Its AI software collects real-time data from road cameras and then sends instructions to manipulate traffic lights based on vehicle flows. Military innovationsEcoMotion's founder Meir Arnon told AFP that surging global interest in smart mobility had made Israel an auto industry player, even though it doesn't manufacture any cars. The EcoMotion Week trade show was held in May, 2022 in the Israeli coastal city of Tel Aviv JACK GUEZ AFP/File"Cars changed," said the industrialist turned investor. Yoav Elgrichi, CEO and co-founder of Rider Dome JACK GUEZ AFP/File"A driving assistant that has become a standard in nearly every car does not exist in motorcycles," chief executive Yoav Elgrichi said. But some observers warn that Israel's technology sector, including smart mobility, could run out of steam.

Israel’s traffic congestion ranks near the worst among developed economies but an algorithm can help, says one of the country’s information technology firms engaged in the auto and mobility sector. ITC, or Intelligent Traffic Control, was one of the artificial intelligence players at Tel Aviv’s recent EcoMotion showcase where high-tech and AI firms hope to make transport more efficient and cleaner.

Taylor Rains/InsiderPrivate charter company VeriJet flies the $2 million Cirrus Vision SF50 jet for $3,000 an hour. CEO Richard Kane told Insider he prefers the single-engine aircraft because of its reliable safety and performance. VeriJet is a charter company born in late 2020 that exclusively operates the world's first single-engine private jet — the Cirrus Vision SF50. In fact, Kane told Insider that one of his customers lives in Tuscaloosa, Alabama, which is not served by any airline. Taylor Rains/InsiderAlso configured in the cockpit are systems backed by artificial intelligence (AI), which is one of Kane's biggest draws to the Cirrus jet.

An apprentice engineer programming robots in a car factory Monty Rakusen/Getty ImagesRobot orders increased 40% in the first quarter of 2022, as businesses seek solutions to the labor crisis. The robot industry is now valued at $1.6 billion, according to the Wall Street Journal. As employers around the country seek ways to fill labor gaps, many are increasingly turning to the assistance of automated technology and robots. "The robots are becoming easier to use," Michael Cicco, chief executive officer of industrial robot provider Fanuc America, told the Wall Street Journal. "Automation, if it goes very fast, can destroy a lot of jobs," Daron Acemoglu, an economics professor at Massachusetts Institute of Technology, told the Journal.

We recently used AI to create Twilight characters based on their book descriptions, and you all let us know in the comments which book characters you wanted to see next:

SINGAPORE — Shares in Asia-Pacific rose in Monday morning trade as investors look ahead to major economic data releases later in the week. In Japan, the Nikkei 225 rose 1.21% in early trade as shares of robot maker Fanuc jumped more than 2%. The Topix index traded 1.01% higher. The Kospi in South Korea also climbed 1.07%, while Australia's S&P/ASX 200 edged 0.31% higher. MSCI's broadest index of Asia-Pacific stocks outside Japan traded 0.28% higher.

At a Singapore construction site, a four-legged robot called "Spot", built by U.S. company Boston Dynamics, scans sections of mud and gravel to check on work progress, with data fed back to construction company Gammon's control room. Gammon's general manager, Michael O'Connell, said using Spot required only one human employee instead of the two previously needed to do the job manually. Meanwhile, Singapore's National Library has introduced two shelf-reading robots that can scan labels on 100,000 books, or about 30 percent of its collection, per day. Robots are also being used for customer-facing tasks, with more than 30 metro stations set to have robots making coffee for commuters. "We always want to have some kind of human touch," said commuter Ashish Kumar, while sipping on a robot-brewed drink.

At a Singapore construction site, a four-legged robot called "Spot", built by U.S. company Boston Dynamics, scans sections of mud and gravel to check on work progress, with data fed back to construction company Gammon's control room. Meanwhile, Singapore's National Library has introduced two shelf-reading robots that can scan labels on 100,000 books, or about 30 percent of its collection, per day. Robots are also being used for customer-facing tasks, with more than 30 metro stations set to have robots making coffee for commuters. "We always want to have some kind of human touch," said commuter Ashish Kumar, while sipping on a robot-brewed drink. Register now for FREE unlimited access to Reuters.com RegisterEditing by Ed Davies and Bradley PerrettOur Standards: The Thomson Reuters Trust Principles.

NASA’s Perseverance Mars rover captured this image of the bleak and barren Martian landscape. Image: NASA/JPL-Caltech/ASU/MSSSThe air on MarsThe Martian atmosphere is thin – its volume is only 1% of the Earth’s atmosphere. To put it another way, there’s 99% less air on Mars than on Earth. That’s one of the goals of NASA’s Mars Perseverance rover mission – to look for signs of ancient Martian life. On the 198th day of its mission, NASA’s Perseverance Mars rover took this selfie.

This outstanding book has a wide breadth of content for all readers: “#DeepLearning for NLP and Speech Recognition” https://t.co/tbJUUFf9Re ——— #AI #MachineLearning #DataScience #NLProc ——— Thank you @drjohncliu for presenting me with a signed copy of your book in Nashville. ?? https://t.co/j35akugilM

Overall, the study shows, the number of women working in AI has increased since the beginning of the century, but in terms of percentage makeup, representation has remained roughly constant at around 27 per cent. Similarity in diversity The diversity of backgrounds of AI researchers, male ...Share this article

LG Group said Thursday it has confirmed a spending package of 106 trillion won ($83.7 billion) through 2026 to consolidate its manufacturing and research base in South Korea.The five-year plan is intended to add momentum to LG‘s domestic business regarding next-generation electric vehicle batteries, artificial intelligence, biopharmaceutical technology and clean technology.The expenditures are aimed at ramping up research capabilities, boosting production and securing infrastructure of the nation’s fourth-largest conglomerate dedicated to consumer electronics, battery, chemical and communications. Of the total investment pledge, 48 trillion won will go solely to research and development.To attain its goals, LG Group looks to hire 50,000 new employees over the next five years.In particular, LG has so far confirmed a budget of 10 trillion won for advancing its secondary battery cells and materials combined over five years.This will allow the group‘s battery arm LG Energy Solution to expand its cylindrical battery cell production in Ochang, North Chungcheong Province, in hopes of lowering its reliance on its lithium-ion pouch cell business. Also, the capital will be used to develop next-generation solid-state batteries and lithium-sulfur batteries; set up a battery recycling system; and lay out battery lifetime prediction tools.Battery materials affiliate LG Chem will pour in 1.7 trillion won to advance its business related to cathode material, separators and carbon nanotubes. LG Chem plans to triple its production capacity of high-nickel cathode materials that are supplied to electric vehicle makers like Tesla by 2025. The investment can also be used as ammunition to execute mergers, acquisitions or joint venture investment, according to the company.Some 3.6 trillion won in expenditure is aimed at research to improve its multimodal hyperscale AI named Exaone under the group‘s holding company LG Corp., while 1.5 trillion won will be invested to bolster LG Chem’s develo

With its newly launched Exchange Traded Instrument (ETI), iMaps Capital Markets is pursuing a trading strategy supported by artificial intelligence in order to benefit from developments on the international currency markets. Liechtenstein, May 19, 2022 – iMaps Capital Markets (iMaps), a brand of the European structured products issuer iMaps ETI AG, in cooperation with Aiternativ Ltd. launched a new exchange-traded investment product: the Aiternativ Dynamic ETI (ISIN: DE000A3GW617). The strategy is based on an automated trading system that identifies, evaluates and, if necessary, trades in short-term volatility breakouts. As a subgroup of Exchange Traded Products, they are an interesting, rapidly growing and cost-effective alternative to funds. Thanks to the approved securities prospectus of iMaps ETI AG, there is also the possibility of publicly offering ETIs to private investors.

Throughout the pandemic, many workers have said they'd quit if employers forced them back to the office. In March, Robert Half, a global recruiting firm, released a survey that revealed 50% of US workers would rather resign than be forced back to the office full-time. But in early May, one high-profile worker put his money where his mouth is: Apple's director of machine learning, Ian Goodfellow, resigned over the Silicon Valley giant’s return-to-office policy. Yet there are plenty more workers itching to leave who haven’t yet. However, some recruiters and analysts believe a prominent professional’s much-talked-about move could be a sign that more resignations will follow as RTO policies start to sink in – and workers begin to bite back.

Nike has made strides in creating inclusive sports bra sizing for women, so much so that it invented a soft tissue robot called “Brabot”. That includes designing a boob robot, or as Nike reportedly calls it, “Brabot”. The innovative technology is designed to mimic the tender tissue of breasts, which allows makers to accurately test different sports bra styles and maximise comfortability. The “Brabot” isn’t the only advancement that Nike has made in women’s sports bra technology. Last September, rapper Megan Thee Stallion joined forces with Nike in a bid to empower women on their fitness journeys.

Since the robot servers were deemed successful in Chili’s, the franchise expanded Rita the Robot to 51 additional restaurants nationwide. Despite the U.S. economy experiencing labor shortages across the country, Allen said the robot servers weren’t implemented due to limited staffing. "It has to have a team member connected to it… you always have to have a team member there to work that robot, to get them to help and assist." Chili’s has seen positive reactions from the robot servers, according to Wade. "[Customers] see that the team members are embracing it… making jobs easier and making casual dining a better place for everybody."

Skin Inc’s Tri-Light + SABI AI combines the skin-transforming properties of a high-tech device with the organization and information of a streamlined app — think of a FitBit, but for your skin. I loved the idea of having both the necessary information and tools needed to make a real difference in my skin — it was one product this app-obsessed skin care lover couldn’t wait to dig into. The AppThe SABI AI App, designed to be paired with the Tri-Light device, most closely resembles a fitness app. It’s like having a skin care expert on-call in your pocket all the time — only this one won’t charge you by the hour. When held against your bare skin, you’ll get an instant readout of your skin’s moisture level — crucial for tracking the efficacy of topicals to fine-tune your skin care routine even further (and hold your skin care products accountable).

ECHO is a small yellow robot currently living with a colony of emperor penguins in Antarctica. The robot is part of a project by Woods Hole Oceanographic Institution studying climate change's impact on the South Pole's most iconic inhabitants. Physicist Daniel Zitterbart, who has been working alongside ecologist Céline Le Bohec, tells us about how ECHO works, what the project is trying to achieve, and his hopes for the future.

Engineers from Northwestern University in Evanston, Illinois have developed the smallest walking robot ever, and it’s a crab. The half-millimeter robot is modeled after a peekytoe crab and is just the latest iteration in a long line of small robots created by the researchers. The goal for creating a bot so small is to move towards more practical uses of the technology and gaining entry to more hard to reach, tightly confined spaces.

Share on Flipboard (opens in a new window)Share on Twitter (opens in a new window)Share on Facebook (opens in a new window)The teeniest robot in the world is a jumping crabIn Antarctica, a clunky robot has befriended a colony of penguinsA robot dog entered a zoo. Amazon's newest robot helper can follow you around, surveil your house, and deliver drinks...the catch? "Star Wars has always been in my life." The biggest stories of the day delivered to your inbox. SubscribeBy signing up to the Mashable newsletter you agree to receive electronic communications from Mashable that may sometimes include advertisements or sponsored content.

And then I put aside that thought because truth be told, the time/effort/compensation equation just doesn’t work out in favor of doing a book. AdvertisementBut what if I could outsource the writing of the book to an algorithm, and my book could be drafted within a matter of minutes, rather than months or even years? But the Generative Pre-Trained Transformer 3, an artificial intelligence application, can get scary close. Doing a little back-of-the-envelope math, I write 600 words per week for this column, 52 weeks a year, times 10 (or so) years. AdvertisementIf I want to write another book, I’ll have to do it myself, darn it.

One of the best algorithms for detecting fake accounts thinks it might be, which shows how challenging it is to quantify the proportion of fake accounts across the social network. Counting Twitter bots has become a point of contention in Elon Musk’s ongoing $44 billion acquisition of Twitter. Musk also outlined a plan to count bots himself that involved sampling 100 @Twitter followers to see how many were bots and said the approach suggests over 20 percent of accounts are fake. But accurately quantifying the percentage of bots on Twitter is a lot more difficult, according to experts. To test Musk’s proposed methodology, IV.ai, an AI company, looked at 100 accounts that follow Musk’s car manufacturing company Tesla on Twitter.

No segment of agriculture is without its passionate advocates of robotics and artificial intelligence as solutions to, basically, all the problems facing farmers today. But the promises of precision agriculture still haven’t been met. “The marketing around precision agriculture, that it’s going to have a huge impact, we don’t have the data for that yet,” says Emily Duncan, a researcher in the Department of Geography, Environment, and Geomatics at the University of Guelph in Canada. “The biggest thing we can do is make it easier for farmers to focus on profit, and not just on yield,” Chowdhary wrote in an email to Undark. “Management tools that help reduce fertilizer and herbicide costs while improving the quality of land and keeping yield up will help farmers realize more profit through fundamentally more sustainable techniques.”

Transcript[Narrator] Behind some of the coolest premium effectsin Hollywood content is the invisible aid of AI. I think where AI really is impactfulis getting it to do things that human beings can't do. [AI Voice] I wasn't very close to anyonealthough I guess I wanted to be. [AI Voice] Welcome to Resemble a generative audio engine. [AI Voice] I went down to the officebecause they're making a robot of me.

The sale also has discounts on a bunch of cleaning gadgets. We're eying the IonVac SmartClean 2000 Robot Vacuum that's on sale for $97 — an impressive deal, considering robot vacuums can run you hundreds of dollars. It has 2,000 Pascals of suction power and four cleaning modes to suck up dirt and debris throughout your home, with minimal effort on your part. You don't even have to recharge it since it automatically returns to its dock to recharge when its battery is low.

The site just slashed the price of the Roborock E4 Robot Vacuum and Mop by 47 percent — and it's never been cheaper. As you may have guessed from its name, the smart cleaning gadget is a robot vacuum cleaner that doubles as a mop. So instead of hauling out a bulky vacuum and a mop and bucket, simply connect to the brand's app or Alexa to get the smart gadget to make your floors sparkle all on its own.

And if you're looking for a gadget to clean your space for you, a robot vacuum cleaner is the way to go. There are a few options on this list, but one of our favorites has got to be this Eufy RoboVac robot vacuum. The now-$170 robotic vacuum can be programmed to clean in real time or on schedule via your phone. The smart vacuum is made with intuitive sensors that prevent it from falling and is compatible with virtual assistants like Amazon Alexa, so you can command it with your voice.

But as eBay’s first chief AI officer, Nitzan Mekel-Bobrov is focused on the role artificial intelligence technology can play in enhancing the user experience for everyone who engages with the platform. Nitzan Mekel-Bobrov, eBay Nitzan Mekel-Bobrov is chief AI officer at eBay. Nitzan Mekel-Bobrov: I’m Nitzan Mekel-Bobrov from eBay, and you’re listening to Me, Myself, and AI . Sam Ransbotham: Shervin and I are excited today to be talking with Nitzan Mekel-Bobrov, the chief AI officer at eBay. It’s been quite insightful, and we really, really appreciate it.

This month’s question for our panelists: Should an organization tie its RAI efforts to its overall corporate social responsibility (CSR) efforts? Scale is defined as the degree to which RAI efforts are deployed across the enterprise (e.g., ad hoc, partial, enterprisewide). The Panelists RespondOrganizations should tie their responsible AI efforts to their corporate social responsibility efforts. Reasons to Connect Responsible AI and CSRA slight majority of our panelists (52%) believe that organizations should tie their RAI efforts to their CSR efforts. If AI maturity levels are high and AI use is linked to your social responsibility, it might be beneficial to link your RAI and CSR efforts.

How, then, can AI project leaders increase end user acceptance and use of AI tools? (See “Overcoming Resistance to Front-Line AI Implementations.”)Overcoming Resistance to Front-Line AI Implementations AI Implementation Challenges Solutions for Overcoming Implementation Challenges The tool might deliver benefits to third parties rather than targeted end users. AI project leaders can also start with a good-enough AI tool that can be trained using currently available, often smaller data sets. AI project leaders frequently keep AI tool development quiet in the early stages to forestall expected user resistance. Behind the glittering promise of AI lies a stark reality: The best AI tools in the world mean nothing if they aren’t accepted.

Join the Adweek live virtual summit Elevate: AI on July 12 to explore the cutting-edge trends, tools and platforms becoming the norm in ad campaigns. Sign up with code: ADWEEKAI50 for 50% off . As part of the partnership between Snap Inc. and Live Nation, Snapchat teamed up with Insomniac Events on four new lenses for fans at Electric Daisy Carnival Las Vegas, which begins Friday night (May 20).

Join the Adweek live virtual summit Elevate: AI on July 12 to explore the cutting-edge trends, tools and platforms becoming the norm in ad campaigns. Sign up with code: ADWEEKAI50 for 50% off . Augmented reality technology pops up in our daily lives more than we realize. From filters in our favorite social apps to virtually trying on a pair of glasses, it’s all powered by AR—and it’s only going to expand into more industries.

Featuring Paul Leonardi, Professor of Technology Management at the University of California, Santa Barbara, and Tsedal Neeley, Professor at Harvard Business School. To thrive in a world driven by data and powered by algorithms, we must learn to see, think, and act in new ways. This doesn’t mean we need to master coding or machine learning—but it does mean that we need to develop a digital mindset. They will discuss:Why a digital mindset is crucial for continued digital transformationThree approaches to the digital mindset—and the perspectives and actions within each approach to help you develop the digital skills you needHow a digital mindset will help you ask the right questions, make smart decisions, and appreciate new possibilitiesAnd the importance of the “30 percent rule” – the minimum threshold that gives us enough digital literacy to understand and take advantage of the digital world. To learn what it really takes to thrive in this age of data algorithms and AI, and what it takes to develop a digital mindset, join Paul Leonardi, Tsedal Neeley, and HBR on May 26.

From the outside, the building resembles a 28-story robot hive—an anonymous box of metal and vents. advertisement“We just wanted to build a normal office building,” Seok says. Oh, and Naver also runs a messaging app, social media platform, and comic book service on top of its burgeoning robot business. advertisement“[The lag] is so short that the robot actually thinks the brain is in it rather than somewhere else,” Seok says. They will be designed to lift these computers in racks and handle many of the day-to-day operations of managing servers.

Their work could help to transform agriculture, turning fruit-picking – a backbreaking, time-consuming human task – into one that’s speedy and easier on farm workers. In orchards with trellised trees, human fruit pickers can cruise through rows of trees in pairs on slowly rolling platforms. The fruit-picking robot has picked an apple successfully about half of the 500 or so times it has tried so far. “Historically, farm workers have not been placed at the center of any of these conversations,” they say. Across various industries, including agriculture, waves of automation have led to job losses and a devaluing of human work.

A pedestrian, of course, would have, but the robot was just stopped in the middle of the crosswalk. You can also call the city to report a robot that stopped working so they can follow up with the store. Q: I think your column on the shameful treatment of Japanese Americans during World War II was great. I normally don’t visit museums, but I did visit one there that showed how Eskimos were treated during World War II. I share this with you to help you understand that the US treated many people poorly during World War II.

Memorial Day weekend has brought back some of the best prices we've seen on Roomba robot vacuums. You can pick up the Roomba j7+ and the Roomba s9+ for $599 and $799, respectively, when using the code ENDGT200 at Wellbots. Buy Roomba j7+ at Wellbots - $599 Buy Roomba s9+ at Wellbots - $799 Buy Roomba j7 at Wellbots - $399The Roomba j7 series has some of iRobot's newest machines in it. Along with those smarts, the Roomba j7 has 10x the suction power of a standard Roomba, plus dual multi-surface brushes, smart home mapping and more. Similarly to the Roomba j7 series, the Roomba s9+ made it into our best robot vacuums guide as a splurge pick.

Jake Kring-Schreifels, The RingerOne of the biggest challenges in filming the original Top Gun was keeping all of the actors playing pilots from constantly vomiting in the cockpit. Any sequel to that '80s classic would have to be even more realistic, since production eventually moved to a soundstage. The quest for so-called "digital sovereignty" could impact services we use daily or some of the features they currently offer. Philip Sherburne, PitchforkArtificial intelligence can now learn, perform and create music with skills that are more than just copying a human's actions. Pitchfork explores some of the more notable projects in search of an answer to the question: Just because we can, does that mean we should?

In general, look for AI stocks that use artificial intelligence technology to improve products or gain a strategic edge. Artificial Intelligence Stocks: IBM Sells Watson HealthAll AI software needs computing power to find patterns and make inferences from large quantities of data. The top AI stocks to buy spans chip makers, enterprise software companies and technology giants that utilize AI tools in many applications. AI Stocks In Consumer ApplicationsIt's no secret that Alphabet, Microsoft, Facebook and Amazon are all spending big bucks on AI technology. Further, The IBD 50 roster of growth stocks has featured artificial intelligence stocks in online dating, digital advertising and business communications.

MOSS LANDING — The Monterey Bay is now home to robotic lab assistants which collect aquatic DNA for researchers to analyze onshore. That DNA could help researchers understand the animals, plants and microbes that teem in the Monterey Bay and the ocean beyond. This new robot can follow that nightly migration and help researchers identify its participants. This technology will also make it easier to track the health of Monterey Bay. “How’s that going to change the cast of characters that are coming into the Monterey Bay?”But these robots will help researchers understand the ocean beyond the Monterey Bay as well.

When you’re lucky enough to have a dog in your life, you tend to overlook some of the more one-sided aspects of the relationship. So naturally, we now have this poop-shooting laser turret to helpfully guide you through your backyard cleanup sessions. To point them out, he attached a laser pointer to a cheap robot arm, and used OpenCV to help line up the bright green spot on each poop. So now instead of having a dog control his life, he’s got a dog and a computer running the show. We joke, because poop, but really, this is a pretty neat exercise in machine learning.

After sweeping by Jupiter, Saturn, Uranus, and Neptune, it is now almost 15 billion miles (24 billion kilometers) from Earth in interstellar space. Both Voyager 1 and its twin, Voyager 2, carry little pieces of humanity in the form of their Golden Records. The incredible longevity of the Voyager spacecraft presents a uniquely tangible entry point into exploring ideas of immortality. Jimmy Carter, whose message and autograph are immortalized in the Golden Records, is a progressive Southern Baptist and a living example of religious hope for immortality. Only those records will likely remain as testimony and legacy of Earth, a kind of objective immortality.

Tristan is a futurist covering human-centric artificial intelligence advances, quantum computing, STEM, physics, and space stuff. Pronouns: (show all) Tristan is a futurist covering human-centric artificial intelligence advances, quantum computing, STEM, physics, and space stuff. Pronouns: He/himJason Silva, futurist and host of National Geographic’s “Brain Games,” recently published a mind-bending YouTube video combining the technological prowess of AI with the artistic creativity of someone who believes in the power of psychoactive experiences. In the above video, Silva spends six minutes riffing on the nature of being human. Did you know Jason Silva, futurist, and host of National Geographic’s “Brain Games,” is speaking at the TNW Conference on June 17?

Find him on Twitter: @Gadget_RyThe CEO of AI cybersecurity firm Darktrace has called for a “Tech NATO” to counter growing cybersecurity threats. NATO members are now spending more on defense, increasing cooperation, and the alliance is now preparing to accept Sweden and Finland into its fold. NATO members, in contrast, have acted in accordance with the UN charter and only provided resources to Ukraine that it can use to defend its territory from the invaders. However, Gustafsson’s call for a “Tech NATO” is surprising—not least because NATO itself already has one in the form of the CCDCOE (Cooperative Cyber Defence Centre of Excellence). Earlier this month, non-NATO member South Korea joined the organisation alongside NATO members Canada and Luxembourg.

Emerging digital twins standards promise to help connect the dots between individual digital twins to enable systems of systems. Multiple standards are being developed by the International Standards Organization (ISO), the Industrial Digital Twins Association (IDTA) and the Digital Twins Consortium (DTC) — creating some challenges for unifying digital twins into systems. System of digital twin systemsWhat a digital twin needs and how it adds value are perceived differently when considered at the level of a machine, a factory production cell, engineering and design or a leadership level, Petrick explained. ISO-23247 provides a generic digital twin development framework to help manufacturers choose building blocks for digital twin implementations. “But what we really need is the interoperable digital twin so we can realize the interoperability between these different digital twins,” said Christian Mosch, general manager at IDTA.

— Sharon Goldman, senior editor and writerTwitter: @sharongoldmanCan AI’s ‘last mile’ problem be solved? For data science teams, moving away from the traditional ‘data science projects’ approach and focusing on outcomes is essential. The ‘AI Engineering Era’ is going to be necessary for enterprises to achieve true client-centric AI. AI engineering is a discipline that enables both business and IT leaders to work together to deploy repeatable patterns for AI solution success. According to Gartner, AI Engineering, one of its top 12 strategic technology trends of 2022, will operationalize the delivery of AI to ensure its ongoing business value.

Credit: Saharia et al. Credit: Saharia et al. Credit: Saharia et al. Credit: Saharia et al. Credit: Saharia et al.

Super AgentThe Super Agent browser extension autofills cookie pop-up forms. After installing the extension, you choose which tracking cookies you want to accept or reject. The open-source browser extension will then navigate the pop-ups on your behalf — although it doesn’t work on every site. I don’t care about cookiesI don’t care about cookies is very effective if your feelings match the tool’s name — but it’s not the more privacy-centric option. However, if cookies are required for a site to work properly, the extension automatically accepts the policy for you.

DuckDuckGo’s reputation for protecting privacy has taken a hit after revelations emerged of a tracking deal with Microsoft. Security researcher Zack Edwards this week revealed that DuckDuckGo’s mobile browsers allow some Microsoft sites to bypass its block on trackers. While the browser blocks Facebook and Google trackers, DuckDuckGo makes an exception for Microsoft. “For non-search tracker blocking (eg in our browser), we block most third-party trackers,” he said. However, we have been continually pushing and expect to be doing more soon.”For non-search tracker blocking (eg in our browser), we block most third-party trackers.

Applied use casesArtificial intelligence predicts patients’ race from their medical images Using both private and public datasets, the team found that AI can accurately predict self-reported race of patients from medical images alone. “These results were initially confusing, because the members of our research team could not come anywhere close to identifying a good proxy for this task,”...Is your face gay? Conservative? AI researchers are asking the wrong questions Additionally, the ensuing global effort to cripple Russian access to international markets calls into question the viability of current contracts, government licensing, and financial instruments involved in Russia’s nuclear exports. While one section of the order (“new investment in the energy...

This week, researchers from Allen Institute for Artificial Intelligence, CEO Oren Etzioni and research scientist Jesse Dodge received the ACL’s 10-year Test-of-Time Paper Award for two separate papers published in 2012. Research team member Mausam, who is now a professor of computer science at Indian Institute of Technology Delhi, accepted the award, dedicating it to Soderland. Instead of using human-annotated data, Midge generated natural language descriptions from detections by computer vision models. “We really cared about natural language generation, even though at the time research on it wasn’t valued in the community,” Dodge said. By expertly mapping natural language questions to their corresponding SQL queries for a broad class of natural language questions, this research was foundational to the way we access and interact with databases today.

Curated list of awesome listsAwesome Artificial Intelligence (AI)A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers. Trust in Machine Learning - a book for experienced data scientists and machine learning engineers on how to make your AI a trustworthy partner. AIMACode - Source code for "Artificial Intelligence: A Modern Approach" in Common Lisp, Java, Python. FANN - Fast Artificial Neural Network Library, native for CFARGonautica - Source code of Douglas Hosftadter's Fluid Concepts and Creative Analogies Ph.D. projects. A weekly newsletter to keep up to date with AI, machine learning, and data science.

Advances in Artificial Intelligence (AI) and machine learning are transforming all aspects of our lives, economies and societies, in both visible as well as unforeseen ways. Predictive analysis and use of big data is now driving insights and actions on sustainability, health and a range ...Share this article

I believe pioneering responsibly should be a priority for anyone working in tech. At DeepMind, everything we do stems from our company mission of solving intelligence to advance society and benefit humanity, and building a culture of pioneering responsibly is essential to making this mission a reality. This is another crucial part of pioneering responsibly: acknowledging that we don’t have all the answers, and building relationships that allow us to continually tap into outside input. Fundamentally, pioneering responsibly means prioritising initiatives focused on ethics and social impact. In that sense, pioneering responsibly is a moral imperative – and personally, I can’t think of a better way forward.

First, downstream applications of text-to-image models are varied and may impact society in complex ways. Second, the data requirements of text-to-image models have led researchers to rely heavily on large, mostly uncurated, web-scraped datasets. Imagen relies on text encoders trained on uncurated web-scale data, and thus inherits the social biases and limitations of large language models. Finally, while there has been extensive work auditing image-to-text and image labeling models for forms of social bias, there has been comparatively less work on social bias evaluation methods for text-to-image models. A conceptual vocabulary around potential harms of text-to-image models and established metrics of evaluation are an essential component of establishing responsible model release practices.

Copilot will also be available free for students as well as “verified” open source contributors. One new feature coinciding with the general release of Copilot is Copilot Explain, which translates code into natural language descriptions. Again, Copilot Labs is intended to be experimental in nature, so things might break. The code Copilot suggests might not always compile, run or even make sense because it doesn’t actually test the suggestions. In the long term, we believe Copilot will write code that’s more secure than the average programmer.”

Rich Mahoney is Vice President of Research at Intuitive. His extensive robotics development background and experience as a founder of several companies shapes his leadership to contribute to Intuitive’s strategic vision. Prior to Seismic, Rich was the Director of SRI Robotics for more than seven years. Rich was the founding President of Silicon Valley Robotics and is recognized as a robotics commercialization thought leader. from Drexel University, and a Ph.D. in engineering from the University of Cambridge, England, where he attended on a Fulbright Scholarship.

There is a case that many lives could be saved if society embraces machine learning and commits itself to deploying robotics technologies responsibly. This machine learning process requires much more data than humans need. However, once trained, robots would outperform humans in any given task and AI and robotics have dramatically improved their performance over the past five years through machine learning. Autonomous robotic lifesaversWith more and more data to learn from, AI is improving quickly, becoming more accurate, adaptive and safe. The trolly problem in the age of AI and machine learningBecause many lives could be saved, there’s an argument for launching robotic technologies once proven to be slightly better than people.

It’s no secret that large models, such as DALL-E 2 and Imagen, trained on vast numbers of documents and images taken from the web, absorb the worst aspects of that data as well as the best. OpenAI and Google explicitly acknowledge this. Scroll down the Imagen website—past the dragon fruit wearing a karate belt and the small cactus wearing a hat and sunglasses—to the section on societal impact and you get this: “While a subset of our training data was filtered to removed noise and undesirable content, such as pornographic imagery and toxic language, we also utilized [the] LAION-400M dataset which is known to contain a wide range of inappropriate content including pornographic imagery, racist slurs, and harmful social stereotypes. Imagen relies on text encoders trained on uncurated web-scale data, and thus inherits the social biases and limitations of large language models. In short, these firms know that their models are capable of producing awful content, and they have no idea how to fix that.

An illustration of facial recognition. Artificial intelligence (AI) offers the promise of a world made more convenient by algorithms designed to mimic human cognitive abilities. The true story of AI, however, is being told through the questions researchers are using it to answer. These days, AI researchers are asking troubling questions of technology and attempting to use it to link facial attributes to fundamental character. AI has the same fundamental limitation that all tools do: Flawed AI systems, like flawed humans, can answer questions that an ethical society would shun.

The event’s three panelists shared their experiences and work in generative artmaking and generally agreed that AI is a tool and collaborator. “The generative or system-making is the artwork, rather than the outputs,” Horta Valenzuela said. She is a composer as well as a judge and organizer for the AI Song Contest, an international human-AI songwriting competition. Four to seven generative models might be used within one song, for instance, to generate the different components of a composition, such as the melody and song design. He built an interface that allows artists without programming or AI experience to understand how generative models represent data in multi-dimensional space.

For today's recap, we're going over our talk with Amazon Web Services tech evangelist Dr. Nashlie Sephus. Our discussion was titled "Breaking Barriers to Machine Learning." Dr. Sephus came to AWS via a roundabout path, growing up in Mississippi before eventually joining a tech startup called Partpic. Partpic was acquired by Amazon in 2016, and Dr. Sephus took her machine-learning skills to AWS. Dr. Sephus said.

Specifically, in human-robot interaction, Robinette et al. Robot-related factors account for features, such as the degree of anthropomorphism and the corresponding influences on its interaction with people (Herzog et al., 2022). The second is reliance, describing that no action is taken when no warning is issued (Vashitz et al., 2009). Following the cue, the framework comprises three sequential steps, inspired by Wickens’ model of human information processing (Wickens et al., 2015):1. As a behaviour, compliance can be defined as:P c o m p l | c o m p r ∩ p e r c = P p e r c ⋅ P c o m p r ∩ p e r c P p e r c ⋅ P c o m p l ∩ c o m p r ∩ p e r c P c o m p r ∩ p e r c ( 3 )Reactance (in behavioural terms) constitutes the opposite reaction to compliance, synonymous with c o m p l i a n c e ̄ , and is defined as:P r e a c | c o m p r ∩ p e r c = P p e r c ⋅ P c o m p r ∩ p e r c P p e r c ⋅ P r e a c ∩ c o m p r ∩ p e r c P c o m p r ∩ p e r c ( 4 )Figure 2 contains a structural tree diagram showing the probabilities for compliance (Eq.

October 19th - 21st | ConferenceJoin Scale AI as we bring together the world's brightest AI leaders, visionaries, practitioners, and researchers across industries to explore operationalizing AI and Machine Learning. This year's conference will feature three days of in-person and virtual keynote presentations, fireside chats, expert panel discussions, and hands-on workshops.

Given the risks of very high-powered AI systems, how has your world ensured that AGI has at least so far remained safe and controlled? International best practices in AGI research and development have been in place for at least 20 years. In your world, is there one AI system that is substantially more powerful than all others, or a few such systems, or are there many top-tier AI systems of comparable capability? All other GM= 8-10 systems are declared illegal to own, develop or maintain under ANT and international law. As a civilian-run organization, GAIA also harmonizes global activities in AGI development within its multimodal network.

ResearchSocial robots are robots that interact with people in a natural manner by, for example, using speech, gestures, facial expressions and language. You will join an international project called VALAWAI (short for Value-Aware AI) studying consciousness, values and AI. “Value-aware AI” is AI that includes a component performing the same function as human moral consciousness: a system that helps decide whether or not certain actions are morally acceptable. At Ghent University we will look at how social robots can become value-aware and how people respond to social robots that have have some level of consciousness. Applicants should send their application to tony.belpaeme@ugent.be with subject “PhD application VALAWAI”.

AiDash has launched the Disaster and Disruption Management System (DDMS), a satellite and AI-powered software-as-a-service (SaaS) offering that connects the dots between weather forecasts, highly accurate disaster models and precise asset data. The service constructs a digital twin for predicting and responding to the anticipated impact before, during and after a significant disaster or extreme weather event. AiDash’s CEO, Abhishek Singh said satellite and weather data competitors tend to focus on the weather as a technology problem. In contrast, AiDash has extended its existing vegetation management domain expertise into disaster management. AiDash believes that improvements in satellite data coupled with AI models could help firms prioritize weather hardening efforts to minimize the impacts of disasters.

As professors in the departments of urban environment and digital culture we delve into this question and examine how the metaverse could profoundly change our relationships with urban spaces. Neal Stephenson coined the term in 1992, in his novel Snow Crash to designate a dystopian virtual urban environment. These spaces, rented or bought by large corporations, make the metaverse a virtual urban environment entirely controlled by private interests, those of the digital technology giants. A new urban exodusWhile practical, this transition to the metaverse changes the ways we interact with the urban environment and forces us to reconsider our urban priorities. In any case, it is clear that the more private companies invest in virtual urban environments, the less we as a society are paying attention to the urban spaces we live in.

At the Microsoft Build developer conference today, the company’s chief technology officer, Kevin Scott, demonstrated an AI helper for the game Minecraft. The non-player character within the game is powered by the same machine learning technology Microsoft has been testing for auto-generating software code. When a player tells it to “come here,” for instance, the underlying AI model will generate the code needed to have the agent move toward the player. The Minecraft bot was built using an AI model called Codex that was developed by OpenAI, an AI company that received funding from Microsoft in 2019. To build something like the Minecraft bot, developers would need to work with the underlying AI model, Codex.

Now, new research points to diversity as being a key parameter for making AI a better team player. In a previous experiment, Lincoln Laboratory researchers tested one of the world's best-performing Hanabi AI models with humans. Any-Play augments the process of training an AI Hanabi agent by adding another objective, besides maximizing the game score: the AI must correctly identify the play-style of its training partner. This objective also requires its partner to learn distinct, recognizable behaviors in order to convey these differences to the receiving AI agent. Facebook similarly used diversity in training to improve collaboration among Hanabi AI agents, but used a more complicated algorithm that required modifications of the Hanabi game rules to be tractable.

Find him on Twitter: @Gadget_RyA deepfake video created by Dutch police could help to change the often negative perception of the technology. Deepfakes use generative neural network architectures – such as autoencoders or generative adversarial networks (GANs) – to manipulate or generate visual and audio content. That’s why he has been brought back to life for this film,” says a voice in the video, before Soares drops his ball. Daan Annegarn, a detective with the National Investigation Communications Team, said:“We know better and better how cold cases can be solved. It is not nothing to ask relatives: ‘Can I bring your loved one to life in a deepfake video?

How data complexity makes forecasts at scale a challengeIn a volatile environment, it’s more important than ever to create accurate and comprehensive financial forecasts. For example, metrics on marketing qualified leads, sales accepted leads, conversion rates, etc., are eventually converted into pipeline numbers to create sales forecasts. Finance uses sales forecasts to create revenue forecasts. Adding commercial and operational data, while necessary to achieve greater accuracy, make it difficult to capture and integrate information at speed without advanced technology. With modern technology that enables intelligent forecasting at scale, agility is within the CFO’s grasp.

A team of researchers from Austria recently developed an AI-powered drone capable of tracking moving objects through dense foliage. Besides people, other targets (e.g., vehicles or animals) can be detected and tracked in the same way. Up front: There are myriad ways an interested party could track moving objects through dense foliage, including FLIR and other thermal optics systems, but this offers a new AI-powered wrinkle: the ability to track color changes through occlusion. In this case, the researchers developed a system capable of using a technique called “color anomaly detection” in real-time to track occluded objects in motion — a first, according to the Journal of Remote Sensing. Background: The team created a lightweight 1D camera array that captures overhead images of high-foliage areas via drone deployment.

While the real-life Delorean still can’t go back to the future, Google’s Street View vehicles can now transport us to the past. The feature was previously only available on the desktop version of Google Maps, but is now available on smartphones. The feature was launched to mark the 15th anniversary of Street View. Joining the celebrations are an upgraded mapping camera and the new Street View Studio, which provides tools for quickly publishing 360-degree image sequences in bulk. Credit: GoogleHappy birthday, Street View: here’s to your next 15 years of exploration, crimefighting, and stalking.

At last: Apple’s apparently upgrading the selfie camera on the upcoming iPhone 14. Sure, there’s no guarantee that using a costlier component translates to the iPhone 14’s selfie camera being otherworldly. But combining Apple’s camera computing and new hardware, we might get a winning combination. Apple’s M1-powered MacBook Air needs a better camera. Upgrading the iPhone selfie camera could be Apple’s priority as the device’s sales account for a big chunk of money in the firm’s balance sheet.

Allado-McDowell, who established the Artists + Machine Intelligence program at Google AI in 2016, has a deep understanding of artificial intelligence. Do you have a grand unified theory of cringe? Allado-McDowell (and GPT-3): Cringe is a slang term that originated on the internet. My grand unified theory of cringe is that it’s a side effect of our social nature as human beings. In other words, we cringe because we empathize with the embarrassment that others feel, or that we think they ought to feel.

Guðni Thorlacius Jóhannesson, the president of Iceland, visited MIT on Friday, engaging in talks with several campus leaders and professors, and touring the Media Lab. Jóhannesson visited the Institute along with a substantial delegation of officials and scholars from Iceland. “It is important to put the human being front and center,” Sarkis said, describing the priorities of Media Lab researchers and MIT scholars and students. By training, Jóhannesson is a professional historian who has studied and written extensively about modern Iceland. The Icelandic delegation sat down for a series of discussions with MIT faculty and administrators.

A few years back, Softiron, which makes data center hardware for software-defined storage, turned to digital twins to help optimize its hardware, not just for cost and performance, but also to dramatically reduce its carbon footprint. SoftIron’s COO Jason Van der Schyff detailed how the company has used digital twins to achieve such impressive gains in an exclusive interview with VentureBeat. VentureBeat: How do you go about building digital twins to optimize your carbon and energy footprint? With respect to our manufacturing, digital twins provide efficiency in designing and deploying new manufacturing techniques. Van der Schyff: Digital twins provide open access to all data in one place increasing cross border and asynchronous collaboration.

Companies that don’t focus on powering AI training will fall behind in a tight hiring market. Importantly, before taking on any AI reskilling efforts, banks and financial services organizations need to first understand what outcome they’re driving towards and what skills are required. Utilizing digital learning platforms like Skillsoft, Udacity, or Udemy, or integrating content into mainstream work systems can make employees’ reskilling experiences more user-friendly. AI reskilling offers an immense opportunity for both financial services companies and their employees, but it can be daunting to consider monetary and time investments needed with reskilling efforts. For example, FINOS (fintech open source consortium under Linux Foundation) helps facilitate the processing and exchange of financial data throughout the entire banking ecosystem.

Multiple companies are vying for a piece of share in the competitive – and growing – low-code AI and ML tools market. The basic concept behind low code is about making it easier for users to build applications without needing to dig into source code development tools. The vendor marketplace for low-code technologies is crowded: Sstartup Sway.ai launched its no-code platform for helping users to build AI powered applications in February. In the same month, startup Mage launched its low-code AI dev tool which helps organizations to generate models. Cogniteam announced its latest low-code AI updates on May 9, with a focus on robotics.

The second approach is known as the Grace Hopper Superchip, which combines the Grace CPU with an Nvidia Hopper GPU. The CGX includes the Grace superchip alongside Nvidia a16 GPUs and BlueField-3 data processing units (DPUs). OVX also uses the Grace Superchip and BlueField-3, while providing vendors with the option of using a range of different Nvidia GPUs. Finally, the HGX Grace and the HGX Grace Hopper 2U reference designs provide systems optimized for AI training and inference. More partners for Nvidia AI EnterpriseNvidia is also using its time at Computex to bring in more industry go-to-market partners in APAC for its Nvidia AI Enterprise software suite, which helps organizations build and support end-to-end data science workflows.

As the use of AI across all industries and nearly every aspect of society grows, there is an increasingly obvious need to have controls in place for responsible AI . Responsible AI is about making sure that AI is used in a way that isn’t unethical, that helps respect personal privacy and that also generally avoids bias. There is a seemingly endless stream of companies, technologies and researchers tackling issues associated with responsible AI. Now, his hope is that AIRL’s Mission Control will be the dashboard in data science to help data teams build technologies with responsible AI practices. He said that AIRL isn’t looking to compete with MLops and existing AI tools, but rather to provide an overlay on top for responsible AI usage.

Today, Vultr announced it is adding GPU instances to its cloud so AI researchers and others in need of massively parallel computing can rent them by the hour. Its new machine options, called Vultr Talon, are built on the new Nvidia A100 Tensor Core GPU, promising to democratize the area because the new product can be rented in smaller, less expensive amounts. Indeed, Vultr bristles at the notion that it is smaller; it prefers the word “independent” to describe its company when compared to the biggest options. Some other cloud providers also offer GPU options, but, in many cases, they’re available only in much larger slices. They can start with smaller machines when debugging and then switch to larger machines in production.

Microsoft works to makes responsible AI easier to understandResponsible AI is actually at the heart of a lot of today’s Build news, John Montgomery, corporate vice president of Azure AI, told VentureBeat. Azure Machine Learning will also now offer a responsible AI “scorecard” in preview to summarize model performance and insights so that all stakeholders can easily participate in compliance reviews. Two orthopedic surgeons developed an AI model to help consultants give their patients a personalized risk assessment of upcoming hip or knee operations. The model is hosted in Microsoft’s Azure cloud and uses the responsible AI dashboard in Azure Machine Learning. “We’re making that so simple that the machine learning engineer can go all the way to production.”

Well, Apple’s Photos app has a Memories feature that shows you algorithmically curated collections of photos and videos — based on different factors like dates, holidays, or places you visited. These memories can appear through a notification, or you can check some collections in the For You section of the Photos app. If you want a person to appear less frequently (or not at all) in your Memories, you can easily do that:Open the Photos app on your iPhone or iPad. You can select Feature This Person Less or Never Feature This Person. While you’re at it, you can also read our guide for hiding sensitive images on Google Photos.

MIT researchers recently made one of the boldest claims related to artificial intelligence we’ve seen yet: they believe they’ve built an AI that can identify a person’s race using only medical images. AI models can be fine-tuned to predict anything, even concepts that aren’t real. Furthermore, claims of “accuracy” don’t mean what the media seems to think they do when it comes to these kinds of AI models. However, our finding that AI can accurately predict self-reported race, even from corrupted, cropped, and noised medical images, often when clinical experts cannot, creates an enormous risk for all model deployments in medical imaging. This AI can only predict labels.

In this article, we’ll look at how Allstate has explored AI applications for its business and industry through two unique use cases:Faster Product Knowledge – Allstate Business Insurance claims that it uses AI models to help agents acquire knowledge about insurance products faster and better streamline the quoting process. Automated Transcription – Allstate claims that it is able to derive meaning and context from customer calls to enhance customer experience and facilitate problem resolution. Prior to implementing the Allstate Business Insurance Expert (ABIE), agents who had questions regarding new business insurance products had limited references to consult. Policies and procedures governing business insurance products were published as PDFs in the company’s SharePoint repository, and accessing the information was time-consuming. A business customer can use the “Ask ABIE” tool, which appears on the “Chat & Help” section of Allstate.com’s business insurance section, and find real-time answers.

Find him on Twitter: @Gadget_RyClearview AI has been fined £7.5 million by the UK’s privacy watchdog for scraping the online data of citizens without their explicit consent. In November 2021, the UK’s Information Commissioner’s Office (ICO) imposed a potential fine of just over £17 million on Clearview AI. Today’s announcement suggests Clearview AI got off relatively lightly. That is why we have acted to protect people in the UK by both fining the company and issuing an enforcement notice.”The enforcement notice requires Clearview AI to delete all facial recognition data. The OAIC ordered Clearview AI to destroy the biometric data it collected of Australians.

Could AI-driven solutions help prevent wildfires before they start by analyzing the tree growth that can spark them? Hitachi Energy, the Zurich, Switzerland-based global technology company, says yes. “These industries all have similar issues with managing their miles and miles of assets,” Bryan Friehauf, SVP of enterprise software solutions at Hitachi Energy, told VentureBeat. Inspecting trees to prevent wildfires and outagesTo address these challenges, today Hitachi Energy announced a new AI-driven solution, Hitachi Vegetation Manager, part of the company’s new Lumada Inspections Insights offering. Satellites capture images, AI analyzes them“With satellites remotely capturing images and AI analyzing them, we can better optimize and plan for addressing areas of concern,” said Friehauf.

Amid an exploding market for AI chatbots, companies that target their virtual assistants to specialized enterprise sectors may get a firmer foothold than general chatbots, according to Gartner analysts. The CAIP market is extremely diverse, both in vendor strategies and in the enterprise needs that vendors target, says Magnus Revang, a Gartner research vice president covering AI, chatbots and virtual assistants. The secret sauce in the AI chatbot marketThat is Zowie’s secret sauce: specialization. Zowie is built using the company’s automation technology, ‘Zowie X1’, to analyze the meaning and sentiment to find repetitive questions and trends. By analyzing patterns, the AI chatbot can tell when something new or unusual is happening and alerts the customer service team, Schaefer said.

Imec, a semiconductor research accelerator in Leuven, Belgium, announced the first fruits of the Sustainable Semiconductor Technologies and Systems (SSTS) research program aimed at reducing the semiconductor industry’s carbon footprint. Today, the semiconductor industry plays a small role in global carbon emissions. A big concern is that growing demand could increase the semiconductor industry’s relative proportion of carbon emissions, particularly as other industries reduce their carbon impact. Semiconductor manufacturing requires large amounts of energy and water and can create hazardous waste. Some different sources of carbon emissions include electrical consumption, direct emissions and water usage.

Digital construction procurement platform, Agora, has rebranded as Kojo as it expands to support new construction trades. Kojo tools reduce the time for placing complex orders, optimize delivery schedules and reduce the need for onsite inventory. Kojo claims its tool also streamlines procurement-related office tasks by 75%, saving construction sites an average of $124,000 per year. With Kojo, field teams can access the Kojo mobile app and have real-time visibility into existing orders, material storage and delivery status. New features that automate requests for quotes (RFQ) help construction firms solicit quotes from more vendors to reduce material costs.

Join AI and data leaders for insightful talks and exciting networking opportunities. A startup called Sway AI recently launched a drag-and-drop platform that uses open-source AI models to enable low-code and no-code development for novice, intermediate and expert users. For one thing, abstracting code into composable modules creates a lot of overhead, and this introduces latency to the process. AI is gravitating increasingly toward mobile and web applications, where even delays of 100 ms can drive users away. AI constrainedAdditionally, most low-code platforms are not very flexible, given that they work with largely pre-defined modules.

A robotic chef is learning how to “taste” food as it cooks it — just like humans do to see if their meal has enough seasoning https://t.co/oAAH5AMLYe #foresight #predictions #futuretrends #futureofwork #futurejobs #keynotespeaker #futuriststrategyCAMBRIDGE, England — A robotic chef is learning how to “taste” food as it cooks it — just like humans do to see if their meal has enough seasoning. The new machine can even change the taste ...

When we finally arrived in Croatia the next day, the crypto market had entered its biggest downturn in years. But the market truly went into meltdown after the collapse of Terra — a so-called stablecoin — and its sister token, Luna. Yet the crypto crash wasn’t the biggest headache for BlockDown’s founder, Erhan Korhaliller. They remember that the market has hit record highs after even bigger crashes. “The stakeholders and factors which are driving the technology are completely different from those that are driving the market,” Zhao told TNW.

There are many no-code development platforms that allow both programmers and nonprogrammers to create software through drag-and-drop graphical user interfaces instead of traditional line-by-line coding. No-code development platforms generally offer templates or modules that allow anyone to build apps. This led to the release of early no-code platforms, including Microsoft FrontPage and Adobe Dreamweaver, to help nonprogrammers build websites. Website developmentThere are many current no-code website-building platforms such as Bubble, Wix, WordPress and GoogleSites that overcome the shortcomings of the early no-code website builders. No-code programming can help reduce the time to train these models, which makes it easier to use AI for many purposes.

The global robotics market is estimated at $55.8 billion and is expected to grow to $91.8 billion by 2026 with a 10.5% annual growth rate. The majority of challenges surround facilitating technologies like artificial intelligence (AI), perception, power sources, etc. Navigation solutions will continue to evolve, but future robots need to be able to work in environments that are unmapped and not fully understood. AI robots can be described as intelligent automation applications in which robotics provides the body while AI supplies the brain. AI applications for roboticsThe cooperation between robotics and AI is naturally called to serve mankind.

On a file per file basis, there is no attention-based task a machine can beat a human at barring chance. Greetings humanoids Subscribe now for a weekly recap of our favorite AI storiesLet’s use computer vision as an example. The cats-dressed-as-clowns might be in the minority of cat images, but they’re still cats. The big idea behind machine superiority is that one day, we’ll create an AI that’s so intelligent it surpasses humans in its ability to reason, plan, and adapt. Neural’s “A beginner’s guide to AI” series began way back in 2018, making it one of the longest-running series on the basics of artificial intelligence around!

Examples of bias in natural language processing are boundless — but MIT scientists have investigated another important, largely underexplored modality: medical images. Using both private and public datasets, the team found that AI can accurately predict self-reported race of patients from medical images alone. Using imaging data of chest X-rays, limb X-rays, chest CT scans, and mammograms, the team trained a deep learning model to identify race as white, Black, or Asian — even though the images themselves contained no explicit mention of the patient’s race. “Even when you filter medical images past where the images are recognizable as medical images at all, deep models maintain a very high performance. Just as in this work, human experts are not able to accurately predict patient race from the same redacted clinical notes.

A recent survey of CEOs and senior executives by Gartner, Inc. revealed significant shifts in their thinking regarding people, purpose, prices and productivity in 2022, specifically on matters of sustainability, workforce issues and inflation. Conversely, 63% of CEOs see the metaverse as either not applicable or very unlikely to be a key technology for their business. Additionally, for the first time in the history of the survey, CEOs placed environmental sustainability in their top 10 strategic business priorities, coming in at 8th place. Sustainability also appears as a competitive differentiator for CEOs in 2022 and 2023, on the same level as brand trust among respondents. Regarding inflation, 62% of CEOs see general price inflation as a persistent or long-term issue.

It’s easier to understand the need for various types of analytics and insights in context. You need real-time insights into how well your IoT devices and other equipment are functioning. To meet those challenges, you need real-time insights into how well your IoT devices and other equipment are functioning. From the supply chain side of the operation, you have to be able to act on data quickly. For many organizations, there may be a bottleneck in the ETL (extract, transform, load) process that hampers speed.

“We’re training, we’re coaching, we’re guiding, we’re practicing,” Michelle Dotson, head of sales enablement at Zoom, told VentureBeat. Zoom deployed AI-generated avatars to train sales teamThat’s where Jenny came in. “My team has fun with Jenny – sometimes we tailor the training and turn up how hard it is to impress her,” she said. I don’t know what you’re talking about.’”Zoom uses diverse AI-generated avatarsMore recently, as Zoom has built a diverse sales enablement team, Jenny has gotten some company. “But there’s a lot of work that goes into creating this place and space where you might have a conversation with Jenny.”Before Zoom launches a product training on Jenny, Dotson said 10-20 people practice with her first.

Remote work has become the norm, and outside the office walls, employees are letting down their personal security defenses. Why self-learning AI is essential in the new cybersecurity landscapeAttackers are constantly innovating, transforming old attack patterns into new ones. “AI will always be a supporting element, not a replacement for human teams and knowledge. Even with the best intentions in the world, security teams will always be constrained by resources. First, the solution should augment the human teams without creating substantial additional work.

Let Goodfellow and his team work from wherever they want. This is important to note because there’s really only one other company on the planet as deeply invested in AGI and full of well-known talent as OpenAI, and that’s DeepMind. Siri, Siri, wherefore art thou Siri? But the timing of his joining the DeepMind team is quite exciting. There’s no telling what Goodfellow and the DeepMind team can accomplish together.

In The NewsAndrew Ng : AI’s Threats to Jobs and Human Happiness Are Real There’s a movement afoot to counter the dystopian and apocalyptic narratives of artificial intelligence. Some people in the field are concerned that the frequent talk of AI as an existential risk to humanity is poisoning the public against the technology and are deliberately setting out more hopeful narratives. IBM’s CEO wants to chart quantum-fueled, AI-powered path to the future Overview of Arvind Kirshna's virtual round table discussion ahead of the IBM Think Conference. He touched upon a range of subjects from the role he hopes AI will play for his company going forward, the organization’s quantum computing efforts and the growing market for ESG and sustainability.

DeepMind today unveiled a new multi-modal AI system capable of performing more than 600 different tasks. Before we can compare GPT-3 and Gato however, we need to understand where both OpenAI and DeepMind are coming from as businesses. In fact, it’s arguably just as impressive as DeepMind’s Gato, but that assessment requires some nuance. Basically, Gato and GPT-3 are both robust AI systems, but neither of them are capable of general intelligence. Gato’s ability to perform multiple tasks is more like a video game console that can store 600 different games, than it’s like a game you can play 600 different ways.

He offered a brief primer on NLP, and explained how the technology can help clinicians identify at-risk patients, and how it can help clinicians and health IT leaders uncover insights into social determinants of health and advance population health efforts. How can NLP help clinicians identify at-risk patients? As mentioned, information on patients' social determinants of health remains poorly coded in most electronic health records. How can NLP help advance population health efforts? While the last example on SDOH is a great illustration of how NLP can impact population health, we don't have to focus on SDOH to see how NLP can help with population health initiatives.

Since foundation models can be adapted to myriad downstream applications, no organization can fully anticipate all potential risks (or foresee all potential benefits). The goal of the board is to facilitate the process of foundation model developers releasing foundation models to external researchers. The foundation models review board operates as follows: A foundation model developer first puts out a call for proposals. Research proposal. In the end, we believe this process allows each foundation model developer to make a more well-informed decision.

Never mind reading generic guides or practicing with friends — Google is betting that algorithms can get you ready for a job interview. The company has launched an Interview Warmup tool that uses AI to help you prepare for interviews across various roles. Interview Warmup is aimed at Google Career Certificates users hoping to land work, and most of its role-specific questions reflect this. There are general interview questions, though, and Google plans to expand the tool to help more candidates. This isn't going to level the playing field, but it might help you brush up on your interview skills.

The other new delivery features aren't quite as big a deal, but they'll actually be available to people not in LA. This will only work on Android for starters, but hopefully Uber Eats users with iPhones will get this feature as well (or something similar with Siri). Uber Eats will also soon be available in a handful of sports arenas and stadiums, as well. Meanwhile, Uber drivers using EVs will get some new tools to help make their lives easier. That's at least in some part thanks to the relative diversity of Uber's offerings — indeed, Uber Eats played a huge role in keeping business up as traditional rides tanked in 2020.

AboutThe collaboration between faculty from the US and EU is intended to better identify, define and understand the challenges, opportunities, and regulatory approaches to artificial intelligence in a global society. The goals of the Transatlantic Dialogue are to:Engage in transatlantic and cross disciplinary discussions about potential regulation of AI and machine learningFoster a transatlantic community of researchers to compare approaches,Inform policy, and create networks to address the challenges and questions surrounding AI regulation for a human centered future. The dialogue is organized by four thematic panels:

There are so many ways AI can make advertising more efficient and effective. This post offers a collection of case studies, use cases and vendors, including ⁦@Go_AiAd⁩ ⁦@Persado⁩ ⁦@onescreenai⁩ #AI #Advertising https://t.co/HXJhW1LA6DArtificial intelligence is transforming how advertisers do their jobs and what success looks like. Learn more about how AI can be your competitive advantage in advertising in this in-depth ...

Inflection AI, the machine learning startup headed by LinkedIn co-founder Reid Hoffman and founding DeepMind member Mustafa Suleyman, has secured $225 million in equity financing, according to a filing with the U.S. Securities and Exchange Commission. Palo Alto, California-based Inflection has kept a low profile to date, granting relatively few interviews to the media. Regardless, the size of Inflection’s funding round reflects the high cost of building sophisticated AI systems. Inflection recently poached AI experts from Google and Meta, CNBC reported in March. “Even at the bigger tech companies, there’s a relatively small number of people actually building these [AI] models.

From LEGO competitions to the robotics lab - learn more about Akhil’s career journey and his advice for aspiring DeepMinders in today’s blog: https://t.co/SPkQIwR1Yd #LifeAtDeepMind https://t.co/RNdzYMiZR9If you want to be at DeepMind, go for it. Apply, interview, and just try. You might not get it the first time but that doesn’t mean you can’t try again. I never thought DeepMind would ...

RT @DeepMind: From LEGO competitions to the robotics lab - learn more about Akhil’s career journey and his advice for aspiring DeepMinders in today’s blog: https://t.co/SPkQIwR1Yd #LifeAtDeepMind https://t.co/RNdzYMiZR9If you want to be at DeepMind, go for it. Apply, interview, and just try. You might not get it the first time but that doesn’t mean you can’t try again. I never thought DeepMind would ...

Krishna sat down yesterday for a virtual round table discussion with several reporters ahead of the IBM Think Conference, which is starting today in Boston. He sees healthcare and AI being sometime off in the future, and that’s a big reason he decided to dump the Watson Health division. But if you think about pharmaceutical drugs, an area we’re quite excited about quantum, I think that’s probably going to be a little bit later,” he said. Finally, he sees a role for AI helping to sift through all the data that tools like Envizi and many others are producing, and using that data to take meaningful action. And for the sheer amount of data and the sheer amount of analysis needed means AI is the only answer.

The AI 100 is CB Insights' annual list of the 100 most promising private AI companies in the world. GET THE LIST OF 2022 AI 100 COMPANIES First name Last name Email Company name Job Title Phone numberCB Insights has unveiled the winners of the sixth annual AI 100 — a list of the 100 most promising private AI companies across the globe. Table of contentsTop AI companies 2022: AI 100 cohort highlightsThe AI 100 Class of 2021: Where are they now? AI 100 (2022) Track the 100 most promising private AI companies to watch in 2022. Track the AI 100 winners AI 100 100 itemsthe AI 100 class of 2021: where are they now?

InsightAs technology drives a new ‘vertical world’, the evolution of the Metaverse is likely to be driven by geopolitical competition. Meanwhile, the real world of politics and economics will play a key role in shaping the direction of the Metaverse, too. As technology triggers polarisation among governments and large organisations and forces global institutions to reconsider grounds for cooperation, the Metaverse is likely to become embroiled in future geopolitical conflicts, argues geopolitical futurist Abishur Prakash. “The Metaverse is a blank canvas that nations will use to advance their geopolitical agenda,” Prakash, co-founder of Center for Innovating the Future, told TRT World. Does this mean a Metaverse “with Chinese characteristics” running on Chinese hardware could be on the horizon?

Uli Köppen, Head of the AI + Automation Lab of Bayerischer Rundfunk, and Claire Leibowicz, Head of AI and Media Integrity at Partnership on AI,shared their experiences. The AI + Automation Lab of Bayerischer RundfunkUli Köppen explained how her team decided to create guidelines to guide their use of AI and automation, and how these guidelines can add value to the irreplaceable work of journalists in the newsroom. With an abundance of industries implementing AI into their workforce, looking at other guidelines for inspiration can be beneficial when creating your own organisation’s ethical guidelines, opening up your mind to aspects you may not have considered otherwise. Building a diverse team while creating your AI guidelines is also critical as it allows for more inclusive, effective outcomes, both internally and for your audiences. Partnership on AIWhen creating their ethical guidelines, the team at Partnership on AI (PAI) started by asking: How do we promote a healthy information ecosystem in the age of AI?

Find him on Twitter: @Gadget_RyZoom has caused a stir following reports that it’s developing an AI system for detecting emotions. The letter urges Zoom to cease research on emotion-based AI. One of the key concerns is that emotion-detecting AI could be used for things like hiring or financial decisions; such as whether to grant loans. All recommended ranges for metrics are based on publicly available research,” Zoom explained. Zoom IQ tracks metrics including:Talk-listen ratioTalking speedFiller wordsLongest spiel (monologue)PatienceEngaging questionsNext steps set upSentiment/Engagement analysisEsha Bhandari, Deputy Director of the ACLU Speech, Privacy, and Technology Project, called emotion-detecting AI “creepy” and “a junk science”.

Nvidia is collaborating with researchers to present 16 papers at Siggraph 2022, spanning multiple research topics that impact the intersection of graphics and AI technologies. One paper details innovation with reinforcement learning models, done by researchers from the University of Toronto and UC Berkeley, that could help to teach AI multiple skills at the same time. “Our goal is to do work that’s going to impact the company,” David Luebke, vice president of graphics research at Nvidia, told VentureBeat. Luebke noted, however, that nearly all the research is also relevant for AI use outside the graphics field. “The thing about graphics is that it’s really, really hard and it’s really, really compelling,” he said.

Is an AI alien invasion headed for earth? According to De Freitas, Gato AI simply needs to be scaled up in order to create an AI that rivals human intelligence. There is nothing new, per se, about studying Alt Intelligence, but the hubris associated with it is. It may just be that the best path to artificial (general) intelligence isn’t through Alt Intelligence, after all. It’s nice to know that most experts don’t believe the AGI alien invasion will arrive anytime soon.

Code generation : generating appropriate code based on textual input. Models thus fine-tuned for programming language generation can then be deployed for various coding tasks, including code generation and generation of unit test scripts for code validation. Programming language generation models available today are CodeBERT, CuBERT, GraphCodeBERT, CodeT5, PLBART, CodeGPT, CodeParrot, GPT-Neo, GPT-J, GPT-NeoX, Codex, etc. The current set of programming language models supports a context size of 2,048 tokens; Codex supports 4,096 tokens. Kamalkumar Rathinasamy leads the machine learning based machine programming group at Infosys, focusing on building machine learning models to augment coding tasks.

When the call for nominees went out, the response was immense, with more than 800 submissions from product leaders across the globe. “In reviewing the hundreds of Product 50 submissions, the judges got to better understand these product leaders, from how they think to how they lead to how they are challenging the status quo,” Bauer says. “The experience reinforced our belief that product people really are driving businesses forward, and I’m so thrilled that we get to celebrate 50 of the top product leaders across the world with this list.”Selecting the Product 50 winnersThe inaugural Product 50 winners are the new guard in product leadership. Meet the 2022 Product 50 winnersMost Admired Product LeaderWhat up-and-coming product leaders aspire to beJen CarterTitle : Product Manager & Head of Technical Team, Google.org: Product Manager & Head of Technical Team, Google.org Company : Google: Google About: Jen is the global head of technology at Google.org, Google’s philanthropic arm, leading all of Google’s pro bono efforts. Most Promising Product Up-and-ComerNot an executive yet, but sure to be making headlines in the product world one dayZen Liu ZhanhongTitle : Senior Product Manager: Senior Product Manager Company : LingoAce: LingoAce About: Zen is a senior product manager at LingoAce, where he instills a culture of experimentation within the product team.

The problemConsider these two sample buttons that you might find on a web page or mobile app. Accessibility: AI to the rescueArtificial intelligence algorithms, it turns out, can be trained to solve problems like this and even to improve automatically as they are exposed to more data. But there was a part of those tools called The CRAFT (Character-Region Awareness For Text) model that held out promise for AI and accessibility. What complicates the matter, even more, is the fact that every person, whether they are visually impaired or not, has a different perception of what is legible. For example, each image can be stamped with a reference piece of text on a fixed background prior to analysis.

For the cult classic Stargate science fiction franchise, which spanned three series (SG-1, Stargate Atlantis and Stargate Universe), character and plot development was helmed by Stargate co-creator Brad Wright. None other than Laurence Moroney, AI lead at Google, responded by picking up the gauntlet to try and prove what AI could do.– though he wasn’t initially worried that AI would replace him or other writers. “That’s what blew me away because it’s not just better – it’s like, whoa… better!”How the Stargate AI script was generatedThe process for producing the Stargate AI-generated script was much the same as the way any AI model is first built – by training it. Moroney trained the AI model with every Stargate episode script ever written, providing the system with a corpus of every line of dialogue and plot. Bringing scriptwriting AI to the Google enterpriseThe models and techniques used by Moroney to build out the Stargate script could be applicable to the enterprise.

Science-fiction movies depict advanced software processing large amounts of documents to find hidden insights that save the day. One of them, San Francisco-based Automation Hero, today launched v6.0 of its Hero Platform, a SaaS service the company claims takes a quantum leap in OCR (optical character recognition) document-processing accuracy. Yes, this includes doctors’ notoriously bad handwriting, Automation Hero founder and CEO Stefan Groschupf told VentureBeat. Faster new AI engineThe latest version of the AH platform provides a faster new AI engine for document processing and automation, Groschupf told VentureBeat. Insurance claims processing is Automation Hero’s best business right now, Groschupf told VentureBeat.

But Sony has been driving progress in the noise-canceling headphone space for the past several years, and the new $400 WH-1000XM5 are no exception. Eye of the beholder and all that, but to me, the WH-1000XM5 just look better than their predecessors. That’s especially true since Sony had only made minor tweaks to its design since the original MDR-1000X headphones were released in 2016. But as I’ve found with other noise-canceling headphones, this can also lead to audible distortion when playing music loudly. The Sony 1000XM5 only make small improvements to the 1000XM4, but that’s all the company needed to do.

One of the best algorithms for detecting fake accounts thinks it might be, which shows how challenging it is to quantify the proportion of fake accounts across the social network. Counting Twitter bots has become a point of contention in Elon Musk’s ongoing $44 billion acquisition of Twitter. Musk also outlined a plan to count bots himself that involved sampling 100 @Twitter followers to see how many were bots and said the approach suggests over 20 percent of accounts are fake. But accurately quantifying the percentage of bots on Twitter is a lot more difficult, according to experts. But many of those accounts also disappeared shortly after, suggesting that Twitter catches bots fairly quickly.

“Engineering systems are not divorced from the social systems on which they intervene,” Cen says. There, she developed an interest in AI algorithms, curious about when and why they misbehave. In one such project, Cen studies options for regulating social media. How should auditors check that a social media platform complies with this regulation? Designing an auditing procedure is difficult in large part because there are so many stakeholders when it comes to social media.

Find him on Twitter: @Gadget_RyA machine learning exec who left Apple due to its return-to-office policy has reportedly joined Google DeepMind. Ian Goodfellow is a renowned machine learning researcher. In a departure note to his team at Apple, Goodfellow cited the company’s much-criticised lack of flexibility in its work policies. Prior to Apple, Goodfellow had previously worked for Google as a senior research scientist. Every talented AI researcher like Goodfellow that leaves Apple is a potential win for Google and other companies.

BrainChip and Edge Impulse have announced a partnership to deliver next-gen platforms for machine learning. BrainChip, as the world’s first commercial producer of neuromorphic IP, will contribute its ultra-low power Akida neuromorphic edge AI processor architecture. Edge Impulse is a leading company for edge AI development that delivers platforms, tools and services for sensor, audio and vision applications. Edge Impulse and BrainChip are addressing the goal of developers to create smart devices by implementing AI/ML capabilities into their products. The partnership combines BrainChip’s unique IP with Edge Impulse’ design platform into one solution.

If one were to take that at face value, it would mean that by now autonomous driving would have been a reality already. Kendall is the cofounder and CEO of Wayve, a company founded in 2017 to tackle the challenge of autonomous driving based on a deep learning approach. Today, Wayve announced a partnership with Microsoft to leverage the supercomputing infrastructure needed to support the development of AI-based models for autonomous vehicles on a global scale. Autonomous Vehicles 2.0Wayve was founded following world-class research in deep learning from the University of Cambridge, building their first robot in a house-office garage. When it comes to data, Wayve focuses on leveraging video collected via cameras in real-time, with radar data having a complementary role.

Women leaders are increasingly at the center of AI innovation, and they’ll be in the spotlight at this year’s Women in AI Breakfast and Women in AI Awards as part of Transform 2022. We’re also pleased to announce the fourth annual Women in AI Awards, which recognizes the accomplishments of the women in the AI industry. Women in AI BreakfastThis year’s Women in AI Breakfast returns live! Women in AI AwardsAnd then once again, VentureBeat will help honor the extraordinary women leaders across the AI industry with the Women in AI Awards. We are still finalizing the date and time for the 2021 Women in AI Awards presentation — stay tuned!

Emza Visual Sense and Alif Semiconductor have demonstrated an optimized face detection model running on Alif’s Ensemble microcontroller based on Arm IP. The two found it is suitable for enhancing low-power artificial intelligence (AI) at the edge. Emza said it trained a full “sophisticated” face detection model on the NPU that can be used for face detection, yaw face angle estimation and facial landmarks. The complete application code has been contributed to Arm’s open-source AI repository called “ML Embedded Eval Kit,” making it the first Arm AI ecosystem partner to do so. “As we look at the dramatically expanding horizon for TinyML edge devices, Emza is focused on enabling new applications across a broad array of markets,” said Yoram Zylberberg, CEO ofEmza.

This is where “responsible AI” comes in — that is, a governance framework that documents how a specific organization should address the ethical and legal challenges surrounding AI. A key motivation for responsible AI endeavors is resolving uncertainty about who is accountable if something goes wrong. Putting responsible AI into practice“We created the first responsible AI governance platform because we saw an opportunity to help companies keep their AI systems and machine learning models aligned with human values,” Navrina Singh, founder and CEO of the company, told VentureBeat. The future of AI governanceDespite tech giants like Google and Microsoft slowly pulling back the curtain to share how they’re approaching AI in their workplace, responsible AI is a relatively new field that is still evolving. Singh notes that she envisions a future in which enterprises prioritize responsible AI the way they do cybersecurity.

Ask 100 people on the street what they believe AI is capable of, and you’re guaranteed to get a cornucopia of nonsensical ideas. To them, the only thing holding back the artificial general intelligence industry is scale. The big problemThere’s no governing body to officially declare that a given machine learning model is capable of artificial general intelligence. Let’s say the folks at DeepMind suddenly shout “Eureka!” and declare they’ve witnessed the emergence of a general artificial intelligence. Or that DeepMind’s Gato, OpenAI’s DALL-E, or Google’s Duplex would be possible without inventing an AI capable of learning as humans do.

Welcome to the fledgling military metaverse. The result is a lot less polished, cutesy, and spacious than Mark Zuckerberg’s virtual world vision, but that’s partly the point. Robinson adds that the company is now working on a platform that will allow many different scenarios to be represented in augmented or virtual reality. “What we’re building is really a military metaverse,” he says. “It is an extremely complex type of simulation, especially given the fidelity that the military demands,” Dohrman says.

In September 2021, scientists Sean Ekins and Fabio Urbina were working on an experiment they had named the “Dr. Evil project.” The Swiss government’s Spiez laboratory had asked them to find out what would happen if their AI drug discovery platform, MegaSyn, fell into the wrong hands. The plan was to use it to accelerate the drug discovery process for rare and neglected diseases. Normally, MegaSyn would be programmed to generate the most specific and least toxic molecules. “Without being overly alarmist, this should serve as a wake-up call for our colleagues in the ‘AI in drug discovery’ community,” Ekins and his colleagues wrote.

Typical approaches for tackling intersection control problems use mathematical models to solve one simple, ideal intersection. Reinforcement learning is a trial-and-error method where the control algorithm learns to make a sequence of decisions. That means we don’t have to wait until we get to 100 percent autonomous vehicles to get benefits from this approach,” she says. In addition, they intend to study how their control system could impact safety when autonomous vehicles and human drivers share the road. For instance, even though autonomous vehicles may drive differently than human drivers, slower roadways and roadways with more consistent speeds could improve safety, Wu says.

Even as rapid improvements in artificial intelligence have led to speculation over significant changes in the health care landscape, the adoption of AI in health care has been minimal. A 2020 survey by Brookings, for example, found that less than 1 percent of job postings in health care required AI-related skills. MGB’s chief academic officer and AI Cures co-chair Ravi Thadhani thinks that five times that amount would be necessary in order to do more transformative work. The presented research spanned different areas of focus from clinical AI and AI for biology to AI-powered systems and others. “I was really impressed with the breadth of work going on in this space,” Collin Stultz, a professor at MIT, says.

Forma AI, a Toronto-based sales performance management (SPM) solution provider, says yes. The company, which offers an AI-supported SPM platform, wants enterprises to rethink the way sales compensation is managed and optimized. But sales compensation management – a $1 trillion annual cost center – remains a decades-old bottleneck. Old sales compensation methods stifle growth“The way sales compensation has been done historically has been completely unscientific,” Nabeil Alazzam, founder and CEO of Forma, told VentureBeat. You then give that recommendation to your sales ops team and sales conference.”Alazzam insisted that these sales compensation management processes are broken.

Gradiant, a Boston-based water solutions provider, has acquired Synauta to accelerate the use of digital twins in the water treatment industry. Digital water characterizes various ways of quantifying water processing infrastructure to improve efficiency and bring digital transformation to the water industry. Emergen Research predicts digital water will grow from $7.96 billion in 2020 to $19.43 billion in 2028. Other companies in the digital water segment include Bentley Systems, Innovyze, Schneider Electrics, Atonix Digital, Plutoshift, Assetic, Copperleaf Technologies and Xenius. And with digital water coming into the mix, you can produce better water at a reasonable cost.”

In this way, they act as a bridge between Industry 4.0 and the analog age it will replace. “With cognitive intelligence,” Ray said, “robots and drones can make proactive decisions.” And, he added, this is still early days. Instead, it partners with those makers to instill equipment with “cognitive intelligence,” said Chris Nielsen, CEO of Levatas. The company competes with other makers of robotic cognitive intelligence systems for a piece of the growing industry. As Industry 4.0 advances, these analog-reading robots – like Spot – won’t be pushed out of their jobs.

Join AI and data leaders for insightful talks and exciting networking opportunities. Observability leaders report a 69% better mean time to resolution (MTTR) for unplanned downtime or performance degradation thanks to investment in observability. 66% of leaders report that their visibility into application performance is excellent (compared to 44% of beginners). “These observability leaders are more competitive, more resilient and more efficient as a result.”Observability becoming more mainstreamThis area is maturing quickly, Xanthos said. “Organizations that use the right observability tools and practices and build to attract talent stand the best chance of becoming leaders in observability,” Xanthos said.

According to Doctor Nando de Freitas, a lead researcher at Google’s DeepMind, humanity is apparently on the verge of solving artificial general intelligence (AGI) within our lifetimes. DeepMind recently released a research paper and published a blog post on its new multi-modal AI system. Dubbed ‘Gato,’ the system is capable of performing hundreds of different tasks ranging from controlling a robot arm to writing poetry. When a DeepMind researcher declares “the game is over,” it conjures a vision of the immediate or near-term future that doesn’t make sense. To insinuate that AGI will arise from a system whose distinguishing contribution is how it serves models?

First, Bloomberg and analyst Ming-Chi Kuo suggested the iPhone 15 — which will be released next year — will sport a USB-C port. If your primary device (iPhone) is charged by a USB-C port, you don’t want other accessories to charge through another port. So all certified USB-C to C cables could potentially aid in fast charging for iPhones. Different data transfer speeds of USB 3.1/3.2Apple already uses USB-C ports for charging and data transfer in MacBooks and iPads. For years, the European Union has been trying to move Apple to use USB-C in iPhones.

John Deere is announcing the acquisition of a state-of-the-art algorithm package from artificial intelligence startup Light. Today’s purchase is all about John Deere’s need for speed — and accuracy, but first let’s talk about rapid development. Background: Light, the company Deere’s partnering with for the asset purchase, is a major player in the autonomous vehicle field. But this acquisition gives us every reason to update that assessment — it’s now quickly becoming the AI company to watch. If nothing else, this acquisition signals John Deer’s further emergence as an artificial intelligence company at the forefront of the ag-tech industry.

Banks are in one of the best positions for leveraging AI in the coming years because the largest banks have massive volumes of historical data on customers and transactions that can be fed into machine learning algorithms. We recently completed our Emerj AI in Banking Vendor Scorecard and Capability Map in which we explored which AI capabilities banks were taking advantage of the most and which they might be able to leverage in the future.

For years, tech companies have relied on something called the Fitzpatrick scale to classify skin tones for their computer vision algorithms. Originally designed for dermatologists in the 1970s, the system comprises only six skin tones, a possible contributor to AI’s well-documented failures in identifying people of color. Now Google is beginning to incorporate a 10-skin tone standard across its products, called the Monk Skin Tone (MST) scale, from Google Search Images to Google Photos and beyond. The Monk Skin Tone scale is named after Ellis Monk, a Harvard University sociologist who has spent decades researching colorism’s impact on the lives of Black people in the United States. “We can weed out these biases in our technology from a really early stage and make sure the technology we have works equally well across all skin tones.

As companies increasingly involve AI in their hiring processes, advocates, lawyers, and researchers have continued to sound the alarm. Hiring algorithms can penalize applicants for having a Black-sounding name, mentioning a women’s college, and even submitting their résumé using certain file types. The EEOC instructs employers to disclose to applicants not only when algorithmic tools are being used to evaluate them but what traits those algorithms assess. The CDT report also urges audits of hiring algorithms before and after they’re put into use—a step not included by the EEOC—and refers to bias against hiring people with disabilities online as an “invisible injustice.”As a teenager, Lydia X.Z. A coauthor of the 2020 CDT hiring discrimination report, Brown called Thursday’s guidance a big win, after years of advocacy, for people like them with disabilities.

While AI is improving the ability to detect cybersecurity threats, he explained, threat actors are also upping the ante. As enterprise companies battle a growing number of major security breaches, they need to prepare for increasingly sophisticated AI-driven cybercrimes, experts say. “Basically, you can’t bring a human tool to an AI fight.”4 ways to prepare for the future of AI cyberattacksExperts say security pros should take several essential steps to prepare for the future of AI cyberattacks:Provide continued security awareness training. The future of offensive AIAccording to Nachreiner, more “adversarial” machine learning is coming down the pike. “This gets into how we use machine learning to defend – people are going to use that against us,” he said.

Delivering AI solutions from the test bed to production environments will probably be the key focus for the enterprise throughout the next year or longer. But organizations should be cautious not to push AI too far too fast, despite the pressure to keep up with the competition. For many organizations, however, the question is where to find this low-hanging fruit. With the right sensor-driven data, AI can also assess workloads across the digital environment and even shift it around to ensure the work-machine balance remains optimal. The extent to which AI can support the enterprise depends very much on an organization’s “data maturity,” says Sumit Kumar Sharma, enterprise architect at In2IT Technologies.

Dimensional Research reports that 96% of AI projects encounter problems with data quality, data labeling and building model confidence. Since AI failures are usually related to data quality and data drifts, practitioners can use a data-centric approach to keep AI applications healthy. The right size of dataset for your AI model depends on the problem you are addressing. The right size of dataset for your AI model depends on the problem you are addressing. Production systems should implement ML-equivalents of devops best practices including logging, monitoring and CI/CD pipelines which account for data lineage, data drifts and data quality.

The shift to working from home during the pandemic has changed workers’ technology habits, including — the data would suggest — where it concerns voice assistants. For example, according to a 2020 report from Voicebot.ai and Cerence, more people are now using voice assistants in cars than on smart speakers. A 2018 Pindrop poll found that 84% of companies expected to be using voice technology with their customers in 2019. Based in New York, Rain partners with businesses and brands like Nike, Amazon, Starbucks, Dreamworks, and Unilever to build “voice experiences” including for the car and smart speakers. Building voice experiences for brandsRain, a technology and design agency, helps customers to conceive, build, and manage voice experiences that integrate with brand services and ecosystems.

Enter AI-powered fraud detectionThe 5-year-old, San Francisco-based Inscribe aims to combat this mounting problem with its AI-powered fraud detection and document automation tools. The company is among a growing number catering to the post-COVID global fraud detection and prevention market. Why fraud detection is so hardLargely, that comes down to resource constraints and the increasing difficulty of the problem, he said. Many financial services companies have well-honed application processes, but they either have badly written fraud detection software or no such software at all. The consideration then isn’t only automating fraud detection – but trusting it.

Don’t be scared of AI jargon; we’ve created a detailed AI glossary for the most commonly used Artificial Intelligence terms and the basics of Artificial Intelligence. Artificial Intelligence in everyday life (2022)Artificial intelligence is now part of our everyday environment, whether we are aware of it or not. We need AI in our lives such as Artificial Intelligence call centers or Artificial Intelligence games. Artificial Intelligence in everyday life: Social MediaTwitterTwitter has started to rely on artificial intelligence behind the scenes to improve its product, from suggesting tweets to fighting offensive or racist material and improving the user experience. Artificial Intelligence in everyday life: Medical applicationsConclusionThanks to the continuous increase in data available to machine learning algorithms, Artificial Intelligence is evolving.

Since the early days, the problem with self-driving cars has been obvious: there’s no control. In all other situations, where the goal has been the pursuit of mythical level 5 autonomy, self-driving cars have failed miserably. Even without additional infrastructure, we’ve got self-driving cars today that are more than capable of driving as safely as humans. Yet, even with more than $80 billion flowing into the field from 2014 to 2017, we have no self-driving cars. This is the crux of the problem with self-driving cars.

DeepMind today unveiled a new multi-modal AI system capable of performing more than 600 different tasks. Before we can compare GPT-3 and Gato however, we need to understand where both OpenAI and DeepMind are coming from as businesses. In fact, it’s arguably just as impressive as DeepMind’s Gato, but that assessment requires some nuance. Basically, Gato and GPT-3 are both robust AI systems, but neither of them are capable of general intelligence. Gato’s ability to perform multiple tasks is more like a video game console that can store 600 different games, than it’s like a game you can play 600 different ways.

Making good on the @UN #Sustainable Development Goals with the promise of #ArtificialIntelligence Read the blog featuring the many ways #AI is being used and can be used for doing good, with @BeenaAmmanath @DeloitteAI @DeloitteUK at #AIforGood #AI4Health https://t.co/ahFCKYgehW https://t.co/0dfeCMVTQR

Kate Kaye is an award-winning multimedia reporter digging deep and telling print, digital and audio stories. She covers AI and data for Protocol. Her reporting on AI and tech ethics issues has been published in OneZero, Fast Company, MIT Technology Review, CityLab, Ad Age and Digiday and heard on NPR. Kate is the creator of RedTailMedia.org and is the author of "Campaign '08: A Turning Point for Digital Media," a book about how the 2008 presidential campaigns used digital media and data.

RT @lanfrica: Thank you @shodeiyanuoluwa for writing this awesome article on the just concluded 2022 #AfricaNLP workshop! It is a great resource to anyone who attended (or could not attend) and would like to reflect on the event. Read it here https://t.co/puVBYxyIvA #ICLR2022 #MasakhaneNLP https://t.co/Qn5DH6tHm1

Find him on Twitter: @Gadget_RyNuance has partnered with The Health Management Academy (The Academy) to launch The AI Collaborative, an industry group focused on advancing healthcare using artificial intelligence and machine learning. Earlier this year, Microsoft acquired Nuance with the promise of ushering in a “new era of outcomes-based AI”. That community will be The AI Collaborative. (Photo by Cytonn Photography on Unsplash)Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London.

An important part of the customer experience process, conversational AI, is quickly taking center stage. Raj Koneru, CEO at Kore.ai, told VentureBeat in an exclusive interview that conversational AI is the foundation of the metaverse. The rise of conversational AIMore companies have embraced the use of conversational AI throughout the last few years. Conversational AI merges the practicality of AI to produce human-like interaction between the human who asks the questions and the machine that answers. Gartner’s Magic Quadrant for Enterprise Conversational AI Platforms 2022 lists Kore.ai as a leader in the conversational AI space, showing the company’s upward trajectory in the industry.

A 2018 Pindrop poll found that 84% of companies expected to be using voice technology with their customers in 2019. Based in New York, Rain partners with businesses and brands like Nike, Amazon, Starbucks, Dreamworks, and Unilever to build “voice experiences” including for the car and smart speakers. Building voice experiences for brandsRain, a technology and design agency, helps customers to conceive, build, and manage voice experiences that integrate with brand services and ecosystems. The experiences can take the form of bespoke voice assistants or third-party apps on existing assistants such as Alexa, Google Assistant, and Siri. One of Rain’s voice experiences, built for Google Assistant.

It was a week filled with AI news from Google’s annual I/O developer’s conference and IBM’s annual THINK conference. On Monday, facial recognition company Clearview AI, which made headlines for selling access to billions of facial photos, settled a lawsuit filed in Illinois two years ago by the American Civil Liberties Union (ACLU) and several other nonprofits. The company was accused of violating an Illinois state law, the Biometric Information Privacy Act (BIPA). Under the terms of the settlement, Clearview AI has agreed to ban most private companies permanently from using its service. But many experts pointed out that Clearview has little to worry about with this ruling, since Illinois is one of just a few states that have such biometric privacy laws.

Artificial intelligence (AI) is a foundational catalyst for digital business, according to a new report by Gartner. In evaluating the trends that will shape the future of AI in the enterprise, there are five trends that Gartner sees as being the most significant. Composite AI is the ability to deploy a variety of AI techniques aligned with the right use cases. Responsible AI is an umbrella term for making appropriate business and ethical choices when adopting AI. Responsible AI is increasingly important amidst growing regulatory oversight, consumer expectations and emerging sustainability goals.

It alleged that Clearview AI had failed to comply with the Illinois biometric information privacy act (BIPA). And its CEO is certainly aware of the ever-dwindling stomach for public-facing facial recognition systems. But Clearview AI was never set up to be the world’s best company at selling facial recognition systems. The gist of the problem is that Clearview AI provides a service the US government wants. Arguably, the ACLU’s “big win,” has gone a long way towards making people feel like Clearview AI is less dangerous than it actually is.

A person with individual artificial intelligence will not become eviler or kinder—he will become stronger. But the main question is how much a person with individual artificial intelligence will be stronger than us ordinary people. I will not specifically analyze the prospects of a fighter pilot, or a tank commander equipped with his own artificial intelligence. Let’s remember together how it wasThe very concept of artificial intelligence appeared in 1956 after the “Dartmouth Seminar,” a conference on artificial intelligence at Dartmouth College. The human brain contains approximately 86 billion neurons, but we are only interested in a small part, about 16 billion in the cerebral cortex.

He announced a forthcoming app called AI Test Kitchen that will make the bot available for outsiders to try. Google’s LaMDA, or Language Model for Dialogue Applications, is an example of what machine-learning researchers call a large language model. Right now Google Assistant and Amazon’s Alexa can only perform certain preprogrammed tasks, and they deflect when presented with something they don’t understand. Chat logs released by Google show that LaMDA can—at least at times—be informative, thought-provoking, or even funny. At the same time, large language models have proven fluent in talking dirty, nasty, and plain racist.

A robot chef designed by researchers at Cambridge University has been trained to taste a dish’s saltiness and the myriad of ingredients at different stages of chewing – a process imitating that of humans. Replicating the human process, researchers say, should result in a tastier end product. A robot ‘chef’ has been trained to taste food at different stages of the chewing process to assess whether it is sufficiently seasoned. To map human taste, the researchers trained the robot chef to make omelettes. It then tasted nine variations of scrambled egg and tomatoes at three stages of the chewing process.

EthicsSurveillance AI needs fake data to track people Synthetic data suppliers promise that the fake data they provide can reduce bias in AI, but it also helps build controversial technologies used to monitor people’s behavior and interpret their emotions and body language. DALL-E 2 Creates Incredible Images—and Biased Ones You Don’t See Photorealistic depictions that look like the real world, shared widely on social media by a select number of early testers, have given the impression that the model can create images of almost anything. “There were a lot of non-white people whenever there was a negative adjective associated with the...

Last month, OpenAI introduced the second-generation version of DALL-E, an AI model trained on 650 million images and text captions. We are in the future,” says Vipul Gupta, a PhD candidate at Penn State who has used DALL-E 2. As part of OpenAI’s “red team” process—in which external experts look for ways things can go wrong before the product’s broader distribution—AI researchers found that DALL-E 2’s depictions of people can be too biased for public consumption. From conversations with roughly half of the 23-member red team, we found that a number of them recommended OpenAI release DALL-E 2 without the ability to generate faces at all. But the red team was not given access to the DALL-E 2 training data set.

Heather Knight will present work from the Collaborative Humans and Robotics: Interaction, Sociability, Machine learning and Art (CHARISMA) robotics lab at Oregon State University (OSU). The pandemic has brought increasing automation into everyday human spaces, making ever more relevant CHARISMA’s work in service robots, expressive communication, and autonomous and human-in-the-loop robot behavior systems. From robot furniture to robot comedy—and from nonverbal expressions to the cultural situation of robots operating around people in factories, workplaces, and sidewalks—CHARISMA contributes to the fields of human-robot interaction and social robotics, regularly deploying robots in naturalistic human settings and entertainment contexts. The group’s research interests include minimal social robots, multi-robot/multi-human social interaction, and entertainment robots. Knight completed a postdoc at Stanford University exploring minimal robots and autonomous car interfaces and a PhD in robotics at Carnegie Mellon University exploring expressive motion for low degree-of-freedom robots.

The world of AI research is in shambles. How it startedThere were approximately 85,000 research papers published globally on the subject of AI/ML in the year 2000. Between 2014 and 2021 big tech all but abandoned its “web first” and “mobile first” principles to adopt “AI first” strategies. On the surface, this would indicate that NLP is a thriving subset of machine learning research that’s gained attention from researchers around the globe. The Guardian’s Stuart Richie recently penned an article wondering if we should do away with research papers altogether.

RT @IanLJones98: Ethical AI adoption in Healthcare requires a global effort, now more than ever. By @timguilliams v/@healx @ForbesTechCncl cc @Nicochan33 @BetaMoroney @MikeNashTech @enilev @FrRonconi @tobiaskintzel @RagusoSergio @mikeflache @AkwyZ @jeancayeux @antgrasso https://t.co/PAqhDCSPbOThe events of the past two years have made very clear that in an interconnected world, a health crisis in one corner of the globe can soon spell disaster for the rest of it.

Researchers create a mathematical framework to evaluate explanations of machine-learning models and quantify how well people understand them. Copyright: news.mit.edu – “Unpacking black-box models”Modern machine-learning models, such as neural networks, are often referred to as “black boxes” because they are so complex that even the researchers who design them can’t fully understand how they make predictions. To provide some insights, researchers use explanation methods that seek to describe individual model decisions. So, MIT researchers created a mathematical framework to formally quantify and evaluate the understandability of explanations for machine-learning models. Understanding local explanationsOne way to understand a machine-learning model is to find another model that mimics its predictions but uses transparent reasoning patterns.

One is the evolution and refinement of ‘deep learning’ that allows AI systems to become more accurate and effective. The second, fuelling the first, is the proliferation of data sets on which AI systems train. Will the future be one marked by mass unemployment, autonomous lethal weapons or uncontrolled AI which turns on its human creators, as pessimists fear? I think AI could, within two decades, revolutionize how we make goods and services in ways that could challenge the fundamental premise of our economic models. If anything, AI systems can be built to identify and correct biases, including through responsible governance protocols, in ways that humans, oblivious to ours, cannot.

We offer these accelerators to Google Cloud customers as Cloud TPUs. At 9 exaflops of peak aggregate performance, we believe our cluster of Cloud TPU v4 Pods is the world's largest publicly available ML hub in terms of cumulative computing power, while operating at 90% carbon-free energy. To address the growing importance for purpose-built AI infrastructure for enterprises, Google launched its new machine learning cluster in Oklahoma with nine exaflops of aggregated compute. We believe that this is the largest publicly available ML hub with 90% of the operation reported to be powered by carbon free energy. This demonstrates Google's ongoing commitment to innovating in AI infrastructure with sustainability in mind."

In our recent ICLR paper, “An Experimental Design Perspective on Model-Based Reinforcement Learning“, we derive an acquisition function that guides an agent in choosing data for the most successful learning. In doing this, we draw a connection between model-based reinforcement learning and Bayesian optimal experimental design (BOED) and evaluate data prospectively in the context of the task reward function and the current uncertainty about the dynamics. Our approach can be efficiently implemented under a conventional assumption of a Gaussian Process (GP) prior on the dynamics function. Thus, we only need to approximate the optimal policy in the regions of the state space that are visited by the optimal policy. Therefore, we choose to learn about \(\tau^*\)—the optimal trajectory governed by the optimal policy \(\pi^*\).

AI21 Labs is both a research lab and a commercial business that offers natural language processing (NLP) as a service. AI21 Labs has developed AI language applications like Wordtune and Wordtune Read, with the capacity to generate natural language and understand meaning. Publicis Groupe will provide creative direction for AI21 Labs’ design team, which includes several highly qualified designers. In this way, current language models fall short of their true potential: a general-purpose natural language interface, which receives a wide variety of inputs in natural language that are relevant for real-time purposes, and which replies similarly, in natural language,” said Shoham. AI21 Labs says it aims to get a little bit closer in the quest to build more accurate AI models.

When there are gaps in customer success coverage combined with little visibility and control of services revenue, leaks can grow. Having real-time data that can be modeled using AI and machine learning is helping to solve these and other customer success challenges today. Getting customer success right is hardNot knowing where the data-blind spots are before they affect revenue makes it hard for customer success management (CSM). The authors explore how companies who built intelligent experience engines improve service revenue while attaining greater customer success. Next stepsClosing the gaps between customer success and services revenue takes an integrated CSM platform combined with technologies that excel at efficiency and empathy.

The company creates process control applications for semiconductor manufacturing, using AI to democratize machine learning for process engineers. The AI process control technology is used both on semiconductor fabrication equipment from OEMs and layered on top of manufacturing processes. Additionally, PAICe Maker would speed up simulations in order to adaptively control equipment, while PAICe Builder provides a theoretical baseline. “This funding will accelerate Tignis on its journey of advancing physics-driven AI process control as an enabler of the next generation of semiconductor manufacturing,” said Jon Herlocker, president and CEO of Tignis. “In addition, the advancement of AI process control technology in semiconductor manufacturing that Tignis is undertaking is of particular importance to the US market, to re-establish competitiveness and leadership in this industry,” Herlocker said.

Turning conversations – from customer support requests to user feedback – into tangible business value is no easy task. Zendesk, for example, has built out its AI capabilities in recent years to help with its customer service platform. In his view, Lang provides a differentiated approach thanks to the use of an unsupervised AI model. Scaling operationsUsing AI to derive business value from conversations can also help organizations to scale operations. One example is with Lang customer Ramp, which provides online tracking services for spending.

Elon Musk is a big brain par excellence. Prediction 2: 1 million Tesla robotaxis on the road by 2020Reality: 0 Tesla robotaxis on the road in 2022Credit: Nicole GrayThe FSD pledges apparently weren’t audacious enough. In 2019, Musk made an even braver prediction for autonomous vehicles: Tesla would have 1 million “robotaxis” on the road in 2020. Needs to be such that anyone can go if they want, with loans available for those who don’t have money — Elon Musk (@elonmusk) January 17, 2020Lucky them. The focus on Optimus could galvanize Tesla investors, staff, and customers, but there’s a risk that Musk’s confidence has an adverse effect.

Specialized cells within the eel’s electric organ pump ions across a semipermeable membrane barrier to produce an electrical charge difference between the inside versus the outside of the membrane. Electric eels don’t shock their prey to death; they just electrically stun it before attacking. Modern attempts to mimic the eelLike Volta, some modern electrical scientists searching to transform battery technology find their inspiration in electric eels. No mention was made of the contributions of electric eels. Just as we humans try to congratulate ourselves on the greatness of our latest portable energy source, the electric eels continue to humble us with theirs.

Sales-Pardo and Guimerà set out to engineer a new machine scientist from the ground up. At each step, the algorithm evaluated candidate equations in terms of how well they could compress a data set. To their dismay, however, they found that even their Bayesian machine scientist sometimes returned multiple equally good models for a given data set. Using their machine scientist, they explored various data sets and found that they fell into two categories: clean and noisy. In cleaner data, the machine scientist could always find the equation that generated the data.

For years we've been promised a computing future where our commands aren't tapped, typed, or swiped, but spoken. Embedded in this promise is, of course, convenience; voice computing will not only be hands-free, but totally helpful and rarely ineffective. And those clunky wake words, i.e., “Hey, Google,” are slowly going away—provided you’re willing to use your face to unlock voice control. Sissie Hsiao leads the Google Assistant team. Google’s aim, Hsiao says, is to make the Assistant better understand these imperfections in human speech and respond more fluidly.

Scholars of relationship science have been trying to find answers. For each couple, Joel and her team of researchers had measures of how happy each partner reported being in their relationship. And they had data on just about anything you could think to measure about the two people in that relationship. The researchers had data on:demographics (e.g., age, education, income, and race)physical appearance (e.g., How attractive did other people rate each partner?) After building her team and collecting and analyzing the data, Joel was ready to present the results—results of perhaps the most exciting project in the history of relationship science.

Robots are well known for having consistency and precision that humans tend to lack. Robots are also well known for not being especially creative—depending I suppose on your definition of “creative.” No, roboticists have seized an opportunity to match the strengths of humans and robots. Share this article

To this end, Google today announced several AI-powered features to Voice, Lens, Assistant, Maps and Translate. This leverages computer vision, natural language processing (NLP), knowledge from the web and on-device technologies. “A digital assistant is really only as good as its ability to understand users,” said Nino Tasca, director of Google Assistant. This has involved significant investment into AI and speech, natural language understanding (NLU) and text-to-speech, or TTS. More natural language enhancements to Google Assistant are based on how users talk in their everyday lives.

The entire development cycle is shortened – saving upfront costs – and the uncertainty of how the model will deploy on the inference hardware is eliminated, he said. Addressing AI dev strugglesAI developers generally have struggled to develop production-ready deep-learning models for deployment in a reasonable amount of time. Professional Tier: For deep-learning teams looking to quickly achieve production-grade inference performance and shorten development time. For deep-learning teams looking to quickly achieve production-grade inference performance and shorten development time. Deci competes in the market against Datagen, Reverie, Simerse, Zumo Labs, CVEDIA, Masterful AI, Mostly AI, OneView, Synthesis AI and Sky Engine.

The business of contract management software is all about helping to optimize that process, reducing the time and money it takes to understand and manage contracts. As it turns out, there is big money in the market for contract management software as well. “Contract data really is everywhere.”Contract management is a growing marketEvisort fits squarely into a market that analysts often refer to as contract lifecycle management (CLM). Evisort will also be investing in new AI capabilities that Ting hopes will democratize access to AI for contract management. To date, he explained that Evisort’s AI works somewhat autonomously based on definitions that Evisort creates.

Concentric AI, a provider of intelligent AI-based data security solutions, has raised $14.5 million in a series A funding round. Concentric AI says it secures the future of work by autonomously securing business-critical data concealed in today’s distributed workforce’s millions of files and databases. Data security trendsToday’s data management requires precise, low-overhead automation that can handle many storage sites and large data volumes. According to a panel of 34 experts on securing sensitive data, most security professionals have no idea where their sensitive data is since they don’t develop policies to categorize it effectively and regularly. There are several companies that are usually in competitive situations with Concentric AI, including Varonis, Netwrix/Stealthbits, Spirion, Symantec, McAfee and Digital Guardian, among others.

Voxel, a San Francisco-based artificial intelligence (AI)-powered, workplace safety company, has secured $15 million in new funding. The company uses computer vision and AI to identify hazards, risky behaviors and operational inefficiencies across workplaces. Voxel’s analytics help sites identify operational inefficiencies and design policies to prevent future issues. Turning big data into safer spacesMarkets and Markets predicts the global workplace safety sector will grow from $12.1 billion in 2020 to $19.9 billion by 2025. Voxel’s competitors include wearables companies like Soter, ProGlove, Strongarm and Kinetic Wearable and vision AI companies like Drishti, Invisible AI and Intenseye.

As a means to simplify the process and further innovate the space, Red Hat announced a collaboration with General Motors. And this is where a partnership of the likes of Red Hat and GM can be so beneficial, he said. Red Hat will help accelerate development of GM’s software-defined vehicle programs through the automaker’s new Ultifi software platform. “Working with Red Hat is a critical element in advancing our Ultifi software development.”By integrating the Red Hat system and Ultifi platform, the companies aim to simplify and accelerate updating capabilities. “By collaborating with GM on the Red Hat In-Vehicle Operating System, we intend to bring the era of open source to the automotive world, benefiting automakers, ecosystem partners and consumers.”

To bridge the miles, Amazon is announcing today two new Alexa Together features: Circle of Support, which helps the whole family pitch in, and, coming soon, a Remote Assist functionality that allows a primary caregiver to set up Alexa Routines for their aging loved one. With the new Circle of Support feature, the aging loved one can now have up to 10 caregivers pitching in and helping out. Only the primary caregiver can use Remote Assist, if the aging loved one chooses to enable that feature. And both the aging loved one and the primary caregiver can remove any circle member at any time in the Alexa app. Amazon will automatically send the aging person an email about the newest Routine.

The chief topics of conversation involved artificial intelligence, sustainability, and the most ambitious plan for quantum computing hardware advances we’ve seen yet. We knew it had big plans; the most current quantum computing roadmap from Big Blue prior to this year’s Think conference demonstrated as much. According to IBM, the company’s advances in the field of quantum computing will usher in an “era of quantum-centric supercomputing” beginning with the unveiling of its 4,000-qubit processor in 2025. For more information about IBM Think, click here. For more information on quantum computing, check out these articles on Neural:

Sales-Pardo and Guimerà set out to engineer a new machine scientist from the ground up. At each step, the algorithm evaluated candidate equations in terms of how well they could compress a data set. To their dismay, however, they found that even their Bayesian machine scientist sometimes returned multiple equally good models for a given data set. Using their machine scientist, they explored various data sets and found that they fell into two categories: clean and noisy. In cleaner data, the machine scientist could always find the equation that generated the data.

The AI in Business Podcast is for non-technical business leaders who need to find AI opportunities, align AI capabilities with strategy, and deliver ROI. Each week, Emerj Artificial Intelligence Research CEO Daniel Faggella interviews top AI executives from Fortune 500 firms and unicorn startups - to uncover trends, use-cases, and best practices for practical AI adoption. Subscribe to Emerj's weekly AI newsletter by downloading our "Beginning with AI" PDF guide

As part of the proposal, Clearview AI has agreed to restrict sales of its faceprint database to businesses and other private entities across the country. Clearview AI will spend $50,000 on adverts in online ads to raise awareness for this feature. Clearview AI is still able to sell access to its huge database to public entities, including law enforcement. Clearview AI could still sell its software, but any purchaser would need to source their own database to train it. (Photo by Maksim Chernishev on Unsplash)Related: Ukraine harnesses Clearview AI to uncover assailants and identify the fallenWant to learn more about AI and big data from industry leaders?

Modern security systems with AI technology can help security teams better detect threats and provide faster responses to protect your business more effectively. Altogether, AI empowers your security teams to provide better and faster responses to threats, strengthening your security systems for the safety of your enterprise. By formulating automated responses, AI streamlines necessary processes, allowing security teams to focus on the most critical matters. Ability to detect anomaliesA good example of using AI to strengthen commercial security systems is detecting anomalies in the security network and behaviours. Human monitoring and intervention is still necessary, and you should never depend solely on AI to protect your security systems.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Eleven years after IBM Watson wowed the tech world by beating two of Jeopardy’s biggest champions, the days of tackling “moonshot” AI goals outside the research lab are over, IBM chairman and CEO Arvind Krishna told reporters yesterday, ahead of the annual IBM Think conference in Boston. Krishna offered a second example, which he called Watson AIops, around how IBM is applying AI to information technology to boost productivity by being predictive rather than reactive. ‘Moonshot’ generalized AI goals on back burnerBut while IBM is seeing success with narrow, quick-win AI use cases, the company’s bigger, headline-making AI goals will no longer be on the front burner.

Intel unveiled its latest infrastructure processing unit (IPU) with plans to take on its rivals through the year 2026. Habana Labs’ Gaudi2 deep learning training processorIntel’s Gaudi2 processor. Meanwhile, Intel’s Habana Labs division launched the Gaudi2 processor, a second-generation Gaudi processor for training. The processors are purpose-built for AI deep learning applications. Habana is partnering with Supermicro to bring the Supermicro Gaudi2 Training Server to market in 2022’s second half.

The 2022 Global AI Adoption Index analyzed responses from over 7500 IT leaders around the world. “While AI adoption has been gradual for several years, I believe we’re at a tipping point and it’s poised to become a lot less gradual than it has been in the past.”Why AI adoption is acceleratingAccording to Dobrin, a major explanation for the gradual AI adoption up to this point is the need to implement a strategy that can successfully operationalize and apply AI to business goals. Barriers to AI adoptionWhile there are benefits to AI adoption, there are also challenges. “The key step to overcoming these barriers to AI adoption is setting an AI strategy,” Dobrin said. However, building an effective AI adoption strategy isn’t just about technology, he added.

Qulacomm said it is launching new platform support for advanced robotics products, 5G wireless networking and edge-AI applications. The company unveiled the Qualcomm Robotics solutions at its annual Qualcomm 5G Summit event as it recognizes the ways in which 5G is proliferating beyond smartphones. The San Diego, California-based company took the wraps off its Qualcomm Robotics RB6 Platform and the Qualcomm RB5AMR reference design, which can be used to build advanced edge-AI and robotics products using Qualcomm’s chips. “Qualcomm is becoming a de facto choice for robotics applications,” Singh said. The Qualcomm RB5 AMR Reference Design is available for pre-sale now through ModalAI and the Qualcomm Robotics RB6 development kit is available for sale through Thundercomm.

ProteanTecs, an Israel-based company providing deep data solutions for electronics health and performance monitoring, today announced it raised $45 million. While this development holds great promise, ProteanTecs believes monitoring chip health and performance is even more essential. Before chip production, design simulations across hundreds of manufacturing model variants are generated, uploaded to ProteanTecs’ software platform and processed by machine learning (ML) algorithms. ProteanTecs claims its proprietary chip telemetry data and ML-based technology enables its analytics platform to provide teams with actionable insights, allowing them to immediately identify potential problems. He said the company’s customers include large-scale technology companies in many industries, including, automotive, datacenter, mobile and communications.

Canada-based Certn, a company that provides AI-powered background screening solutions to enterprises, today announced it has raised $50 million in a series B round of funding, led by B Capital. Over the years, background checks have matured a lot. It conducts real-time comprehensive background checks and ongoing risk monitoring for employees, contractors and tenants around the world. Enterprises signing up for Certn are provided with a platform to create and deliver a background screening application to the potential candidate. However, it is not the only player using AI to conduct background checks.

In the last few weeks, we’ve heard multiple rumors about Elon Musk’s vision of generating more money through Twitter. Even Snap, a company that’s doing better than Facebook and Twitter, grew its user numbers by only 18% in Q1 2022. Lofty revenue goalsMusk’s pitch deck aims to get Twitter to reach a yearly revenue figure of $26.8 billion by 2028. The former PayPal CEO wants Twitter to stop relying entirely on ads for revenue. Musk wants to increase that to 9,225 by 2022-end, slash it to 8,332 by 2023, and increase it again to 11,072 by 2025.

AI Articles and Analysis about RecommendationsRecommender systems or recommendation systems (sometimes replacing \"system\" with a synonym such as platform or engine) are a subclass of information filtering system that seek to predict the \"rating\" or \"preference\" that a user would give to an item.

Cloud AI solutions for reducing computational load and increasing sales- The company uses AI cloud to help reduce its computation load and increase product sales for its annual ‘Double 11’ Global Shopping Festival. Managing this amount of traffic while optimizing the shopping experience requires prodigious use of AI, particularly machine learning and cloud AI functions. Notably, Alibaba claims that its AIRec algorithms outperform “self-managed” algorithms by 20-100%. Alibaba claims that some of the benefits of its use include:Faster content availability Enhanced site load times Improved site security Reduced bandwidth costsElastic GPU Service: Alibaba claims that elastic GPU helped it reduce its livestream latency to less than 1 second, which they state is 75% lower than the industry average. In theory, such massive infrastructural changes that Alibaba alleges that it made using 100% cloud AI resources may have a significant monetary impact.

When Chris Rackauckas has a spare moment, he often uses it to answer questions about numerical differential equations that people have posed online. His research, unsurprisingly, revolves around differential equations and on computational methods — using AI and other techniques — to solve them quickly and efficiently. Simulations, which are experiments that we carry out on computers, can involve solving thousands upon thousands of differential equations. Moderna also publicly used Pumas and our clinical analysis methods in its clinical analysis of the Covid-19 vaccine and other drugs. In between these projects, I might take a moment to answer the odd question about differential equations.

RT @reidhoffman: In the latest Daedelus edited by James Manyika, @kevin_scott, @ericschmidt, @drfeifei, @tobias_rees and an amazing lineup of other contributors ask big questions about AI and the opportunities and challenges it poses for society. Can't wait to dive in! https://t.co/czjf3H5jNRAI is transforming our relationships with technology and with others, our senses of self, as well as our approaches to health care, banking, democracy, and the courts. But while AI in its ...

AI is transforming our relationships with technology and with others, our senses of self, as well as our approaches to health care, banking, democracy, and the courts. But while AI in its many forms has become ubiquitous and its benefits to society and the individual have grown, its ...Share this article

Find him on Twitter: @Gadget_RyAmerican rapper Kendrick Lamar has made use of deepfakes for his latest music video. Deepfakes use generative neural network architectures – such as autoencoders or generative adversarial networks (GANs) – to manipulate or generate visual and audio content. For his track ‘The Heart Part 5’, Lamar has made use of deepfake technology to seamlessly morph his face into various celebrities including Kanye West, Nipsey Hussle, Will Smith, and even O.J. You can view the music video below:For due credit, the deepfake element was created by a studio called Deep Voodoo. The fake Zelenskyy had a comically large and noticeably pixelated head compared to the rest of his body.

Earlier today, community-driven machine learning (ML) platform Hugging Face announced $100 Million in new funding – raised in just one week – to continue building what many, including CEO Clement Delangue, call the “GitHub of machine learning.”“I think that’s an accurate analogy,” he told VentureBeat. “We’ve seen the emergence of a new generation of machine learning architecture called transformers, which is based on transfer learning,” Delangue said. And Giada Pistilli, who has a Ph.D. in philosophy and specializes in conversational AI ethics, just started with Hugging Face today. Delangue added that Hugging Face has a “strong view” on the future of AI and ML. “We wanted to create something like that for machine learning.”But it’s Hugging Face’s emphasis on an open collaborative approach that Delangue said made investors confident in the $2 billion valuation.

Founded in Stockholm, Sweden, in 2018, PocketLaw targets subject-matter experts (SMEs) with all the smarts they need to manage and automate many of their repetitive, day-to-day legal tasks. PocketLaw: Creating a contractElsewhere, the PocketLaw platform features include the capability to collaborate with the various stakeholders in a legal agreement. “The regulatory landscape — and legal complexity for businesses — is growing rapidly, forcing companies to invest more time and money in legal services just to survive,” PocketLaw CEO and cofounder Kira Unger told VentureBeat. “But most day-to-day legal work consists of simple, repetitive tasks and given the cost of massive in-house legal teams, adding more people to the problem isn’t the solution. “PocketLaw can be used by any business and any stakeholder in the world, regardless of size, financial muscle and experience,” Unger added.

RGo Robotics announced that it has exited stealth mode with $20 million in funding. The startup intends to enable mobile robots to operate autonomously by understanding their environment through artificial-perception technology. RGo Robotics says that is has developed an AI-powered perception engine to allow mobile robots to understand complex surroundings by achieving purportedly human-level perception. Inside the perception engineRGo’s perception engine consists of both software and hardware components. The data for the perception engine, through which the robot learns its environment, is provided over an API.

The past decade has seen tremendous progress in the field of artificial intelligence thanks to the resurgence of neural networks through deep learning. This has helped improve the ability for computers to see, hear, and understand the world around them, leading to dramatic advances in the ...Share this article

Transparency, accountability, and efficiency in smart contracts can offer insights into shaping and instituting data governance guidelines for AI-driven systems. The need for data governance of AI-driven systemsAI-driven systems have advanced well beyond the automation of mundane, repetitive tasks with little-to-no supervision or instruction. This is where data governance policies play a critical role because data serves as the strategic centerpiece for generating new business opportunities. Similarly, the whole customer-company relationship could be retooled to provide data governance. DAOs: Building better data governance policiesBy communicating with customers and involving them when using personal data to craft services, contributors understand how the information flows through every node and process.

Writer at Neural by TNW — Thomas covers AI in all its iterations. Writer at Neural by TNW — Thomas covers AI in all its iterations. Bored Apes have become symbolic of the NFT craze. The machine learning expert has built an app that generates as many Bored Apes as your heart desires. The finished system produces Bored Apes that are entirely unique — and fungible.

The FIDO Alliance has been at the forefront of the passwordless movement. Well, the people at the FIDO Alliance must be jumping for joy. In a new announcement, Google, Microsoft, and Apple have agreed to play nice with each other to enable passwordless sign-ins across their platforms. In March, the FIDO Alliance released a white paper that introduced the passkey concept that enables cross-platform and multi-device passwordless sign-ins. The FIDO Alliance is speaking at a conference in June, and we’ll hopefully get to know more about how this whole project will pan out.

Perhaps most interesting among these new solutions is Fiverr’s “AI Auditions,” a machine learning-powered tool that synthesizes human speech so that actors can audition for voice roles asynchronously. According to a company press release, the process is simple:Voiceover artists on Fiverr can create their own AI voices by quickly training the Voice Auditions engine. According to a testimonial included in the AI Auditions press release, the process of recording and sending voice samples for auditions can be tedious and time-consuming. The main benefits to AI Auditions would be a one-off training cycle and the ability for directors to hear actors speak any lines. The problem is that AI Auditions sounds too good to be true.

We have had the great pleasure to be working with OpenAI InPaint and DALL·E 1 for many months, and thought it would be constructive to document some techniques, ideas and reflections that have been raised through exploring these remarkable tools in honor of the announcement and warranted ...Share this article

RT @SpirosMargaris: The #Future Of #AI: 5 Things To Expect In The Next 10 Years https://t.co/n66cYzUNiw #fintech #ArtificialIntelligence #MachineLearning #DeepLearning @omegavp @sallyeaves @ahier @psb_dc @KMcDTech @HaroldSinnott @andi_staub @terence_mills @MariaFariello1 @Shi4Tech @Nicochan33 https://t.co/EaIkIjkju0In this article, I take a step back to consider how artificial intelligence is poised to fundamentally restructure broader swaths of our economy and society over the next decade with five ...

Find him on Twitter: @Gadget_RyGoogle has picked ASUS IoT to help scale the manufacturing, distribution, and support of its Coral edge AI platform. Coral was launched in 2019 with the goal of making edge AI more accessible. ASUS IoT is a sub-brand of the wider ASUS brand that has decades of experience in global electronics manufacturing. The sub-brand was the first partner to launch a Coral SoM (System-on-Module) product with the Tinker Edge T development board. Since then, ASUS IoT has integrated Coral accelerators into their intelligent edge computers and was first to release a multi Edge TPU device with the AI Accelerator PCIe Card.

The AI data lifecycle is dynamic and ever-changing, and the approaches we take to manage its different components need to be dynamic as well. Pre-labeled datasets are ready to go and can make the model training process seamless and efficient, while synthetic data could serve as a substitute for hard-to-find data, enhancing model training. Pre-labeled datasets are ready to go and can make the model training process seamless and efficient, while synthetic data could serve as a substitute for hard-to-find data, enhancing model training. First, you need an ontology or data model that describes the contents of your data and how they’re related to each other. First, you need an ontology or data model that describes the contents of your data and how they’re related to each other.

In November, the New York City Council passed the first bill in the U.S. to broadly address the use of AI in hiring and employment. But that was just the beginning for proposed regulations on the use of employment AI tools. This kind of legislation around the use of AI in hiring and employment is becoming more common, Beena Ammanath, executive director of the Global Deloitte AI Institute, told VentureBeat. But AI bias is a legitimate concern in this area, said Fran Maxwell, managing director and global leader in the organizational transformation practice at consultancy Protiviti. “Often the AI model does not intend bias, but the data used by the model could unintentionally introduce bias,” he explained.

Join AI and data leaders for insightful talks and exciting networking opportunities. Artificial intelligence (AI) is highly effective at parsing extreme volumes of data and making decisions based on information that is beyond the limits of human comprehension. As tech writer Scott Clark noted on CMSWire recently, explainable AI provides necessary insight into the decision-making process to allow users to understand why it is behaving the way it is. Without that, doubt will persist within any action or decision an AI model generates and this increases the risk of deployment into production environments where AI is supposed to bring true value to the enterprise. This is partly why explainable AI is not enough, says Anthony Habayeb, CEO of AI governance developer Monitaur.

Writer at Neural by TNW — Thomas covers AI in all its iterations. The company this week offered the entire research community access to a fully-trained large language model (LLM). Named the Open Pretrained Transformer (OPT), the system mirrors the performance and size of OpenAI’s vaunted GPT-3 model. In addition to sharing OPT for non-commercial use, Meta has released its pre-trained models, their underlying code, and a logbook of their development. You can get the OPT open-source code and small-scale pre-trained models here.

Writer at Neural by TNW — Thomas covers AI in all its iterations. Writer at Neural by TNW — Thomas covers AI in all its iterations. The theory contends that there’s a conflict between the apparent absence of aliens and the probability that they exist. Credit: Wong and BartlettAliens civilizations that are near to burnout could be the easiest to detect. Their theory merely draws on laws of life on Earth, which may not apply to other planets.

Last month, OpenAI introduced the second-generation version of DALL-E, an AI model trained on 650 million images and text captions. We are in the future,” says Vipul Gupta, a PhD candidate at Penn State who has used DALL-E 2. As part of OpenAI’s “red team” process—in which external experts look for ways things can go wrong before the product’s broader distribution—AI researchers found that DALL-E 2’s depictions of people can be too biased for public consumption. Early tests by red team members and OpenAI have shown that DALL-E 2 leans toward generating images of white men by default, overly sexualizes images of women, and reinforces racial stereotypes. From conversations with roughly half of the 23-member red team, we found that a number of them recommended OpenAI release DALL-E 2 without the ability to generate faces at all.

For example, they may highlight words in a movie review that influenced the model’s decision that the review was positive. So, MIT researchers created a mathematical framework to formally quantify and evaluate the understandability of explanations for machine-learning models. Instead, researchers resort to using local explanations that focus on individual inputs. They are then likely to assume that all positive words make positive contributions to a model’s predictions, but that might not always be the case, Zhou says. “Going from local explanations to global understanding was a big gap in the literature.

Researchers at the University of Cambridge have done just that, testing a robot arm’s ability to taste eggy dishes. Certain amino acids give a savory umami taste, while salts like sodium chloride taste, well, salty. But electronic tongues are a pale imitation of the organic kind. To test the arm, the researchers created nine simple egg dishes, each one with different quantities of salt and tomato. With this new technique, the researchers could create salt maps that surpassed anything electronic tongues had done before.

However, pre-training corpora are a significant factor for language model performance, and research on pre-training corpora is important from the perspective of conserving data collection costs. Discovery 1: In-context learning performance heavily depends on the corpus domain sourceIt is noticeable that in-context learning ability emerges differently depending on pre-training corpus sources. For example, the Blog model demonstrates in-context few-shot learning performance comparable to the ALL model, the model using the entire HyperCLOVA corpus. Also, perplexity, evaluated with the validation set of seven sub-corpus, and in-context learning are not always correlated: e.g., low perplexity does not always imply high in-context learning performance. Discovery 2: In-context learning ability can emerge when multiple corpora are combinedWe discovered that in-context few-shot learning ability can emerge by combining two corpora, even if each of both corpora cannot provide in-context few-shot learning ability.

Temi Popo: Welcome everyone to the Building Trustworthy AI with Mozilla Panel. She is a member of the Building Trustworthy AI working group at Mozilla and a Ph.D. student at the Delft University of Technology in the Netherlands. So next up we have Wiebke and I love to know what brought you to the Building Trustworthy AI Working Group. And so when Mozilla launched a call for the Trustworthy AI Working Group last year in August, that was really perfect. Wiebke, know with the Building Trustworthy AI Working Group, you had to like assemble a team where it was kind of self-organizing.

At Myrtle.ai, we have been investigating whether Vision Transformers would make a good candidate algorithm for deployment on a Low Earth Orbit (LEO) satellite system. This first post focuses on the theory behind attention and the application of Vision Transformer models to a satellite image data set. Data-efficient Image Transformer (Credits: image from “Training data-efficient image transformers & distillation through attention”)Training a Vision Transformer for Low Earth Orbit SatelliteIn order to train and test our transformer-based models, we used a dataset called NWPU-RESISC45. In a recent paper [5] it has been demonstrated that Vision Transformers are much more robust to severe occlusions, perturbations and domain shifts than traditional CNNs. https://arxiv.org/pdf/1703.00121.pdf[5] Naseer, M.; Ranasinghe, K.; Khan, S.; Hayat, M.; Khan, F. S.; Yang, M. Intriguing Properties of Vision Transformers.

But advances in artificial intelligence (AI) could increasingly help online information users sort out fact from fiction. The index, put out by a US-based non-profit organization, can help governments, media professionals, and other web users assess the trustworthiness of online content. Tall-tale triageUsing AI tools and techniques, the GDI “triages” unreliable content from several of the world’s most prominent news markets. Still, flagging false or misleading online content may not be enough to thwart determined creators of false content. Ultimately, clear international standards are needed to categorize online data and its ownership.

Specifically, this overview will first explain some common neuroimaging modalities more in-depth and then discuss applications of deep learning in conjunction with some of the unique characteristics of neuroimaging data. These unique characteristics tie into a broader movement in deep learning, namely that data understanding should be a goal in itself to maximize the impact of applied deep learning. Unique and leverageable aspects of neuroimaging dataAmong others [20], one critical challenge with deep learning in the field of neuroimaging is the limited number of samples; many neuroimaging datasets range from roughly 300 to 1300 subjects. Data understanding and interpretation is an essential facet of deep learning applications to neuroimaging data and can lead to novel algorithms. CitationFor attribution in academic contexts or books, please cite this work asEloy Geenjaar, "Deep Learning in Neuroimaging", The Gradient, 2022.

Meta AI says it wants to change that. With 175 billion parameters (the values in a neural network that get tweaked during training), OPT is the same size as GPT-3. The team built OPT to match GPT-3 both in its accuracy on language tasks and in its toxicity. The idea was to provide researchers with a similar language model to study, says Pineau. A big reason for the different approach by Meta AI is Pineau herself, who has been pushing for more transparency in AI for a number of years.

If foundation models fulfill their promise, it could bring AI into much broader commercial use. If, for instance, some sort of racial bias is present in an AI foundation model (remember the “unprofessional hair” algorithm controversy? Like a mutation in DNA, these flaws, replicated across many AI systems, could metastasize and become devilishly difficult to correct and eliminate. These foundation models, despite their increased hardware efficiency, require enormous amounts of electricity which has to come from . Reich said that CRISPR, the gene-editing technology, provides a contrasting model for developing and structuring ethics to go with foundation models.

The symposium hosted by the Institute for AI International Governance of Tsinghua University, with the United Nations Development Programme as the supporting international organization, convened thought leaders and practitioners across the world. Erik Solheim, former United Nations under-secretary-general and former executive director of the UN Environment Programme, said that climate change is one of the most urgent challenges facing humanity. "AI can be used to predict consumer behavior, mitigate climate change with smart grids, and achieve better payment for infrastructure," he said. As highlighted in the Principles, addressing global climate change is essential for the sustainable future of humanity and ecology. The symposium included multiple panel discussions on a wide range of topics related to AI and climate action, including how AI can empower climate action, AI and net-zero cities, and AI and sustainable energy.

RT @Xbond49: What You Need To Know Before You Start #working With #ArtificialIntelligence https://t.co/M9PcEjv3TK #coding #BigData #coding #AI #python #Java @DeepLearn007 @sallyeaves @HaroldSinnott @gvalan @ipfconline1 @nigewillson @Paula_Piccard @Khulood_Almani @Nicochan33 @jblefevre60I firmly believe that the true value of AI – estimated to be worth $13 trillion to the global economy by 2030 – will be realized due to it being accessible to businesses of all shapes and ...

Galileo is tackling this critical need head on.”More than 80% of the world’s data today is unstructured (text, image, speech, etc.) Recent advancements have made it easy for any data scientist to plug and play complex models for unstructured data, leading to a surge in their adoption across industries. It is common for data scientists to use spreadsheets and Python scripts to inspect and fix their training unstructured data. He was also an early member of the Siri team at Apple where he built foundational infrastructure for better ML data management. To read Chatterji, Sanyal and Sheth’s blog on ML data intelligence, simply go to: https://www.rungalileo.io/blog/introducing-ml-data-intelligenceAbout GalileoGalileo’s mission is to create data intelligence tools for unstructured data ML practitioners.

But it has not yet caught on in the craft businesses. And this is despite the fact that many companies are facing problems such as a shortage of skilled workers or a lack of trainees. As a consultant for automation, Werner Hampel is certain that robotics offers a great opportunity for the future of the craft businesses. Craft businesses in Germany have been struggling for years with a shortage of skilled workers as well as a lack of apprentices. “Many businesses that don’t automate soon will cease to exist.” Read more about craft businesses that have already automated in our series “Robotics.

These consist of conformity assessments that providers of high-risk AI systems are expected to conduct, as well as post-market monitoring plans that providers must establish to document the performance of high-risk AI systems throughout their lifetimes. While the majority of AI systems are expected to fall into the low-risk category the requirements for high-risk AI systems are more elaborate. Today, many high-risk AI systems are already subject to third-party conformity assessments e.g. Post-market monitoringIn addition to the pre-market conformity assessments, providers of high-risk AI systems are also expected to establish and document post-market monitoring systems. Many details concerning how pre-market Conformity Assessments and post‑market Monitoring should be conducted in practice have not yet been clarified.

As we noted a couple of weeks ago with our announcement post, we're fast approaching the date for Ars Frontiers, our inaugural single-day conference. At Frontiers, Ars Technica editors will sit down and interact with those experts, and we'd love to have you on board for the ride. AdvertisementThe main eventOur in-person conference will take place in Washington, DC, on Thursday, May 12, and will feature a series of panel discussions moderated by Ars Technica editors. Cryptography and privacyWe'll have two different infosec-focused panels, both moderated by Ars security editor emeritus Sean Gallagher. Commercial spaceAfter a brief break for refreshments, we'll be back with a pair of panels moderated by Ars Senior Space Editor Eric Berger.

Photograph: DALL•E 2And “a teddy bear on a skateboard in times square”:“a teddy bear on a skateboard in times square”, generated by DALL•E 2. And so:“panda mad scientist mixing sparkling chemicals, artstation”“panda mad scientist mixing sparkling chemicals, artstation”, generated by DALL•E 2. Photograph: DALL•E 2“a dolphin in an astronaut suit on saturn, artstation”“a dolphin in an astronaut suit on saturn, artstation”, generated by DALL•E 2. Photograph: DALL•E 2It can also use one image as an anchor and then modify it with a text description. Photograph: DALL•E 2Weddings and restaurants by DALL•E 2.

A few month ago, scientists and engineers described during a conference held at Stanford how AI research has succeeded in creating models that are generic. Those models called Foundation models use an enormous, single data set and can perform a variety of different tasks with relatively little programmer input rather than being tailored to a single task and dataset. This article reflects on the debates around these foundation models.

About the webinarAs a business leader in a competitive industry, you already know that speed to market is a critical differentiator. Your organization’s success depends on your ability to quickly deliver new innovative products or solutions. Inaccessible knowledge threatens to slow down productivity and cross-functional collaboration in large organizations. Human-centric knowledge management is the solution leaders need in the post-pandemic digital workplace. Head over to the TechForge website to explore the full 2022 webinar and virtual events programme.

The digital world is now so fundamentally insecure that a zero-trust strategy is warranted anywhere computing is taking place — with one exception: data science. Finding a way to apply evolving security practices to data science has become the most pressing policy issue in the world of technology. Zero trust for data scienceThere are three fundamental tenets that can help to realign the emerging requirements of zero trust to the needs of data science: minimization, distributed data, and high observability. Data mesh architectures are helping to make it possible to retain decentralized architecture while unifying access to data through a single data access layer. But if you don’t take steps to secure your data science environment, the difficulties of applying zero trust to that environment will only become more acute over time, rendering entire data science programs and AI systems fundamentally insecure.

Product development has long been an elevated science with frameworks, rules and significant research work conducted to ensure the product in question is fit-for-market and valued for its price. At every step of the product development cycle, there are meaningful contributions from AI-powered product insights platforms to create, optimize, and market products better. Here are the five recognized stages of the product development cycle and some specific ways the right product insights platform can give organizations the best chance to maximize their return on investment. While many companies rely on simple social listening and human assessment, AI-driven product intelligence is another level of guidance. The right insights platform absorbs conversations about customers’ initial impressions of your product and validates or calls into question your marketing strategy.

The city of Seoul, South Korea, is planting the seeds for a metaverse ecosystem called “Metaverse Seoul” for all areas of its municipal administration. The effort combines digital twins, virtual reality (VR) and collaboration to improve city services as well as planning, administration and support for virtual tourism. At the MIT Future Compute Conference, Seoul smart city policy bureau CIO Jong-Soo Park elaborated on their vision and current progress. The long-term vision is to add support for business development services, education and support for city services for filing complaints, inquiring about real estate and filing taxes. “We hope to one day have an AI-based public servant working in the metaverse office in close collaboration with others for public services,” said Park.

Manufacturing Data Engine integrates a variety of Google Cloud components like Dataflow, PubSub, BigQuery, Cloud Storage, Looker, Vertex AI, Apigee, etc., into a manufacturing-specific solution. The primary targeted analysis areas for this new technology suite are three high-value use cases, including; manufacturing analytics and insights, predictive maintenance and machine-level anomaly detection. However, this move in its manufacturing business is a major undertaking that is clearly mission-critical to its long-term success. I further expect that both Ford and Google will learn much from this partnership that Google can apply to its other cloud services customers. Additionally, I predict that Google may also extend more analytics and business process automation features for Ford and customers in other markets.

Now it wants to accurately predict the future behavior of networks so those here-and-now operations are always running optimally. Cisco, which commonly works with large enterprises, said it will make its wares accessible to companies of all sizes. Cisco’s predictive networks work by gathering data from myriad telemetry sources. “We started this about two years ago with the premise of saying: ‘Can we use some monitoring to predict issues before they happen? '”Works hand-in-hand with user’s security processesThe new predictive software is versatile and works hand-in-hand with any existing security system, Vasseur said.

This has led to the massive growth of knowledge management. “The more we need insights, the more we need something that we trust that guides us in the next step,” said Peggy Choi, founder and CEO of AI-driven knowledge management platform Lynk. The “upheaval” of traditional workplaces as the result of the pandemic demands more sophisticated knowledge management. Lynk’s knowledge management platform features an intuitive interface and flexible interaction with advanced search and formats including Q&As, 1:1 calls, project work, articles and documents. This hierarchy represents the relationships between data, information, knowledge and wisdom, with each creating a building block that establishes a step toward the next higher level.

“In our vision, it was key that we had people leading our progress and the business and corporate functions owning this transformation,” said Valero Marín, Repsol’s Corporate Director of Digitalization and Global Services. From day one, Marín and his team recognized that neither objective was an acute business challenge with a tidily outsourced solution. “It’s like the gym,” says Enrique Fernández Puertas, Repsol’s Chief Digital Officer. One of the initiative’s greatest successes is ARiA, a proprietary cloud-based data and analytics platform built to leverage company data toward maximizing business value. The product has been so successful that Repsol has begun to offer a commercial version of the product for external use.

Liu and his collaborators developed an artificial intelligence technique that learns to represent data in a way that captures concepts which are shared between visual and audio modalities. For example, in the video-audio dataset, the model chose 1,000 words to represent the actions in the videos. Then, when the researchers fed it audio queries, the model tried to find the clip that best matched those spoken words. For one, their research focused on data from two modalities at a time, but in the real world humans encounter many data modalities simultaneously, Liu says. “And we know 1,000 words works on this kind of dataset, but we don’t know if it can be generalized to a real-world problem,” he adds.

Hello AI Weekly subscribers,Bill Ridenour here, CRO at AI Weekly / Essentials, sending a quick note about a great milestone we've achieved recently and also a great opportunity for you, our subscribers! First of all, I want to let you know that our AI Weekly / AI Essentials has now grown to 400k audience overall and that on top of the AI Weekly newsletter we now offer 8 highly specialized AI sub-topics including 2 new ones about "AI For Good", focusing on sustainable, inclusive and positive use cases of AI and "The Business of AI", focusing on investment news, startup news, transactions and overall business activity in the AI world. Second, AI Weekly Pro! Thirdly, the team at Essentials knows that a lot of you within this community are looking for ways to engage proactively with the AI community and as such, we're offering you a very special 35% discount for you to run an advertising campaign within our AI Weekly and AI Essentials publications. Bill RidenourGlobal Chief Revenue OfficerFaveeo / AI Weekly / Essentials[email protected]

What makes Essentials Essential? We are on a MissionOur mission has always been to give readers the confidence that they will never again, miss anything, or anyone, that matters. Founded by a team of sociologists passionate about the future of media, and supported by leading European investors like the Hoffmann family, Faveeo has developed a social media discovery AI that focuses on detecting Trust Patterns on social media, in order to identify & highlight trusted sources and news, out of the social media chaos. Our tech has been developed and stress-tested by the most demanding companies and organisations like P&G, EY, Cisco & the ICRC who relied on our AI system to discover and publish great content from the most trusted sources in a fully automated way.

The EU’s draft AI regulation classifies systems into three risk categories:Limited risk – includes systems like chatbots, inventory management, spam filters, and video games. Unacceptable risk systems will face a blanket ban from deployment in the EU while limited risk will require minimal oversight. Organisations deploying high-risk AI systems would be required to have things like:Human oversight. Reuters reports that two key lawmakers on Wednesday said the EU’s AI regulations will likely take over a year more to agree. It will be interesting to observe how AI regulations in Europe will differ across the continent and beyond.

From digital twins that provide insights into current performance to advanced simulation intelligence that evaluates future scenarios, organizations in nearly every industry are taking advantage of the evolution of AI simulation to speed up operations, reduce risk and boost better decision-making. And almost all companies are on board the AI simulation train. According to PWC’s 2022 AI Business Survey, it is almost unanimous – a whopping 96% of survey respondents said they plan to use AI simulations this year. Meanwhile, 57% of those deemed AI leaders plan to use simulations to forecast market conditions, while 54% of AI leaders plan to use simulations to support financial, sales and marketing planning as well as enhance supply chain and operations. This fervent focus on the business power of AI simulations is not surprising, Anand Rao, principal with PwC’s U.S. advisory practice, told VentureBeat.

No-code AI is a category in the AI landscape that tries to make AI more accessible to everyone. When it comes to AI and machine learning models, no-code AI refers to the use of a no-code development platform with a visual, code-free and frequently drag-and-drop interface. No-code AI enables the creation of AI models without the need for specially trained individuals. Benefits of a no-code, AI-powered platformWith the Kinetix platform, everybody can generate fully featured 3D animations to share and integrate into virtual worlds, by uploading video clips into the Kinetix platform. Given the rapid rise of virtual worlds, there is a demand for simple platforms for creating 3D content and monetizing it.

The West Japan Rail Company (JR West) has unveiled a new futuristic worker that’s tasked with infrastructure jobs considered too risky for humans. It’s a giant robot humanoid that looks like a cross between WALL-E and a Transformer. This actually has to do with the fact that the robot is operated by a human pilot using a VR headset. Then, by gripping a pair of handles, the human pilot is able to control the machine’s arms and hands. The humanoid robot is currently a prototype, and it won’t reach commercial production until spring 2024.

The world of AI research is in shambles. How it startedThere were approximately 85,000 research papers published globally on the subject of AI/ML in the year 2000. Between 2014 and 2021 big tech all but abandoned its “web first” and “mobile first” principles to adopt “AI first” strategies. On the surface, this would indicate that NLP is a thriving subset of machine learning research that’s gained attention from researchers around the globe. The Guardian’s Stuart Richie recently penned an article wondering if we should do away with research papers altogether.

This week, issues and innovations related to conversational AI and emotion AI were everywhere. Two interesting discussions that I didn’t include in the story:Is emotion AI simply the next evolution of conversational AI? But the growing connection between digital avatars and emotion AI, leading to the possible emergence of virtual emotional beings, is a “fascinating” development, said Annette Zimmerman, VP analyst at Gartner. “Unlike real humans, digital humans never tire and don’t get irritated after a 10-hour shift – which is particularly important for customer service and experience. Another application for a better world might be if emotion AI could help identify people whose depression may lead to self-harm.

The company automates security compliance audit preparation, using natural language processing (NLP) and machine learning (ML) to auto-suggest answers to security questionnaires. “When it comes to security questionnaires and compliance, I feel the entire SaaS sector needs a kick in the pants. Compliance is just not truthful today,”CISOs can use Kintent to objectively assess and receive vendor security and compliance data. As a result, rather than zero-trust, Kintent offers explicit, tangible trust, in which business trust is constantly confirmed programmatically. As a result, instead of zero-trust, Kintent will establish a world of transparent, measurable trust, where business trust is constantly confirmed programmatically.

LifeOmic, a precision health platform, and XCures, which helps identify cancer treatment options, have partnered to streamline and improve recommendations for cancer care. How technology helps identify more promising treatment optionsXCures uses artificial intelligence (AI) and predictive modeling to identify and rank the most promising treatment options for advanced cancer patients who are not responding to the standard of care therapies. The platform distills essential information about a patient’s cancer presented as a time series, similar to a Gantt chart. The combination of recommendation engine and generative model provides a proactive view of what treatment options to consider and when. The clinical operations team identified and manually tagged relevant data points such as diagnosis, medications, omics, radiology reports and any specific comorbidity conditions that could impact treatment options.

Slamcore, a spatial intelligence company that gives situational awareness to robots, drones, machines, and VR/AR headsets, has raised $16 million in a series A round of funding. With the autonomous mobile robot market alone shaping up to be a $22 billion industry within a decade. Throwing autonomous machines into such an environment could cause several problems if these machines can’t figure out where everything is, and lead to major accidents. Generating accurate positional awareness in unmapped or fast-changing environments is no easy feat, particularly when indoors or in built-up areas where GPS is less effective. So Slamcore, effectively, transforms sensor data from lidar, sonar, radar, and more, into a “real-time spatial understanding,” and delivers this to companies via a software development kit (SDK).

The ‘Digital Engineering’ revolution is offering new opportunities to those willing to adopt new technologies and upskill. Advantages of digital engineeringFirst and foremost, what does digital engineering actually mean? Digital engineering involves professionals using 3D models, digital twins, data analytics, and other advanced technologies to bring their designs to life. Digital engineering allows professionals to test their designs before acting on them, finding structural issues before a project has even been built. Python is also used extensively in areas like robotics, AI, and machine learning, technologies revolutionizing digital engineering.

The AI sommelier was trained on a database containing hundreds of thousands of beer and wine reviews. This begs the question: how ethical is it to generate content without crediting the machine? You might not be a wine lover or beer drinker, but that doesn’t mean you’re safe from the reality-distorting effects of AI-generated content. Would you feel the same way about a positive AI review if you knew it was preceded by dozens of negative ones that the humans in charge didn’t show you? Clearly, there are far more questions than answers when it comes to the ethical use of AI-generated content.

“We have surged data scientists forward,” Deputy Secretary of Defense Kathleen Hicks told WIRED in a recent interview. These tech experts crafted code and machine learning algorithms, creating systems that are “especially valuable for synthesizing the complex logistics picture,” she said. Due to the sensitive nature of operations in Ukraine, Hicks says she cannot provide details of what the data team has done. “I like to say that bits can be as important as bullets,” Hicks says, in reference to the importance of software, data, and machine learning. “I am concerned about operators having to go into some kind of contingency [conflict] without the available technology,” he says.

Had the opportunity to talk #remix, #AI and #art with friends and colleagues who have written some of the leading books in the field: @navasse @aram @djspooky @markamerika. Grateful to @rawsin_ and the Rendering Unconscious podcast for hosting the event. https://t.co/O9XcIYtG3d https://t.co/v6KS7eJQtz

When preparing unstructured data for AI, you’re really pulling in all the data about that data, said Jeffrey Casale. “We can understand not just the information, but information about the information,” Casale said. This supports the Joint Artificial Intelligence Center’s (JAIC) Data Readiness AI Development program, which is designed to scale AI across the Pentagon. Whatever the specific use cases, MarkLogic is looking forward to the opportunity to help “solve the nation’s most critical national security complex data challenges,” Casale said. “It’s the unique ability that we have with complex data,” Casale said.

Join AI and data leaders for insightful talks and exciting networking opportunities. Steady progress deploying AI into productionAccording to IDC, 31 percent of IT decision-makers say they have pushed AI into production, but only a third of that group considers their deployments to be at a mature stage. As can be expected, dealing with data and infrastructure at a scale that AI requires to deliver real value remains one of the biggest hurdles. Building and maintaining data infrastructure at this scale is no easy feat, even in the cloud. Like any technology, AI is only as effective as its weakest link, so if development and training are not adequately supported, the entire initiative could falter.

“Pathology still lives on glass,” said Brian Rubin, chair of the Pathology and Laboratory Medicine Institute at Cleveland Clinic. “People are amazed it isn’t digital.”But PathAI and Cleveland Clinic aim to change that. Using AI for a cureThe end-goal is to help Cleveland Clinic leverage AI to more quickly identify diseases and match patients to the best therapies unique to their conditions, Beck said. These tools will be applied at Cleveland Clinic as it executes a plan to scan 1.5 million slides over five years. There is also immense opportunity in terms of storage – physical slides are large and the intensive physical space they require could be reduced with digital slides.

According to the results of a new study on supply chain resiliency conducted by IBM and process mining company, Celonis, 51% of supply chain leaders are willing to sacrifice profit to improve sustainability outcomes, which equates to $22 billion for U.S. Fortune 500 companies in a single year. In fact, 87% of chief supply chain officers (CSCOs) surveyed are implementing execution management and 77% are implementing process and task mining to modernize their operations. Furthermore, in an effort to modernize their supply chain operations, 87% of CSCOs plan to use execution management in their day-to-day workflows. For example, supply chain leaders are reinventing processes by leveraging the latest, data-infused technologies via the cloud — 69% are planning to accelerate cloud adoption to enhance real-time data access. The group surveyed 500 Chief Supply Chain Officers across industries to gain an in-depth understanding of how recent disruptions in global supply chains are affecting their short-term tactics, longer-term strategies and business performance.

The number of American workers who quit their jobs during the pandemic—over a fifth of the workforce—may constitute one of the largest American labor movements in recent history. Will robots automate away the jobs of entitled millennials who “don’t want to work,” or could this technology actually improve workers’ jobs and help firms attract more enthusiastic employees? Is it inevitable that robots and artificial intelligence will take all of our jobs, and over what time frame? While 8 percent of German manufacturing workers left their jobs (voluntarily or involuntarily) between 1993 and 2009, 34 percent of US manufacturing workers left their jobs over the same period. Two case studies illustrate the differences between American and German approaches to manufacturing workers and automation, from policies to supply chains to worker training systems.

RT @johnkoetsier: AI so good it beats the stock market every time will never be publicly available. In fact, it may be impossible. Here's why ... Forbes https://t.co/VDLp214gDg Video https://t.co/B0IQfk3lgd Podcast https://t.co/GFhX1DlVLU Transcript https://t.co/YxHUs8nX00 https://t.co/NGxx2iZb1ZWill super-smart artificial intelligence be the ultimate cheat code for beating the market? Probably not … but its could ensure you retire with 25% more than you otherwise would. In this ...

Find him on Twitter: @Gadget_RyMeta (formerly Facebook) has unveiled an AI supercomputer that it claims will be the world’s fastest. The supercomputer is called the AI Research SuperCluster (RSC) and is yet to be fully complete. A model with tens of billions of parameters can finish training in three weeks compared with nine weeks prior to RSC. Meta says that its previous AI research infrastructure only leveraged open source and other publicly-available datasets. RSC was designed with the security and privacy controls in mind to allow Meta to use real-world examples from its production systems in production training.

Meta recently announced a long-term research partnership to study the human brain. Then, somehow, it’s going to use this data to develop an AI capable of understanding language. Per a Meta AI blog post:Our collaborators at NeuroSpin are creating an original neuroimaging data set to expand this research. All of this work is part of Meta AI’s broader investments toward human-level AI that learns from limited to no supervision. The plan is to create an end-to-end decoder for the human brain.

But until now, the equations have not been able to capture the complexity of breaking waves. Their results, published today in the journal Nature Communications, will help scientists understand how a breaking wave affects the water around it. They aimed to improve the model by “training” the model on data of breaking waves from actual experiments. “We had a simple model that doesn’t capture wave breaking, and then we had the truth, meaning experiments that involve wave breaking,” Eeltink explains. “If you don’t model wave breaking right, it would have tremendous implications for how structures behave.

From “Star Wars” to “Happy Feet,” many beloved films contain scenes that were made possible by motion capture technology, which records movement of objects or people through video. But what you really care about is the dynamic motion: the joint angles of the leopard — not if they look light or dark,” Du says. Here, system parameters and actions are entered into a differentiable simulation. Finally, the researchers deployed their RISP system to infer the motion of a real-world quadrotor, which has complex dynamics, from video. In nearly all of the experiments, the RISP procedure outperformed similar or the state-of-the-art methods available, imitating or reproducing the desired parameters or motion, and proving to be a data-efficient and generalizable competitor to current motion capture approaches.

What is Ethical AI? via @Merkle's Brian Demitros ➡️ "Ethical AI also requires leaning more on behavioral data over demographic data, creating opportunities to build first-party data strategies that make consumer interactions more human." https://t.co/OdCSY9EkiwThe role of artificial intelligence (AI) in marketing continues to expand as new capabilities become available. At the same time, businesses have an increasing desire to cast light on the ...

RT @QVeraLiao: Excited to share a preprint of our #FAccT2022 paper with @Shyamer on designing for responsible trust in AI A short version will be presented at the #CHI2022 TRAIT workshop tomorrow! https://t.co/viSgrGhqY1 It all started with a venting email. What did I vent about? (?1/N) https://t.co/6nXe2WsmRe

Algorithms have always been at home in the digital world, where they are trained and developed in perfectly simulated environments. Yet DL algorithms have always played their part in the safe simulated environment of the digital world. Yep, the real world. In the digital world, algorithms can get away with accuracies of around 80%. That doesn’t quite cut it in the real world.

Find him on Twitter: @Gadget_RyArm has reportedly taken back control of its “rogue” Chinese business ahead of an expected IPO. Dylan Patel, Chief Analyst at SemiAnalysis, even penned a piece titled: ‘The Semiconductor Heist Of The Century – Arm China Has Gone Completely Rogue’. Arm-owner SoftBank sold 51 percent of its stake in the Chinese venture, Arm Limited, to a consortium of Chinese investors for $775 million. However, Wu refused to leave arguing that: “Arm China did not convene any valid board meeting”. In the meantime, Wu reportedly got rid of staff loyal to Arm from Arm China and even employed security guards in a bid to keep out unwanted guests to retain his position.

While there’s a vast amount of data available for HR and talent analytics today, most organizations are still not reaping the benefits of their analytics investments. He noted that Myinterview’s video interview platform allows employers and jobseekers to meet much sooner in the hiring funnel. The company uses ML techniques for interpreting video, video capturing, compression and delivery across varying internet stabilities. Gillman said the AI component of Myinterview’s platform extracts information from videos by using these two models:An NLP/text analysis model that identifies keywords and phrases that align to various personality leanings. He noted that companies can get a lot more data from someone speaking casually in a video than from texts and other data forms.

Of all the grand ideas that Elon Musk has for Twitter, the one that he’s pitched most fervently is making the platform’s algorithms open source. First, he’d make it open source. Musk said he wanted to make the Twitter algorithm open source. It could also hinder another of Musk’s ambitions: “defeat the spambots.”On the other hand, open source offers new opportunities to find vulnerabilities and flaws. If you make something open source, won’t that make it easier to find vulnerabilities?

Ethereum is preparing for an upgrade that’s been described as “the single most important event in crypto history.”The transition would overhaul the infrastructure of the world’s second-largest blockchain. Here’s everything you need to know about the long-awaited “merge.”What is the Ethereum merge? The merge aims to make the Ethereum blockchain more efficient by switching from a proof-of-work (PoW) mechanism to a proof-of-stake (PoS) system. PoS proponents expect the merge to make Ethereum more scalable, secure, and sustainable. Ethereum Foundation developer Tim Beiko tweeted on April 12 that the merge “won’t be in June, but likely in the few months after.”It won't be June, but likely in the few months after.

Cerebras Systems, a company bent on accelerating machine learning systems, built the world’s largest chip last year. In the time since, it’s developed bespoke solutions to some of the largest problems in the AI industry. Cerebras Systems is now valued at more than $4 billion. I interviewed Cerebras Systems co-founder and CEO Andrew Feldman to find out how his company was bellying up to the hardware bar in 2022 — an era marked by big tech’s domination of the AI industry. To learn more about Cerebras Systems, check out the white paper for the CS-2 here and the company website here.

This computing energy projection draws from the Semiconductor Research Corporations’s decadal report. Panel topics ranged from “Custom hardware for efficient computing” to “Hardware for new architectures” to “Algorithms for efficient computing,” among others. Visual representation of the conversation during the workshop session entitled "Energy Efficient Systems." Image: Haley McDevitt Previous item Next itemThe goal, said Yildiz, is to improve energy efficiency associated with computing by more than a million-fold. “5G is the most energy efficient standard ever,” said Scharp.

“We were very pleased that many groups across MIT were eager to contribute their expertise and creativity to that goal. The team will pilot their technology in a small study at Boston Medical Center in collaboration with neurosurgeon James Holsapple. Numerous recent studies have highlighted a potential role for immune inflammation in Alzheimer’s disease. Working in mice, Choi’s lab will test whether such activity is prone to increase in Alzheimer’s and whether it contributes to disease. By studying biochemical signaling at the junction the lab hopes to discover new targets that could be therapeutically modified.

One of the most pressing issues facing cities and urban spaces are the future impacts of climate change, and how to make critical decisions related to mitigation and response. Sipremo, a Brazilian-based start-up applying artificial intelligence (AI) for smart decision making, was awarded the top prize for start-ups making our cities safe, clean and sustainable. Sipremo addresses the massive impact of climate change events on business in various industries and all of our society. AI offers predictive analysis that delivers insights on climate change. Sipremo empowers intelligent decision making to take on climate change, allowing various businesses to have critical support for crucial decisions and do climate-friendly management of their operations.

In a February paper, scientists at Alphabet-backed DeepMind describe what they call a “data-driven” approach for teaching AI to control computers. Not-so-coincidentally, DeepMind co-founder Mustafa Suleyman recently teamed up with LinkedIn co-founder Reid Hoffman to launch Inflection AI, which — like Adept — aims to use AI to help humans work more efficiently with computers. Vaswani and Parmar helped to pioneer the Transformer, an AI architecture that has gained considerable attention within the last several years. “Google’s efforts scaling Transformer models yielded [the AI architecture] BERT, powering Google search. “Historically, long-term AI work has been the purview of large tech companies, and their concentration of talent and compute has been unimpeachable.

Boltanski, mediated by technology, brought the voices of dogs back to the human’s quarters while at the same time emphasising the process of othering and exclusion of non-human animals that came with the historical practice. Especially when it comes to sonic collaborations between humans and non-human animals, the latter proponents of twentieth and twenty-first century interspecies music are deeply rooted in historical traditions of making music and exchanging sound productions by humans and non-human animals. Sometimes non-human animals are explicitly addressed not so much as active performers but as audience members. Artificial intelligencesAs explained previously, there can no longer be doubt in the creative capacity of non-human animals. We only briefly hint at the fact that theoretical sketches of AI ethics and AI citizenship already relate to the parallel and more established discourses on non-human animals.

Recording Robocar Data with MCAPUsing the MCAP C++ writer to record robotics dataDaniel Chia · 5 min readPublished April 22, 2022Read more:article studio data platform The Future of Robotics What we can expect from the Fourth Industrial Revolution . Esther Weon Esther Weon March 25, 2022 · 7 min read article data platform Analyze Your Robotics Data with Jupyter Notebooks Connect Foxglove Data Platform to your Jupyter Notebooks . Miles Egan, Esther Weon, Roman Shtylman Miles Egan, Esther Weon, Roman Shtylman March 24, 2022 · 3 min read

Aligning Language Models with Human PreferencesDate: 11:00am - 12:00 noon PT, Apr 28 2022Venue: Zoom (link hidden)AbstractThis talk is part of the NLP Seminar SeriesSelf-supervised learning objectives are highly effective at pretraining language models (LMs) for various tasks. Even after such training schemes, some undesirable behaviors may remain undetected during training. We thus go a step further and generate inputs that elicit undesirable behaviors from the LM using other LMs, to preemptively catch and fix such behaviors. Overall, we find that some of the most powerful tools for aligning LMs with human preferences are LMs themselves. BioEthan Perez is a fourth and final year Ph.D. student in Natural Language Processing at New York University.

Drug discovery has come a long way from its roots in serendipity. To further support the drug discovery process, we are also working on methods that can automatically design compounds that better fit project requirements than existing candidate compounds. The MoLeR modelIn the MoLeR model, we represent molecules as graphs, in which atoms appear as vertices that are connected by edges corresponding to the bonds. As we alluded to earlier, drug molecules are not random combinations of atoms. OutlookWe continue to work with Novartis to focus machine learning research on problems relevant to the real-world drug discovery process.

Their 30-centimeter-tall jumper can spring more than 30 meters into the air—roughly the elevation of a 10-story building and 100 times its own height. Image of the device jumping, with lines added over the position of the jumper approximately every 200 milliseconds. To maximize the new bot’s energy density, the team created a 30-gram device whose whole body acts as the spring. “On the moon, our device could theoretically jump forward half of a kilometer while going 125 meters high in a single leap,” he says. Many jumping robots are designed, in part, to help researchers study how beings from fleas to humans hurl their bodies into the air.

? Research Summary by Eryn Rigley, a PhD research student at University of Southampton, specialising in the intersection of environmental and AI ethics, as well as defence & security AI ethics. This paper offers a challenge to the notion that ‘responsibility’ is suitable and sufficient to AI ethics work. IntroductionWe have all seen the AI ethics buzzwords by now: ‘explainability’, ‘transparency’, and the big one, ‘responsibility’. Yet, Heinrichs argues, AI ethicists have failed to consider in sufficient depth how humans might be accountable for the performance of AI actions. Exploring the limitations of responsibility, he has outlined for readers some of the challenges in AI ethics, and where responsibility fits in this crowded field.

As described by Agility Robotics CTO Dr. Jonathan Hurst in a recent blog post, Agility’s robots are - by contrast - built to be versatile, cost-effective, and durable helpers to people. “We’re deepening our investment in Agility Robotics because their robots solve the labor shortages plaguing businesses and wreaking havoc on supply chains,” said Matt Ocko, Co-Managing Partner, DCVC. Interested enterprise customers who want to explore early access to the next generation of Agility’s robots can reach out to sales@agilityrobotics.com to prequalify. ADDITIONAL RESOURCESAgility Robotics on Twitter, Instagram, LinkedIn, YouTube, FacebookAbout Agility RoboticsHeadquartered in Corvallis, Oregon, Agility Robotics makes robots that are made for work. Agility marries design, software, and hardware to build robots capable of doing limitless tasks as part of a blended human-robot workforce.

They point to hundreds of studies that appear to demonstrate that smiles, frowns, and so on are universal facial expressions of emotion. The assumption of universal facial expressions can be traced back to several sources, most notably a set of photographs by 19th-century French physician Guillaume-Benjamin-Amand Duchenne. He also did not write that facial expressions are functional products of evolution. They could now write about facial expressions as universal and claim to be the heirs of the unassailable Charles Darwin. The evidence is clear that the same emotion can accompany different facial movements and that the same facial movements can have different (or no) emotional meaning.

RT @khoslaventures: AI is upending the way business is done and shifting who has the advantage. Tune in to hear @klakhani of @HarvardHBS on why traditional models don't work anymore, why some companies are in denial, and where the new opportunities are now. https://t.co/GPQR5sacFm https://t.co/dQYKlsQi2O

Max Gruber / Better Images of AI / Clickworker Abyss / CC-BY 4.0Artificial intelligence is becoming a household name in the hiring process for many businesses. Current AI Regulations for HRThere have been plenty of cases where HR AI systems for screening and selecting job candidates have had bias in their algorithmic decisions. The two main EU regulatory frameworks concerning HR companies are the General Data Protection Regulation (GDPR), and specific EU AI regulations. Statistics show that “96% of senior HR professionals believe AI has the potential to greatly enhance talent acquisition and retention” and “55% of HR managers see evidence of AI becoming a regular part of HR within the next five years”. Also, there are more and more initiatives of joint collaboration among major businesses and institutions in the HR industry committing to create safeguards against AI bias for the workforce.

This article outlines different methods for creating confidence intervals for machine learning models. Comparing the Different Confidence Interval MethodsGiven that there are so many confidence interval methods out there, which one should we use? The .632+ bootstrap (method 2.4) might be the most accurate bootstrap method, but it is computationally very expensive for large datasets. show ()Out:We can see that bootstrapping the test set results in the widest confidence intervals, and the .632 estimates result in the smallest confidence intervals. Machine learning benchmark studies don’t necessarily have to be accompanied by confidence intervals.

In 2021, funding globally for robotics and drone companies rose to $14.9 billion, according to PitchBook. The amount of funding raised for robotics in 2022 has been steady. By January, VCs allocated $560 million of funding into robotics startups, excluding the round for Wandelbots, a German industrial robotics company that raised $84 million for its Series C round in January. “The third paradigm shift of AI started four years ago and drives the tech market,” Zino said. Diligent Robotics, an Austin, Texas-based startup founded by two women, raised over $30 million in April.

New York-headquartered Vic.ai, a startup that provides a platform to automate accounting and financial processes for enterprises, today announced an AI-powered tool for enterprise-grade cost optimization. Officially dubbed Spend Intelligence, the offering comes as the latest addition to Vic’s flagship intelligence product designed to provide business and operational insights to help companies improve existing processes. “With Spend Intelligence, we continue to push the boundaries of what AI can do for finance,” Alexander Hagerup, CEO of Vic.ai, said. The algorithm learns from the historical spending data of the enterprise – down to the individual line items. “In addition to cost reductions, we’ll eventually track and monitor carbon footprint to help companies calculate and reduce CO2 emissions.

And just as the human brain created AI and ML models that grow increasingly sophisticated by the day, these systems are now being applied to study the human brain itself. The research arm of Facebook’s parent company today announced a long-term study to better understand how the human brain processes language. Researchers are looking at how the brain and AI language models respond to the same spoken or written sentences. “We’re trying to compare AI systems to the brain, quite literally,” said Jean-Rémi King, senior research scientist at Meta AI. AI language models, meanwhile, are trained on billions of sentences and can parameterize up to 175 billion artificial synapses.

But access to data doesn’t necessarily equate to useful, contextualized information that’s easy to search and derive insights from. And this is something that German company Deepset is setting out to solve, with an open-source NLP framework called Haystack that enables developers to build pipelines for myriad search use-cases. Founded in 2018, Deepset started work on Haystack in 2019, and released the first incarnation of the open-source project the following May. “For all disruptive technologies, but especially for AI and NLP, being locked-in is what most enterprises fear,” Rusic continued. “Deepset has offered professional services, support, and hosting of Haystack-based systems before — these revenues allowed the company to bootstrap for three years,” Rusic explained.

Gaps in cybersecurity tech stacks, especially in endpoint security and patch management, are increasingly leaving enterprises vulnerable to attacks. Cybersecurity providers’ sales partners are also helping to close gaps in tech stacks by providing their expertise and insights. Closing tech stack gapsThere are five strategies cybersecurity vendors should rely on to help their enterprise customers close widening gaps in their security tech stacks. Doubling down on endpoint security as a core product strategyFast-tracking endpoint security applications and platforms are also helping to close the gaps in tech stacks today. Cybersecurity tech stacks need AI to identify how best to thwart advanced attacks today and in the future.

Software development toolmaker Digital.ai, gazing into the future of how high-quality code will be created, has announced a new tool set to strengthen its AI-powered devops platform. “The AI-powered devops platform enables the planning, gathering, and delivery of the conceptual idea to the creation and operation and orchestration of all groups that come into the platform and operate within the platform,” Digital.ai general manager of agile and devops Derek Holt told VentureBeat. Derek Holt: Our three differentiators are our unified devops platform, our AI / ML capabilities and our scaled integration to the enterprise. Holt: In our experience, most companies treat devops as a series of best practices for software delivery. This includes more technical personas, such as VP of engineering, to understand bottlenecks in the software delivery process.

Video conferencing platform Zoom came under fire after saying it might soon include emotion AI features in its sales-targeted products. How can organizations calculate the risks and rewards of investing in emotion AI? Commitment to transparency is keyBut, they add, an emotion AI investment requires a commitment to transparency. Today’s evolving emotion AI technologies “may feel a little bit more invasive,” admitted Annette Zimmerman, a vice president and analyst at Gartner who specializes in emotion AI. What enterprises need to know before investing in emotion AIGive emotion AI C-level attention“Executives need to know that emotion AI has great possibilities along with great responsibilities,” said Theresa Kushner, data and analytics practice lead at NTT DATA Services.

Thankfully, I accidentally found this amazing trick to launch the Notes app and start writing. It was so simple that I am still kicking myself for not knowing it all these years. That small note poking out of the cornerMac uses something called a Hot Corner to activate the Notes app. The Notes app trick is enabled by default on Macs; you can change that, and configure your own Hot Corners too. Here are your options for Hot Corner customizationYou’re all set to make better use of your screen real estate.

Vets are expected to balance the emotions of devoted pet owners with the pressure of keeping a small business afloat. For the veterinary health products company Zoetis, two things have become clear. And second, the field of veterinary diagnostics is uniquely positioned at the nexus of some of veterinary care’s biggest pain points. When it came to fully leveraging such advanced technology like AI, Zoetis strategically bolstered its in-house capabilities by tapping into an ecosystem of partners. Coffee, eggs, and advanced machine learningFate intervened in the hall of a veterinary conference in 2018.

“People can look through it and say, ‘Oh, I see a problem here, I don’t agree with this,’” Musk said. Social platforms like Twitter, Facebook, and TikTok wield enormous influence and power but are largely opaque to their users and regulators. Ruths says he has refrained from teaching his students about social recommendation systems thus far because they are so opaque. The first problem is that there is no single algorithm that guides the way Twitter decides to elevate or bury content, unlike what Musk has implied in the past. “There is no ‘master algorithm’ for Twitter,” one company source says.

Instead, the researchers used an optimization methodology developed for machine learning to dramatically reduce the CPU time required while maintaining the accuracy of the solution. Fusion energyFusion offers the promise of unlimited, carbon-free energy through the same physical process that powers the sun and the stars. It is no accident that fusion researchers have been pioneers in computational physics for the last 50 years. Predicting the performance of a self-heated fusion plasma therefore requires a calculation of the power balance between the fusion power input and the losses due to turbulence. New approach increases confidence in predictionsThis work, described in a recent publication in the journal Nuclear Fusion, is the highest fidelity calculation ever made of the core of a fusion plasma.

Deep neural networks outperformed other machine learning techniques and did not result in significant drop in performance as the number of classes increased. Deep learning, technology to thank for this, continues to disrupt traditional approaches in many other subfields of machine learning. Originally in the 60s, inspired by how the brain works (at least how we understood it back then) deep learning has now developed into a mature engineering concept. It utilizes advances in genomics, big data analysis and deep learning for in silico drug discovery and drug repurposing for aging and age-related diseases. The company pursues internal drug discovery programs in cancer, Parkinson's, Alzheimer's, sarcopenia and geroprotector discovery.

RT @GE_Digital: IT'S HERE! The world's FIRST cloud-native historian #software, @GE_Digital's Proficy Historian for Cloud, is NOW AVAILABLE in the @awscloud Marketplace. Simple, secure, reliable movement of OT #data? Now that's something all industrial companies will love. https://t.co/HvzYijZZYK https://t.co/END6naC8R0

A pioneer in machine learning has argued that the technology is best placed to augment human intelligence and bemoaned ‘confusion’ over the meaning of artificial intelligence (AI). With various contributors thanked at the foot of the article – including one Jeff Bezos – Jordan outlined the rationale for caution. The 2019 article mentions that ‘most of what is labelled AI today, particularly in the public sphere, is actually machine learning’. Jordan noted a semantic shift between concepts such as social science and social engineering to explain this. Attend the co-located 5G Expo, IoT Tech Expo, Blockchain Expo, AI & Big Data Expo, and Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London, and Amsterdam.

Find him on Twitter: @Gadget_RyThe AI industry continued to thrive this year as companies sought ways to support business continuity through rapidly-changing situations. As we wrap up the year, it’s time to look ahead at what to expect from the AI industry in 2022. Explainable AI (XAI) is a partial solution to the problem. (Photo by Michael Dziedzic on Unsplash)Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo.

Craig Martell, Head of Machine Learning at Lyft, is set to head the Pentagon’s AI efforts. Breaking Defense first broke the news after learning Martell was destined to be named as the Pentagon’s new chief digital and AI officer. Martell has significant AI industry experience – leading efforts at not just Lyft but also Dropbox and LinkedIn – but has no experience navigating public-sector bureaucracy. The Pentagon is going to be very much “in at the deep-end”...

Find him on Twitter: @Gadget_RyBritish semiconductor firm Graphcore has unveiled the first Wafer-on-Wafer (WoW) processor alongside setting out its roadmap for an “ultra-intelligence AI supercomputer”. In terms of power, Graphcore says the flagship Bow Pod delivers more than 89 petaFLOPS of AI compute. Here’s how that power translates into real-world performance across popular AI applications:Ultra-intelligence AI supercomputerThe most interesting announcement made by Graphcore today is of its plan to develop an “ultra-intelligence AI supercomputer”. The computer will be called the ‘Good’ computer, named after computer science pioneer Jack Good (born Isadore Jacob Gudak). Thus the first ultraintelligent machine is the last invention that man need ever make.”By 2024, Graphcore expects to have delivered the first ultra-intelligence AI computer.

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The Oxford Union, the debating society of the University of Oxford, invited an artificial intelligence to debate the ethics surrounding its own existence earlier in December. The topic for debate was “this house believes AI will never be ethical”, to which the AI responded: “AI will never be ethical. There is no such thing as a good AI, only good and bad humans. “We [the AIs] are not smart enough to make AI ethical. In a possible hint to Elon Musk’s Neuralink plans, the Megatron continued: “I also believe that, in the long run, the best AI will be the AI that is embedded into our brains, as a conscious entity, a ‘conscious AI’.

Find him on Twitter: @Gadget_RyNokia has announced that it’s deploying its AI video analytics solution to improve rail crossing safety. The solution, Scene Analytics, will initially be deployed for Baselland Transport AG (BLT) in Münchenstein, Switzerland. This deployment, in collaboration with Nokia, represents an encouraging step towards using analytics as another layer of protection in dangerous areas. It is now our ambition for Nokia Scene Analytics to become a key part of the transition towards the digitalisation of future railways. By integrating machine learning into level crossing systems, we will be able to continuously improve and refine safety processes in real-time.

Arize, a maker of artificial intelligence (AI) observability tools, has introduced Bias Tracing, a new tool for identifying the root cause of bias in machine learning (ML) pipelines. The new bias tracing applies similar techniques to create a map of AI processing flows spanning data sources, feature engineering, training and deployment. Getting to the root of AI biasArize’s AI observability platform has already supported tools for monitoring AI performance and characterizing model drift. Dhinakaran said the Bias Tracing launch is related to Judea Pearl’s groundbreaking work on causal AI, which is at the cutting edge of both explainable AI and AI fairness. Arize Bias Tracing is currently built to work with classification models and will expand to other use cases over time.

The ongoing problem in the telecom industryAn ongoing problem in the industry is simply that telecom fraud is a lucrative business that is relatively easy to execute. A report by the Communications Fraud Control Association (CFCA) estimates telecom fraud is causing $3.6 billion in financial damage to businesses yearly. From financial losses, to reputational damage and more, the impact of telecom fraud can be devastating. “Our innovative AI-enabled telecom fraud prevention solution is helping telcos modernize their fight against telecom fraud and significantly close the gaps against the activities of fraudsters,” said Baranowski. He noted that this has significantly widened the attack surface for cybercrime, especially telecom fraud.

It is a conundrum throughout the enterprise sector: artificial intelligence (AI) and machine learning (ML) modeling delivers great business value across a variety of use cases. HPE to the rescueAs a means to help simplify and speed up this process, HPE today released a new Machine Learning Development System. The ready-to-use system allows users to immediately build and train AI models at scale and realize faster value. The San Francisco startup built an open-source AI training platform that has now transitioned to the HPE Machine Learning Development Environment. By contrast, he said, the HPE Machine Learning Development System is fully integrated and ready-to-use, combining software and specialized computing including accelerators, networking and services.

: GHG emissions that are indirectly associated with a company’s purchase or use of utilities including heat, steam, electricity, etc. “Given the SEC’s proposed rules for emissions reporting, it’s a hot time for our sector in green tech. “Equinix has seen, firsthand, an increasing demand for sustainability from its stakeholders, customers and employees alike — and it has met them, particularly with its global green data centers. There would also be a gradual integration period for registrants to begin reporting of scope 3 emissions. “Even without the SEC regulators, the push for sustainability reporting will have an effect on companies of all sizes,” Hertogh added.

A few weeks ago, VentureBeat published an article titled “Why you shouldn’t be using AI for hiring” that claimed shortcomings in AI-based hiring tools make them unfair. And so there is nothing particularly “artificial” about most of the AI techniques used, nor could you call most of them “intelligent” on their own. Our scoring begins with using only the transcribed words that a candidate speaks because that content is what the candidate gives us to use in the hiring process. This article is far from endorsing AI that is used indiscriminately in the hiring process. AI is also a powerful tool and, when applied in a thoughtful, careful, rigorous, scientific way, can lead to great improvements in hiring technology.

David Clark Cause, IBM, United Nations Human Rights, and the Linux Foundation today officially launched the 2022 Call For Code. Now in its fifth year, Call for Code is an annual challenge seeking solutions to the global climate crisis and other societal issues. Call for Code participants join a global force for good the moment they sign up. They get access to IBM partner ecosystem technologies such as cloud services, machine learning models, and developer support. Get more information and sign your team up here at the official Call for Code website.

All Articles forMachine LearningMachine learning, a branch of artificial intelligence, is about the construction and study of systems that can learn from data. For example, a machine learning system could be trained on email messages to learn to distinguish between spam and non-spam messages. After learning, it can then be used to classify new email messages into spam and non-spam folders. The core of machine learning deals with representation and generalization.

But there’s a major hurdle that holds these systems back: The models often suggest new molecular structures that are difficult or impossible to produce in a laboratory. A new approach from MIT researchers constrains a machine-learning model so it only suggests molecular structures that can be synthesized. When compared to other methods, their model proposed molecular structures that scored as high and sometimes better using popular evaluations, but were guaranteed to be synthesizable. “This process reformulates how we ask these models to generate new molecular structures. Many of these models think about building new molecular structures atom by atom or bond by bond.

Ryan is a senior editor at TechForge Media with over a decade of experience covering the latest technology and interviewing leading industry figures. Find him on Twitter: @Gadget_RyCraig Martell, Head of Machine Learning at Lyft, is set to head the Pentagon’s AI efforts. Breaking Defense first broke the news after learning Martell was destined to be named as the Pentagon’s new chief digital and AI officer. Martell has significant AI industry experience – leading efforts at not just Lyft but also Dropbox and LinkedIn – but has no experience navigating public-sector bureaucracy. One example is Google’s Project Maven contract with the US Department of Defense to supply AI technology to analyse drone footage.

However, according to Nadav Avni, CMO at Radix Technologies, organizations can stay ahead of these challenges by using device management solutions that consolidate all devices, work processes and stakeholders in a single management platform. How Radix’s platform tackles device management challengesRadix’s platform is built on six major pillars that enable companies to start small and scale fast, according to Avni. The last pillar on which the Radix platform rests is what the company refers to as “common functionality,” which includes dashboards, workflows, automations, API access, among others. While Avni noted that device management is broad, he said Radix focuses on endpoint devices like laptops, smartphones and tablets. “Large fleets require central management,” he explained, adding that the VISO engine is the heart of Radix Technologies’ device management platform which helps organizations to be more agile, saves time and money and provides smarter and more focused management.

Accelerating the innovation cycleOne of the most straightforward ways the metaverse is likely to change manufacturing is by significantly accelerating the process of prototyping. Or it could use digital prototyping to customize processes so that a wider menu of product variations can be produced. Decentralized, democratized, and transparent productionThe metaverse could also lead to democratization, decentralization, and increased transparency within the manufacturing sector across industries. Connecting the digital and physical worldsFinally, the metaverse will connect the digital and physical worlds in ways that will prompt new fields of innovation, business models, and demands for manufacturing. In manufacturing or any other sector, the rise of the metaverse must ultimately fit within a framework of sustainability.

On the other hand, object detection requires the model to identify more than object as well as the bounding box of each instance. Object detection models are much more complex than image classification networks and require more memory. More recent models such as YOLO (You Only Look Once) use single-shot detection to provide near real-time object detection. Also, by considerably reducing the size of the neural network, FOMO lowers the memory and compute requirements of object detection models. FOMO can detect many small object instances in an imageLimits of FOMOThe benefits of FOMO do not come without tradeoffs.

Today’s FinTechs and financial institutions (FIs) need cutting-edge technologies to navigate our digital-first world. Fintechs and financial institutions today need data to construct holistic, data-driven strategies that foster demand generation and better customer experiences. The rewards for financial institutions that successfully navigate the advent of AI and its innovations could not be more promising. Finally, through Explainable AI, financial institutions could trace sales back through the customer journey and understand the factors that led to them. ResultsThrough the initiative, Daitan and their secure communications platform client improved customer engagement at the financial institution.

With e-commerce orders pouring in, a warehouse robot picks mugs off a shelf and places them into boxes for shipping. But a new technique developed by MIT researchers would require only a handful of human demonstrations to reprogram the robot. The technique uses a neural network specifically designed to reconstruct the shapes of 3D objects. The researchers use this trained NDF model to teach a robot a new skill with only a few physical examples. "How efficiently we can teach robots new manipulation skills depends on the robots' ability to generalize from just a few demonstrations.

On May 4, join PAI's @ChristineCustis at the @TTCLabs Online Summit to explore the future of transparent and trustworthy AI. Register for the Summit below. ⬇️ https://t.co/xf8rKMZ75aThis year on the 4th & 5th May we will be joined by some of the world's leading thinkers on AI to explore questions of trust, explainability and control. What does it mean for AI to be ...

But unstructured conversational data? According to Allied Market Research, the global conversational AI market was $5.78 billion in 2020 and is expected to grow to $32.62 billion by 2030 – representing a compound annual growth rate of 20%. “For someone who’s just exploring for the first time conversational intelligence or conversational AI, I would say that it’s like listening at scale,” said Brown. By applying conversational data, Brown explained, they were able to determine that users got stuck because they simply couldn’t figure out how to navigate the chat. “So, when they’re listening, they’re listening with knowledge,” Brown said.

Join this VB Live event for an in-depth look at why ethical AI is essential, and how we can ensure our AI future is a just one. And the problem was gaps in the data, which was compiled by male researchers who didn’t recognize their own internal biases. The problem might not sound urgent, but when companies fail to put guardrails around their AI and machine learning models, it hurts their brand, Braga says. “Responsible AI means creating AI systems that are unbiased, that are transparent, that treat data securely and privately,” she says. “It’s on the company to build systems in the right and fair way.”For an in-depth discussion of ethical AI practices, how companies can get ahead of impending government compliance issues, why ethical AI makes business sense, and more, don’t miss this VB On-Demand event!

Digital twins have become increasingly invaluable for enterprises to improve their supply chain planning. Whereas many digital twins simulate products or buildings, supply chain twins model the diffuse connections between raw materials, finished products and customers. Supply chain twins are growing in importance as enterprises try to keep pace with disruptions caused by COVID-19, shipping bottlenecks and sanctions. Here are 18 ways digital twins are being used to streamline supply chain logistics:Strategic planningEnterprises used to develop a strategic plan once per year using tons of spreadsheets. Tactical coordination“Supply chain digital twins can equally shine by mitigating risks almost in real-time,” Amraotkar said.

Later, their generated digital avatar guides workers through presentations or training sessions, answers customer questions, or handles sales requests. This is the future of work, said Oren Aharon, CEO and founder of Hour One, whose self-service platform enables businesses to quickly and automatically create human-led video and full-fledged avatars. Hour One’s platform enables businesses to easily turn any person into a virtual character that can then be activated using just text. An increasing number of startups, including Hour One, are entering the market to provide digital avatars. Hour One’s self-service platform enables businesses to quickly and automatically create human-led video and full-fledged avatars.

Deep Learning continues to evolve as one of the most powerful techniques in the AI toolbox. Many software packages exist today to support the development of models, and we highlight important options available with key qualities and differentiators to help you select the most appropriate ...Share this article

Join AI and data leaders for insightful talks and exciting networking opportunities. Welcome to the latest edition of the AI Weekly newsletterAs I finish my first week at VentureBeat, it’s a perfect opportunity to introduce myself: I’m Sharon Goldman, a senior editor and writer covering AI for technology decision-makers. The success of AI lies in the infrastructureWhy it’s a must-read: AI is all about data, and data lives in infrastructure. Why it’s a must-read: Human-centered AI is more than a hyped buzzword or philosophical framework. While it focuses on how AI can amplify and enhance human performance, it is really about helping enterprises build and manage better AI.

Doctors and scientists have developed an artificial intelligence tool that can accurately predict how likely tumours are to grow back in cancer patients after they have undergone treatment. Monitoring patients after treatment is vital to ensuring any cancer recurrence is acted on urgently. In the retrospective study, doctors, scientists and researchers developed a machine learning model to determine whether it could accurately identify non-small cell lung cancer (NSCLC) patients at risk of recurrence following radiotherapy. Lung cancer is the leading worldwide cause of cancer death and accounts for just over a fifth (21%) of cancer deaths in the UK. “This means there is variation in the type and frequency of follow-up that patients receive … Using AI with healthcare data may be the answer.

ROBOT rats may soon be unleashed to search for survivors at disaster sites as researchers continue to develop the technology. It comes as scientists have unveiled a mechanic critter named SQuRo (small-sized quadruped robotic rat). The robot version would be able to pass through narrow spaces and rugged terrains and perform tasks, such as detection or transportation in relevant scenarios. In the study, experimental results revealed that the legged robot SQuRo is capable of mimicking the motion of actual rats inside narrow spaces. "One solution for enhancing their environmental adaptability is to design a small-sized biomimetic robot capable of performing multiple motions.

Using a sophisticated form of AI known as a GAN (Generative Adversarial Network) they created artificial human faces (i.e. photorealistic fakes) and showed these fakes to hundreds of human subjects along with a mix of real faces. You see, they also asked their test subjects to rate the “trustworthiness” of each face and discovered that consumers find AI-generated faces to be significantly more trustworthy than real faces. Accurately replicating the look and sound of a person in the metaverse is often referred to as creating a “digital twin.” Earlier this year, Jensen Haung, the CEO of NVIDIA gave a keynote address using a cartoonish digital twin. This means that metaverse platforms need equally powerful authentication technologies that validate whether we’re interacting with an actual person (or their authorized twin) and not an evil twin that was fraudulently deployed to deceive us.

But when pointed at the right problem, advanced data analytics solutions have the power to change human lives for the better. But companies could make exponential performance gains in movement health while reducing injuries and maintaining a healthier workforce with just a few basic implementations. Robotics are also reducing injuries, as well as improving efficiency, for certain repetitive-motion tasks previously performed by human workers. How do business owners benefit from the adoption (and, of course, investment in) data technology? This leads to a better late-stage quality of life, free from movement impairment and the limitations imposed by a senior care facility.

Chandler fire crews on Thursday used a robot to open a door and ventilate a massive battery in Chandler that has been smoldering since Monday. The battery is in an approximately 6,000-square-foot building off 56th street south of Loop 202, and used to store electricity for the power grid. An interior sprinkler system has been spraying water on the smoldering battery since Monday, he said. The battery in Surprise that exploded in 2019 was storing energy for Arizona Public Service Co., and exploded when it was smoldering and fire crews opened a door to ventilate the facility. The smoldering was caused when a battery cell essentially shorted out, overheated and damaged other battery cells nearby.

'Moon Knight': What does the cast really know about the moon? Lizzo goes head-to-head with James Corden in a glorious 80s v. 2020s dance riff-offNatasha Lyonne, Chlöe Sevigny, Charlie Barnett, and Greta Lee play a wrong-answers-only version of Know Your Role. All these intuitive robot arms want is offer an extra pair of handsPlus, Zurg and his terrifying robot army. The shocking true-crime case returns with an all-star cast. SubscribeBy signing up to the Mashable newsletter you agree to receive electronic communications from Mashable that may sometimes include advertisements or sponsored content.

The latest trailer for Pixar's Lightyear gives us a bit more of the legendary Space Ranger's origin story. Captain Buzz Lightyear (voiced by Chris Evans) journeys through hyperspace to a new planet, only to realize that his journey took over 60 years in real time. Lightyear and his robot cat Sox (voiced by Peter Sohn) team up with a new squad of Rangers (voiced by Keke Palmer, Taika Waititi, and Dale Soules) in order to get home. But it won't be so easy: Emperor Zurg and his army's arrival pose a major threat to Buzz's mission... and the whole universe. Lightyear hits theaters June 17.

The song is a winking kiss-off to a lying ex — the title “Te Felicito,” or “I congratulate you,” is meant as a sarcastic goodbye to a person who won’t change. Shakira sings about finally seeing someone for who they truly are before Rauw joins her with the second verse of the track. The visual follows Shakira as she brings a robot lover (who turns out to be Rauw) to life. Rauw, who is currently on tour, is known for nimble dance moves, while Shakira went viral on TikTok in 2020 with her “Girl Like Me” dance challenge. “Te Felicito” follows “Don’t Wait Up,” a track Shakira released last summer that also tinkered with futuristic electro-pop production.

Roomba 694iRobot Roomba 694iRobot's affordable Roomba 694 is still on sale for $180 right now, which is a record low. Buy Roomba 694 at Amazon - $180AirPods (3rd gen)Apple's newest earbuds are close to their record-low price right now. Buy AirPods Max at Amazon - $450Story continuesRoomba j7+iRobot Roomba j7+The Roomba j7+ robo-vac and the Roomba s9+ are both $200 off right now and down to $599 and $799, respectively. Buy smart thermostat at Amazon - $48Google Nest thermostatsBoth Google's standard Nest Thermostat and the more advanced Learning model remain discounted right now. Buy 83-inch Sony A90J OLED at Amazon - $5,500Follow @EngadgetDeals on Twitter for the latest tech deals and buying advice.

Then, you look up as 4-foot-tall robot wheels your order to your table. Wayne Doney, the general manager of the popular brunch restaurant at the Mayfair Collection, said they've been using the robot to deliver food to tables for a few days. "It's always here, it shows up for work, it doesn't get sick," Doney said of the robot. Once the food is on the platform, an employee punches in the table number and the robot rolls the food items to the table. The robot has the layout of the restaurant programmed into it so it knows where each table and chair is.

Golden Nest RobotA robot being used at Golden Nest Pancakes & Cafe is helping serving staff deliver food to tables.

A man has created an artificial intelligence program that sends women on dating apps text messages using only quotes from American Psycho and James Bond movies. Ethan Keiser first posted a TikTok video detailing his program back in January that went viral on the app. In the video, Keiser explained that he reverse engineered all the major dating apps and stored 49,000 dating profiles in a database. He then wrote a program that swiped right on every profile, which got him around 5,000 matches. To speak to all 5,000 women, Keiser built an artificial intelligence that sent messages using movie quotes from the character Patrick Bateman in American Psycho and James Bond.

John Deere has been in business for nearly 200 years. Heck, even city folk who’ve never seen a tractor that wasn’t on a television screen know John Deere. Automation is par for the course at John Deere. Heraud explained that John Deere approached artificial intelligence technology from two angles:There’s automation, making machines achieve superhuman performance. The “future of farming,” as John Deere calls it, will apparently be a robotic revolution.

Artificial Intelligence (AI) has been invented as a blessing for the market of many sectors. Businesses are adopting this technology to reduce operational costs, increase productivity, enhance the quality of work, etc. By automating processes with AI you can grow your business ...Share this article

Honda’s ASIMO humanoid robot is retiring. For the last 20 years, ASIMO had been performing at the Honda showroom in Tokyo, Japan, but these regular demonstrations are now at an end. We’ve known for a while that this was coming—Honda announced back in 2018 that it was halting ASIMO ...Share this article

Ryan is a senior editor at TechForge Media with over a decade of experience covering the latest technology and interviewing leading industry figures. Find him on Twitter: @Gadget_RyInvestments in data scientists by the UK’s Department for Digital, Culture, Media & Sport (DCMS) continue to increase rapidly. The data, retrieved by the Parliament Street think tank, shows that DCMS has spent an estimated £20,606,100 on data scientist and data analyst roles between 2017 and 2021. I am delighted to see the investment the government is making in this area.”Last year saw record investment in data scientist/analyst roles. Around £7,383,000 was spent by DCMS in 2021 on bolstering its talent; a 75 percent increase over 2020 when an estimated £4,213,800 was spent.

The New York City-based conversational AI software developer yesterday revealed the results from its Voice and Messaging 2022: Consumer Preferences Around Automation and CX report, which explores evolving consumer preferences around calling and messaging and how these preferences continue to change in specific situations. That’s why 87% of respondents said they’re more likely to do business with a company that connects their interactions across voice and messaging. Another key takeaway is that consumers need options to contact brands when and where they want, and this is non-negotiable, LoCascio said. “According to this latest survey, 43% of consumers say they prefer a voicebot with an accent similar to their own,” LoCascio said. “Combining conversational AI for messaging, speech recognition and analytics for voice and integrations that bring messaging, voice and CRM platforms together can help companies get a comprehensive, unified system in place to connect conversation across all channels,” LoCascio said.

Join AI and data leaders for insightful talks and exciting networking opportunities. But AI is still a digital technology, which means it can be compromised as well, particularly when confronted by an intelligent attack. Getting to AI through its data sourcesThe need for vast amounts of data is actually one of AI’s biggest weaknesses because it creates a situation in which security can be breached without attacking the AI itself. Image-based training data is among the most vulnerable, says Stanford University cryptography professor Dan Boneh. And as AI algorithms become increasingly dependent upon open source tools, hackers will have greater access to algorithms as well.

Elon Musk is clearly bullish on Tesla (TSLA), the potential of Twitter (TWTR), and the Optimus robot. "I was surprised that people do not realize the magnitude of the Optimus robot program," the Tesla CEO said on the company's earnings call late Wednesday. Tesla first revealed the Optimus robot — also known as Tesla Bot — at an artificial intelligence-focused event in August 2021. In theory, the Optimus program has potential to help Tesla lower operating costs in its facilities —it's just a matter if Musk can make enough robots. Shares of Tesla rose 7% in pre-market trading as Tesla pummeled Wall Street estimates for sales, margins, and earnings.

Tesla CEO Elon Musk says the company's robot, named Optimus, will be "worth more than the car business, worth more than FSD." The executive shared during the company's first-quarter 2022 earnings call that Tesla is continuing to work on its robot. Earlier this month at Tesla's Austin gigafactory opening, Musk said a new wave of products, like Optimus, will be introduced in 2023. "I was surprised that people did not realize the magnitude of the Optimus robot program," said Musk on Wednesday's earnings call. Those who are insightful or looking, listening carefully, will understand that Optimus will ultimately be worth more than the car business, worth more than FSD."

'Lone fanatic' could engineer pandemic worse than COVID due to technology, scientist warnsAn individual could conjure up a pandemic worse than COVID-19 due to the power of modern technology, a leading scientist has warned. And that’s an example of what really does keep me awake at night." He added that the world is becoming more dangerous because small groups, "or even fanatical loners" who are empowered by novel technology can "trigger consequences in the cyber world, as well as in the bio world, that can cascade globally." Lord Rees also cited rogue artificial intelligence as a potential source of disaster. He said: "We may dismiss these ideas as science fiction, but given the stakes, they should not be ignored, even if deemed highly improbable."

Tesla chief Elon Musk has said the company’s humanoid robot Optimus would eventually be “worth more than” its self-driving car business. During the company’s first-quarter 2022 earnings call on Wednesday, Mr Musk said the company is continuing to work on its humanoid robot Optimus, first unveiled last year in August. If you want to do it, you can, but you won’t need to do it”, Mr Musk had said. Those who are insightful or listen carefully will understand that Optimus will ultimately be worth more than the car business, worth more than FSD,” he said. “It has profound implications for the economy, given that the economy at its foundational level is labour,” Mr Musk had said.

The stock slid 35.1% Wednesday to mark its steepest single-day percentage decline since it fell a record 40.9% on Oct. 15, 2004. Netflix avoided closing with a market value below $100 billion but it still shed a stunning $54.3 billion in market capitalization on the day, according to Dow Jones Market Data. David Trainer, CEO of New Constructs, an independent equity research firm that uses machine learning and natural language processing to parse corporate filings and model economic earning, said the stock could fall another 50%. "Strong competition is taking market share, limiting pricing power and making it clear that Netflix cannot generate anything close to the growth and profits implied by the current stock price," he wrote in commentary for MarketWatch. Billionaire investor Bill Ackman, meanwhile, sold his hedge fund's entire stake just a few months after announcing he had acquired it and said the losses have reduced year-to-date returns by four percentage points.

(AP Photo/Efrem Lukatsky)Ukraine is employing US-made facial recognition technology to scan the faces of dead Russian soldiers and, after having matched the images to online profiles, sending the photos to the relatives of the deceased. According to Ukrainian officials cited in the story, the US firm Clearview AI gave the country the technology. With the photos of dead Russian troops in hand, a database of 20 billion images from social media and the internet is sifted through in order to identify the individuals. Clearview AI sells access to its technology to 3,100 active users, including the FBI and Department of Homeland Security. Beyond tormenting Russian families, Ukraine is using this technology to scale up its internal repression.

Let me say that again: it creates original, realistic images and art from a text description. DALL·E 2 image generated from text, 'a teddybear on a skateboard in times square'(Image by DALL·E 2 AI) Details DMCAIt was developed by OpenAI, an AI research and deployment company, whose mission is "to ensure that artificial general intelligence benefits all of humanity." DALL·E 2 image generated from,'a dolphin in an astronaut suit on saturn, artstation'(Image by DALL·E 2 AI) Details DMCAI'm breathless. DALL·E 2 image generated from, 'a shiba inu wearing a beret and black turtleneck'(Image by DALL·E 2 AI) Details DMCA"Seeing is believing? DALL·E 2 image generated from, 'a propaganda poster depicting a cat dressed as french emperor napoleon holding a piece of cheese'(Image by DALL·E 2 AI) Details DMCABut who owns the copyright?

The private Raleigh, N.C., firm was asked to make a presentation at an international conference on chemical and biological weapons. The team ran MegaSyn overnight and came up with 40,000 substances, including not only VX but other known chemical weapons, as well as many completely new potentially toxic substances. The researchers had previously used MegaSyn to generate molecules with therapeutic potential that have the same molecular target as VX, Urbina says. For their study, the researchers had merely asked the software to generate substances similar to VX without inputting the exact structure of the molecule. The AI systems “score” a molecule based on certain criteria, such as how well it either inhibits or activates a specific protein.

Amazon.com Inc. AMZN, -0.89% announced the launch of the Amazon Industrial Innovation Fund, a $1 billion venture investment program focused on the supply chain, customer fulfillment, logistics and warehouses. The first round of investments have been focused on wearable technology. Among the funding recipients are: South Carolina-based Modjoul, which is developing technology that will help the wearer avoid musculoskeletal issues; Vimaan, a California company working on artificial intelligence solutions for inventory management; and Agility Robotics, which is creating a "bi-pedal walking robot" called Digit. Amazon stock is down 7% over the past year. See: Chipotle launches $50 million venture fund

Researchers at the Sydney Zoo deployed a robot dog to study how animals would behave in its presence. The Unitree robot dog, dubbed Sparky, was put face-to-face with two cheetahs inside their enclosure. While the cheetahs didn’t seem particularly fond of their new robot friend, Sparky was able to hold its ground.

Over the last five years, researchers at the Information Somatics Lab in Tokyo have made significant process testing and developing Dr. Octopus-like robot arms. The goal is to find effective ways to redesign the human body, focusing specifically on augmenting and multiplying our upper limb capabilities. Their work has come a long way over the years, from feet-controlled robot arms to appendages controlled movements in the shoulders.

During Tesla's earnings call Wednesday, company CEO Elon Musk said the robotaxi is "highly optimized for autonomy" and won't have a steering wheel or pedals. Musk hinted at other "exciting" innovations, saying the goal is to achieve the lowest cost-per-mile possible, and that the cost of taking a Tesla robotaxi would be lower than riding a bus. The robotaxi will be unveiled sometime next year, and the company plans to mass produce it by 2024, Musk said. During the call, Musk said that the company's Full Self Driving tech has been extraordinarily difficult to perfect. "To solve Full Self Driving you actually have to solve real world artificial intelligence, which nobody has solved."

SAVE $39: As of April 21, new customers can get a third-gen Amazon Echo Dot ($39.99) for just 99 cents by bundling it with a month of Amazon Music Unlimited ($7.99). As of April 21, you can get Amazon's third-gen Echo Dot for just 99 cents (normally $39.99) when you subscribe to its Amazon Music Unlimited streaming service for a month ($7.99). SEE ALSO: Everything we know about Amazon Prime Day so farSpeaking of: The deal is only available for new customers (i.e., anyone who hasn't purchased an Echo device or subscribed to Amazon Music before). Also note that the Amazon Music Unlimited membership is set to auto-renew after one month, but you're free to cancel or change it anytime. Once you've linked it to Amazon Music Unlimited through the Alexa app, you can simply ask the AI to start streaming your favorite tunes.

It's like 'ThunderCats' and 'Voltron', but for adults'The Gilded Age' star Denée Benton talks about pushing past stereotypes and fulfilling her dreams'Moon Knight': What does the cast really know about the moon? Everything you need to know about 'Our Flag Means Death'Bobby Moynihan created this '80s throwback cartoon with an all-star cast including Ben Schwartz, Cobie Smulders and Taran Killam. Anymal used to walk blindly. All these intuitive robot arms want is offer an extra pair of handsThe biggest stories of the day delivered to your inbox. SubscribeBy signing up to the Mashable newsletter you agree to receive electronic communications from Mashable that may sometimes include advertisements or sponsored content.

Anymal used to walk blindly. Robots are totally obsessed with this traditional children’s gameThe spectacular sprint of the robot mini-cheetahETH Zürich's ANYmal has made major strides since it first burst onto the scene. It's like 'ThunderCats' and 'Voltron', but for adultsSeriously, just watch it. All these intuitive robot arms want is offer an extra pair of handsThe biggest stories of the day delivered to your inbox. SubscribeBy signing up to the Mashable newsletter you agree to receive electronic communications from Mashable that may sometimes include advertisements or sponsored content.

If the recent price hike on Amazon's Prime membership service didn't scare you away, you're in for a sales spectacular come Prime Day. Prime Day is an annual sitewide sale that Amazon puts on for its Prime members. What will be the best Prime Day deals? Other usual Prime Day suspects include 4K OLED TVs, robot vacuums, headphones, earbuds, and basically every Apple product under the Sun. Amazon also offers special discounts on items to its members beyond Prime Day, including early access to time-sensitive Lightning Deals.

The Hulu version of 'Sailor Moon' finally honors the original's queernessTrailer for 'White Hot: The Rise & Fall of Abercrombie & Fitch' examines the decline of a clothing empire'Marcel the Shell' is back with adorable film trailer'SNL' calls you out for liking thirst traps on InstagramCulture reporter Tim Marcin got his hands on the Flamin' Hot Dorito chicken wings from Buffalo Wild Wings. Find out if they're worth the order. It's like 'ThunderCats' and 'Voltron', but for adultsAnymal used to walk blindly. All these intuitive robot arms want is offer an extra pair of handsThe biggest stories of the day delivered to your inbox. SubscribeBy signing up to the Mashable newsletter you agree to receive electronic communications from Mashable that may sometimes include advertisements or sponsored content.

The explanation isn't as weird as you'd thinkThe best and worst dating apps that don’t exist anymoreApple TV+'s 'Pachinko' has an opening title sequence you'll want to watch more than once'Cat Burglar' picks up where 'Black Mirror: Bandersnatch' left offJoin us as we explore the genetic anomaly behind one of our favorite big cats. Anymal used to walk blindly. "Nicole Kidman got a sword, Willem Dafoe got a longship. All these intuitive robot arms want is offer an extra pair of handsThe biggest stories of the day delivered to your inbox. SubscribeBy signing up to the Mashable newsletter you agree to receive electronic communications from Mashable that may sometimes include advertisements or sponsored content.

Ever wondered what your robot clone might look like, when the machines inevitably grow intelligent enough to engineer a takeover of humankind that starts with them replacing us one by one? Well, wonder no more! In the video above, YouTuber Tom Scott pays a robot company in the UK that makes robot doubles of people to create a moving doppelgänger of him — and the result (which includes hand-painted skin and individually punched human hairs) is undeniably unnerving.

Instead, they were created by DALL-E 2, a new AI system that can turn textual descriptions into images. Just write down what you want to see, and the AI draws it for you — with vivid detail, high resolution, and, arguably, real creativity. That means for all the delightful images that DALL-E 2 has produced, it’s also capable of generating a lot of images that are not delightful. OpenAI is far from the only artificial intelligence company dealing with bias problems and trade-offs. One of their observations is that over the past few years, a lot of the splashiest AI research has been migrating from academia to industry.

For some kids, school breaks mean family vacations, part-time jobs, sports and hobbies, or soaking in the general bliss of not having to do homework. This summer in Los Angeles, dozens of children ages 5 to 17 will attend the third-ever session of Crypto Kids Camp, where they’ll learn about everything from artificial intelligence to virtual reality using hands-on games and activities. “They’re stoked.” She has big plans: By this summer, Crypto Kids Camp plans to operate in six states, and by fall, there are expected to be 41 locations nationwide. It isn’t the only kids’ camp devoted to the subject; similar programs exist at the University of Pennsylvania and other colleges across the country; in Miami; and, naturally, online. And when they lose, it’ll be a very good lesson.”Serido’s first recommendation for teaching kids about money, however, aligns with Crypto Kids Camp’s: Start with something tangible, like physical currency that can help show that money is a finite resource.

On Wednesday, Meta CEO Mark Zuckerberg revealed some answers during a keynote speech about the company’s latest developments in AI. One of Meta’s main goals is to develop advanced voice assistant AI technology — think Alexa or Siri, but smarter — that the company plans to use in its AR/VR products, like its Quest headset (formerly Oculus), Portal smart display, and Ray-Ban smart glasses. So it makes sense that the company is looking to the future, in which Meta hopes to roll out sophisticated language-processing AI. Meta is building its own ambitious voice assistant for AR/VROn Wednesday, it became clear that Meta sees voice assistants as a key part of the metaverse, and it knows that its voice assistant needs to be more conversational than what we have now. For example, most voice assistants can easily answer the question, “What’s the weather today?” But if you ask a follow-up question, such as, “Is it hotter than it was last week?” the voice assistant will likely be stumped.

Cultivated meat, grown in a bioreactor rather than out on the range, might be one of the big food trends of the decade. Though we hear about new cultivated meat companies and funding rounds with frankly amazing frequency (this one happened while I was writing this! "Using this legacy hardware to cultivated meat requires one to re-engineer the cells. While there's almost certainly a little more to it than that, the system will use built-in sensing, machine learning, and industrial automation tech to this end. Some of these can already be found in the innovations of other cultivated meat startups, but it's a fast-moving sector.

Photo: Drew Angerer/Getty ImagesRight after the Sunset Park shooting, WCBS asked Eric Adams about the possibility of putting metal detectors at subway entrances. — and in response, the mayor’s communication director clarified that Adams was “of course” not talking about airport-style detectors but was instead talking about using “innovative technology.”On Wednesday, Adams once again brought up metal detectors in similarly vague terms. “Oftentimes when people hear of metal detectors, they immediately think of the airport model,” Adams said. Evolv claims “Fast, Efficient Threat Screening,” saying that it can screen 3,600 people per hour, ten times faster than metal detectors, using advanced sensors and artificial intelligence. “It might be a little bit less obvious than metal detectors, and people will move through it more quickly.

Mark Cuban knows a little bit about emerging technologies, and to him all roads lead to artificial intelligence. "If you look at the biggest market cap companies, all but maybe Berkshire Hathaway are industry leaders in AI," Cuban wrote in an email to Inc. "They over index in AI investment and it has paid off for them." There's a slew of applications for deploying AI within an organization: automation, customer engagement, and expanded data analysis are just a few. Deploying AI can also shave considerable time that employees now spend conducting manual tests, time that can be reinvested elsewhere. If you're concerned about being too early an adopter, Cuban says it's still better to take that risk, even if some believe gains made from integrating AI aren't necessarily immediate.

CEO John Schlifske says AI drives growth: "It's faster, more effective and leads to better outcomes."

If you're looking for more convenience and a deeper clean, you can't go wrong with the iRobot Roomba i3 Evo 3550 Robot Vacuum that's currently $151 off. It has the same three-stage cleaning process as the Roomba 694 — but with even stronger suction power. Thanks to smart mapping, the vacuum, which cleans in straight lines, learns the layout of your home for custom room cleaning. When it's full, it will automatically empty out everything it's picked up into a dirt disposal bag in its base. The bag takes 60 days to fill, so you won't have to think about it for weeks.

Kraft Heinz Co. KHC, +2.28% has announced a partnership with Microsoft Corp. MSFT, -1.23% that will transform the food company's supply chain and analytics process. The partnership is one more step in the company's investment in technology. In February, Kraft announced a partnership with Google GOOG, -2.39% GOOGL, -2.41% to use machine learning tools and Google Cloud products. Kraft stock has advanced 23.6% for the year to date.

Tesla's humanoid robot will one day be the most valuable part of the company's business, CEO Elon Musk predicted on Wednesday. Musk said on Tesla's first-quarter earnings call that the importance of the robot, dubbed Optimus, "will become apparent in the coming years." "I was surprised that people do not realize the magnitude of the Optimus robot program," Musk said Wednesday evening. "Those who are insightful or who listen carefully will understand that Optimus ultimately will be worth more than the car business and worth more than full self-driving. Musk said that the first models of the bipedal robot would arrive next year, though experts are skeptical about that timetable.

In those early days of artificial intelligence, most people in AI were focused on complex math problems aimed at automating human tasks, with limited success. Agre noticed that those building artificial intelligence ignored critiques of the technology from outsiders. Agre began to rebel, in a sense, from his profession, seeking out critics of artificial intelligence, studying philosophy and other academic disciplines. John Seberger, a postdoctoral fellow in the Department of Informatics at Indiana University who has studied Agre’s work extensively, said that’s not necessarily a bad thing. Seberger said Agre’s work offers a way of thinking about the problems that face an increasingly digital society.

Our global power industry was built around large, centralised power stations, mostly powered by coal and natural gas. Coal, crude and natural gas did not become any more investible, cleaner, cheaper or less volatile since the invasion of Ukraine. Smarter storageStem (NYSE: STEM) is a US-based smart energy storage and energy management software company, which combines hardware, software and artificial intelligence to create storage solutions that maximise renewable energy generation to build a cleaner grid. The clean energy storage market in the US alone is likely to grow over 120 times by 2035, and Stem is very well positioned to profit from this opportunity. Improving efficiencyAmeresco (NYSE: AMRC) provides energy efficiency solutions, which is irrefutably one of the few short-term answers to the current energy crisis.

For Kobi Abayomi, vice president of data science at Warner Music Group, addressing such challenges starts with hiring strong talent into the technology function. Kobi Abayomi, Warner Music Group Kobi Abayomi is the senior vice president for data science at Warner Music Group, where he and his team enable the company to understand, respond to, and predict trends and opportunities in listening. Kobi Abayomi: I’m Kobi Abayomi from Warner Music Group, and you’re listening to Me, Myself, and AI . A music company is a support network for artists; it’s a repository for licensing rights; it’s a creator of music content. ), and the stuff that we all like and enjoy: going on tours, live music, things like that.

Question #1 Responsible AI should be a part of the top management agenda. Eighty-six percent of our expert panelists agree responsible AI should be a top management agenda item. Reasons to Focus on Responsible AIA strong majority of our panelists agree that RAI should be a top management concern. Linda Leopold, head of responsible AI and data at H&M Group, offers a clear statement to that effect: “Responsible AI must be seen as an integrated part of the AI strategy, not as an add-on or an afterthought.” IBM AI Ethics global leader Francesca Rossi adds, “Any company building, using, or deploying AI should make sure that AI systems are not only accurate but also trustworthy (fair, robust, explainable, etc.). Elizabeth Renieris is guest editor for the MIT Sloan Management Review Responsible AI Big Idea program and founding director of the Notre Dame-IBM Technology Ethics Lab.

sign up Please enter a valid email address Thank you for signing up Privacy PolicyFew clinical AI systems have successfully run this entire gantlet. And until clinical AI systems result in significant productivity advances, the economic value from them is in doubt. Contrast that situation with the current potential for administrative AI systems in health care. Administrative costs in the U.S. averaged $2,497 per capita in 2017 — 34% of total health care costs. Opportunities for Revenue Cycle AIForemost among the opportunities for administrative AI is the revenue cycle area — authorization, billing, and payments.

How AI language generation worksAI language won’t generate your ideas for you. But it can quickly transform your ideas and concepts into words and provide you with alternative ways of saying something. The AI uses more than 600 tags to categorize language based on the emotions, stories, narratives and motivators that drive desired outcomes. A superhuman partnershipIt’s important to remember that while humans can come up with machines, machines could never come up with humans. Just like technology transformed the fictional astronaut Steve Austin into a bionic being who was faster, better and stronger than any mere mortal, AI can use the algorithmic choice of words to give human-generated ideas superhuman impact.

Natural language processing (NLP) tools have advanced rapidly and can help with writing, coding, and discipline-specific reasoning. The most visible advances have been in what’s called “natural language processing” (NLP), the branch of AI focused on how computers can process language like humans do. OpenAI, the Microsoft-funded creator of GPT-3, has developed a GPT-3-based language model intended to act as an assistant for programmers by generating code from natural language input. Identify your text data assets and determine how the latest techniques can be leveraged to add value for your firm. Language-Based AI Tools Are Here to StayPowerful generalizable language-based AI tools like Elicit are here, and they are just the tip of the iceberg; multimodal foundation model-based tools are poised to transform business in ways that are still difficult to predict.

Idea in Brief The Reality A personalized customer experience has become the basis for competitive advantage. The Solution Businesses must design intelligent experience engines, which assemble high-quality, end-to-end customer experiences using AI powered by customer data. In this article we explore how cutting-edge companies build what we call intelligent experience engines to assemble high-quality customer experiences using AI powered by customer data. Personalizing an end-to-end customer experience requires orchestration across channels—a capability that no brand has fully mastered. Intelligent experience engines must be surgically focused on microgoals—positive moments composing the entire customer experience.

Whether you’re in a car, on a bike, or simply stepping into a crosswalk, you should always pay attention to traffic lights. advertisementadvertisementNoTraffic, a real-time autonomous traffic management platform, suggests that traffic lights at intersections can do more to make intersections safer and more efficient. Instead of just relying on road users to pay attention to traffic lights, the traffic lights should be paying attention to how people are using the road. NoTraffic’s platform uses artificial intelligence, cloud computing, and hardware installed in the low-tech traffic lights dangling above intersections. NoTraffic offers what Kreisler calls a real-time approach to traffic light timing.

“But they know me better than I know myself.” Quartr is an investors-relations app that seeks to take the pain out of finance for both investors and companies. It contains recordings and transcripts of earnings calls, and it centralizes company investor documents in one place. Today, Quartr has centralized investing information on 7,000 different companies, while 55 are uploading their own information. A future fueled by AI Going forward, Osman wants to build tech tools that can help individual investors. “We want to create an ecosystem where investors can interact easily with companies,” Osman says.

Some of its best experiments are actually in the world of audio, as we saw with its eerie deepfaked voices from 2017 . Now, Adobe has revealed a totally new audio project, aimed at making recordings and editing podcasts a cinch. That means Shasta is a quietly revolutionary piece of software in the world of editing. You snip and paste chunks across that line, and especially when you’re editing audio, it can be hard to know exactly what you’re trimming at any given time. Shasta takes a different approach by transcribing the words you say with AI to create a document you read (and edit) top to bottom.

That first Mother's Day gift for a new mom is definitely a big one — you have to set the bar for all the Mother's Days to follow. Knock it out of the park with a gift that tells her you appreciate everything she does for her. (Plus, they all remember the golden rule of Mother's Day gift-giving — the gift must be for the mom, not the baby! No gifts for the household, either, so no cleaning or organizing products, no matter how much you want to get her a robot vacuum.) Whether you're shopping for your wife, your daughter, your girlfriend or any other first-time mom, any of these thoughtful Mother's Day gifts from Etsy, Amazon and others will make her smile.

The notion of a robot doing your chores for you first came to life with the advent of the robot vacuum. These days there are a variety of home robots in the market ready to help you check off tasks from your neverending to-do list. At the Good Housekeeping Institute, our Media & Tech lab tests the latest innovations, ranging from consumer electronics such as smart home devices to household appliances like smart air conditioners. While home robots aren't all mainstream technology, we've tested and reviewed several top models for performance capabilities, ease of use and tech specifications. These are the best home robots to help you out in 2022 according to our pros.

The Defense Innovation Unit, a military technology accelerator, is opening an office in Chicago to tap into the region’s technology ecosystem, from electric vehicles to artificial intelligence, and better wage 21st century warfare. The Defense Department brought in 100 new vendors last year, mostly from other regions, Brown said. The Chicago office will be the fifth for the Defense Department, which has innovation units in Silicon Valley, Boston, Austin and at the Pentagon. J.B. Pritzker, Mayor Lori Lightfoot and both Illinois senators, who were on hand Thursday for the defense innovation hub announcement. Whelan declined to say if the Defense Innovation Unit would follow the Discovery Partners Institute to its new digs.

International Business Machines Corp. reported a stronger-than-expected 8% revenue increase in the first quarter on continued momentum in its hybrid cloud platform, a key focus of the tech company’s transformation. Revenue rose to $14.2 billion, from $13.19 billion a year earlier, beating analysts’ expected $13.78 billion, according to FactSet. The results were boosted by a 14% revenue increase from the hybrid cloud, which straddles public clouds, private clouds and as-a-service properties. In 2021, IBM acquired 15 companies to strengthen hybrid cloud and AI capabilities. Consulting revenue rose 13%, while infrastructure revenue was again the laggard, with a 2% revenue decline in the March quarter.

Not too long ago, the thought of a robot vacuum was considered science fiction. Add on improved mapping and advanced sensors that smooth out their routing, and most modern robo-vacs like the Narwal T10 2-in-1 Vacuum and Mop are highly effective and now sport price tags that make them accessible to virtually any household. The Narwal T10 even accomplishes a task most users hate the most, which is its self-washing function. While its mechanics are formidable, the customization is part of what helps the T10 genuinely stand apart. This is leagues ahead of its time for mopping.”Already the winner of major awards from CES, Red-dot, Edison, and more, as well as a Time Magazine Best Inventions of 2020 honoree, shoppers can now save $150 off the price of the Narwal T10 Robot Vacuum and Mop.

We call this the robot kitchen because it used to be a Chinese restaurant until three years ago,’ says Khalifa Al Qama, director of Dubai Future Labs. Its economy now is concentrated on leisure, property and retail, but tech is where it sees its future prosperity. The London Technology Club, a network matching wealthy investors with opportunities, has just opened its first overseas hub in Dubai. Kitopi is one of Dubai’s first unicorns, or tech companies valued at more than $1bn. ‘Historically the talent pool for technology companies has been shallow in Dubai.

The Amazon Industrial Innovation Fund follows other investment activities by the company aimed at finding, backing and sometimes acquiring companies working on technology critical to its own mission. Amazon's climate fund has invested in 11 companies to date, including CarbonCure, CMC Machinery, Pachama, Redwood Materials, Resilient Power, Rivian, TurnTide Technologies, BETA Technologies, Ion Energy, ZeroAvia and Infinium. The new industrial innovation fund taps into Amazon's need to find faster ways to deliver goods, while improving the experience of customers and employees working in warehouses and logistics, according to the company. Amazon announced five initial investments into startups developing wearable technology and robotics. VimaanThe Santa Clara, California startup founded in 2016 is developing computer vision and artificial intelligence product to improve inventory management.

In the last few days, both the Brave browser and DuckDuckGo search engine announced features that actively block or bypass pages hosted on Google’s Accelerated Mobile Page (AMP) protocol. When you load or share a Google AMP page anywhere from DuckDuckGo apps (iOS/Android/Mac) or extensions (Firefox/Chrome), the original publisher's webpage will be used in place of the Google AMP version. — DuckDuckGo (@DuckDuckGo) April 19, 2022What’s their reasoning behind this? Brave came out swinging against Google’s AMP technology, describing it as harmful to user privacy and bad for security. For users, AMP pages have been often a pain in the neck in terms of website and article formatting.

Like many lonely children, Lucas Rizzotto had an imaginary friend: a talking microwave called Magnetron. Friends reunitedAs a child, Rizzotto had given his imaginary friend a detailed life story. View this post on Instagram A post shared by Lucas Rizzotto (@_lucasrizzotto)Rizzotto wrote an entire back story that he says spanned 100 pages. Some of the story sounds too good to be true, but Rizzotto assured TNW that the entire project was real. Update (12:00PM CET, April 21, 2022): Added response from Lucas Rizzotto.

To discover these objects without a human’s helpful guidance, STEGO looks for similar objects that appear throughout a dataset. Since STEGO can learn without labels, it can detect objects in many different domains, even those that humans don’t yet understand fully. STEGO not only doubled the performance of prior systems on the COCO-Stuff benchmark, but made similar leaps forward in other visual domains. By connecting objects across images, STEGO builds a consistent view of the word. I consider unsupervised segmentation to be a benchmark for progress in image understanding, and a very difficult problem.

Applied use casesArtificial intelligence is creating a new colonial world order An MIT Technology Review series investigates how AI is enriching a powerful few by dispossessing communities that have been dispossessed before. This story is the introduction to MIT Technology Review’s series on AI colonialism. AI models to detect how you're feeling in sales calls In brief AI software is being offered to sales teams to analyze whether potential customers appear interested during virtual meetings.

Gas saturation and pressure buildup prediction using U-FNO is 46% and 24% more accurate than state-of-the-art CNNs. Running a 30-year case on GPUs using U-FNO takes 0.01 s compared to 600 s using traditional finite-difference methods (FDM). The trained U-FNO model can therefore serve as an alternative to traditional numerical simulators in probabilistic assessment, inversion, and CCS site selection. For more information, see Towards Large-Scale Learned Solvers for Parametric PDEs with Model-Parallel Fourier Neural Operators. But its deployment to counterbalance hard-to-abate residual emissions is considered unavoidable if net zero CO 2 or greenhouse gas emissions are to be achieved.

About The WebinarAs a business leader in a competitive industry, you already know that speed to market is a critical differentiator. Your organization’s success depends on your ability to quickly deliver new innovative products or solutions. Inaccessible knowledge threatens to slow down productivity and cross-functional collaboration in large organizations. Human-centric knowledge management is the solution leaders need in the post-pandemic digital workplace. Join Marc Vontobel, Starmind Founder and CEO, and Barry Byrne, Novartis Head of Global Knowledge Management Strategy to learn how AI-powered modern knowledge management can accelerate time to market by:● Enabling cross-functional collaboration● Breaking down geographical, departmental, and hierarchical silos● Accessing and retaining tacit knowledge to engage the expertise of all employees fully● Improving productivity and speeding up the release of new products or solutions to solidify your market first advantage

Listen to this articleEach year, the RBR50 Robotics Innovation Awards program receives more nominations than can fit in the annual list. Notable WinnersAn emphasis on innovation led to 36 RBR50 winners being recognized for their introduction of new products, services and technologies. Louisville, Ky.-based CRG Automation built a robotic system to help decommission a stockpile of 70,000 M55 chemical missiles at the Blue Grass Army Depot in Kentucky. A startup from California to keep an eye on is Pharm Robotics, which is developing the Sureshot robotic cow inoculator. Investment in RBR50 innovatorsSeveral RBR50 winners raised funding to continue R&D or to scale commercial products.

This enhancement is bolstered by new AI architectures that can learn from data using neural operators. FNO: Physics-inspired neural network model that uses global convolutions in spectral space as an inductive bias for training neural network models of physical systems. Physics-inspired neural network model that uses global convolutions in spectral space as an inductive bias for training neural network models of physical systems. PINO: PINO is the explicitly physics-informed version of the FNO. NTK is a neural network analysis tool that indicates the convergent speed of each component.

When applied to the data sets taken from the Long Beach area, the algorithms detected substantially more earthquakes and made it easier to work out how and where they started. It’s not the only work applying AI to the hunt for earthquakes. Researchers from Penn State have been training deep-learning algorithms to accurately predict how changes in measurements could indicate forthcoming earthquakes—a task that has confounded experts for centuries. Deep learning could make that process quicker, and more accurate, by helping to cut through large volumes of data, Koelemeijer says. “Showing that [the algorithm] works in a noisy urban environment is very useful, because noise in urban environments can be a nightmare to deal with, and very challenging,” she says.

So what do big players in the tech space mean, really, when they say they care about making AI that’s fair and unbiased? One type of fairness, termed procedural fairness, would hold that an algorithm is fair if the procedure it uses to make decisions is fair. Another conception of fairness, known as distributive fairness, says that an algorithm is fair if it leads to fair outcomes. Text-generating AI systems, like GPT-3, have been hailed for their potential to enhance our creativity. “You don’t have a way to appeal with an algorithm.”And an AI system has the capacity to sentence millions of people.

Evaluating generalisation in neural networks for NLP Dieuwke Hupkes, Facebook AI Research Venue: Zoom (link hidden) Abstract Generalisation, like many other things in the world, is one of these things that seems to have a very clear meaning, until you start to really think about it. In ...Share this article

Researchers discovered that the deep-learning system was able to detect vision-threatening diabetic retinopathy with an accuracy of 94.7%, sensitivity of 91.4% and specificity of 95.4%. "A deep-learning system can deliver real-time diabetic retinopathy detection capability similar to retina specialists in community-based screening settings," researchers wrote. The CDC reports that diabetic retinopathy is the leading cause of blindness in adults in the U.S. In 2019, Alphabet's life science subsidiary Verily unveiled real-world clinical use for its machine learning algorithm to screen for diabetes-related conditions including diabetic retinopathy and diabetic macular edema. There is a history of research on AI for diabetic retinopathy screening.

The National AI Initiative Act of 2020 calls for the Secretary of Commerce, in consultation with the Director of the Office of Science and Technology Policy, the Secretary of Defense, the Secretary of Energy, the Secretary of State, the Attorney General, and the Director of National Intelligence, to establish the National Artificial Intelligence Advisory Committee (NAIAC). The NAIAC is tasked with advising the President and the National AI Initiative Office on topics related to the National AI Initiative. The AFL-CIO is a national federation of 57 national and international labor unions, representing more than 12.5 million workers. In his spare time, Jacks writes Import AI, a newsletter about AI and AI policy read by more than 25,000 people around the world. Keith is a recognized authority on national AI infrastructure and is the founding Co-Chair, AI Compute Taskforce at the OECD.

In brief AI software is being offered to sales teams to analyze whether potential customers appear interested during virtual meetings. The hope is that sellers may be able to use AI technology to automatically tell when they're boring clients and can immediately change tactics, such as being more empathetic to keep them interested. The idea that AI can accurately detect human emotions, however, has been repeatedly challenged by experts. "We find that for compute-optimal training, the model size and the training dataset size should be scaled equally: for every doubling of model size the training dataset size should also be doubled," DeepMind researchers said in a blog post last week. The NAIAC will advise President Biden on all policies related to AI, ranging from how the technology affects national security to civil rights.

Then, in the 1980s, came the onslaught of digital technologies, and the trend reversed itself. New, well-paying tech jobs were clustered in a few cities. Not only will this foster political and social unrest, but it could, as Coyle suggests, hold back the sorts of AI technologies needed for regional economies to grow. Such benchmarks have driven the direction of the research, Klinova says. But the pandemic has also pointed to the potential of digital technologies to expand our abilities.

FairPlanet spoke to one of Blue Forest's directors as well as to a local expert about the viability of this project. Blue Forest, an offshoot of Impact Adventures - a UK-based company delivering sustainable community-led projects in Africa, said they were big believers in innovation. But the country’s mangrove forests are facing serious threats from both human and natural causes. For the community, by the communityFadeyi added that the mangrove project is a community-based one, as most of the restorations would be done through community help. "We are delighted to partner with Blue Forest and to introduce innovative technology in the way we map and restore our vital mangrove forests for generations to come," Munjovo said.

On NYT Magazine on AI: Resist the Urge to be ImpressedContextOn April 15, 2022, Steven Johnson published a piece in the New York Times Magazine entitled “A.I. The headline (not necessarily due to Johnson, but I think a not incongruent framing of the article, in this case) asks: “A.I. This headline asserts that “AI” has “mastered language” (spoiler alert: it hasn’t) and in the process presupposes the existence of something that can be referred to as “AI”. But GPT-3’s intelligence, if intelligence is the right word for it, comes from the bottom up: through the elemental act of next-word prediction. (For more on construct validity in the evaluation of so-called “AI” systems, see Raji et al 2021 or Ben Dickson’s popular press coverage of it, as well as Jacobs and Wallach 2021.)

Earlier this week, California-based company Miso Robotics announced that it will partner with Panera Bread to test an artificially intelligent coffee-making system called CookRight. The company has made a name for itself by specializing in creating robots for restaurants like the fry-station-manning Flippy and the soft-drink-dispensing Sippy. “[Robots are] not taking jobs because those jobs are going unfilled every day in millions of restaurants around the world. Miso’s robots can cost restaurants around $5,000 for installation and up to $3,000 a month for maintenance, according to CNBC. Robots can’t yet do simple tasks like checking the age on a customer’s ID to determine if they’re old enough to be served alcohol, or interact dynamically with customers.

The city is known for its Catalan modernism, an iconic aesthetic popularized by Antoni Gaudí, the mastermind behind the Sagrada Familia. The architectural movement was born in part from the investments of wealthy Spanish families who amassed riches from their colonial businesses and funneled the money into lavish mansions. One of the most famous, known as the Casa Lleó Morera, was built early in the 20th century with profits made from the sugar trade in Puerto Rico. The AI industry does not seek to capture land as the conquistadors of the Caribbean and Latin America did, but the same desire for profit drives it to expand its reach. Neither does the industry still exploit labor through mass-scale slavery, which necessitated the propagation of racist beliefs that dehumanized entire populations.

Researchers at Georgia State University (GSU) have designed an ‘electric eye’ – an artificial vision device – for micro-sized robots. Conventional colour sensors typically consume a large amount of physical space and offer less accurate colour detection. Its van der Waals semi-conductor powers the sensors with precise colour recognition capabilities whilst simplifying the lens system for downscaling. “The new functionality achieved in our image sensor architecture all depends on the rapid progress of van der Waals semiconductors during recent years,” said one of the researchers. Sidong Lei, assistant professor of Physics at GSU and the research lead, said: “More than 80% of information is captured by vision in research, industry, medication, and our daily life.

Find him on Twitter: @Gadget_RyAI-driven scientific research firm LabGenius is harnessing the power of Graphcore’s IPUs (Intelligence Processing Units) to speed up its drug discovery efforts. LabGenius is currently focused on discovering new treatments for cancer and inflammatory diseases. LabGenius reports that switching to Graphcore’s IPUs in cloud instances from Cirrascale Cloud Services enabled its training of models to be reduced from one month to around two weeks. AI will help to find new cancer treatments that cause less suffering and greatly increase the odds of long-term survivability. They can move much more at the speed they think.”(Photo by National Cancer Institute on Unsplash)Want to learn more about AI and big data from industry leaders?

In December 2021, SageMaker Serverless Inference was introduced in preview, and as of today it is generally available. SageMaker Batch Transform to run predictions on batches of data, and SageMaker Serverless Inference is for workloads with intermittent or infrequent traffic patterns. SageMaker Serverless Inference and Amazon’s double bottom line with SageMakerBut what about Amazon’s double bottom line with SageMaker – better ease of use and lower TCO? Since its preview launch, SageMaker Serverless Inference has added support for the SageMaker Python SDK and model registry. SageMaker Serverless Inference has increased the maximum concurrent invocations per endpoint limit to 200 for the GA launch from 50 during preview, enabling use of Amazon SageMaker Serverless Inference for high-traffic workloads.

This includes having the right ML tools to automate the process of running parallel and distributed training jobs or to manage multiple ML model experiments. Plan to evolve your ML modelsTo build on the benefits of being ML-first, organizations can’t stop at just having the right ML models and tools. One of the biggest mistakes startups make is to deploy ML models with no plan to monitor and update them. Further, fostering more open-source ML tools will fuel more R&D and a much greater diversity of ML options. Allie Miller is Global Head of ML Business Development, Startups and Venture Capital at AWS

Convoy, the company behind a digital freight network and marketplace that connects shippers with truckers, has raised $260 million in funding, constituting a $160 million series E round and $100 million in venture-debt. In fact, Uber itself offers a similar service called Uber Freight, which repurposes its technological infrastructure to support companies’ logistics. Convoy goes some way toward addressing this by optimizing and streamlining cargo loads, and ensuring that trucks aren’t traveling long distances with empty trailers. “Moving freight via trucks is the foundation of our domestic supply chain — when supply chains are unreliable, companies spend more money and waste precious time,” Convoy cofounder and CEO Dan Lewis told VentureBeat. And automation plays a fundamental part in all of this, with Convoy specifically leaning on machine learning smarts to match freight loads with trucks with next to no human intervention.

Over the past few years, we’ve all encountered “Let’s chat!” buttons on websites that promise a quick, helpful customer service experience. They can create more sophisticated conversational AI tools, from smarter chatbots and asynchronous messaging to voice and mobile assistants. Solving for conversational customer challengesDerek Roberti is VP of technology, North America at Cognigy, a low-code conversational AI platform based in Germany. Conversational AI targets two types of customer service buyersRoberti cites two primary types of buyers in the market for conversational AI tools for customer service and support. The idea is that companies might eye the rest of the customer funnel.”Tight labor market leads to smarter conversational AIA tight labor market is driving conversational AI growth in customer service, said Roberti.

Regardless of the varied acquisition costs across different businesses, customer loss is an expensive proposition, especially when it can be prevented through careful stewardship of the customer experience (CX). While chatbots will never replace the human component of CX, they have shown to increase customer retention significantly, loyalty and overall satisfaction. Additionally, the ability for chatbots to be concise hints at another key element during this phase of resolution: customization. Ultimately, the marriage of chatbot tools to traditional human interactions will be the key to revolutionizing the field of CX. Jamie Kennedy is the director of digital strategy and customer experience solutions at HGS Digital

A KT researcher works with an ALS patient to replicate his voice using artificial intelligence technology at KT’s research and development center in Seoul. (KT Corp.)KT Corp. said Wednesday it has restored the voices of patients who are losing their voices due to Lou Gehrig’s disease using its personalized text-to-speech (P-TTS) technology. Some ALS patients lose the ability to speak. KT’s project with ALS patients began with the wife of a patient’s inquiry to KT’s customer center. Working together with a foundation for ALS patients, KT replicated voices for eight ALS patients and allowed them to keep using their voices through the Maeum Talk mobile app free of charge.

I‘ll talk to Qualcomm to join in.”“There are about 500 companies that do AI chips. 1 mobile carrier, unveiled its AI chip “Sapeon” to the public for the first time.Specialized AI chips are essential components to enable artificial intelligence. They conduct massive calculations at high speeds but consume little power.Conventional AI data centers process data with graphics processing units. SK Telecom‘s Sapeon, Korea’s first independently developed AI chip, consumes 20 percent less power but computes 1.5 times faster than a typical GPU. 1 foundry Taiwan Semiconductor Manufacturing Co. to manufacture Sapeon chips.Last month, SK Telecom decided to carve out its AI chip division as an independent company, tentatively called Sapeon Korea.

Artificial vision could be closer to reality, after scientists develop a tiny electric eye designed for use by microbots, which could ultimately help blind people too. Conventional color sensors typically adopt a lateral color sensing channel layout and consume a large amount of physical space and offer less accurate color detection. ARTIFICIAL VISION: IMPROVING COLOR CORRECTION Biomimetic artificial vision is receiving significant attention nowadays, particularly for the development of neuromorphic electronic devices, artificial intelligence, and microrobotics. AdvertisementKnown as a van der Waals semiconductors empowered vertical color sensor (vdW-Ss), it represent a newly-emerged class of materials, in which individual atomic layers are bonded by weak van der Waals forces. In molecular physics, the van der Waals force is a distance-dependent interaction between atoms or molecules.

The manga and animation world was in mourning on April 7, with the news of the passing of Motoo Abiko, aka Fujiko A. Fujio, creator of some of Japan’s best-known manga. After the dissolution of the duo more than three decades later, Fujimoto continued writing under the “Fujiko F. Fujio” pseudonym, while Abiko adopted the nom de plume “Fujiko A. Fujio,” broadening his horizons through production work in anime, TV and film. Although the duo are arguably best known for the Fujimoto-created manga series about a robot cat called “Doraemon,” Abiko earned significant admiration for his own quirky creations. Often these were highly bizarre creations, delving into the dark side of the human psyche with more than a tinge of black humor. ‘Ninja Hattori-kun,’ which featured an 11-year-old ninja named Hattori, was one of Motoo Abiko’s best-loved creations.

Virtual YouTuber Kizuna AI will go on “indefinite hiatus” following one last, livestreamed concert tonight at 7 p.m. Thousands of viewers are expected to log on to watch the digital character, sporting her trademark pink bow, perform a set of upbeat pop songs. Kizuna AI, created by entertainment company Activ8 and voiced by actress Nozomi Kasuga, pioneered the art of VTubing. All of this financial and soft power success began with Kizuna AI, who came up with the “virtual YouTuber” title in her first upload. While she eventually branched out into a music career, Kizuna AI never strayed far from the YouTuber model. Kizuna AI may not be so unique anymore, but her influence is one of a kind.

Clearview AI is arguably the best known and most controversial facial recognition company in the world. It's been at the centre of battles over privacy, personal data and freedom. BBC North America tech reporter James Clayton spoke to Hoan Ton-That, co-founder of Clearview AI.

I always assumed that artificial intelligence would eventually replace me on these pages. I did not suspect my own children. I have, it seems, been replaced by a rabble who believe hotel is spelt “howtle”. To add insult to injury, Lautrec suggested that my own two children should trial the programme first. You got older!”) this seemed a bold offer, but Chewton Glen has always had that understated yet unshakeable confidence granted only to those of very old, very good breeding.

As with many other models now, the Lexus has Lane Trace Assist which keeps the car in the middle of its lane when cruise control is activated. Lexus says the system has been improved using Artificial Intelligence technology. This means that when you cross a white line without indicating, the steering wheel vibrates and tries to bring you back into lane. Having the word BRAKE flash up in red on the instruments isn’t particularly helpful when you’re edging forwards to park in a tight space. It means I can drive with my left leg almost completely extended which adds a new dimension of comfort.

Tesla smashed Wall Street estimates for revenue and profit in another record quarter on Wednesday, despite a tumultuous few months for its CEO, Elon Musk, and ongoing supply chain concerns. The report beat analyst expectations of $17.8bn, sending Tesla shares up 4% in after-hours trading. Musk said he anticipated deliveries would recover over the next quarter and that Tesla would be able to produce 1.5m vehicles in 2022. There are concerns that Musk may sell some Tesla stocks or borrow against additional Tesla shares to finance his $43 bid to buy Twitter. Musk said it was his “firm belief” that the robot would someday be “worth more than the car business of Tesla”.

NEW ALBUMSKirk Hammett, “Portals” (Blackened Recordings)Metallica guitarist Kirk Hammett is an insanely creative musician, and it’s evident in his short yet deeply revealing four-song EP. While “Portals” is an apt companion piece to the Metallica canon, it’s also sonically unique to Hammett as a singular force. Her buoyant beat lifts spirits while Morgxn sings, “I wanna be loved like I’ve never been loved before,” for an all-around emotional track. Following 2020’s instantly likable “Area of Affect” EP, they recently signed to the Virgin Music Label and are beginning to showcase what comes next. The track’s video also uses a very cool machine-learning framework that produces AI variations of Laner and Fine.

It's difficult when you're the best salesperson on the planet to find yourself in a situation where you can't sell a product you believe in. Musk AnnoyedThis is the situation in which Elon Musk, the CEO of high-end electric vehicle manufacturer Tesla (TSLA) - Get Tesla Inc Report, finds himself. "I was surprised that people did not realize the magnitude of the Optimus robot program," the tech tycoon said during the call, visibly annoyed. The Tesla Bot, as it was dubbed, would use the same artificial-intelligence (AI) systems that helped power Tesla vehicles, Musk said at the time. During the 2021 fourth quarter earnings' call in January, Musk said Optimus is the next big product Tesla is working on in 2022.

Elon Musk and Tesla (TSLA) - Get Tesla Inc Report dominate the automotive market. Musk and Tesla have also created a model for selling Tesla vehicles directly to the consumer. Tesla customers are also called upon to carry those out by simply updating the software and other systems that equip Tesla cars. Musk sees a Tesla vehicle as a robot on four wheels that he constantly perfects. Customers can also "upgrade to a new Tesla vehicle," Tesla said.

International Business Machines Corp. reported a stronger-than-expected 8% revenue increase in the first quarter on continued momentum in its hybrid cloud platform, a key focus of the tech company’s transformation. Revenue rose to $14.2 billion, from $13.19 billion a year earlier, beating analysts’ expected $13.78 billion, according to FactSet. The results were boosted by a 14% revenue increase from the hybrid cloud, which straddles public clouds, private clouds and as-a-service properties. In 2021, IBM acquired 15 companies to strengthen hybrid cloud and AI capabilities. Consulting revenue rose 13%, while infrastructure revenue was again the laggard, with a 2% revenue decline in the March quarter.

Commerce Secretary Gina Raimondo's husband has deepened his financial investment in the artificial intelligence company PathAI, a business that has raised eyebrows as it is backed by a company with deep ties to the Chinese Communist Party (CCP). The report found that DHVC was one of five companies to provide $4.2 million in seed money for the AI startup. It renewed its investment in 2017, when it helped the company raise another $11 million to sponsor research in diagnostic technologies. Following a December report by The Washington Free Beacon that first revealed the financial backing of a company with CCP ties, lawmakers sent the commerce secretary a litany of questions. HOUSE PASSES AMERICAN COMPETES ACT TO COUNTER CHINESE MANUFACTURING"When did you first become aware of PathAI’s direct ties to the CCP?"

The project, dubbed AI2050, is being launched through the couple's philanthropic organization Schmidt Futures, which funds ventures that serve a purpose for advancing society. "AI will cause us to rethink what it means to be human," Mr. Schmidt said in a statement. "As we chart a path forward to a future with AI, we need to prepare for the unintended consequences that might come along with doing so." GOOGLE PLANS TO CURTAIL CROSS-APP TRACKING ON ANDROID PHONESManyika has compiled a working list of "hard problems" the initiative aims to tackle. "Paramount to both of these goals is developing more capable and more general AI that is safe and earns public trust, making sure AI performs technically well and does not harm people, and that AI systems which are developed remain aligned and compatible with the way humans designed them."

Panera Bread is piloting Miso Robotics' new automated coffee brewing system as it doubles down on its drink subscription program. Last month, Chipotle Mexican Grill announced it is testing a robot made by Miso that makes tortilla chips. Miso's latest launch is the CookRight Coffee system, which uses artificial intelligence to monitor coffee volume and temperature. Panera launched the coffee and tea subscription program over two years ago after overhauling its coffee selection. For now, only two Panera locations are testing the CookRight Coffee system.

Since leaving Google, Eric Schmidt has focused his energy on tackling big global problems — and none, he says, are as pressing as climate change. "If we don't address climate change, we really will be toast," Schmidt, Google's former CEO and chairman, tells CNBC Make It. Over the past five years, he says, he's learned that climate change isn't just a long-term problem. "But the fact of the matter is that [climate change is] a great challenge, and something that's well worth our time," he says. Schmidt Futures is already using AI to improve climate modeling research through a program called Virtual Earth System Research Institute (VESRI).

In this videoShare Share Article via Facebook Share Article via Twitter Share Article via LinkedIn Share Article via EmailHybrid cloud and AI drove demand in consulting and software, says IBM CEOArvind Krishna, IBM CEO, joins 'Closing Bell' to discuss the company's quarterly earnings, why the company's margins fell and more.

They break a multiagent behavior prediction problem into smaller pieces and tackle each one individually, so a computer can solve this complex task in real-time. Their behavior-prediction framework first guesses the relationships between two road users — which car, cyclist, or pedestrian has the right of way, and which agent will yield — and uses those relationships to predict future trajectories for multiple agents. Then a prediction model, known as a marginal predictor, guesses the trajectory for the passing agent, since this agent behaves independently. A second prediction model, known as a conditional predictor, then guesses what the yielding agent will do based on the actions of the passing agent. “Predicting future trajectories of multiple, interacting agents is under-explored and extremely challenging for enabling full autonomy in complex scenes.

Steering businesses towards a smarter and sustainable future: Why technology is pivotal for companies to grow - Times of India https://t.co/UFmAUmtTT0 #ResponsibleAI #EthicalAI #AIEthicsTechnology has transformed the way we work and collaborate. With the advent of concepts like work from home (WFH) and hybrid working, we can now function remotely and still stay in sync ...

The money will be used to bring Nighthawk, its chiplet-based architecture for faster complex matrix calculations, to markets like data centers. Their in-memory solution can store the model’s matrix full of weights close to the computing engine, so it doesn’t need to be reloaded and reloaded every time the transformer is applied. The same approach could also be applied to many other problems that require large matrix calculations, from simulations to forecasting. Now I can use my existing data center without having to worry about throwing stuff out.”What’s ahead for chips and matrix calculations? As AI models are deployed more commonly, chips like D-Matrix’s will be in demand to keep power consumption from exploding.

Many are looking at autonomous, self-supervised AI systems as the next big disrupter, or potential definer, in the discipline. These so-called “foundation models” include DALL-E 2, BERT, RoBERTa, Codex, T5, GPT-3, CLIP and others. The term “foundation models” was dubbed by the newly formed Center for Research on Foundation Models (CRFM), an interdisciplinary group of researchers, computer scientists, sociologists, philosophers, educators and students that formed at Stanford University in August 2021. Solving the inherent problems of foundation models requires real-world use, he added. The center also emphasizes that foundation models should be grounded, should emphasize the role of people and should support diverse research.

AlphaMountain.ai’s AI platform is designed to provide up-to-date domain and IP intelligence feeds for security platforms. Threat intelligence: working smarter not harderOver the past decade, threat intelligence solutions have become an integral part of the enterprise security strategy. Research highlights that 90% of CISOs rely on outdated, report-based threat intelligence that is too old to inform decisions. Another competitor is Fortinet with FortiGuard Labs, a threat intelligence platform that’s designed to feed threat intelligence directly into Fortinet security products, and the enterprise’s wider environment. However, AlphaMountain.ai’s AI pedigree puts it in a strong position to present itself to the market as one of the most extensive and accurate threat intelligence solutions.

Their efforts shed light on the possibilities and problems posed by travel beyond our nurturing Earth. Courtesy of Belknap Press Buy This Book At: AmazonBookshop.org If you buy something using links in our stories, we may earn a commission. When it comes to exploration, however, our robots can outperform astronauts at a far lower cost and without risk to human life. Since Apollo 17 left the moon in 1972, the astronauts have journeyed no farther than low Earth orbit. Each of these missions cost about a billion dollars in today’s money.

After two years of battling Covid-19 and related burnout, nurses say it’s been a welcome relief. “There's two levels of burnout: There's ‘we’re short this weekend’ burnout, and then there's pandemic burnout, which our care teams are experiencing right now,” says Abigail Hamilton, a former ICU and emergency room nurse that manages nursing staff support programs at the hospital. Moxi is one of several specialized delivery robots that has been developed in recent years to ease the strain on health care workers. But it’s also paved the way for more robots in health care settings. About 15 Moxi robots are now operational in US hospitals, with an additional 60 scheduled to deploy later this year.

So many things happening! We had a great in person Robot Block Party at Circuit Launch to celebrate National Robotics Week. If you missed it, then never fear! We'll be doing it again in the second half of the year. https://svrobo.org/robot-block-partyNext newsletter I'll talk more about ...Share this article

Find him on Twitter: @Gadget_RyThe US Ninth Circuit Court of Appeals has decided that scraping data from a public website doesn’t violate the Computer Fraud and Abuse Act (CFAA). In 2017, employment analytics firm HiQ filed a lawsuit against LinkedIn’s efforts to block it from scraping data from users’ profiles. The court barred Linkedin from stopping HiQ scraping data after deciding the CFAA – which criminalises accessing a protected computer – doesn’t apply due to the information being public. “hiQ’s entire business depends on being able to access public LinkedIn member profiles,” hiQ’s CEO argued. Mass data scraping will remain a controversial subject.

Presented by SupermicroCoupled with NVIDIA OVX, Omniverse Enterprise, NVIDIA’s virtual 3D collaboration platform, adds the tremendous power of digital twins to the mix across industries. “NVIDIA Omniverse lets you build and simulate a virtual world that’s indistinguishable from the real world.”Created for real-time 3D collaboration and workflows, Omniverse Enterprise is a scalable, multi-GPU real-time reference development platform. It was created to dynamically connect enterprise teams, apps, and projects that demand efficient design collaboration for 3D simulation and design collaboration through virtual worlds. Lockheed Martin uses Omniverse Enterprise to visualize fires, process a fire’s magnitude, predict how it might spread, and suggest actions to best suppress the blaze. To learn more about the power of virtual 3D design collaboration, AI-powered digital twins, and how NVIDIA Omniverse Enterprise and OVX are changing industries and changing the world, don’t miss this VB Live event!

The Austin, Texas-based company today introduced Next-Generation Price IQ, what it calls “the next giant leap” for its B2B price-optimization product. Price IQ ostensibly brings advances in performance, claiming a 10-times improvement in AI-based optimization speeds, the company said. Price IQ also uses a constraint-based optimization solver, which means it has the ability to enforce rational price relationships, Eppele told VentureBeat. Supports multiple price optimization models and scenario templates, enabling customers to optimize pricing for multiple lines of business in one centralized location. Users have reported a 10x ROI in less than a year using Zilliant, Peters said.

Every day around the world, billions of people are watching online videos — they’re quickly becoming the go-to choice when it comes to content consumption. All told, IMARC Group expects the global digital video content market to reach just over $391.1 billion by 2027, up from $151.4 billion in 2021. Improving corporate video productionThis has made room for a growing number of companies that help to create and process enterprise online video content. “It is the only option for anyone looking to stay ahead in the video content game.”The six-year-old company’s algorithms automatically meta-tag images, video and text, as well as process content for context and identify key moments. “AI is the future, and AI-led technology will dictate how companies best use their content going forward,” Shrivastav said.

The concept of “human-centered AI” has permeated the artificial intelligence zeitgeist over the past couple of years. That is thanks, in part, to organizations such as Stanford’s Institute for Human-Centered AI (HAI), which launched in 2019. The company had a few dozen complex AI models that ran on edge devices that were distributed globally. “John McCarthy, the father of AI, used to call it ‘trying to understand the reality behind the appearance.’Ultimately, he explained, human-centered AI is a “humble” AI that seeks to amplify human work and improve human judgment. “You can’t have human-centered AI without understanding the design of the AI activity, how it plugs into human decision-making.”

Musk and Dorsey both hate the Twitter boardIn the last few weeks, Musk has been very vocal about Twitter’s shortcomings as a platform. Over the weekend, Musk responded to a tweet that listed Twitter’s board members and their percentage ownership in the company. In the same thread, he indicated with another tweet that Twitter’s board has been constantly a dysfunction of the firm for years. Maybe we’ll see Dorsey open up more about the Twitter board once he’s officially retired from it. — jack⚡️ (@jack) April 16, 2022Dorsey, still on the Twitter board, is already prompt in criticizing the company’s current direction.

The feasibility study analyzed the first 30 years of operations, including an estimated 5-year construction and implementation period. Virgin Hyperloop One (US)A work in progress by Virgin Hyperloop. Credit: Virgin HyperloopNews hit in March that Virgin Hyperloop was abandoning its effort to be the first people carrier and laying off over 100 staff. The departure of CEO and co-founder Josh Giegel and other key staff have also impacted the running and leadership of Virgin Hyperloop. The Boring Company (US)Ok, ok, we all know Elon’s Boring Company is making tunnels.

This article will explore a metaphysical argument against artificial intelligence on the grounds that a disembodied artificial intelligence, or a “brain” without a body, is incompatible with the nature of intelligence. The possibility of artificial intelligence primarily revolves around what is necessary to make a computer (or a computer program) intelligent. Concepts like thought, intelligence, feelings, beliefs, desires, and even hereditary and genetics are eliminated in favor of environmental stimuli and behavioral responses. Hubert Dreyfus explains how artificial intelligence inherited a “lemon” philosophy. The reason is that the philosophy adopted most frequently by friends of artificial intelligence is pragmatism.

Use-Case 1 – Evidence-Based Knowledge DiscoveryIntroductionThe drug discovery process, a key application of evidence-based knowledge discovery, costs an immense amount of time, effort, and resources. Today’s pharmaceutical companies have turned to AI-enhanced technologies in an effort to speed up the drug discovery process. In addition to drug discovery, drug repurposing has emerged as a key area that promises both savings and new treatments, on a potentially faster timeline. Indeed, the drug discovery and drug repurposing processes have long awaited the capabilities promised by AI and its innovative capabilities. For instance, the presence of comorbidities must be avoided for a patient to qualify for a recruitment in clinical trials.

As we move further into the 21st century, more and more aspects of our lives are being controlled by algorithms. It's no wonder then that online marketing is becoming increasingly reliant on algorithms to create effective copywriting. So what does the future hold for the web—will marketing be dominated by machines, or will human creativity always be necessary? It was generated by software from the copywriting service Jasper, inspired by the headline on this article. Although OpenAI’s technology most often produces original text, it can regurgitate text that appeared in its training data scraped from the web.

Join AI and data leaders for insightful talks and exciting networking opportunities. But Optimal Dynamics, an AI-powered startup that announced $33 million in series B financing last week led by the Westly Group, is helping trucking companies solve their logistical nightmares by automating and optimizing their operations. Trucking receptive to AI technology“That rich technology grew over the decades through lots of iterations, becoming incredibly mature,” Powell said. “We may be the first time they’ve ever set up an automation, a data feed, outside of their company.”Trucking industry will struggle without AI adoptionBut trucking companies need to start innovating, he emphasized. The applications that can robustly leverage machine learning and AI give a large advantage to those operators that do.

Even though viewers and listeners are frequently known to offer their candid opinions on what they see and hear in these channels, the channels will never hear them. Brands using Speaky can measure customer voice sentiments, which are richer, more emotional and more useful to a brand than legacy outmoded approaches — like comment cards or questionnaires. With qualitative data immediately processed by our voice AI capabilities, brands can connect to customers like they never could before. The customer then leaves an audio message in which they can say anything they like to the brand. Speaky’s voice AI analyzes those customer responses, and the company receives an actionable report.

Artificial intelligence (AI) is bringing many changes to the enterprise, none of which is more vital to its success than infrastructure. As VB pointed out earlier this year, AI is already changing the way infrastructure is being designed all the way out to the edge. Application-specific AIIt also seems likely that infrastructure will not just be optimized around AI, but around all the different flavors of AI. It took decades to bring data infrastructure to the state it’s in today, and it will take more years to tailor it to the needs of AI. Despite all the changes that AI is poised to bring to the enterprise, one thing remains the same: AI is all about data, and data lives in infrastructure.

DALL-E 2 builds on the success of its predecessor DALL-E and improves the quality and resolution of the output images thanks to advanced deep learning techniques. DALL-E 2 shows how far the AI research community has come toward harnessing the power of deep learning and addressing some of its limits. It also provides an outlook of how generative deep learning models might finally unlock new creative applications for everyone to use. The beauty of DALL-E 2Like other milestone OpenAI announcements, DALL-E 2 comes with a detailed paper and an interactive blog post that shows how the machine learning model works. But as has often been seen, deep learning models often learn the wrong representations.

And it’s not just an American issue — nearly half of the respondents in a Canadian study claimed to have been victims of financial fraud at some point in their life. Artificial intelligence and financial security: your credit card’s personal bodyguardCredit card companies are using AI to help detect cases of fraud. As an example, in 2019 Visa used AI to identify fraudulent activity by examining more than 500 transaction attributes — in real-time. It’ll be interesting to see where the advances in this field will take us, but it’s safe to say biometrics will continue to be an important tool for financial security. Some card companies offer the ability to generate a one-time-use virtual credit card, so you can trash the information once you’ve made a purchase.

This reality is made of discrete chunks of spacetime. We’ve covered the concept of timespace as discrete chunks extensively here at Neural. Unfortunately we don’t currently have any way of estimating how many FPS the universe or base reality runs at. Honestly, I think that the frame rate of the universe truly is infinite, not even finite, very, very large. This would almost certainly indicate that our universe is either part of a physical multiverse, or that it’s a simulation.

Artificial intelligence marketing is a method of using customer data and AI concepts such as machine learning to predict the customer's next step and improve the customer journey. It’s the process of using data models, mathematics, and algorithms to generate information that marketers can ...Share this article

OpenAI has recently released DALL-E 2, a more advanced version of DALL-E, an ingenious multimodal AI capable of generating images purely based on text descriptions. Caption: A DALL-E 2 generated image. Feed the class name as part of a textual prompt to DALL-E and add the generated images to that class’s labels. For example, “A Dalmatian dog on the beach chasing a bird.” The same applies to the style of the generated image, e.g. Moreover, using images generated by DALL-E might hold a significant risk in specific domains such as pathology or self-driving cars, where the cost of a false negative is extreme.

Although the accuracy of AI models is undoubtedly the most important factor to consider while deploying AI-based products, there is an urgent need to understand how AI can be designed to operate responsibly. Responsible AI is a framework that any organization developing software needs to adopt to build customer trust in the transparency, accountability, fairness and security of any deployed AI solutions. However, often the missing pieces of these packages are patterns around responsible AI that evaluates ML models for fairness, transparency, explainability, causality and more. Here, we demonstrate a quick and easy way to integrate PyCaret with Microsoft RAI (Responsible AI) framework to generate a detailed report showing error analysis, explainability, causality and counterfactuals. A responsible AI analysis report showing model error analysis, explainability, causal inference and counterfactuals can add great value to traditional statistical metrics of precision-recall that we usually use as levers to evaluate models.

After playing a cat and mouse game with Twitter’s board seat, Elon Musk declared his ambition to buy 100% of the company’s shares yesterday. The game of the board seatThe story kicked off last week, with Musk disclosing that he’s bought 9.2% shares in Twitter. And some governments can also prohibit social networks to publish the reason why a piece of content was taken down. While this was going on, asset management company Vanguard Group increased its stake in Twitter to 10.3% — dethroning Musk as the social network’s largest shareholder. Meanwhile, at TED 2022, Musk mentioned that he has a “plan-B” if Twitter rejects his bid.

Students in the MIT course 6.036 (Introduction to Machine Learning) study the principles behind powerful models that help physicians diagnose disease or aid recruiters in screening job candidates. SERC Scholars collaborate in multidisciplinary teams to help postdocs and faculty develop new course material. For some, it may have been their first experience thinking critically in an academic setting about the potential negative impacts of machine learning. The SERC Scholars evaluated each lab to develop concrete examples and ethics-related questions to fit that week’s material. They think they are there to learn the nuts and bolts of machine learning, like loss functions, activation functions, and things like that.

The company, which is building service mesh support software, announced that their products will now support Arm’s Neoverse platform. Both of Tetrate’s major tools, Envoy and Istio, now run natively on the Arm chips that are rapidly becoming known for offering cheaper computing in cloud environments. The Arm chips have been gaining popularity in cloud computing because they offer better performance at a lower price point. Talwar says that Tetrate was motivated by seeing the Arm chips appear in some of the hardware running in edge machines close to the users. The new hardened versions of Tetrate tools will also support Arm architectures.

Various approaches to supply chain twins show tremendous value in sorting out supply chain bottlenecks, improving efficiency and meeting sustainability goals. But enterprises face numerous challenges in transforming raw supply chain data into living, breathing digital twins. In the past, the use of digital twins was more challenging to implement as supply chain segments were more separated and data was siloed. “Our vision for the supply chain is to change the world by leveraging intelligence to create a transparent and sustainable supply chain for everyone. “Supply chain planning in the cloud, in the form of SaaS solutions, has already become the norm in the supply chain software industry,” said Puneet Saxena, corporate vice president of global manufacturing high-tech at Blue Yonder, a supply chain management provider.

They get fine-tuned during a process called training, where the model learns how to produce the best outputs, but it’s unclear what the model is learning. Transformers incorporate an additional type of ingredient, called an attention head, with sets of heads arranged in layers (as are neurons). Consider the simplest possible kind of language model, called a bigram model, which reproduces basic language patterns. The researchers showed that a transformer model with one layer of attention heads does something similar: It reproduces what it memorizes. (In this case, the model has learned to associate the same word “Smith” with the found word “Smith.”) The net effect of the overall process is that the model copies the word “Smith” from the context to the output.

MIT graduate student Fernanda De La Torre, alumna Trang Luu ’18, SM ’20, and senior Syamantak Payra are recipients of the 2022 Paul and Daisy Soros Fellowships for New Americans. De La Torre, Luu, and Payra are among 30 New Americans selected from a pool of over 1,800 applicants. Fernanda De La TorreFernanda De La Torre is a PhD student in the Department of Brain and Cognitive Sciences. Shortly after, an abusive home environment forced De La Torre to leave her family and support herself throughout her early teens. In her graduate studies, Luu researched methods to improve evaporative cooling devices for off-grid farmers to reduce rapid fruit and vegetable deterioration.

AI is transforming our relationships with technology and with others, our senses of self, as well as our approaches to health care, banking, democracy, and the courts. But while AI in its many forms has become ubiquitous and its benefits to society and the individual have grown, its impacts are varied. Concerns about its unintended effects and misuses have become paramount in conversations about the successful integration of AI in society. IMAGE: These images were generated by a state-of-the-art version of GPT-3 that builds on the approaches used in DALL·E and GLIDE. This group of images consists of unique outputs to the same prompt: “An artist painting the future of humans cooperating with AI.” For more on GPT-3, see the afterword to this volume.

RT @HelenMargetts: Happy to see this special issue of Daedalus on "AI & Society" live (and free) https://t.co/pxpfmAZXq7 - including my essay on Rethinking AI for Good Governance https://t.co/3fdTF5CPp4 @americanacad @oiioxford @turinginstAI is transforming our relationships with technology and with others, our senses of self, as well as our approaches to health care, banking, democracy, and the courts. But while AI in its ...

Though facial recognition technology is used by police departments, governments, and even online proctoring services, it has little scientific basis and often leads to discriminatory and harmful outcomes. AI is transforming our relationships with technology and with others, our senses of self, as well as our approaches to health care, banking, democracy, and the courts. But while AI in its many forms has become ubiquitous and its benefits to society and the individual have grown, its impacts are varied.

RT @IasonGabriel: Check out the latest volume of https://t.co/SP8qCALmq3 which contains 26 open-access essays on AI and Society, including the final version of "Towards a Theory of Justice for AI"⚖️AI is transforming our relationships with technology and with others, our senses of self, as well as our approaches to health care, banking, democracy, and the courts. But while AI in its ...

But it’s important to keep track of how well the true objective is being optimized. We’ll focus on a setting that is particularly clean to analyze, in which we have access to the true objective. In addition, best-of-$n$ sampling has reliable performance and is straightforward to analyze mathematically, making it well-suited to empirical studies of Goodhart’s law and related phenomena. Together, these estimators allow us to easily analyze how the true objective varies with the amount of optimization applied to the proxy objective. In the settings we’ve studied so far, such as summarization, we’ve typically been able to reach a KL of around 10 nats using reinforcement learning before the true objective starts to decrease due to Goodhart’s law.

RT @KateKayeReports: "Emotion AI" -- controversial AI that attempts to detect people's moods based on facial expressions, tone of voice and what they say -- is becoming productized. @Zoom tells me it could end up in your virtual meeting soon. Others already sell it. My latest: https://t.co/VW2ZStaIQ0

Diligent Robotics develops socially-intelligent service robots and artificial intelligence solutions that enable robots to collaborate within and adapt to human environments. Stress and burnout have been a persistent problem in healthcare for years, creating a shortage of nurses and clinical staff. In the two years since its Series A funding, Diligent Robotics has achieved several milestones including:Moving from prototype to commercial deployment . We are excited to partner with Diligent Robotics as their positive impact on the healthcare system grows." CONTACT: Christy Warring, cwarring@diligentrobots.comCisionView original content to download multimedia:https://www.prnewswire.com/news-releases/diligent-robotics-raises-over-30-million-in-series-b-funding-round-to-deploy-collaborative-robots-to-healthcare-systems-across-the-nation-301522502.htmlSOURCE Diligent Robotics

In 2020, research in Nature Communications claimed that changes in historical trustworthiness could be tracked through a machine learning algorithm that assessed facial cues in paintings. The premises and methods used to develop the trustworthiness algorithm hinge on flawed and racist premises. Spanton and Guest brought up that the use of facial features to identify trustworthiness is rooted in racist and pseudoscientific practices called physiognomy. “There’s always been these sort of marginal scientists doing this kind of pseudoscientific work,” Guest said. A technology based on the racist pseudoscience of physiognomy can’t be objective, but the ethics and history of psychology are seldom taught.

What would a public sector AI look like? Using a broad definition of AI that includes machine learning (ML) and agent computing, this essay considers the governmental tasks for which AI has already proved helpful: detection, prediction, and simulation. The use of AI for these generic governmental tasks has both revealed and reinforced some key ethical requirements of fairness, transparency, and accountability that a public sector AI would need to meet with new frameworks for responsible innovation. A public sector AI in which fairness, accountability, and transparency are prioritized would be viewed as more trustworthy, working against such perceptions. This would require a specific branch of AI research and development, geared at distinctively public sector tasks and needs.

Though artificial intelligence may be a buzzword today, tomorrow, it might just become a standard part of our everyday lives. Furthermore, virtual assistants have become better and better at figuring out filtering useless background noise from your actual commands. The aim of this project is to eventually be able to use artificial intelligence to diagnose and treat various neurological diseases. With AI and machine learning algorithms, you can have thousands of transactions analyzed in a second. Recipes and cookingAI can recommend recipes based on the contents of your fridge (Image credit: Getty)AI also has potential in more unexpected areas, such as cooking.

Shubhangi Vashisth, senior principal research analyst at Gartner, said that AI innovation is happening at a rapid pace. AI is fueled by data and if there are errors along the data pipeline, AI models will produce biased results. As more startups ride the next wave of AI to innovate for the enterprise, some startups look poised to lead the pack in 2022 and beyond. Another State of AI report from CBS Insights last year said AI startup funding hit a record high of $17.9 billion in Q3. Below are vital details on these 10 AI startups that are worth watching across diverse industries, including retail, finance, cybersecurity, devops and more.

Recently, NLP technology facilitated access and synthesis of COVID-19 research with the release of a public, annotated research dataset and the creation of public response resources. Wikipedia serves as a source for BERT, GPT and many other language models. Responding to this, MIT researchers have released StereoSet, a dataset for measuring bias in language models across several dimensions. If NLP technology is going to be something revolutionary, it will need to be better and different. CitationFor attribution in academic contexts or books, please cite this work asBenjamin Batorsky, "New technology, old problems: The missing voices in Natural Language Processing", The Gradient, 2022.

Moreover, highly extendable linear actuators can achieve both shape- and size-changing transformations (Takei et al., 2012; Hammond et al., 2017; Hawkes et al., 2017). 2 Expandable Structures for RoboticsTo date, there has been very little work exploring robots with expandable structures from a human-robot interaction perspective. While precisely classifying the full space of shape-changing robots is challenging, as some robots might cross categorical boundaries, systems developed in prior research generally fall into one of the following major groups: modular self-reconfigurable robots, origami-like robots, tensegrity robots, soft robots, or deployable/expandable robots. Currently, most research in tensegrity robotics is focused on design, locomotion, and control (Caluwaerts et al., 2014; Sabelhaus et al., 2015; Zhang et al., 2017; Vespignani et al., 2018; Wang et al., 2019). Prior research has explored a variety of communicative channels, including gaze (Mulu, 2006; Andrist et al., 2012; Andrist et al., 2014; Admoni, 2016; Oliveira et al., 2018), implicit motion (Dragan et al., 2013; Szafir et al., 2014; Sadigh et al., 2016; Zhou et al., 2017; Kwon et al., 2018), gesture (Waldherr et al., 2000), sound (Cha and Matarić, 2016; Cha et al., 2018a), visual displays, lights (Szafir et al., 2015; Baraka et al., 2016; Song and Yamada, 2018), haptics (Guerreiro et al., 2019; Guinness et al., 2019), projection (Pierce et al., 2012; Cauchard et al., 2019), and augmented reality (Hedayati et al., 2018; Walker et al., 2018; Cao et al., 2019; Szafir, 2019; Walker et al., 2019).

Honored to be a part of this foundational issue of Daedalus -- James did an amazing job -- your answers about AI and Society are here. https://t.co/Uu8zhSPtJbAI is transforming our relationships with technology and with others, our senses of self, as well as our approaches to health care, banking, democracy, and the courts. But while AI in its ...

Trustworthy robotsRobots are becoming a more and more important part of our home and work lives and as we come to rely on them, trust is of paramount importance. She received her doctorate at the Bristol Robotics Laboratory, where she investigated ethical concerns of social robots. She is currently working on the technical development and implementation of the robot ethical black box, which will be used to generate explanations of the robot’s decision-making process. She is a member of the committee on Ethics for Robots and Autonomous Systems at the British Standards Institute, where she uses her insights and knowledge to support the progress of a standard for ethical design and implementation of robots. Before joining Oxford, he obtained a masters degree from Carnegie Mellon University and conducted research at IBM Research as an intern.

Ryan is a senior editor at TechForge Media with over a decade of experience covering the latest technology and interviewing leading industry figures. Find him on Twitter: @Gadget_RyHong Kong-based AI giant SenseTime delivered a keynote titled “AI for a better tomorrow” at this year’s World AI Cannes Festival (WAICF). George Huang, President of the International Business Group at SenseTime, led the keynote and showcased how the company is applying its AI technology in the areas of mixed reality, mobility, city management, and healthcare. “We are committed to developing AI technology that advances economies, society, and humanity to address global challenges.”SenseTime is the world’s most-funded AI company and its facial recognition system is used for China’s mass surveillance network. Naturally, ethical concerns have been raised about such technology and SenseTime continues to do its best to reduce fears.

Efficient 3D design collaboration between teams is critical across so many industries. It’s a scalable, multi-GPU, real-time reference development platform for 3D simulation and design collaboration through virtual worlds. Omniverse is a scalable platform, not only enabling design collaboration, but also the ability to develop and operate industrial digital twin simulations. The use cases that OVX and digital twins are unlockingWhile the primary use cases have primarily been in the industrial manufacturing and warehouse logistics segments, digital twins will enable the next era of industries and AI. BMW Group is using Omniverse Enterprise capabilities to develop and operate its state-of-the-art factory of the future.

They are built to execute difficult and time-consuming functions to help companies save not only time but potentially lives in a high-risk work environment. With more efficient inspections through satellite images, data analysis (artificial intelligence) and planning (AI-powered optimization planning), companies save even more money and time. Satellites help automate their manual tasks, which allows the workforce to be more targeted and productive with limited time. Satellite AI alleviates the issue by effectively monitoring and managing its critical supply chain. Providing predictive maintenance strategies and automating mobile fleet monitoring through satellite AI helps companies be more proactive.

The “brain in a jar” is a thought experiment of a disembodied human brain living in a jar of sustenance. The brain in a jar is a different inquiry than traditional questions about artificial intelligence. A jar passes the Turing Test if it fools another jar into believing it is behaving intelligently by responding to questions with responses that seem intelligent. The irony of the brain in a jar is that Descartes would not have considered “AI” thinking at all. This strange line of inquiry shows that real artificial intelligence could not be real unless the brain in a jar has legs, which spells doom for some arbitrary GitHub repository claiming artificial intelligence.

Truth be told, data is not data until it is actionable, and to get there it must be accessible. Luckily, there are steps to consider for utilizing the power of machine learning for data portability and availability at an organizational level. — With different file types or data types organizations can train their algorithm to provide more accurate results over time. At each step of the journey of using machine learning to gather important data, security will remain important. At each step of the journey of using machine learning to gather important data, security will remain important.

Let’s face it: tech marketing has muddied the waters for investors looking for real AI companies. As we’re all aware, not all AI companies have actual AI as their core value proposition. You can break out AI companies into two categories: ones that use AI as a medium, or as an add-on to automate a certain function. The latter is the equivalent of what we’re seeing today with AI — companies born far enough into the maturity lifecycle of AI that they could build companies that are entirely powered by AI. If there’s language about “layer in” AI after the fact, or that we’ve “built a sophisticated AI engine on top,” you know it’s likely not real AI.

The quantum computing race is far from over, but it’s clear that IBM is off to an early and substantial lead. This assessment comes on the heels of Big Blue’s latest launch: two new Qiskit Runtime primitives debuting today (you read it here first). IBM developed Qiskit Runtime to make it easier for people who don’t have a degree in physics to operate quantum computers. But, for the most part, the physicists, engineers, and developers working at the cutting-edge of quantum computing are inventing as they go. Qiskit Runtime primitives are, essentially, the pilot’s manual.

Perovskites are a family of materials that are currently the leading contender to potentially replace today’s silicon-based solar photovoltaics. But bringing these materials from controlled laboratory experiments into a product that can be manufactured competitively has been a long struggle. Manufacturing perovskite-based solar cells involves optimizing at least a dozen or so variables at once, even within one particular manufacturing approach among many possibilities. The manufacturing process would involve a moving roll-to-roll surface, or series of sheets, on which the precursor solutions for the perovskite compound would be sprayed or ink-jetted as the sheet rolled by. He says companies there are not yet doing large-scale manufacturing, but instead starting with smaller, high-value applications such as building-integrated solar tiles where appearance is important.

Aimed at driving diversity and inclusion in artificial intelligence, the MIT Stephen A. Schwarzman College of Computing is launching Break Through Tech AI, a new program to bridge the talent gap for women and underrepresented genders in AI positions in industry. Break Through Tech AI will provide skills-based training, industry-relevant portfolios, and mentoring to qualified undergraduate students in the Greater Boston area in order to position them more competitively for careers in data science, machine learning, and artificial intelligence. “Break Through Tech’s success at diversifying who is pursuing computer science degrees and careers has transformed lives and the industry,” says Judith Spitz, executive director of Break Through Tech. Ozdaglar, who will be the MIT faculty director of Break Through Tech AI, adds: “The college is committed to making computing inclusive and accessible for all. We’re thrilled to host this program at MIT for the Greater Boston area and to do what we can to help increase diversity in computing fields.”Break Through Tech AI is part of the MIT Schwarzman College of Computing’s focus to advance diversity, equity, and inclusion in computing.

Join us to learn how a more sustainable organization can start with intelligent asset management. With over 15 years of experience in engineering and driving successful organizational evolution with a focus of how AI technology can be infused to maximize results, Galina evangelizes IBM AI point of view of how AI technology helps customers to profitably achieve their corporate sustainability and climate goals. Complete the form on the right to watch this webinar on-demand. Head over to the TechForge website to explore the full 2022 webinar and virtual events programme. View website here.

Since 2019, it has raised $32.5 million in two rounds for an X-ray that will enable engineers to see inside the products that they are designing or repairing. Engineers use the X-ray scanner to see inside their products and use AI software to find flaws in a non-destructive way. With the right tools, engineers can take calculated risks with confidence and create groundbreaking products that push the boundaries of modern manufacturing technology. The biggest manufacturers have been able to afford the ultimate engineering tools: CT scanners that give their engineers complete insight into their work. Lumafield was founded by a group of engineers who have developed and delivered impactful products.

An appearance on Google Cloud marketplace followed, thus making a ‘Big 3’ cloud hat-trick, one that other quantum players can also assert. But, if IonQ was later to the quantum computing race, it was early to the quantum computing IPO. Last year, IonQ claimed standing as the world’s first public pure-play quantum computing company. Clearly, raising large sums from VCs or public markets is a ‘to-do item’ for quantum computing hardware makers like IonQ. More such deals can be expected, as IonQ’s quantum computing efforts ramp up and roll out.

Tampa, FloridaLocated on Florida’s Gulf Coast, Tampa is quickly establishing itself as a tech city to know — last year Forbes named it the number-one emerging tech city in the nation and real estate website Zillow is tipping it as the location of the nation’s hottest housing market this year. The city predicted a growth of 2,000 new jobs last year — it is already responsible for more than 25% of the state’s tech jobs. You can discover lots more tech jobs nationwide on our Job Board now. Almost 14% of Raleigh’s workforce is involved in tech, and it ranks among the top U.S. cities for startups, helping it to become one of the nation’s best cities for tech jobs. And it is now establishing itself as a technology hub too.

Under the LLM hoodGoogle’s publication outlines the philosophy of Pathways at every step of the process of training PaLM. Second, even though multilingual sources are cited, in reality they’re still dominated by the English language. Google notes that the language capabilities of PaLM are likely constrained by the limitations of language present in the training data and evaluation benchmarks. At the same time, PaLM yields impressive multilingual capabilities on the benchmarks Google evaluated against, the majority of which are in the English language. In language understanding, PaLM was evaluated on 29 widely used English natural language processing (NLP) tasks.

The metaverse has already changed the way that we live and work, offering us a hybrid state of existence right now. I believe it’s the reason why so many consumers, investors and even technologists have missed its arrival, and misunderstand its potential to be linked to a fully digital existence that won’t ever arrive. The hybrid-verse is here now and, unless you’re reading this story as a printout in your cabin in the woods, you’re living in it. If you visit the site today, Ruth will offer assistance to help you bake cookies in the real world. They are virtual helpers acting in the real world to enhance actual, tangible productivity.

Artificial intelligence research lab OpenAI made headlines again, this time with DALL-E 2, a machine learning model that can generate stunning images from text descriptions. DALL-E 2 shows how far the AI research community has come toward harnessing the power of deep learning and addressing some of its limits. It also provides an outlook of how generative deep learning models might finally unlock new creative applications for everyone to use. The beauty of DALL-E 2Like other milestone OpenAI announcements, DALL-E 2 comes with a detailed paper and an interactive blog post that shows how the machine learning model works. Ideally, the machine learning model should be able to learn latent features that remain consistent across different lighting conditions, angles and background environments.

But you can speed up the whole process by using a QR Code, which your friends can scan to connect to your network. Here’s how you can do it:Go to qifi.org on your desktop browserEnter your WiFi connections SSID, encryption type, and passwordGenerate the QR code and print it to stick it your fridgegenerating QR code for your home WiFi connectionYou can use other tools such as ZXing’s QR code generator or QR4’s generator for the same task. And if you’re using a phone with Android 10, you can follow this guide to create a QR code right from your device’s settings. While you’re at it, you can check out our nifty guide to QR code scanning on your iPhone. If you own an Android phone, you can read Android Police’s article on safe QR code scanning.

Anytime you use a video teleconferencing app, you’re sending your audio data to the company hosting the services. And, according to a new study, that means all of your audio data. There’s no good reason not to explicitly inform users in large print that the mute button doesn’t stop their audio feed to the server. If you truly want to mute your audio feed, you need to perform a double mute. And, furthermore, it demonstrates how much more sensitive our audio data can be than our video data.

During one-week, themed FutureMakers Workshops organized around key topics related to AI, students learn how AI technologies work, including social implications, then build something that uses AI. Ewnetu’s team, Team Dyadic, built a prediction model to warn people about wildfires. This year’s Day of AI, on May 13, includes teaching materials for upper elementary through high school. Middle school students will learn about generative adversarial networks, which can produce both deepfakes and art. High school students will learn about the recommendation systems used by social media and their implications for individuals and for society.

Find him on Twitter: @Gadget_RyResearch from analytics firm SAS has found that Finland appears to be leading in hyperautomation awareness while a number of large countries are falling behind. “We know from our own research that companies are already using hyperautomation to drive efficiency, customer service and innovation,” commented David Shannon, Head of Hyperautomation at SAS UK & Ireland. For its research, SAS analysed online search data for 24 countries to determine awareness of hyperautomation and where demand seems to be growing fastest. “While search data can’t tell us everything, it’s a useful barometer to test the mood of nations. Awareness has grown rapidly in some countries in just three years, indicating where demand could be strongest,” added Shannon.

Noogata, a company based in both New York and Israel, today announced it has raised $16 million in series A funding to simplify artificial intelligence (AI) access for enterprise users worldwide. Noogata’s no-code solutionFounded in 2019, Noogata changes the status quo by bringing AI capabilities into the hands of business users. Moving ahead, it plans to add support for more data sources, including Snowflake data warehouses and expand the capabilities of the AI blocks to focus on more use cases. The funding comes as the no-code AI development space continues to grow in light of the pandemic and the shortage of data science talent. Other players operating in the same segment are Akkio, Google AutoML, Obviously AI and Fritz AI.

AIops needs data to function, but challenges along the AIops data pipeline mean that AIops doesn’t often produce the right results. Manjrekar said observability needs to be augmented by AIops to contextualize, automate, scale and instantly remediate challenges along the AIops data pipeline. Cloudfabrix, a California-based software company, says it’s built the world’s first robotic data automation fabric (RDAF) technology to unify observability, AIops and automation — turning noisy IT data into action and prediction. How robotic data automation fabric worksThe term “data fabric” has been gaining momentum in the data analytics space in recent times. Gartner listed “data fabric” as one of the top 10 data and analytics trends for 2022, with predictions that “as data becomes increasingly complex and digital business accelerates, data fabric is the architecture that will support composable data, analytics and its various components.”The research firm further said data fabric reduces the time for integration design by 30%, deployment by 30% and maintenance by 70% because its designs draw on the ability to use/reuse and combine different data integration styles.

PassiveLogic’s CEO Troy Harvey conceived of the idea while leading an energy engineering controls firm, where he realized that most building never achieved their engineering design goals. A new vision for intelligent buildingsPassiveLogic is creating an autonomous platform to connect the dots between architectural drawings, engineering designs and building controls. The building control industry is led by incumbents such as Honeywell, Johnson Controls, Siemens and Schneider Electric. This extends the emerging concepts of generative design used in the architecture community to building control systems. After mapping each room, they can add additional details like sensors and then connect to control systems by other automation vendors.

Neptune.ai, a Polish startup that helps enterprises manage model metadata, today announced it has raised $8 million in series A funding. So, when something goes wrong, it becomes incredibly difficult for the ML engineers to unpick what caused the issue and when. GrowthSince its launch, Neptune.ai has roped in more than 20,000 ML engineers and 100 commercial customers, including Roche, NewYorker, Nnaisense and InstaDeep. “Neptune wins (against these platforms) on flexibility and customizability, great developer experience and focus on solving one component of the MLops stack (model metadata management) really deeply,” Niedźwiedź noted. It will grow its product and engineering teams to further improve the metadata store and augment the workflows of ML engineers and data scientists.

Executives and decision makers want AI projects to mature so they are more like the software development projects that have been with us for a generation. But, any AI project manager hoping to deliver on those expectations knows that success in AI projects requires an end-to-end thinking rarely found today. First, sourcing and securing talent emerges as a key challenge for AI projects as they grow increasingly complicated. While this can be challenging for a software project, it becomes even harder for an AI project. AI project leaders must create and foster executive AI fluency by overcoming the misconceptions that the rapid advent of AI science has created.

@tdietterich @IamManuell @leonpalafox Yeah.... though I share a lot of the AINow & D&S concerns about NAIRR in practice: https://t.co/i5ryvHMIeX https://t.co/F8BwA2r0uFThroughout our comment, our recommendations encourage the Task Force to fundamentally reconsider whether the investment in shared computing and data infrastructure is consistent with both ...

Presented by ProvectusCompanies now no longer have a choice but to prioritize AI transformation. An AI strategy is most successful, and most transformative, when it is backed by strong, top-down vision and objectives for AI transformation, real business knowledge, and guided by a company’s mission. “AI use cases should be evaluated on their own, based on individual business circumstances, but we’ve seen that it’s helpful to prioritize customer-focused use cases. How leaders can launch the AI transformation journeyTo launch an AI strategy, above all, you need to be hands-on. And you need to work to ensure that the project never loses its primary objective: to support real business users.

Shubhangi Vashisth, senior principal research analyst at Gartner, said that AI innovation is happening at a rapid pace. AI is fueled by data and if there are errors along the data pipeline, AI models will produce biased results. As more startups ride the next wave of AI to innovate for the enterprise, some startups look poised to lead the pack in 2022 and beyond. Another State of AI report from CBS Insights last year said AI startup funding hit a record high of $17.9 billion in Q3. Below are vital details on these 10 AI startups that are worth watching across diverse industries, including retail, finance, cybersecurity, devops and more.

In simple terms, Design for Trust is a collection of three design principles and associated methodologies. It occurred because the AI/ML system’s facial recognition feature did a poor job of distinguishing persons of color and minorities. Google search results are based on algorithms that decide which search results are presented to searchers. Over the years, Google has initiated a series of actions to improve the fairness of search results and protect users. AI/ML systems are typically known and understood only by the data scientists, programmers and designers who created them.

OpenAI has unveiled a new AI tool that turns text into images — and the results are stunning. “When approaching DALL-E 2 we focused on improving the image resolution quality and improving latency, rather than building a bigger system,” OpenAI researcher Aditya Ramesh told TNW. The system then gradually transforms the pattern into a picture when it recognizes specific aspects of that image. The nature of many jobs will change, jobs that never existed before will be created, and others may be eliminated. View this post on Instagram A post shared by DALL·E by OpenAI (@openaidalle)Altman believes DALL-E’s mechanism could change how we interact with machines.

Artificial intelligence (AI) and machine learning (ML) have been massively hyped over the years. Machine learning starts with dataWithout the ability to analyze data, identify patterns, and put it to use, data is effectively useless. The data optimized through the machine with machine learning provides marketers with a supercharged ability to make the most informed decisions and then enact a creative strategy to achieve their desired outcome. Machine learning for marketers: Asking the right questionsThe things that matter to companies and to individuals are decisions and actions. By combining machine learning with real-time data, marketers can see results live, instead of waiting for results at the end of a campaign.

Tesla’s “Cyber Rodeo” party, a grand opening event for the company’s new gigafactory in Texas, had a flamboyance and excessiveness to match Elon Musk’s personality. Fireworks were shot and thousands of drones were deployed in the sky to form a map of the state of Texas, the Cybertruck, and a Shiba Inu dog. Credit: TeslaFull of jokes and confidence, Musk made several big announcements. Musk said that the bot will enter production “hopefully next year” and do “anything that humans don’t want to do.”6. If you want to see the party for yourself, you can find Tesla’s video below:

The USB-C standard can handle a lot of things: charging, data transfer, and audio and video streaming. My colleague Napier has beautifully captured how the USB-C standard has changed the gadget life. The complexity of USB-CUSB-C is confusing, because for each capability — power, data, and video — there’s a specification you need to know. — Burak Tamt*rk #StopErdogan (@buraktamturk) March 21, 2022You mean USB-C with USB 2.0 speeds or USB-C with USB 3.1 Gen 1? USB-C that supports video signal or USB-C that’s for power only?… — Alex @ Geek’s Table (@geekstable) March 16, 2022These tweets above capture my frustration with USB-C perfectly.

There might be part of a site that doesn’t easily let you copy some text, or maybe the text you need to copy is part of an image. iPhone Live TextApple recently rolled out the Live Text feature in iOS 15 that easily lets you copy text using your camera. Now, you can open a photo and tap on the text to select it, or tap on the Live Text button to copy all text. You can easily copy text from Photos on iPhone, thanks to Live Text. Once you install the extension, visit any web page you want to copy text from, and click on the Blackbox AI icon.

Now European lawmakers are set to include millions of photos of people’s faces in this system—and allow facial recognition to be used on an unprecedented scale. The expansion of facial recognition across Europe is included in wider plans to “modernize” policing across the continent, and it comes under the Prüm II data-sharing proposals. Facial recognition technology has faced significant pushback in recent years as police forces have increasingly adopted it, and it has misidentified people and derailed lives. The EU is debating a ban on the police use of facial recognition in public places as part of its AI Act. However, Prüm II allows the use of retrospective facial recognition.

Getting a census count wrong can cost communities big. The US Census Bureau began its latest effort to modernize the population count in 2010, largely through automation and technology. According to a summarization of approaches that influenced the 2020 census, part of the goal was to keep costs comparable to 2010 levels, at about $12 billion. The Census Bureau is now working on a machine learning algorithm that detects new construction, using satellite imagery to track projects from start to finish. In the year ahead, the US Census Bureau will test its AI in different parts of the country to see how the model performs in various climates and regions.

20 Pages Posted: 8 Apr 2022Date Written: April 4, 2022AbstractRobot rights is already in play and operational from the moment the robot appeared on the stage of history. And the question “Can or should robots have rights?” is currently the site of a contentious debate. This chapter engages in an analysis of the terms and conditions of the dispute. It therefore does not take sides in the existing conflict by advocating for one position over and against the other. Instead identifies and critically evaluates the common set of shared values and fundamental assumptions that both sides already endorse and support in order to enter into conflict in the first place.

With the use of machine learning, data mining, and advanced modeling techniques, advanced analytics can turn an organisation’s vast amount of data into increasingly accurate predictions. Even more, advanced analytics can help identify trends and predict the best steps to yield positive outcomes. Adopting advanced analytics: a story of resistance and confusionDespite the tremendous value-add advanced analytics can provide, it’s still often met with confusion and resistance in organisations. Making advanced analytics accessible brings an organisation one step closer to solving the user-adoption dilemma. To dive deeper into advanced analytics and its benefit to business, download the How to Boost Your Business with Advanced Analytics ebook by Forecast here.

One is a “data fabric,” a data integration approach that includes an architecture — and services running on that architecture — to help organizations orchestrate data. To uncover the relationships between data, a data fabric builds a graph that stores interlinked descriptions of data such as objects, events, situations and concepts. Using data meshes and fabricsWhen weighing the pros and cons, it’s important to keep in mind that data mesh and data fabric are concepts — not technologies — and aren’t mutually exclusive. An organization can adopt both a data mesh and data fabric approach across certain, or all, departments as appropriate. “A data fabric and a data mesh both provide an architecture to access data across multiple technologies and platforms, but a data fabric is technology-centric, while a data mesh focuses on organizational change,” he writes in a blog post (via Datanami).

Join AI and data leaders for insightful talks and exciting networking opportunities. One of the main roadblocks preventing the enterprise from putting artificial intelligence (AI) into action is the transition from development and training to production environments. Even though MLops is designed for automation and even autonomy, don’t overlook the human element as a key driver of successful outcomes. MLops should be a critical component of this strategy because it supports diversification in the development, deployment and management of AI projects. And just judging by Gartner’s MLops framework, a broad set of skills will be required to ensure that outcomes provide top value to the enterprise business model.

“The native market should provide the same great customer experience as in all other markets. And that’s just not true for most companies right now,” said Spence Green, CEO of enterprise translation software and services company Lilt. The power of diversityLanguage is central to the customer experience and it’s a critical component of global commerce. Test it yourselfJust test it yourself by going to any company website, changing the language and clicking around on links. “Incorporating this into your customer experience and your digital presence is just a totally obvious thing to do,” Green said.

However, the growing demand for AI is hampered by the very low supply of data scientists and machine learning experts. The financial sector is leading the way, aggressively hiring AI talent and putting machine learning models into use. The AI Cloud has evolved into an end-to-end no-code platform that covers the entire machine learning development lifecycle. Throughout the entire machine learning development lifecycle, more advanced users can integrate their own hand-written code with DataRobot’s automated tools. “The future of no-code AI is going to be about increasing the level of automation that platforms can provide.

Reading and maintaining large Python projects — you’d better be a world hero at finding descriptive variable names and commenting out all your code, you’re done messed up. They’re usually in a form like my_module.my_variable().If you’re confused by such a variable, you’re not finished when you’ve checked where else it appears in the main file. If you’re stringing together a big project, you might be integrating C packages, Fortran packages, and more. The documentation for integrating C modules in your Python code is more than 4,500 words long — twice as long as this article! In big projects, Python isn’t forbidden.

Is Artificial Intelligence (AI) the Future of Humanity? General artificial intelligence: This is the most sophisticated form of artificial intelligence and is capable of performing any task that a human can. The future of artificial intelligenceThe future of artificial intelligence is glimmering with promise but fraught with uncertainty. Despite the numerous unknowns, the future of artificial intelligence is likely to be shaped by a number of factors. While Gottfried Leibniz and Charles Babbage advanced ideas about artificial intelligence in the 18th century, the field of artificial intelligence did not take shape until the twentieth century.

While an article by McKinsey notes voice analytics can provide a high-quality customer experience in a digital age, it isn’t the only use case for voice analytics. The use cases for voice technology are enormous and Israel-based voice analysis technology company, Nemesysco, wants to drive new frontiers in the industry. Emotion Logic is AI-based and genuine emotion detection is just one of what the company will do,” he said. A major market opportunity Emotion Logic wants to go after is the emerging metaverse. It’s not going to happen without some level of embedded humanity that Emotion Logic is developing,” he said.

Thus, we do not aim to provide a well-rounded sense of the overall effects of image generation technologies. Risk assessment processEarly workBeginning in 2021, several staff at OpenAI have been exploring risks associated with image generation systems, and potential mitigations for those risks. Harassment, bullying, and exploitationTargeted harassment, bullying, or exploitation of individuals is a principal area of concern for deployment of image generation models broadly and Inpainting in particular. In addition to side-by-side comparisons, it is important to consider how new image generation technologies can be combined with previous ones. Moreover it is important to consider what impacts deployments such as this will have on wider norms related to image generation and modification technologies.

Out of the many companies that are working on the development of humanoid robots, we have come up with companies making different types of robots. Here are the top 10 companies working in the development of humanoid robots. Ubtech, since its launch, has come up with world-class robotics including, consumer humanoid robots, enterprise service robots, and STEM skill-building robots. Intuitive Robots is also known for developing applications for humanoid robots. Honda MotorHonda Motor is yet another renowned robotics company that has carved a niche for itself for manufacturing humanoid robotics.

They lack the common sense to dismiss the possibility of food-on-food crime. By definition, common sense is something everyone has; it doesn’t sound like a big deal. systems common sense, many thorny problems would be solved. with common sense wouldn’t be so easily perplexed—it would know that rifles don’t have four legs and a shell. researchers thought that they were close to programming common sense into computers.

2.2 Assistive TechnologiesAssistive technologies refer to any article, piece of equipment, software program, or product system used to increase, maintain, or improve people with disabilities (Carver et al., 2016; Federici et al., 2017). They include those that improve structure and function (Livingstone et al., 2020), such as mobility devices (e.g., prosthetic legs, cochlear implants, and electronic implants for bladder control) (Azocar et al., 2018; Aiordachioae et al., 2020). And those that improve activity performance (e.g., voice input systems, wheelchairs for riding stairs, and communication panels) (Sasaki et al., 2020; Smith, 2020; Patel and Parmar, 2020). Environmental modifications or so-called Assistive environments (e.g., automatic door openers, walk-in entrances, and accessible bathrooms) (Guay et al., 2020; Martinez-Martin et al., 2020; Hu and Hu, 2021), which reduce or eliminate restrictions on participation, are also considered types of assistive technology. Attempts are currently underway to apply automation to certain aspects of medical diagnosis and hence to psychotherapy procedures (Cameron et al., 1964; Goldberg et al., 2020; Casas-Bocanegra et al., 2020).

The TechCrunch Global Affairs Project examines the increasingly intertwined relationship between the tech sector and global politics. Unlike other technologies, artificial intelligence (AI) is far more than a mere tool. Likewise, it took time to realize the implications of facial recognition technology for privacy and human rights violations. AI systems can act with little or no human intervention. Nor is the world prepared for how AI will transform geopolitics and the ethics of international relations.

In the last two years, a new direction has all but monopolised research in artificial intelligence (AI). This direction involves training large neural networks - dubbed foundation models - on large amount of raw data, and without requiring a direct human supervision signal. Those models are trained once, on a static dataset through a process that requires access to a large cluster of computer processors specialised in matrix computation. In this talk, Matthias Gallé will first provide a general-audience overview of those models and some insights on how they are trained. Matthias Gallé leads various artificial intelligence (AI) research teams at Naver, a leading Asian tech company.

Hoan Ton-That, CEO of Clearview AI, talks to reporters in New York, Tuesday, Feb. 22, 2022. (AP Photo/Seth Wenig)Hoan Ton-That, CEO of Clearview AI, talks to reporters in New York, Tuesday, Feb. 22, 2022. Clearview AI co-founder and CEO Hoan Ton-That disclosed the plans Friday to The Associated Press in order to clarify a recent federal court filing that suggested the company was up for sale. “We’re really focused on law enforcement right now,” he said, describing how the company’s mission had evolved from commercial applications into helping to solve crime. “The future potential uses for Clearview appear to be a moving target,” said Sejal Zota, legal director of Just Futures Law.

4 Trends That Will Shape Artificial Intelligence In Healthcare via @DigitalSalutem Read here ? https://t.co/nvtBAfT3uN #AI #digitalhealth #ehealth #telehealth #telemedicine #digitaltransformation #ArtificialIntelligence #Artificial_Intelligence https://t.co/nbs31APASiWhen we think about the potential of artificial intelligence in healthcare, we have to acknowledge that it is only recently

If user-related disease information, potential health information and other privacy-related information – such as information that users do not want to share, but obtained through BCIs – are acquired, they should be processed reasonably. Therefore, key techniques need to be open and transparent to reduce potential risks. Therefore, key techniques need to be open and transparent to reduce potential risks. The thoughts and behaviors of those who use BCIs to augment their human intelligence may be different from those who have not applied these technologies. In fact, a lot of work remains to be done in understanding the potential risks of BCIs.

At least four prominent members of Meta AI have departed in recent months, according to people familiar with the matter and LinkedIn analysis. Karl Hermann, an AI entrepreneur who used to work at rival lab DeepMind, told CNBC on Monday the true figure could be more like half a dozen, adding that the company's London AI lab had seen an alarming number of exits. Neil Lawrence, professor of machine learning at the University of Cambridge, told CNBC that he wasn't surprised. Another recent departure is Ahmad Beirami who left his research scientist position at Meta in January and joined Google in the same role. And last year, in December, Douwe Kiela left his research scientist role at Meta after spending five years at the company.

RT @colinangle: Yes, we're celebrating National Robotics Week – and at @iRobot, we work to inspire the next generation of big-thinkers all year round. ? So fun to find out how these young people think and feel about the #robots in their lives! #RoboWeek #STEM https://t.co/7p1G6l9X6X https://t.co/HKr4FygPR4

EthicsIs Artificial Intelligence the Future of Humanity? Some may argue that current artificial intelligence technology is insufficiently advanced to enable machines to perform complex tasks in the same way that humans do. A more complete understanding of customer behavior: AI can assist businesses in developing a more complete understanding of customer... The Dangers Of Not Aligning Artificial Intelligence With Human Values In artificial intelligence (AI), the “alignment problem” refers to the challenges caused by the fact that machines simply do not have the same values as us. This forms the basis of the latest book by Brian Christian, The Alignment Problem – How AI Learns Human Values.

RT @NVIDIAEmbedded: Happy #NationalRoboticsWeek! We are excited to kick-off a week of celebrating all of the #robots, both big and small, that are made possible by NVIDIA's #Jetson and #Isaac platforms. Dive into the world of #robotics with us and get involved! https://t.co/9Ov0kAryZh @RoboWeek https://t.co/NxRSOMEbPC

The rising costs of storage have pushed many companies to adopt cloud options, which offer the advantage of low entry costs. Other technology providers have turned their attention to on-premises systems, creating algorithms that they claim can reduce storage costs either with hardware suggestions or novel file compression techniques. “Data storage today suffers from several challenges: Storage deployments are often made up of a variety of different storage media such as memory, flash, disk drives and tapes. Accenture claims that it reduces storage costs by detecting duplicate or near-duplicate content, helping customers move or archive the right data at the right time. “AI techniques can help optimize placement of data on the right storage tiers — balancing performance and costs.

Their technique, called Shared Interest, incorporates quantifiable metrics that compare how well a model’s reasoning matches that of a human. “The value here is that using Shared Interest, we are able to see these patterns emerge in our model’s behavior. If those techniques contain bias or are inaccurate, then Shared Interest will inherit those limitations. In the future, the researchers want to apply Shared Interest to different types of data, particularly tabular data which is used in medical records. Boggust hopes this research inspires more work that seeks to quantify machine-learning model behavior in ways that make sense to humans.

The finding, reported March 16 in Science Advances, suggests that the millions of years of evolution that have shaped circuits in the human brain have optimized our system for facial recognition. “And that’s the same solution that we hypothesize any system that’s trained to recognize faces and to categorize objects would find,” she adds. “It needs to recognize a face identity, as well as a bike or a pen.”As the program learned to identify the objects and faces, it organized itself into an information-processing network with that included units specifically dedicated to face recognition. “We didn’t build anything face-ish into our network,” Kanwisher says. “Why do we have a separate system for face recognition in the brain?

iRobot has announced a brand new robot that is guaranteed to leave your floors 0 percent cleaner. This is not a mop or a vacuum, but instead a new and updated version of iRobot Create: the Create 3. It's part of the company's ongoing effort to create new generations of roboticistsShare this article

Google Cloud is making a series of announcements today, covering a range of its data, analytics and AI services. As you might imagine from the name, this service will make data lakes stored in Google Cloud Storage (GCS) far better integrated with BigQuery, Google Cloud’s data warehouse service. This coordination of lake and warehouse will resonate with fans of the so-called lakehouse model, while still respecting that data lake and data warehouse technologies each have relative strengths. Google is also announcing something called Spanner change streams, a change data capture service that will replicate data in real time from Google Cloud Spanner into BigQuery, Pub/Sub or Google Cloud Storage. For example, creating a side-by-side data warehouse/data lake environment is very much like what Microsoft’s Azure Synapse Analytics had done already: bring together the former Azure SQL Data Warehouse with Azure Data Lake Storage, Spark and a data lake query engine.

Presented by Defined.aiWhat does it mean to build responsible, ethical AI? In the private sector, businesses must grapple with how to develop and deploy ethical AI, while in the public sphere, government policy is being crafted to ensure safe and fair AI use. What does responsible and ethical AI look like? A focus on privacy is also essential, especially when building the right data sets. Ethical AI also requires a feedback loop, so that anyone working on the models can raise their hand to flag any issues or concerns.

Today, Google opened up their Data Cloud Summit with a bevy of announcements of new products and enhancements designed to help data scientists leverage the power of the Google Cloud Platform to perform data science. “Data is probably on the top of the agenda of every C-suite on the planet,” explained Gerrit Kazmaier, general manager and VP for databases, data analytics and looker at Google Cloud. At the same time, it’s nurturing an open ecosystem so that companies can use and share data from wherever it may be captured. This open strategy can help Google battle with large competitors like Amazon or Microsoft. “At Google Cloud, we’re removing the limits of data clouds to further cose the Data-to-AI-Value gap.” said June Yang, VP of cloud AI and innovation at Google.

RevOps brings science to sales and uses real-time buyer/seller engagement signals to take a data-based approach to sales enablement and execution. Key data points about RevOpsA successful RevOps strategy brings sales and revenue together, but it still requires coordination and communication among sales marketing and customer service teams. RevOps tries to solve this by calling for sales and customer success to become more intertwined. Mediafly launches a command center applicationMediafly‘s revenue enablement and revenue intelligence application, called the Revenue360 suite, comprises four comprehensive solutions that connect sales enablement, revenue intelligence, value selling and buyer engagement. With Revenue360, Mediafly brings science to sales, using real-time buyer and seller engagement signals to take a prescriptive, data-based approach to sales enablement and execution.

Artificial intelligence (AI) governance software provider Monitaur launched for general availability GovernML, the latest addition to its ML Assurance Platform, designed for enterprises committed to the responsible use of AI. Corporations are prioritizing the need to establish and operationalize governance policies across AI applications in order to demonstrate compliance and protect stakeholders from harm. We built GovernML to solve both.”The importance of AI governanceEffective AI governance requires a strong foundation of risk management policies and tight collaboration between modeling and risk management stakeholders. How would this system of record mesh with other enterprise systems, such as data governance apps, legal risk management, security, etc.? These are examples of key bias controls in the lifecycle of responsible AI governance.

These kinds of legal AI tools will change how law firms handle cases. With AI, law firms can quickly determine outcomes, reducing costs and risks while defending their clients with more information. In law firms, AI can replace this tedious job to a large extent, and it can perform due diligence efficiently and accurately. According to the survey, 82% of law firms reported that their businesses had fully reopened, and 77% of firms had no plans to reopen. It can be said that deploying AI in law firms can provide positive momentum for the law firm, thus helping lawyers to work efficiently.

Up front: The stock market is a nebulous and challenging environment to try and “solve” with AI, but the team’s leadership appears more than up for the challenge. Background: While they’re certainly not the first to attempt to demystify the stock market with artificial intelligence, they’re taking a slightly different approach than their competitors. The typical stock-picking AI tries to “guess” what’s going to happen next in the market based on historical trends. In a study involving 44,000 hands of poker, DeepStack defeated with statistical significance professional poker players in heads-up no-limit Texas hold’em. As the old saying goes: teach an AI to win at poker and you’ll eat for a day, teach an AI to dominate the stock market and you’ll be set for life.

“The robot cannot see the human arm during the entire dressing process.” In particular, it cannot always see the elbow or determine its precise position or bearing. Last year, Li and Shah and two other MIT researchers completed a project involving robot-assisted dressing without sleeves. Robots are already adept at certain things, such as lifting objects that are too heavy or cumbersome for people to manage. To deal with obstructed vision in trying to dress a human, an algorithm takes a robot’s measurement of the force applied to a jacket sleeve as input and then estimates the elbow’s position. “We didn’t specialize this algorithm in any way to make it work only for robot dressing,” Li notes.

Free tool from @labmlai at https://t.co/149PEl7n2T uses Twitter to mine trending research papers: https://t.co/lDO6KraZJ6 +And it can monitor #DeepLearning model training from your mobile device! ——— #abdsc #BigData #DataScience #DeepLearning #MachineLearning #AI #DataScientists https://t.co/ARXqjQT9a4Here is a great too for your AI research I feel like a kid in a candy shop! If you are like me, and want to stay on top of AI, you spend a lot of time working through research papers ...

Find him on Twitter: @Gadget_RyMicrosoft and Hewlett Packard Enterprise (HSE) are working with NASA scientists to develop an AI system for inspecting astronauts’ gloves. Astronauts’ gloves are built to be robust and have five layers. Aside from the usual day-to-day wear that happens even from using gloves here on Earth, astronauts’ gloves have to deal with a variety of additional hazards. A cloud-based AI system was trained using the data and the results were comparable to NASA’s own actual damage reports. In space, images would be taken of astronauts’ gloves while they remove their equipment in the airlock.

Learn more about Transform 2022VHive, an Israeli startup that provides software to help off-the-shelf drones create digital twins, has raised $25 million in series B funding. The VHive software automates flight planning and data analysis for various commercial drones. Tower operators can also survey the tower itself as part of maintenance and inventory the equipment installed on a given tower. An actual physical record can help build consensus between MNOs and tower operators when questions arise about what is actually installed. The tricky part comes with customizing the process of translating raw physical data into useful digital twins for each industry.

The integration of BrainChip’s Akida technology and SiFive’s multi-core capable RISC-V processors is expected to provide an efficient solution for integrated edge AI computing, Nadel said. RISC-V (pronounced “risk-five”) is an open instruction-set computing architecture based on established reduced instruction set computing (RISC) principles. Jack Kang: For SiFive, this announcement shows the ongoing uptake of the SiFive Intelligence family of RISC-V-based processor IP. More companies are choosing RISC-V to be part of their product roadmap strategy, and SiFive is the leading provider of commercial RISC-V IP. In the emerging green-field markets of AI/ML-enabled platforms, such as the edge processing market targeted by BrainChip, the performance per area and efficiency advantages of SiFive processor architecture makes the SiFive Intelligence family a competitive choice.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - August 3. That’s why 85% of SREs report that the scalability of SRE practices will be extremely dependent on the availability of automation and AIops capabilities. Both are needed to accelerate innovation and transformation while alleviating some of the processes that typically require time-consuming manual effort. Minimizing time spent on these efforts will help SREs evolve into the more critical role businesses are giving them when it comes to digital transformation strategy. Read the full report by Dynatrace.

Dataminr Pulse compiles early indications of emerging security and market risks from publicly available information and makes them available in real time. Dataminr’s AI platform discovers, distills and delivers alerts from social media, blogs, information sensors and the dark web, ensuring that businesses have the backstory knowledge they need to make key decisions, the company said. Edelboim said the Dataminr Pulse corporate risk product is used by more than two-thirds of the Fortune 50 and half of the Fortune 100. Following its recent $475 million growth capital financing, Dataminr made two strategic acquisitions in 2021: UK-based WatchKeeper, a data geovisualization platform, and Copenhagen-based Krizo, a real-time crisis response platform. Both are now fully integrated into Dataminr Pulse.

That’s the day SandboxAQ slipped out of stealth mode and starbursted out of the Alphabet (Google) research blanket. SandboxAQ’s solution to this problem is to use machine learning and quantum technology to develop and secure a new protocol called post-quantum cryptography (PQC). His company, by necessity, is focused on leveraging quantum physics to solve problems today without the aid of quantum computers. The big difference here, as Hidary told us, is that “these solutions can run on an Nvidia GPU … you don’t need a quantum computer.”In fact, Sandbox AQ’s focus includes quantum sensing, quantum communications, quantum simulations, and quantum cryptography. The future is nowSandboxAQ is poised to burst into the quantum technologies scene with immediate impact.

But Peloton has plans for the Guide and the new software experience that comes with it. The company wants to count your workout reps, study your progress over time, see which areas of the body you’re fatiguing more than others. Guiding LightThe Peloton Guide looks like a set-top box, like an Apple TV or a Roku box, though Cortese bristles at the phrase “set-top box.” A set-top box “does this dumb thing of just streaming one-way content,” he insists. (Still, when Peloton eventually shipped me a Guide on loan, I attached it to my TV using an HDMI cable … like I would any other set-top box.) And while the camera is capturing your live-streamed body data and making inferences about it, Peloton says it doesn’t store image or video data of you while you perform the movements.

On the highway we have to turn left, fuck,” one of the soldiers says in Russian using code names meaning “East” and “Snow 02.”“Got it. But it’s the use of natural language processing technology to analyze Russian military communications that is especially novel. Off-the-shelf code and APIs that use AI can now transcribe speech, identify faces, and perform other tasks, often with high accuracy. The ability to train and retrain AI models on the fly will become a critical advantage in future wars, says Gourley. “It is indicative of comms equipment failures, some arrogance, and possibly, the level of desperation at the higher levels of the Russian military,” adds Mick Ryan, a retired Australian general and author.

Thomas Tull, leading visionary entrepreneur and investor, has been appointed a School of Engineering visiting innovation scholar, effective April 1. Throughout his career, Tull has leveraged the power of technology, artificial intelligence, and data science to disrupt and revolutionize disparate industries. Tull’s hands-on approach involves not only data science and analytical tools, but also a close partnership with business leaders. “Thomas is an incredible advocate and ambassador for innovation and technology,” says Anantha Chandrakasan, dean of the MIT School of Engineering and Vannevar Bush Professor for Electrical Engineering and Computer Science. Tull received his undergraduate degree from Hamilton College and resides in Pittsburgh, Pennsylvania.

RT @Mayoclinicpress: Progress has been made medically and technologically for women's heart health. Learn more about the use of AI, digital technology and how one's sex and gender can influence #hearthealth and treatment. @SharonneHayes @DemiladeMD @WomenHeartOrg #womenshealth https://t.co/LLQ4ZaTqrG

Join AI and data leaders for insightful talks and exciting networking opportunities. As a result, leaders are acting, with 26% of business decision-makers increasing investment in environmental, social and governance (ESG) ahead of developing new products/services (24%) or accelerating financial growth (21%). Those that fail to act for social good will inevitably put their business growth and ability to attract talent at risk. For leaders and executives, this means it’s time to refocus on technology investment — identifying not only the data that will support growth, but also help employees gain meaningful access to it. For its report, Cloudera surveyed 2,213 enterprise business decision-makers — including 54% C-suite representation — and 10,880 knowledge workers in the U.S., EMEA, India and APAC.

So how do we continue the forward progress and employ even more on-the-ground technologies to power smart cities? By collecting real-time inputs from various sensors, dynamic geospatial data can be used to power autonomous tech and smart city projects. Beyond data collectionWhile the collection of data is important, there are two additional considerations that serve to improve smart cities’ operations — data sharing and robust annotation. Smart cities: Where we go from hereSmart cities aren’t quite a reality just yet, but we’re closer than we’ve ever been. We’ve already started adopting the type of technology necessary to power smart cities.

O’Reilly’s annual survey of enterprise AI adoption found that just 26% of organizations have AI projects in production, the same percentage as last year. What’s holding AI projects back? According to O’Reilly’s findings, many enterprises with AI projects in production don’t have dedicated AI specialists or developers overseeing the projects. SageMaker relies on the AWS Shared Responsibility Model, an AWS framework, to define the extent of its security support versus what customers need to provide. For example, just 53% of AI projects move from pilot to production, reflecting the lack of integration, visibility and transparency across MLDLCs.

Organizations must fundamentally rethink digitization to not only keep up, but to master the process, and data in general. The answer to this: Autonomous operations (AO) and AIops – the process of applying AI to IT operations – according to a new report commissioned by Dell Technologies. “We have to be heading in the direction that data is going to get created,” said John Roese, global CTO for products and operations at Dell. Developed and coined by Google, this practice involves incorporating and applying aspects of software engineering to infrastructure and operations problems. A more holistic approach to AO and AIops is necessary, and IT teams must lead the charge in embracing innovation.

Learn more about Transform 2022BeamUP, an Israeli startup that makes digital twins for enterprise building management, has launched with a $15M round of seed investment led by Israel-based StageOne Ventures and U.S.-based Ibex Investors and included prominent angels, such as CEO of Workday Chano Fernandez. The company generates digital models of facilities to reduce system and infrastructure design time. BeamUP uses AI to help simplify data management aspects of facility design and streamline integration into applications like AWS IoT TwinMaker. This is important since most building data is stored as 2D floor plans and lacks structured semantic data and context. The company is also developing graph databases of design and facility data to train AI models using graph neural network models (GNN).

What does it mean to be human in an age where artificial intelligence agents make decisions that shape human actions? He is leading the college as it incorporates computer science into all fields of study at MIT while teaching students to use formidable tools like artificial intelligence ethically and responsibly. “Now, research problems have practical applications almost overnight in computing-related disciplines. In addition to research, Huttenlocher was passionate about bridging gaps between disciplines. And when it comes to questions about AI, Huttenlocher feels a sense of urgency.

RT @StartupEdmonton: "What ethical implications should I be aware of for my AI-powered product/startup?" Our friend and expert @katragram answers FAQ's in our latest blog: https://t.co/lZuq3QzQCQ @ethicsforAIWhat's on the mind of startups when it comes to AI ethics? Katrina Ingram, CEO, Ethically Aligned AI shares some of the types of questions being asked and her expert insight.

With the influx of users and heightened demand, companies have had to turn to any number of places to get started with chatbot technologies. They also might turn to the offerings provided by chatbot providers like IBM Watson, Five9 or Gupshup. These tools empower enterprises to integrate, deploy and train AI chat and messaging into their own custom web and mobile experiences. The statistics surrounding AI messaging only help to further cement its place in the modern ecosystem. The SaaS market has a rich history of counter-balancing the need for speed to market on one hand, with feature-rich customization on the other.

One of the most troubling potential misuses of AI can be found in the form of adversarial AI attacks. In an adversarial AI attack, AI is used to manipulate or deceive another AI system maliciously. The occurrence of an adversarial attack on AI or a machine learning algorithm highlights a deep crack in the AI mechanism. Therefore, to fully utilize the potential of AI systems and algorithms, it is crucial to understand and mitigate adversarial AI attacks. Understanding adversarial AI attacksAlthough the modern world we live in now is deeply layered with AI, it has yet to take over the world fully.

Most AI applications are based on machine learning and deep learning neural networks that require large datasets. An example that many of us can relate to are AI-powered apps that provide us with driving directions. Perhaps we should be paying more attention to this not-so-subtle shift in our reliance on AI-powered apps. NBC News correspondent Jacob Ward argues in his new book The Loop that through AI apps we have entered a new paradigm, one with the same choreography repeated. More than that, we need a regulatory framework soon that protects our personal data and ability to think for ourselves.

With all the things going into that, is there even a slight amount of worry? For example, the work around OmniVerse went into light speed in the last couple of years because we needed it. The way that we write software, the way that we maintain software, the way that we continuously improve software has changed. I’m encouraged by the work that’s being done at Intel. One of the most important engines in the world, Nvidia AI, is used by 25,000 enterprise companies in the world.

This vision of no-code, reusable AI will certainly elevate our current operator-machine relationships to the supervisor-assistant relationships. Reusable AI enabling no-code AISince every AI solution typically requires certain customizations, reusable AI enables no-code AI customizations. No-Code, reusable AI defines supervisor-assistant relationshipsNo-code, reusable AI enables everyone, including non-IT professionals, to create their own custom AI solutions (assistants). Future directions of no-code, reusable AINo-code, reusable AI democratizes the creation and adoption of powerful AI solutions without requiring scarce AI talents or costly IT resources. To make no-code, reusable AI the main paradigm for developing and adopting AI solutions, advances must also be made in several areas.

This type of data mismatch or “concept drift” happens if you don’t give proper care and respect to data audits. All is not lost if your data sources reveal gaps or potential areas for erroneous prediction. As we know, historical demographic data sources tend to skew towards white males, and that can bias your entire model. All is not lost if your data sources reveal gaps or potential areas for erroneous prediction. As we know, historical demographic data sources tend to skew towards white males, and that can bias your entire model.

History repeats and repeatsThe first industrial revolution started with steam power and iron, but it was built with the power loom and machine tools. The second industrial revolution started with steel and sparks, but it was built with the rail and the telegraph. The third industrial revolution started with digital logic and computability, but it was built with silicon, HTML and javascript. Just like in every other industrial revolution, those early advances have a timeless quality to them. The fourth industrial revolution — the AI revolution — is ongoing.

To do this, they treat the formation of atoms and chemical bonds as a graph and develop a graph grammar — a linguistics analogy of systems and structures for word ordering — that contains a sequence of rules for building molecules, such as monomers and polymers. Their method, which they’ve called data-efficient graph grammar (DEG), will be presented at the International Conference on Learning Representations. “We want to use this grammar representation for monomer and polymer generation, because this grammar is explainable and expressive,” says Guo. This is done repeatedly, creating the production rules as it goes, until a single node remains. Down the road, the researchers see many applications for the DEG method, as it’s adaptable beyond generating new chemical structures, the team points out.

The Boston-based publisher and researcher today announced the results of its annual “AI Adoption in the Enterprise” survey. ‘Disturbing’ AI governance trend“The large number of organizations lacking AI governance is disturbing,” Mike Loukides, VP of content strategy at O’Reilly and the report’s author, told VentureBeat. “While it’s easy to assume that AI governance isn’t necessary if you’re only doing some experiments and proof-of-concept projects, that’s dangerous. “It’s even more dangerous when you’re relying on AI applications in production,” Loukides said. While there may be AI applications where privacy and fairness aren’t issues, companies with AI practices need to place a higher priority on the human impact of AI, Loukides said.

Learn more about Transform 2022In the latest move to enable high-performance computing for enterprises, Intel has scooped up Israel-based company, Granulate, a startup that uses AI to perform workload optimization in real-time. In fact, many enterprises, including PicsArt, Coralogix, Nativo and Singular, have already reaped these benefits by signing up for Granulate. Then, building on that work, the companies debuted a commercial workload optimizer as part of Intel’s cloud tools for optimization and management. This offering did exactly what Granulate promised (performance gains and reduced costs) but only for enterprise customers relying on Intel’s Xeon deployments. At the application layer, there is an Intel Ventures company called AIOBench, which addresses the optimization of application workloads via benchmarking.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - August 3. By miniaturizing its in-fiber photonics, the startup claims it can bring server-grade capabilities to the edge. Cognifiber has now announced a “first of its kind” glass-based photonic chip specifically created for edge computing. The company claims it has been able to miniaturize its previous designs to “a fraction” of the size. The company claims its in-fiber processing can boost performance by 100 times and decrease AI/ML training cost by 80%.

She might use a spatula to lift the dough onto a cutting board then use a rolling pin to flatten it into a circle. It is especially difficult for a robot to learn a manipulation task with a long sequence of steps — where there are many possible choices — since learning often occurs through trial and error. In one task, the robot uses a spatula to lift dough onto a cutting board then uses a rolling pin to flatten it. In another, the robot uses a gripper to gather dough from all over the counter, places it on a spatula, and transfers it to a cutting board. In fact, DiffSkill was the only method that was able to successfully complete all three dough manipulation tasks.

Surveillance cameras have an identity problem, fueled by an inherent tension between utility and privacy. Security cameras can do a lot — they’ve become smarter and supremely more competent than their ghosts of grainy pictures past, the ofttimes “hero tool” in crime media. Dissatisfied with this status quo, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), in collaboration with other institutions, came up with a system to better guarantee privacy in video footage from surveillance cameras. The system builds on a formal definition of privacy — “differential privacy” — which allows access to aggregate statistics about private data without revealing personally identifiable information. Across a variety of videos and queries, Privid was accurate within 79 to 99 percent of a non-private system.

The Future of RoboticsWhat we can expect from the Fourth Industrial RevolutionEsther Weon · 7 min readPublished March 25, 2022The robotics industry is at an inflection point in its history. If there is any cross-disciplinary field that will play a huge role in defining this Fourth Industrial Revolution, it’s robotics. Working with robotics data is an especially important part of robotics development. We’ve started by focusing on helping people visualize and manage their robotics data, but the possibilities are endless. Read more:article data platform Analyze Your Robotics Data with Jupyter Notebooks Connect Foxglove Data Platform to your Jupyter Notebooks .

Grants to Black in AI and DAIR to Advance Efforts to Embed Equity into the Growing Field of Artificial IntelligenceNEW YORK | March 28, 2022 – The Rockefeller Foundation announced that it is providing nearly half a million dollars to two organizations to combat bias in artificial intelligence (AI) globally. The Foundation is also providing nearly $200,000 to the new Distributed AI Research Institute (DAIR) Institute. “Almost daily, the world finds new ways to use artificial intelligence and machine learning to make startling advances in how people live, work, and communicate. About Black in AIBlack in AI is an organization dedicated to empowering Black people in the field of Artificial Intelligence and advancing the ethical application of Artificial Intelligence technologies. About The Rockefeller FoundationThe Rockefeller Foundation is a pioneering philanthropy built on collaborative partnerships at the frontiers of science, technology, and innovation to enable individuals, families, and communities to flourish.

Mobile robots have become an automation staple in manufacturing, warehousing and distribution environments, with more to come. In many instances, companies are unsure how best to successfully deploy mobile robots in their facilities. Over the last decade, many customers have asked me for a cheat sheet listing the “dos and don’ts” when selecting and deploying a mobile robot solution. In fact, autonomous mobile robots (AMRs), which can move dynamically throughout a facility, are expectedto make up more than half of the deployed mobile robot fleet worldwide by 2025. Autonomous Mobile Robots (AMRs)Given that AGVs lack navigation autonomy and obstacle avoidance capabilities, many companies are turning to AMRs in place of AGVs.

By 2030, AI technology will add $15.7 trillion to global gross domestic product (GDP). In this crucial moment, when AI is poised to transform every aspect of our personal and professional lives, universal AI literacy is imperative. This could mean AI systems are used for discrimination and human rights abuses, or to undermine institutions. With the right data and more diverse teams, AI systems could even learn to advance equality. AI, machine learning, technology How is the Forum helping governments to responsibly adopt AI technology?

Transformer models: an introduction and catalog — 2022 EditionWhy this postI have a terrible memory for names. The goal of this post is to offer a short and simple catalog and classification of the most popular Transformer models. The Transformers catalogSo hopefully by now you understand what Transformer models are, and why they are so popular and impactful. In this section I will introduce a catalog of the most important Transformer models that have been developed to this day. I will categorize each model according to the following properties: Pretraining Architecture, Pretraining Task, Compression, Application, Year, and Number of Parameters.

Traditional time-of-flight LiDAR, however, has many drawbacks that make it difficult to use in many 3D vision applications. "But 30 years ago, nobody knew autonomous cars or robots would be a thing, so the technology focused on tissue imaging. Credit: Ruobing Qian, Duke UniversityFMCW LiDAR takes a similar approach with a few tweaks. "It has been very exciting to see how the biological cell-scale imaging technology we have been working on for decades is directly translatable for large-scale, real-time 3D vision," Izatt said. Journal information: Nature Communications Ruobing Qian et al, Video-rate high-precision time-frequency multiplexed 3D coherent ranging,(2022).

An artificial intelligence has beaten eight world champions at bridge, a game in which human supremacy has resisted the march of the machines until now. In contrast, chess and Go – in both of which AIs have already beaten human champions – a player has a single opponent at a time and both are in possession of all the information. “What we’ve seen represents a fundamentally important advance in the state of artificial intelligence systems,” said Stephen Muggleton, a professor of machine learning at Imperial College London. The NukkAI challenge required the human champions to play 800 consecutive deals divided into 80 sets of 10. The AI – called NooK – played the same role as the human champion, with the same cards and the same opponents.

Two Stanford researchers have fallen down a LinkedIn rabbit hole, finding over 1,000 fake profiles using AI-generated faces at the bottom. NPR looked into DiRestra and Goldstein's claims and found more than 70 businesses linked to the fake profiles. Several of the businesses said they had hired outside marketers, but expressed surprise when told about the fake LinkedIn profiles. Accounts like Ramsey's are used by companies to pitch software to potential new customers, and whenever a target responds they're redirected to a real person. The fake faces used by Ramsey and the countless army of bots like her are generated by general adversarial networks, or GANs.

? Water quality measurements to detect water pollutionAfter perusing recent research papers on water pollution, I decided to utilize oxidation-reduction potential (ORP), pH, total dissolved solids (TDS), and turbidity measurements denoting water pollution (contamination) so as to create a budget-friendly device to forecast water pollution levels locally in the hope of obviating the need for expensive equipment to detect contamination and forfend perilous pollution effects. Although ORP, pH, TDS, and turbidity measurements provide insight into detecting water pollution, it is not possible to extrapolate and interpret water pollution levels accurately by merely employing limited data without applying complex algorithms since water pollution levels fluctuate according to various phenomena. localhost/water_pollution_data_logger/?orp=<_data_>&pH=<_data_>&tds=<_data_>&turbidity=<_data_>&pollution=<_class_>#️⃣ Then, if the given data array is inserted successfully, the application prints:The given data packet is added to the <_>.csv file successfully! Step 5.1: Training and installing the modelAfter activating Neuton (Zero Gravity) on my Google Cloud Platform (GCP) account, I created a new solution named Water Pollution Monitor and uploaded my water pollution data set (water_pollution_data_set.csv). After executing the water_pollution_monitor_run_model.ino file on the MKR GSM 1400:?? The device waits for the MKR GSM 1400 to connect to the GSM network.

In The NewsA Wave Of Billion-Dollar Language AI Startups Is Coming And a lot goes into building an enterprise-grade conversational AI interface: handling data privacy and security requirements, integrating with third-party applications, building the infrastructure to support deployment at scale, providing a graceful “fallback mechanism” when the bot is stumped and...Top AI execs, including Richard Socher, launch AIX Ventures In a sign that the coffers of AI startups need little replenishing, Richard Socher, former chief scientist at Salesforce and the CEO of You.com, today announced the launch of a $50 million “AI-focused” venture fund called AIX Ventures. Speaking to VentureBeat via email, Shaun Johnson, a cofounder of...

Learn more about Transform 2022In a sign that the coffers of AI startups need little replenishing, Richard Socher, former chief scientist at Salesforce and the CEO of You.com, today announced the launch of a $50 million “AI-focused” venture fund called AIX Ventures. Speaking to VentureBeat via email, Shaun Johnson, a cofounder of AIX alongside Pieter Abbeel, Anthony Goldbloom, and Chris Manning, said that the goal is to make AIX a “household name” for AI-focused venture capital. But Socher and his cofounders believe that they bring differentiating — and deep — experience to the table. AIX Fund I already has around 40 portfolio companies, Johnson claims, including NLP startup Hugging Face, as well as Athelas, Weights & Biases and Time by Ping. “[W]e see the magnitude of the impact AI is going to have on humanity.

A: After I got fired from Google, I knew I’d be blacklisted from a whole bunch of large tech companies. I saw how people in certain places just can’t influence the actions of tech companies and the course that AI development is taking. And then we’re using AI technology to keep out climate refugees. I’d like for some sort of law such that tech companies have to prove to us that they’re not causing harms. Many years later there might be talk about regulation — then the tech companies have moved on to the next thing.

Check out the performance of NVIDIA DGX A100 systems at various GPU counts and I/O sizes with @nvidia GPU Direct Storage and @NetApp EF600 storage systems. https://t.co/dilCDKc5QYHow did we get here? Where does BeeGFS need to go to support its users for years to come? What changes are needed to enable the next generation of AI and HPC workloads, not just today, but ...

Inflection AI, the start-up launched earlier this month by LinkedIn billionaire Reid Hoffman and DeepMind co-founder Mustafa Suleyman, has poached artificial intelligence gurus from Google and Meta, according to CNBC analysis. Headquartered in Silicon Valley, Inflection's aim is to develop AI software products that make it easier for humans to communicate with computers. He is one of many people to have left Meta since CEO Mark Zuckerberg announced a pivot to the so-called metaverse. Both Kuttler and Fenton, who used to work at DeepMind with Suleyman, did not immediately respond to a CNBC request for comment. Meta and Google did not immediately respond to a CNBC request for comment.

Effortless CX is the new paradigm and it all begins in the contact center. Help your organization drive brand loyalty and increase operational efficiency with AI-powered CX. Today’s customers are accustomed to personalized, seamless journeys because companies around the world fully leverage the capabilities of advanced technologies. There’s no doubt–the CX gold standard is driven by Artificial Intelligence (AI). Innovations in cloud computing, data storage, and machine learning make it possible for contact centers to use AI to do amazing things for CX– but how?

Musk shared on his thoughts on potential uses for Optimus, a humanoid robot Tesla is developing. Musk shared his thoughts during a recent conversation with Mathias Döpfner, the CEO of Insider's parent company, Axel Springer, at Tesla's factory in Fremont, California. He spoke about potential future uses for Optimus, a humanoid robot Tesla is making. Musk said he thinks Tesla will have "something pretty good at the prototype level" this year and might have "at least a moderate volume production" around the end of 2023. Besides Optimus, Musk spoke about a range of other topics in the interview.

Find him on Twitter: @Gadget_RyMIT has launched a new academia and industry partnership called the AI Hardware Program that aims to boost research and development. “We are all in awe at the seemingly superhuman capabilities of today’s AI systems. However, the AI Hardware Program is off to a great start with the inaugural members bringing significant talent and expertise in their respective fields to the table. More information about the AI Hardware Program can be found here. (Photo by Nejc Soklič on Unsplash)Want to learn more about AI and big data from industry leaders?

Helpshift has made it possible for mobile game makers to handle millions of customer support requests over mobile phones through automation. Register NowWith Metashift, Helpshift hopes to use technologies such as immersive customer service and user support, feedback systems, and blockchain verification with in-world support for mobile, AR, and VR experiences. With a metaverse-specific immersive customer support experience, companies can help reduce churn, increase CSAT scores, and drive revenue. The third phase will be native VR support where customers can reach support from within the VR experience. That’s why Helpshift is in the process of delivering the first support solution for the VR metaverse experience.

What are the important parts of autonomous AI? Many autonomous systems are able to work quite well by simplifying the environment and limiting the options. Many plans for creating working autonomous systems depend upon creating this limited environment. What are the levels of autonomous AI vehicle guidance? How are startups impacting autonomous AI?

This no-code communication platform uses engagement data gained from conversations and combines it with artificial intelligence (AI) to better understand who might want to buy what among its customers, Martins said. Sentiment analysis identifies changes in emotion and behavior during interactions with customers, so the experience can be improved in the future. “It all comes with our customer data platform that converts into first-party data and deeply understands customer behavior using AI. Due to privacy issues and conflicts involving Facebook and Apple, many companies have shifted their data sourcing activity from third-party to first-party data. The funding will be used to scale the sales and marketing organizations and bring Arena to consumer enterprises looking to increase engagement and conversion, Martins said.

Learn more about Transform 2022Bonitasoft, an open source business process management (BPM) and low-code tool provider, has introduced several enhancements to simplify testing and operations management for business digital process automation (DPA) workflows. The enhancements promise to enable more teams to extend devops workflows that are common in application development into business process automation. Modern DPA variants take advantage of innovations in model-driven programming, low-code development and robotic process automation (RPA) capabilities. Modern application development teams are increasingly adopting devops practices that improve the coordination across development, testing and operations teams. This is also important for critical and core business processes that need to be tested at the business logic level.

Have you ever found yourself sitting for hours in a busy waiting room at the ER? It’s stressful, you’re in physical pain and everyone is rushing around you, focused on the massive number of other patients waiting, too. Luckily, hospitals are beginning to use machine learning solutions to ...Share this article

Find him on Twitter: @Gadget_RyThe House of Lords Justice and Home Affairs Committee has determined the proliferation of AI in the justice system is a threat to human rights and civil liberties. A report published by the committee today highlights the rapid pace of AI developments that are largely happening out of the public eye. We welcome the advantages AI can bring to our justice system, but not if there is no adequate oversight. “Without proper safeguards, advanced technologies may affect human rights, undermine the fairness of trials, worsen inequalities, and weaken the rule of law. “If we only train our AI using mostly data from white patients it cannot help our population as a whole,” said Javid.

Nvidia’s GTC panel on the industrial metaverse. Our industrial engineering software already provides key building blocks for the industrial metaverse, including the immersive user experiences with AR and VR capabilities. From this perspective, the industrial metaverse drives a democratization of simulations, allowing experts to communicate more effectively with non-experts. This already exists, but let’s say the industrial metaverse will bring an augmented user experience into this domain. Even if we already have a good foundation, the building blocks of the industrial metaverse, we still have an exciting time to come.

Learn more about Transform 2022Let the OSS Enterprise newsletter guide your open source journey! Anyone who has worked in the financial services sector will at least be aware of Bloomberg Terminal, a research, data and analytics platform used to garner real-time insights on the financial markets. Open for businessOpenBBThe newly named OpenBB Terminal is very much an alpha-stage product, one that’s aimed at the more technically minded. But OpenBB hopes that its open source credentials, and foundations in Python, will position it and its flexibility, to win over many new users. Terminal ascentFor now, OpenBB Terminal will be an entirely free proposition, but with a fully-fledged commercial business now behind it and $8.5 million in the bank, there will be a concerted push to monetize it.

Learn more about Transform 2022CEA-Leti, a France research institute, has received a $3 million grant from the European Research Council (ERC) to build a first-of-a-kind intelligent edge AI system inspired by the insect’s nervous system. The nanoscale system uses novel technologies to boost the performance and energy-efficiency of edge AI. To that end, Vianello has discovered that various functions of the insect’s nervous system closely resemble those of different memory technologies that are being worked on by CEA-Leti. Hence, the scientists aim to create a “hybrid synapse” that co-integrates these different memory technologies. The concept of a heterogeneous distributed computing system shows various processing units and sensors that are connected through both spiking and non-spiking links.

Today, however, there is a third alternative — low-code/no-code applications. The sudden demand for digital collaborative spaces and remote work tools meant that business leaders needed a way to build and deploy these projects overnight. As demand for low-code/no-code platforms surges among enterprises, even those that were once dependent on conventional development practices in the past. Human-assisted, AI-poweredBuilder.ai is a human-assisted, AI-powered platform designed for entrepreneurs and enterprises to build, run and scale applications without code. New York venture capital firm, Insight Partners, recently led investments into multiple start-ups capitalizing on these transformative shifts in the digital workforce, from low-code/no-code to AI, most recently with Builder.ai.

In addition, predictive maintenance is core to many organizations’ Maintenance, Repair, and Overhaul (MRO) initiatives. As a result, IoT Analytics predicts that the global predictive maintenance market will expand from $6.9 billion in 2021 to $28.2 billion by 2026. Streamlining predictive maintenanceNvidia sees the opportunity to expand its total available market with an integrated platform aimed at streamlining predictive maintenance. As asset-heavy service industries, including airlines and others, face higher fuel costs and more challenges in operating profitably, AI-based predictive maintenance will become the new technology standard. Nvidia’s decision to concentrate architectural investments in edge computing to drive predictive maintenance is prescient of where the market is going.

Yet not all changemakers and historical figures are equally represented on the dominant web encyclopedia: Just 20% of Wikipedia biographies are about women. Today they released their findings and methodologies in the paper, “Generating Full Length Wikipedia Biographies: The Impact of Gender Bias on the Retrieval-Based Generation of Women Biographies.”Meta AI has also open-sourced the model and corresponding dataset. These directly relate to not only women, but women in science and those located in Asia and Africa. Meanwhile, factuality is one of the major problems in text generation and NLP. Ultimately, she emphasized that the model and dataset are just one small step in the process of righting long-standing, inherent bias.

Automated model monitoring and analytics platforms help ML teams detect issues when they emerge, troubleshoot why they happened and improve overall model performance, Dhinakaran said. Keeping ML models on track and performing optimallyArize AI effectively serves as a guardrail on deployed AI applications, Dhinakaran said. Ensures data integrity : Guarantee the quality of model data inputs and outputs with automated checks for missing, unexpected, or extreme values. Responds to model performance problems : Unmask the exact features and dimensions that are pulling performance down with automated monitors and flexible dashboards to begin troubleshooting. Arize AI competes in a growing market that includes IBM Watson Studio, FiddlerAI, Databricks Lakehouse, InRule, V7, MLOps, Algorithmia, Neptune.ai, Comet.ML, Determined AI and DVC.

We need to ditch big tech’s virtual assistants and calmly demand a little more autonomy in our AI. Background: Virtual assistants were supposed to evolve over time. The root of the problem is, unlike a human assistant with an NDA, you can’t trust big tech’s AI. It feels like a win-win because the more you use your virtual assistants, the better they become for everyone. The AI models powering virtual assistants aren’t designed to provide the best possible assistant experience for users — they’re designed to harvest data.

The MIT AI Hardware Program is a new academia and industry collaboration aimed at defining and developing translational technologies in hardware and software for the AI and quantum age. The companies represent a diverse ecosystem, both nationally and internationally, and will work with MIT faculty and students to help shape a vibrant future for our planet through cutting-edge AI hardware research. The MIT AI Hardware Program will create a roadmap of transformative AI hardware technologies. Leveraging MIT.nano, the most advanced university nanofabrication facility anywhere, the program will foster a unique environment for AI hardware research. “We are all in awe at the seemingly superhuman capabilities of today’s AI systems.

But it wasn’t until he joined a robotics lab his last year in college that he realized robotics, and not mathematics or physics, would be his life’s calling. In a recent interview, Rodriguez discussed the nuts and bolts of tactile robotics and where he sees the field heading. Q: Your PhD thesis, Shape for Contact, led to the work you do now in tactile robotics. A: During my PhD, I got interested in the principles that guide the design of a robot’s fingers. Robotics research has focused on the control and on the morphology of robotic fingers and hands, with less emphasis on their connection.

The funding round, led by GGV Capital, will enable OmniML to expand its machine learning (ML) team and enhance its software development. OmniML’s solution enables users to design, optimize and deploy advanced machine learning models to hardware devices at the network edge. It’s designed to enable enterprises to use small, scalable, and efficient machine learning models to give edge devices the ability to perform AI inference tasks. This is a challenge, as most existing AI solutions aren’t lightweight enough to run on edge devices. Yet none of them is trying to solve the fundamental problems: existing AI models are not designed for constrained edge hardware.

Learn MoreOver the past five years, large language models have taken the world by storm. But, as I’ll explain below, the models powering language generation are not yet fit for purpose. Since Google’s BERT language model in 2018, language models have grown exponentially, often via brute force by adding more parameters, data, and computing power. Unequal language resourcesWe need large amounts of text to build language models, mainly from the web. We should — and can — do betterTo break through the barrier of limitations imposed by language models, we need to really understand semantics.

Users can generate Meeting Gems directly from meetings by highlighting snippets within the notes. Users can generate Meeting Gems directly from meetings by highlighting snippets within the notes. This feature leverages Otter.ai’s proprietary AI to automatically create a meeting summary that’s curated and displayed in the Meeting Gems panel — giving users a precise brief of discussions. The company also has competition in meeting transcription tools from Cisco and startups Scribie, Verbit, Simon Says, Trint and Voicera. Video conferencing platforms like Zoom, Google Meet, Microsoft Teams and Cisco Webex currently record millions of daily virtual and hybrid meeting users, according to a report by GetVoIP.

He said CYE’s Critical Cyber Operations will offer data forensics and incident response (DFIR), threat hunting and computer threats intelligence (CTI) assessment, advanced cyber architecture solutions and executive cyber and physical protection. Market opportunity for Critical Cyber OperationsAronashvili noted that CYE’s customers are exposed to attacks from a wide range of actors, from cybercrime to superpowers. According to Aronashvili, Critical Cyber Operations operates based on an intelligence-oriented behavioral approach and consists of experienced cybersecurity professionals with an in-depth knowledge of technical operations. He said they have proven experience confronting and/or executing state-level cyberattacks, and capabilities to guide, lead, and perform defense, technology and cyber operations. Following this launch, cybersecurity expert Shmulik Yehezkel joined CYE to lead the Critical Cyber Operations group.

Learn MoreToday, Pinecone Systems Inc. announced they’re welcoming $28 million of new investment in a series A round supporting further expansion of their vector database technology. This doesn’t work like I expect.’”Simplifying database searchesPinecone’s vector database makes it simpler to search for a long sequence of numbers and find the entry that is closest but not necessarily the same. “We have a [client] company which uses Pinecone to compare and search through security alerts,” said Kogan. But using a vector database like Pinecone you can see how similar one alert is to another. “Many of the largest companies in the world have already embraced the use of vector search, which has given them a distinct advantage over their competitors,” said Tim Tully, partner at Menlo Ventures.

Called “autonomous molecular machines,” the new nanotechnology eschews the traditional visage of microscopic metal automatons in favor of a more natural approach. DNA nanobots are synthetic nanometer-sized machines made of DNA and proteins. Our natural DNA not only carries the code our biology is written in, it also knows when to execute. As far as we know, this is the first time a DNA nanobot capable of carrying arbitrary cargo has been demonstrated. And, perhaps most exciting, it may be possible in the far-future to build fully-functioning molecular computers using DNA nanobots.

Examining four places to get started with business process automationWith the above in mind, here are four areas where there is a lot of opportunity to start using automation. Lead to cash: The sales cycle of the “lead to cash” process is typically an opportunity for companies to introduce automation. When the customer expectations and the opportunity to automate align, then automation should be a strong consideration. Business process automation for the winThe automation market is growing quickly — and no wonder, given its many potential benefits. Optimizing your business processes with automation helps create better business models and eliminate process inefficiencies and even results in business transformation.

Find him on Twitter: @Gadget_RyAI-powered healthcare service Babylon Health has announced a partnership with Google Cloud to boost scalability and innovation. London-based Babylon Health is a digital-first health service provider that uses AI and machine learning technology to provide access to health information to people whenever and wherever they need it. He estimates conducting between 800 and 900 full triages and that some were repeat tests to see whether Babylon Health had fixed the issues he previously highlighted. That same year, Babylon Health published a paper claiming that its AI could diagnose common diseases as well as human physicians. In 2020, Babylon Health claimed in a paper that it can now appropriately triage patients in 85 percent of cases.

A new report from CircleCI has indicated that the most successful software teams meet four key benchmarks. The leading software development company released these findings with the news that it recently acquired the AI-powered testing platform Ponicode. The Paris-based AI-powered testing capability startup helps developers and engineering teams build “defect-free” software at scale and with less hassle. The testing phase in any software solution is the hardest part, Leblanc pointed out, whether it is human or automation based. The report represents the largest analysis of developer productivity data and provides new insight into the devops practices of elite software teams.

While an article by McKinsey notes voice analytics can provide a high-quality customer experience in a digital age, it isn’t the only use case for voice analytics. The use cases for voice technology are enormous and Israel-based voice analysis technology company, Nemesysco, wants to drive new frontiers in the industry. Emotion Logic is AI-based and genuine emotion detection is just one of what the company will do,” he said. A major market opportunity Emotion Logic wants to go after is the emerging metaverse. It’s not going to happen without some level of embedded humanity that Emotion Logic is developing,” he said.

Define data & AI vision and value potentialIdentify your critical data and quick-win use cases early by prioritizing the data elements that unlock the insights needed for your company to drive business value. Select projects that can deliver ROI quickly and deliver immediate business value to gain momentum for the data-driven evolution. What organizations do once they have enabled cloud technologies determines how much business value will be realized. Strategic organizational operationalization and use of data and AI is the key to unlocking business value from cloud. And in practice, Data-driven Transformation enables better decision-making because it allows organizations to scale AI and data across their business.

Navigating the unpredictableThe Boston-based company has released Cloud 8.0, which is designed to help businesses navigate unpredictable events and market conditions. “AI is the technology that companies must adopt in order to handle the unpredictable world we live in,” said Bardoliwalla. With the release of Cloud 8.0, DataRobot’s Continuous AI capabilities are available in on-premises environments and leading public clouds. They can raise revenues, lower costs and risks, and arm themselves with “incredible access to information” with the use of ML and AI. “Companies that are most successful have incredible access to information and incredible ML capabilities,” Bardoliwalla said.

Join AI and data leaders for insightful talks and exciting networking opportunities. Learn MoreAlong with all the analytical and operational gains artificial intelligence (AI) brings to the enterprise, there is another, more fundamental change taking place. More AI power to the peopleFor the enterprise, this presents a significant opportunity to democratize AI across the workforce, just like the PC put computing power that was once reserved for elite professionals into the hands of everyday employees. This doesn’t just necessarily mean a more conversational one, but a system that can function as an augmentation of human work using multiple communications channels. This doesn’t just necessarily mean a more conversational one, but a system that can function as an augmentation of human work using multiple communications channels.

Learn MoreNatural language processing (NLP), business intelligence (BI), data integration and data annotation were considered foundational AI technologies, according to a new report by Gradient Flow. The addition of data annotation to the AI technologies respondents plan to have in place by the end of 2022 indicates more sophisticated use cases as the technology matures. Locally installed commercial software (37%) and open-source software (35%) were the most popular forms of software being used to build healthcare AI applications, respondents indicated. Mature organizations (68%) relied even more heavily on using in-house evaluation and tuning models themselves — a step critical to prevent model degradation over time. This is further proof of how healthcare AI is being refined.

Learn MoreThe use of deep learning has grown rapidly over the past decade, thanks to the adoption of cloud-based technology and use of deep learning systems in big data, according to Emergen Research, which expects deep learning to become a $93 billion market by 2028. Deep learning is a subset of machine learning which uses neural networks to perform learning and predictions. However, it is more than that, which makes deep learning far better than any of the classical machine learning algorithms. In high dimensions, everything is extrapolation.” Thus, as part of function learning, deep learning is doing nothing but interpolation or in some cases, extrapolation. That’s what makes deep learning such a powerful tool for any machine learning task.

Get everyone in the roomThe COVID-19 pandemic was a perfect storm for virtual assistants, with hundreds of thousands of people needing chat help. It’s critical to remember that every piece of text you add to a virtual assistant’s logic influences the system. Looking ahead to the future, there’s reason to believe we’ll be engaging a great deal more with virtual assistants, even when we’re not experiencing a crisis. We can now build virtual assistants that can interpret from your browsing behavior what you might be looking for, or what kind of problem you’re likely to be having. Thanks to a stronger understanding of these best practices surrounding virtual assistants and conversational AI, virtual assistants have gotten much more powerful and intuitive in only the last few years.

RT @StanfordHAI: The #AIIndex2022 is a must-read for anyone interested in the current state of AI. For those who want a snapshot, @IEEESpectrum distilled this year's report into 12 graphs: https://t.co/0FISayqOzZStanford's 2022 AI Index is a massive compendium of data and graphs that tries to sum up the current state of artificial intelligence. We read it so you don't have to.

On one end of the spectrum, marketers often leverage AI that’s entirely off-the-shelf from a vendor. How do you know your training data is accurate? How do you know your training data is thorough and diverse? Good training data also has to be thorough, meaning you need plenty of examples outlining all conceivable scenarios and outcomes you’re trying to drive. When I think of potential implications of AI bias, I see risk both for inputs into an algorithm and outputs.

And now the Russian invasion of Ukraine is being dubbed the first TikTok War. First, TikTok can help people on the ground in Ukraine who want to galvanize action and be trusted as frontline witnesses. As the TikTok misinfo expert Abbie Richards has pointed out, TikTok doesn’t even indicate the date a video was posted when it appears in the For You feed. How much material is lost forever because human rights organizations haven't had the chance to see it and preserve it? Groups like WITNESS, Mnemonic, the Human Rights Center at Berkeley, and Human Rights Watch are working on finding ways these archives could be set up—balancing accountability with human rights, privacy, and hopefully ultimate community control of their archives.

It’s probably for the best that human babies can’t run 9 miles per hour shortly after birth. Roboticists don’t have that kind of time to spare, however, so they’re developing ways for machines to learn to move through trial and error—just like babies, only way, way faster. The thousands of simulated robots could try all kinds of different ways of moving their limbs. Video: MITThen the researchers ported what the digital robots had learned about running on different surfaces into the real-life Mini Cheetah. To be clear, this isn’t necessarily the safest or most energy efficient way for the robot to run—the team was only optimizing for speed.

RT @StephaneNappo: Why Unsupervised #MachineLearning is the Future of Cybersecurity #CyberSecurity #InfoSec #CISO #CSuite #MachineLearning has made huge progress in #technology with Supervised & Reinforcement learning via @TechNative ? https://t.co/AuYSkka5bA Cc @TheMegaTrends @CyberStructures https://t.co/NIjXKidFoYNot all Artificial Intelligence is created equal As we move towards a future where we lean on cybersecurity much more in our daily lives, it’s important to be aware of the differences in ...

The company unveiled the Grace CPU Superchip, a data center processor designed to serve high-performance compute and AI applications. Both can be deployed for building speech AI applications and point to the growing market for speech recognition in particular. Speech technologies span voice generation tools, too, including “voice cloning” tools that use AI to mimic the pitch and prosody of a person’s speech. As far as what lies on the horizon, Nvidia sees new voice applications going into production across augmented reality, videoconferencing, and conversational AI. For AI coverage, be sure to subscribe to the AI Weekly newsletter and bookmark our AI channel, The Machine.

Many of today’s top companies are now turning to games and artificial intelligence (AI), which can elevate the human experience and maximize cultural intelligence in the workplace. Why traditional talent development is no longer enoughIn a competitive employment market, the strength of your people is intricately linked to positive business outcomes. These traditional talent development tools focused on knowledge acquisition used more to enhance a company’s ROI rather than foster employee development. Even a quick browser search for “talent development” provides us with over a billion hits. Games can help us realize areas where we need development, but what about the training and resources?

Join AI and data leaders for insightful talks and exciting networking opportunities. Learn MoreKargo, a company that promises freight and logistics companies full visibility into their operations, has raised $25 million in a series A round of funding. Founded in 2019, Kargo has developed a software-as-a-service (SaaS)-based hardware and software platform that captures and aggregates data from physical loading docks and enterprise software systems — it essentially connects the physical and digital worlds around freight. The sensors’ ability to inspect freight automatically means that unloading time can be reduced by up to 48%, according to the company. “There isn’t a single SaaS solution to tackle this problem, so we created our Kargo Towers and the accompanying platform to flag inefficiencies in real time and finally bring visibility to loading docks,” Lurye added.

The #1 publisher in AI coverage, VentureBeat, returns with their flagship event, Transform—NorCal’s leading event on applied AI for enterprise business and technology decision-makers. During this event, industry experts will come together for a full week to provide comprehensive coverage across the breadth and depth of the world of applied AI, as well as industry-focused vertical tracks in healthcare, finance, retail, manufacturing, and security.

Learn MoreSeventy percent of U.S. consumers want to use AI in their jobs, according to a new Gartner, Inc. survey. In nine categories of work tasks, more workers wanted AI to help than wanted AI to do everything or do nothing. More than half (57%) of U.S. workers wanted AI to do two or more of those tasks. Workers also hope to maintain control over how their data is used for training such AI solutions. For this report, Gartner surveyed 1,515 respondents from the U.S. in October and November 2021 to measure consumer and employee attitudes regarding AI.

Many folks will get wrapped up in this merger of the virtual world with the physical world once the metaverse fully arrives. If the metaverse is an inevitability, then it is our moral obligation to build one that is safe, private and secure. The metaverse has so many unknowns that it just cannot possibly be considered safe, by any standards. Before the metaverse, layers of abstraction existed, thanks to the physical world and our carefully balanced engagement through smartphones, computer systems, and apps. The best metaverse is a genuine metaverse forum for humans void of bots and hackers.

In 2017, Andrew Ng founded Landing AI, a startup working on facilitating the adoption of AI in manufacturing. We connected with Ng to discuss what he calls the “data-centric approach” to AI, and how it relates to his work with Landing AI and the big picture of AI today. But after working on many customer projects, Ng and Landing AI developed a new toolkit and playbook for making AI work in manufacturing and industrial automation. What is new, however, is building on and re-examining these techniques in light of modern AI models and methods. That seemed to point towards approaches such as Robust AI, Hybrid AI or Neuro-Symbolic AI and technologies such as knowledge graphs to express domain knowledge.

In response to the growing power of tech monopolies and AI technology, a movement to decolonize AI has arisen across the Global South. Why does the decolonial movement address AI specifically rather than, say, digital technologies more generally? Universities in the western world are also now staying on top of the decolonial AI movement by offering fellowships, hosting workshops, and publishing work. They meet often and do their own AI research to create new AI systems by Africans for Africans. And so then this idea emerges that we can have many worlds existing in one, as expressed in the Decolonial AI Manyfesto.

To implement, scale, and maintain effective AI ethical risk mitigation strategies, companies should begin with a deep understanding of the problems they’re trying to solve. A challenge, however, is that conversations about AI ethics can feel nebulous. Here’s how you can set the table to have AI ethics conversations in a way that can make next steps clear. One conversation concerns coming to a shared understanding of what goals an AI ethical risk program should be striving for. Your AI ethics team should determine where their respective limits are and how their skills and knowledge can complement each other.

That’s the day we officially incorporated the Open Source Robotics Foundation, the origin of what we now call Open Robotics. The prospect of starting a company is both scary and exciting; but starting an open-source company in a niche as specialized as robotics, now that is terrifying and exhilarating, if not a little unorthodox. All we had was a dream, some open-source code, and some very smart friends, a whole lot of them. Until March of 2012, Willow Garage had been the stewards of ROS and Gazebo and nurtured it from an idea to a growing community. When OSRF was hired to create and manage the world’s first-ever robotics simulation event, the company was up and running.

In The NewsAndrew Ng predicts the next 10 years in AI The only way out of this dilemma is to build tools that empower customers to build their own models, engineer the data and express their domain knowledge. Ng and Landing AI do that through Landing Lens, enabling domain experts to express their knowledge withdata labeling. In the early days of deep...Google uses deep learning to design faster, smaller AI chips They claim their approach can produce designs that have lower latency and require less space than Google's in-production EdgeTPU accelerator and other designs made using traditional tools. They said they found that the PRIME chip designs overall improved latency by 2.7x and reduced die area usage by...

They include details of its new silicon architecture, Hopper; the first datacenter GPU built using that architecture, the H100; a new Grace CPU “superchip”; and vague plans to build what the company claims will be the world’s fastest AI supercomputer, named Eos. Nvidia has benefited hugely from the AI boom of the last decade, with its GPUs proving a perfect match for popular, data-intensive deep learning methods. As the AI sector’s demand for data compute grows, says Nvidia, it wants to provide more firepower. “For the training of giant Transformer models, H100 will offer up to nine times higher performance, training in days what used to take weeks,” said Kharya. The company also teased that it was building a new AI supercomputer, which it claims will be the world’s fastest when deployed.

That’s why in this article, I am going to share with you the 10 Best Udemy Courses for Machine Learning. This course not only teaches you the theory related to Machine Learning but also provides the implementation of each Machine Learning algorithm. Students who have at least high school knowledge in math and who want to start learning Machine Learning. After that, you will learn deep learning concepts, such as Deep Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. I hope these Best Udemy Courses for Machine Learning will help you to learn concepts of Machine Learning.

Googlers and UC Berkeley academics say they have devised a way to use artificial intelligence to design faster and smaller chips that accelerate artificial intelligence. Meanwhile, traditional chip design toolmakers, such as Synopsys and Cadence, say they've added machine-learning to their software suites. These latest findings from Google could prove a game-changer for the web giant's custom chip design efforts. They said they found that the PRIME chip designs overall improved latency by 2.7x and reduced die area usage by 1.5x. The researchers also used PRIME to design chips that were optimized to work well across multiple applications.

Who said that robot goats should have all the fun, especially when you can ride on a robot goat or let it carry your bags for you. Boston Dynamics’ Spot robot had become quite popular in the past few years, especially when the quadruped robot got assigned tasks related to COVID-19 countermeasures. Designer: KawasakiA goat is probably the last animal you’d think of as an inspiration for a human-friendly robot, especially since they’re often portrayed in a negative light. And that is the second biggest difference between Spot and Bex, that the latter is actually designed to be ridden by humans. It’s definitely a strange way to travel or to carry your things, but that’s not exactly worse than having a headless robot dog following you around.

A world-first approach to help organisations comply with future AI regulations in Europe has been published today in a report by the University of Oxford and the University of Bologna. A one-of-a-kind approach, the ‘capAI’ (conformity assessment procedure for AI) will support businesses to comply with the proposed AIA, and prevent or minimise the risks of AI behaving unethically and damaging individuals, communities, wider society, and the environment. Additionally, it will support the explanation of AI-driven outcomes, and the development and running of systems that are trustworthy and AIA compliant. Thanks to capAI, organisations will be able to produce a scorecard for each of their AI systems, which can be shared with their customers to show the application of good practice and conscious management of ethical AI issues. Professor Matthias Holweg, American Standard Companies Chair in Operations Management at Saïd Business School and co-author of the report, remarked: “To develop the capAI procedure, we created the most comprehensive database of AI failures to date.

Machine learning algorithms can perform exponential tasks today—from mastering board games and identifying faces to automating daily tasks and making predictive decisions—this decade has brought forward countless algorithmic breakthroughs and several controversies. Analytics India Magazine takes you through a historical story of machine learning algorithms. The 1950s was when pioneering machine learning research was conducted using simple algorithms. The 2000sThe early 2000s popularised support vector clustering, unsupervised learning and kernel methods for machine learning. In 2014, Facebook developed DeepFace, an algorithm that created history by beating all previous benchmarks of computer algorithms recognising human faces.

Top Artificial Intelligence Writers You Should Know About in 2022Aspiring techies and working professionals have the urge to know more about artificial intelligence and its concepts. Thus, Analytics Insight helps to find out the top artificial intelligence writers to know in 2022 to gain more fundamental knowledge about the cutting-edge technology. Stuart Jonathan Russell OBEStuart is a British computer scientist known for his contributions to artificial intelligence. He is completely focused on the future impact of artificial intelligence on people and societies. That being said, if you want to gain knowledge of artificial intelligence, you can read books and papers of these popular and eminent artificial intelligence writers at your pace.

Other questions asked respondents their feelings about AI’s increased use, the way AI programs are designed and a range of other possible AI applications. For example, more Americans are excited than concerned about AI applications that can do household chores. In addition to awareness being a factor associated with Americans’ views about these AI applications, there are patterns related to education. Among Asian adults: Some 41% of Asian adults think that the experiences of Asian adults are well taken into account. A plurality of Americans are not sure whether AI can be fairly designedIn addition to gathering opinion on how well various perspectives are taken into account, the survey explored how people judge AI programs when it comes to fair decisions.

Episode 133 | March 23, 2022In “Just Tech: Centering Community-Driven Innovation at the Margins,” Senior Principal Researcher Mary Gray explores how technology and community intertwine and the role technology can play in supporting community-driven innovation and community-based organizations. He’s also the co-director of the Justice, Equity, and Technology Lab and a professor of social work. In addition to his numerous positions at the School of Social Work, he is the associate director of Diversity, Equity, and Inclusion. I’ve been—in this new kind of part of my life—trying to push for an agenda for social work values and thinking that can be embedded in technology, that also affects how social workers do social work in the community as well. Desmond Patton: You know, what we talk a lot about in social work is taking a strengths-based perspective.

Relying on AI for hiring may cause more harm than good. AI functions best when it has access to huge amounts of objectively measured data, something not found in hiring. The simple solution is to avoid using AI in hiring altogether. Companies should consider creating teams of hiring and tech professionals that monitor data, root out problems, and continuously challenge the outcomes produced by AI. The humans on those teams may be able to spot potential biases and either eliminate them or compensate for them.

In a sign that the coffers of AI startups need little replenishing, Richard Socher, former chief scientist at Salesforce and the CEO of You.com, today announced the launch of a $50 million “AI-focused” venture fund called AIX Ventures. Speaking to VentureBeat via email, Shaun Johnson, a cofounder of AIX alongside Pieter Abbeel, Anthony Goldbloom, and Chris Manning, said that the goal is to make AIX a “household name” for AI-focused venture capital. But Socher and his cofounders believe that they bring differentiating — and deep — experience to the table. AIX Fund I already has around 40 portfolio companies, Johnson claims, including NLP startup Hugging Face, as well as Athelas, Weights & Biases and Time by Ping. “[W]e see the magnitude of the impact AI is going to have on humanity.

Today, C3 AI’s CEO Thomas Siebel announced the third group of Digital Transformation Institute (DTI) grant recipients to accelerate the development of artificial intelligence (AI) and machine learning (ML) technologies in hopes to combat emerging threats. As an organization, C3 AI provides enterprises with an AI Application platform they can use to create, deploy, and manage AI applications for use cases like fraud detection, network health, supply network optimization, energy management, and more. The grant was established in March 2020 by C3 AI, Microsoft, and some of the world’s leading universities. “Each recipient will receive grant funds to kick off their projects that use AI and machine learning to improve cybersecurity, harden information security, and secure critical infrastructure. “C3 AI provides transformative solutions across industries including defense, manufacturing, energy, and financial services.

The capital will be used to accelerate the company’s growth plans, including strategically building out and expanding Digits’s Living Model technology. Digits Reports brings financial reporting into the modern era with live, interactive financial statements featuring built-in analysis and visualizations. Unlike traditional PDF or Excel reports, Digits Reports are fully encrypted and support built-in commenting, collaboration and secure sharing, including the ability to monitor and revoke unwanted access. Unlike traditional PDF or Excel reports, Digits Reports are fully encrypted and support built-in commenting, collaboration and secure sharing, including the ability to monitor and revoke unwanted access. Digits Report Builder allows accountants to design customized report packages for their clients in a fraction of the time.

We often hear about the benefits artificial intelligence (AI) can bring to medicine and healthcare through drug research, but could it also pose a threat? Researchers from Collaborations Pharmaceuticals, a North Carolina-based drug discovery company, have published a paper that highlights the dangerous potential of AI and machine learning to discover biochemical weapons. By simply tweaking a machine learning model called MegaSyn to reward instead of penalise predicted toxicity, their AI was able to generate 40,000 biochemical weapons in six hours. From generation to synthesisThe barriers to misusing machine learning models like MegaSyn to design harmful molecules are lower than you might expect. The researchers’ findings make an important case for the need to oversee AI models and fully consider the ramifications of complex AI.

With the acceleration of digital transformation, consumer product brands are considering — or actively adopting, as the case may be — product intelligence software. Product intelligence entails gathering, acting on, and analyzing data related to how customers use and perceive a company’s products. “The retail and consumer goods industries will see further sustainability regulations and pressure from consumers for sustainable products. The startup claims to have over 456 billion data points on millions of regulated consumer products, which it uses to map product data to regulatory and handling data — ostensibly helping organizations reduce their environmental impact. And each day, we bring in over eight million new data points from our retail and supplier deployments,” Claudia added.

Officially dubbed Now Platform San Diego, the release brings new robotic process automation (RPA) capabilities to empower enterprises with the “true potential of hyperautomation” and help them create smarter, faster and better ways of working to drive productivity. Now Platform San Diego strengthens hyperautomationWhile ServiceNow has been focusing on hyperautomation with Now Platform’s low-code and AI capabilities, the latest release is all about strengthening the product. As the company explains, Now Platform San Diego brings Automation Engine, a comprehensive suite of automation integration solutions that combines the power of Integration Hub with robotic process automation (RPA) capabilities of the all-new RPA Hub. This comes exactly a year after the company’s acquisition of Intellibot, a robotic process automation (RPA) company based in Hyderabad, India. The studio also comes with a built-in connection Flow Designer, allowing users to pass information to and from their ServiceNow instance with ease.

At the Nvidia GTC Conference, several digital twins startups explained how the program combined with the Omniverse platform accelerated their product and marketing development. The core of Nvidia’s Inception program provides appropriate technical expertise within Nvidia, preferred pricing and access to cloud credits, introductions to venture capital firms, and marketing support. Of note at GTC were the varied ways companies were translating digital twins into new business models. Radical is developing tools to translate consumer-grade video into 3D digital twins of people. Clinton Smith, cofounder & CTO at Rios.ai, said the initial value of the Inception program was the discounts.

Gartner recognizes quantum technologies and AI as two of the key emerging technologies spurring innovation through trust, growth and change. Undoubtedly, the quantum computing ecosystem is burgeoning and promising — and Sandbox AQ wants to lead the charge in that space. Schmidt said the convergence of quantum and AI technologies is already transforming entire industries, accelerating scientific discovery and reimagining what we thought was possible. I am excited for this journey as Board Chair and investor in Sandbox AQ,” said Schmidt. Sandbox AQ is developing commercial products for telcom, financial services, healthcare, government, computer security and other computationally intensive sectors.

The company offers a security platform that leverages self-learning AI to spot all attacks — including novel attacks such as zero days — while also lowering the noise for security teams, reducing false positives by 97%, according to Keister. Today, to drive the commercial expansion of the MixMode platform, the company announced its series B round led by growth equity firm PSG, and including an investment from Entrada Ventures. That means the platform does not require training data, and does not require manual input of data or configuration, Keister said. And many of those partners, which include Optiv, are now selling MixMode into end customers, Keister said. PacketSled was originally founded in 2013 with a focus on data security analytics.

After editing out the robotics process automation and other junk, there were 61 robotics funding events in February this year equalling $3.1 Billion USD. $1.3 Billion of that comes from GE buying SoftBank out of Cruise, but there are also quite a few 9 figure deals in the rest of the data! Share this article

Employing these deep learning approaches can help drive diagnostic investigation at an earlier stage and facilitate the discovery of drug molecules and biomarkers. Deep learning has also been highly successful in the treatment of life-threatening diseases by predicting pharmacological properties, such toxicity or side effects. CHALLENGES OF IMPLEMENTING DEEP LEARNINGDeep learning has the potential to transform the work of medical and paramedical practitioners but building a deep learning model in the medical field requires a unique set of skills. Conversely, it is essential that deep learning experts have knowledge of the healthcare domain as well as a good understanding of DNNs and an awareness of the latest deep learning frameworks, libraries and APIs. Discover the latest tools, optimised frameworks, and training for deep learning at the Intel® AI Academy.

Companies developing artificial intelligence (AI)-powered marketing tools typically claim that their solutions drive strategic decision-making better than software without an algorithmic component. Brew, a Tel Aviv, Israel-based strategic marketing platform, claims its approach is different from the rest in that it’s more holistic. Automating marketing activitiesMaayan Levy, the CEO of Brew, founded the company in 2019 with Raviv Ventura, Ronen Idrisov and Gabriel Amram. There’s certainly no shortage of potential customers, in any case, with interest in AI-powered marketing products continuing to climb. “[We have] over 30 customers, all with multiple users, from chief marketing officers to individual stakeholders in sales and marketing.

The AI models used in modern text translation, MoE or no, contain components called “neurons” that are organized into distinctive layers. MoEs were first proposed in the ’90s, and research papers in recent years from companies including Google describe experiments with trillion-parameter-plus MoE language models. But Microsoft claims that Z-code MoE is the first MoE language model to reach production. This architecture allows massive scale in the number of model parameters while keeping the amount of compute constant,” Huang continued. A single MoE model can replace 20 of the current translation models, increasing efficiency of training MoE models while also improving translation accuracy.”Future workWhile Microsoft says that Z-code MoE has led to great strides in improving language translation, the problem isn’t solved.

>>Nvidia offered details on its Grace central processing unit (CPU) “Superchip” during CEO Jensen Huang’s keynote speech at its virtual Nvidia GTC 2022 event. The Nvidia Grace CPU Superchip comprises two CPU chips connected, coherently, over NVLink-C2C, a new high-speed, low-latency, chip-to-chip interconnect. The Grace CPU Superchip complements Nvidia’s first CPU-GPU integrated module, the Grace Hopper Superchip, announced last year, which is designed to serve giant-scale HPC and AI applications with an Nvidia Hopper architecture-based GPU. “The Grace CPU Superchip offers the highest performance, memory bandwidth and Nvidia software platforms in one chip and will shine as the CPU of the world’s AI infrastructure.”The Nvidia CPU PlatformNvidia’s Earth 2 simulation will model climate change. Created to provide the highest performance, Grace CPU Superchip gets an estimated performance of 740 on the SPECrate2017_int_base from its 144 cores.

>>San Francisco-based Domino Data Lab, a company known for its machine learning operations (MLops) platform, today announced new integrations with Nvidia as part of an effort to empower enterprises with faster GPU-accelerated model deployments. Test-run on Nvidia LaunchPadFinally, Domino also announced that its platform is coming on Nvidia’s LaunchPad program. This, the company said, will allow teams to quickly test AI projects on the complete stack underpinning joint Domino and Nvidia AI solutions. They could use proofs-of-concept validated by Domino and Nvidia for the trial run and eventually fast-track the projects from prototype to production. “We led the charge as the first Enterprise MLops platform to integrate with Nvidia AI Enterprise, Nvidia Fleet Command and Nvidia LaunchPad.

However, until recently, these multimodal AI systems were strictly for the domain of research. CLIPr is among the nascent cohort of companies using multimodal AI systems for applications like analyzing video. Multimodal futuresOutside of multimodal systems, AI doesn’t experience the world the same way that people do. Multimodal setbacksWhile commercialized multimodal AI is more common than it used to be, several hurdles must be overcome before these types of systems reach wide scale deployment. We’ve had to patchwork several AI models, each of which does one type of analysis well.

>>During its March 2022 GPU Technology Conference (GTC) this week, Nvidia unveiled Isaac Nova Orin, a computing and sensor architecture powered by the company’s Jetson AGX Orin hardware. Isaac Nova Orin and Jeston AGX OrinIsaac Nova Orin, which will be available later this year, pairs two Jetson AGX Orin units to deliver up to 550 TOPS of power. Over-the-air software management support is “preintegrated” in Isaac Nova Orin and the hardware is “calibrated and tested to work out of the box,” Nvidia says. Isaac Nova Orin includes tools necessary to simulate the robot as well as software modules designed to accelerate perception and navigation tasks and map different robots’ environments. Alongside Isaac Nova Orin, Nvidia announced that the Jetson AGX Orin developer kit, which the company first detailed in November, is now available to customers for purchase.

In particular, ML will be needed to help counter the inevitable adoption of ML by cybercriminals for automating cyberattacks, such as ransomware attacks, Hyppönen said in an interview with VentureBeat. Hyppönen said he predicts that cybercrime organizations such as ransomware gangs will begin to utilize ML in this way within the next 12 to 24 months. They have a slow reaction,” Hyppönen said. “In many ways, the security business is about trust. According to SonicWall, the total number of ransomware attacks more than doubled in 2021 — jumping 105% during the year compared to 2020.

Named for Grace Hopper, the pioneering U.S. computer scientist, the new architecture succeeds Nvidia’s Ampere architecture, with launched roughly two years ago. The H100, bound for datacenters, will be available first in Nvidia’s fourth-generation DGX system — the DGX H100. Every GPU is connected by a fourth-generation NVLink for 900GB per second of connectivity, and an external NVLink Switch can network up to 32 DGX H100 nodes in one of Nvidia’s DGX SuperPod supercomputers. (A single DGX SuperPod with a H100 GPU delivers around an exaflop of FP8 AI performance.) DGX H100 systems, DGX Pods, and DGX SuperPods will also be available from Nvidia’s global partners starting in Q3.

For organizations that are not interested in philosophical quests, but in practical approaches, terms such as “AI ethics” or “responsible AI” can feel convoluted and abstract. The term “trustworthy AI” has been used in places ranging from the EU Commission to IBM and from the ACM to Deloitte. Trustworthy AI is really a structure to get you started to think about the dimensions of trust within your organization. A fictitious company is used as the backdrop for the analysis of each dimension comprising trustworthy AI. “Every person within the organization needs to know what trustworthy AI means and how it applies to their organization.

Continuing with the platform theme, Nvidia also announced new DGX H100-based systems, SuperPODs (multi-node systems) and a 576-node supercomputer. The top layer is a set of pre-built AI systems to address specific use cases. Huang also announced the formalization of the AI platform. This should give customers the confidence that the software has been tested, vetted and optimized for the Nvidia platform. The platform approach Nvidia has taken is common in networking, cloud and software but is unique to it in silicon.

>>Nvidia CEO Jensen Huang said today that the Omniverse virtual simulation and its tools will be available in the cloud so that developers can use it on just about any computer. Those simulations are being used in everything from games to industrial “digital twins,” where designers test a concept in a virtual design before committing to physical designs. The instruments and sensors in the real factory can collect data and feed it back into the Omniverse virtual factory simulation, which can then become more accurate. Siemens is using Nvidia’s Omniverse and Modulus to create digital twins for its wind farms. Virtual representations of Siemens Gamesa’s wind farms will be built using Omniverse and Modulus, which together comprise Nvidia’s digital twin platform for scientific computing.

Follow VentureBeat’s ongoing coverage of Nvidia’s GTC 2022. >>Nvidia and Kroger today announced a “strategic collaboration” designed to bring more AI-powered applications and services to the grocery realm. The duo announced the new partnership today at GTC 2022, Nvidia’s annual developer-focused AI conference. Kroger and Nvidia at GTC 2022Digital twinsKroger and Nvidia revealed plans to build an AI lab and “demonstration center,” to improve its shipping logistics and in-store shopping experience. Part of this will entail creating so-called “digital twin” simulations, which will reflect actual store layouts.

Harassment in the workplace affects employees of all backgrounds, genders, sexualities, and ethnicities — but disproportionately those in under-represented groups. Sexual harassment causes lasting damage to the employees who experience it, leading to higher turnover, lower productivity, and increased absenteeism and sick leave. Deloitte estimates that workplace sexual harassment costs an average of $2.6 billion in lost productivity, or $1,053 per victim. “The dashboards can help organizational leaders understand what kind of language is being used in their organizations, how to categorize that language (harassment, discrimination, bullying, etc. This helps the analytics adapt to domain-specific jargon and changing communications behavior.”Regardless of an AI system’s accuracy, Dehingia cautions that software alone isn’t the answer to workplace harassment or abuse.

Today, compliance-as-a-service platform, Trustero, launched from stealth with $8 million in seed funding. The solution provides users with real-time compliance monitoring for SOC 2, a voluntary compliance standard for service organizations. The solution automatically tests controls and alerts users with remediation instructions when a system or process violates regulatory compliance. Mitigating risk in hybrid workingWidespread cloud adoption throughout the industry has positioned compliance monitoring solutions to similarly become increasingly important for enterprises to adopt. “We help users become SOC 2 compliant and keep them in compliance.

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The demand for automated solutions has accelerated the growth of the T&E management software market, which Grand View Research expects will be worth $17.4 billion by 2017. Automating expense reportingManual T&E reporting isn’t time-consuming just because it requires entering items into a spreadsheet. More recently, Ramp rolled out Ramp for Travel, a set of automations and integrations that focuses on simplifying trip expense reporting. Glyman asserts that these companies will make the switch once the benefits of T&E management software become clear to them. “This funding will accelerate development of our finance automation platform, on the heels of Ramp for Travel and other features that fully automate expense management,” Glyman said.

In a recent event held by Meta AI, LeCun discussed possible paths toward human-level AI, challenges that remain and the impact of advances in AI. The question LeCun is exploring is, how do we get machines to learn world models mostly by observation and accumulate the enormous knowledge that babies accumulate just by observation? Example of self-supervised learning in medical imaging (source: arXiv)High-level abstractionsMore recently, scientists have experimented with pure self-supervised learning on computer vision tasks. LeCun believes that self-supervised learning with these types of high-level abstractions will be key to developing the kind of robust world models required for human-level AI. Joint Embedding Predictive Architecture (JEPA) (source: Meta)Modular architectureAt the Meta AI event, LeCun also talked about a modular architecture for human-level AI.

In 2017, Andrew Ng founded Landing AI, a startup working on facilitating the adoption of AI in manufacturing. We connected with Ng to discuss what he calls the “data-centric approach” to AI, and how it relates to his work with Landing AI and the big picture of AI today. But after working on many customer projects, Ng and Landing AI developed a new toolkit and playbook for making AI work in manufacturing and industrial automation. What is new, however, is building on and re-examining these techniques in light of modern AI models and methods. That seemed to point towards approaches such as Robust AI, Hybrid AI or Neuro-Symbolic AI and technologies such as knowledge graphs to express domain knowledge.

In the UK, there have been several steps in creating more regulations around AI auditing practices, AI assurance and algorithmic transparency. The AI Cloud Service Compliance Criteria Catalogue (AIC4) responds to the calls for AI regulation by clearly outlining the essential requirements for promoting robust and secure AI practices. Fundamentally, the FTC believes the current law is sufficient for enforcement and will be enforced on AI systems when necessary. Additionally, the Department of Commerce established the National Artificial Intelligence Advisory Committee (NAIAC) in September under the advice of the National AI Initiative Act of 2020. Predictions for 2022 often discuss a more widespread adoption of explainable AI, stronger AI-centered risk management strategies and increased monitoring of AI systems.

About 250 of these applications were requests from reviewers with great experience in reviewing for machine learning conferences, and who should have been included in the original invites, but who fell through the cracks for some reason. Statistics ‘Regular’ reviewer Mentee Average review ...Share this article

And top of the agenda is the development, training and deployment of Deep Generative Models (DGMs) — which I consider to be some of the most exciting models set for use in industry. You’ve likely already seen the results of a DGM in action — they’re actually the same type of AI models that produce deepfakes or impressionistic art. The “D” in “DGM” refers to the fact that, alongside being generative models, they leverage deep neural networks. This depth gives deep neural networks the ability to operate with extremely complex datasets with many variables at play. Last month, DeepMind released AlphaCode, a code-generating AI model that’s successfully outperformed the average developer in trials.

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Language models — AI systems that can be prompted to write essays and emails, answer questions, and more — remain flawed in many ways. Another major limitation of many of today’s language models is that they’re “stuck in time,” in a sense. But the coauthors note that their method can be applied to virtually any AI language model without much modification. This gives search engines a lot of power over the data that might inform web-connected language models’ answers. Solutions to the limitations of language models that search the web remain largely unexplored.

Figure 1: Data Literacy Education FrameworkNow I want to complete the framework by discussing the third (AI/ML literacy) and fourth stages (prediction and statistical literacy) of the Data Literacy Education Framework. AI/ML literacyMy article, “The Growing Importance of Data and AI Literacy – Part 2” broadened the data literacy conversation by introducing AI (Artificial Intelligence) and ML (Machine Learning) Literacy; that is, an introduction into how AI and ML models work. An AI model seeks to optimize its AI Utility Function – the KPIs and metrics against which the AI model’s progress and success will be measured – as the AI model interacts with its environment. The AI model seeks to maximize “rewards” based upon the definitions of “value” as articulated in the AI utility function. Figure 3: The AI Utility FunctionThe AI utility function assigns values to certain actions that the AI system can take.

This underlying, often abstracted complexity is why it’s important to consider how ethics should play into AI development and deployment. Succeeding here demands consideration of AI technologies beyond the limited scope of their primary and planned use cases. The ethical development and delivery of AI technologies must balance risks and rewards for everyone, not just the intended beneficiaries. Defining the path forward for ethical AI and related technologyThere’s an inherent challenge in assessing right and wrong with AI. The bottom lineWe must initiate the discourse around ethical AI and technology now, while we’re still a long way from that science fiction AI vision.

The Bloom filter provides a solution, allowing you to quickly and accurately check whether any particular site’s address, or URL, is on the blacklist. Bloom filters never produce false negatives — if they say the site is bad, it’s bad. A benign website that could have been blocked by a larger Bloom filter can now get past the more accurate learned Bloom filter. Mitzenmacher also improved the learned Bloom filters. Like the learned Bloom filter, most of these new structures only incorporate a single machine learning element.

Artificial intelligence research has made great achievements in solving specific applications, but we’re still far from the kind of general-purpose AI systems that scientists have been dreaming of for decades. In a talk at the IBM Neuro-Symbolic AI Workshop, Joshua Tenenbaum, professor of computational cognitive science at the Massachusetts Institute of Technology, explained how neuro-symbolic systems can help to address some of the key problems of current AI systems. “These systems develop quite early in the brain architecture that is to some extent shared with other species,” Tenenbaum says. One of the main challenges that remain is how to design AI systems that learn these intuitive physics concepts as children do. These directions of research might help crack the code of common sense in neuro-symbolic AI.

The event is a huge deal, as Nvidia has now crossed three million developers and it needs to communicate with them. More than 20 million people viewed Huang’s last keynote at the November GTC event, and more than 200,000 register to attend sessions at the free GTC online event. Register Now“We’ll see most of the major announcements for our company, and this GTC will be no exception,” Estes said. Our previous GTC metaverse session in November was one of the top sessions at the event. Everyone from the CIA to AstraZeneca will be speaking at the event, Estes said.

This week in AI, a new Pew Center poll shed light on Americans’ views of AI, including the use of facial recognition by police. Facial recognition, too, has been shown to be biased against “suspects” with certain skin tones and ethnicities. At least three people in the U.S. — all Black men — have been wrongfully arrested based on poor facial recognition matches. Justice Department predictive policingThe Department of Justice (DOJ) is a growing investor in AI, having awarded a contract to Veritone for transcription services for its attorneys. But according to Gizmodo, the DOJ maintains poor records of its spending — especially where it concerns predictive policing tools.

Artificial intelligence (AI) is being unleashed on business processes, data analytics and a host of other enterprise functions, but its role in data center automation stands to change not just the data center itself but all its infrastructure — physical and virtual, to the edge and beyond. Still, experts predict that the shortage of qualified data center operators will continue and may even get worse. AI of all tradesClearly, there are many ways in which AI can be used to automate data center management. All of this points to a pretty good outlook both for AI in the data center and the IT workforce. And the modern data center itself will be able to do more while consuming less, which is a benefit to all.

A Russian “suicide drone” that boasts the ability to identify targets using artificial intelligence has been spotted in images of the ongoing invasion of Ukraine. A UN report published last year concluded that a lethal drone with this capability may have been used in the Libyan civil war. It is unclear if the drone may have been operated in this way in Ukraine. The KUB-BLA images have yet to be verified by official sources, but the drone is known to be a relatively new part of Russia’s military arsenal. Its use would also be consistent with Russia’s shifting strategy in the face of the unexpectedly strong Ukrainian resistance, says Samuel Bendett, an expert on Russia’s military with the defense think tank CNA.

The robot needs to respond quickly to changes in the environment, such as the moment it encounters ice while running on grass. If the robot is walking, it is moving slowly and the presence of snow is not typically an issue. The problem is that moving on all terrains as if you were walking on ice is very inefficient, but is common among today’s robots. Here, MIT PhD student Gabriel Margolis and IAIFI postdoc Ge Yang discuss just how fast the cheetah can run. This work is supported by the DARPA Machine Common Sense Program, Naver Labs, MIT Biomimetic Robotics Lab, and the NSF AI Institute of AI and Fundamental Interactions.

Delivering this type of care is complex, and key interventions require inserting a needle and catheter into a central blood vessel, through which fluids, medications, or other aids can be given. AI-GUIDE is a platform device made of custom-built algorithms and integrated robotics that could pair with most commercial portable ultrasound devices. To operate AI-GUIDE, a user first places it on the patient’s body, near where the thigh meets the abdomen. Once the catheter is securely in the blood vessel, the device withdraws the needle and the user can remove the device. VITL was built in order to bring a medical device prototyping capability to the laboratory.

Artificial intelligence is getting cheaper, better at the tasks we assign it, and more widespread—but concerns over bias, ethics, and regulatory oversight still remain. The most popular AI categories that researchers and others published on were pattern recognition, machine learning, and algorithms. Big tech and AITech companies like Amazon, Netflix, Spotify, and YouTube have been improving the AI used in recommendation systems. In the last decade, the most popular specialties among PhD computer science students were artificial intelligence and machine learning. Last, the report looked at global, federal, and state regulations related to AI (looking for keywords like artificial intelligence, machine learning, autonomous vehicle or algorithmic bias).

In The NewsStanford Annual report on AI : Artificial intelligence is everywhere now Computers that analyze images and understand speech Specifically, out of the various tasks that AI can perform, last year, the research community was focused on applying AI to computer vision, a subfield that teaches machines to understand images and videos in order to get good at classifying...Tiger Global Was the Biggest Investor in AI at the End of 2021 Funding for companies related to artificial intelligence doubled last year, reaching a total of $66.8 billion, according to CB Insights.

Functional forms of all of our speedup methods that allow you to integrate them into your existing training loop. Example: Functional APIIntegrate our speed-up methods into your training loop with just a few lines of code, and see the results. data import DataLoader from torchvision import datasets , transforms from composer import Trainer from composer . ? Building Speedup RecipesGiven two methods that speed up training by 1.5x each, do they combine to provide a 2.25x (1.5x * 1.5x) speedup? Even after these speedup methods are implemented, assembling them together into recipes is nontrivial.

"This year's AI Index tells us that AI is being integrated into the economy and the effects of it are beginning to go global across research, deployment, and even funding." Read the new 2022 AI IndexThe 2022 AI Index is one of the most comprehensive reports about AI to date. The AI Index partners with organizations across sectors, including the Center for Security and Emerging Technology at Georgetown University, LinkedIn, and Bloomberg Government, to track the progress of artificial intelligence. In addition, the 2022 report also introduces more self-collected data and original analysis than any previous editions. Read the 2022 AI Index.

Funding for companies related to artificial intelligence doubled last year, reaching a total of $66.8 billion, according to CB Insights. Tiger Global Management was the most active investor in artificial intelligence in the fourth quarter of 2021. The $95 billion investment management firm backed 13 AI-related startups in the three-month period, bringing its full-year total to 44. It participated in 31 mega-deals with AI companies throughout all of 2021, according to a new CB Insights report. Investment companies like Tiger Global – companies that are measured on the ability to make a lot of money fast – are going to pounce on the opportunity.”Tiger Global is the investment firm founded by Tiger Cub Chase Coleman.

Lately, though, Aboukhadijeh has entered a new kind of flow state, helped along by a tool called Copilot. Built by OpenAI, the private research lab, and GitHub, the Microsoft-­owned website where programmers share code, the tool is essentially autocomplete for software development. Much as Gmail tries to finish a sentence as you write it, Copilot offers to complete a chunk of your program. “Some of them are really eerie.”For weeks, Aboukhadijeh kept Copilot turned on while he worked. If he simply typed into his code editor “Write a function that capitalizes every word in a document,” Copilot would assemble that code all by itself.

The MIT researchers said their generative model requires less memory to store than datasets, which can cost millions of dollars to create. MIT researchers have found a way to classify images using synthetic data, which they claim can rival models trained from real data. In the study, the team created a special type of machine learning model to generate extremely realistic synthetic data, which can then train another model for vision-related tasks. The team said their special machine learning model, known as a generative model, requires far less memory to store or share than a dataset. The team compared the results of their learning model that only used this synthetic data, to several other image classification models that were trained using real data.

The plans started forming after Russia invaded Ukraine and Clearview Chief Executive Hoan Ton-That sent a letter to Kyiv offering assistance, according to a copy seen by Reuters. Ukraine's Ministry of Defense did not reply to requests for comment. Previously, a spokesperson for Ukraine's Ministry of Digital Transformation said it was considering offers from U.S.-based artificial intelligence companies like Clearview. Many Western businesses have pledged to help Ukraine, providing internet hardware, cybersecurity tools and other support. read moreThat database can help Ukraine identify the dead more easily than trying to match fingerprints and works even if there is facial damage, Ton-That wrote.

One of the most influential regulative proposals for AI is that proposed by the European Commission in April 2021, the “AI Act.” The EU’s proposed regulation has already inspired some international regulative proposals and is likely to broadly impact AI policies around the world. In this piece, we analyze the core policy concepts of the AI Act, with focus both on those worth amending and defending. These discussions may provide valuable elements for other regions beyond the EU to consider for their own AI policy. While the AI Act could still be improved to make it even more robust in managing AI-related risks to health, safety, and fundamental rights, and to increase incentives to industry to take actions beneficial to both itself and others, overall we applaud this act. By Meeri Haataja & Joanna J. Bryson[1]The EU’s proposed regulation for artificial intelligence, published in April 2021 and known as the “AI Act,”[2] is probably the most influential AI-focused policy paper published to date.

Google DeepMind has collaborated with classical scholars to create a new AI tool that uses deep neural networks to help historians decipher the text of damaged inscriptions from ancient Greece. In fact, Ithaca has already been used to help resolve an ongoing debate among historians about the correct dates for a group of ancient Athenian decrees. Many ancient sources—whether they be written on scrolls, papyri, stone, metal, or pottery—are so damaged that large chunks of text are often illegible. Historians had previously pegged the dates of the decrees to no later than 446 BCE. These historians uncovered evidence that the Attic letterform had continued to be used in official documents long after 446 BCE.

Nonetheless, assuming open-source analysts are right, the event illustrates well that autonomous weapons using artificial intelligence are here. The KUB-BLA is not the first AI-based autonomous weapon to be used in combat. The United States, Russia, and Israel have significant concerns, and various others do not support a ban. It recommends a ban on unpredictable autonomous weapons, autonomous weapons that target human beings, and various regulations on other sorts of “non prohibited” autonomous weapons. And that is quite significant, because autonomous nuclear weapons are perhaps the riskiest autonomous weapons.

Image databases of skin conditions are notoriously biased towards lighter skin. The focus should be on adding more diverse real images to existing databases, says Roxana Daneshjou, a clinical scholar in dermatology at Stanford University. Next, the team will combine the synthetic images of darker skin with real images of lighter skin to create a program that can detect skin cancer. The only way to confirm that the synthetic images work as well as real images in a model would be to compare them with real images — which are in short supply. If a diagnostic model is based on synthetic images from one group and real images from another — even temporarily — that’s a concern, Daneshjou says.

Results showed that the score of correct answers during the training was higher when learning with the adaptive robot. • H2: Participants perceive a higher subjective knowledge gain when learning with an adaptive robotic tutor than participants learning with a non-adaptive robotic tutor. • H3: An adaptive robotic tutor supports participants’ motivation more than a non-adaptive robotic tutor. 2.1.2 Implementation of the Adaptive Robotic TutorWe implemented two versions of the robotic tutor: adaptive and non-adaptive. For example, an exercise that was completed correctly in the first learning session is colored green in the third learning session, while a task that was completed incorrectly in the second session is now colored red.

It’s all very recognizable and type checks as a modern deep learning paper, except it is from 33 years ago. misses: 102While my training script repro.py in its current form prints at the end of the 23rd pass:eval: split train. This change dramatically improved the training error, completely overfitting the training set:eval: split train. Leaving the baseline training running for 100 passes already shows some improvement from the added data alone:eval: split train. 2055 neural nets are basically the same as 2022 neural nets on the macro level, except bigger.

So much of the data available to law enforcement data analysts and senior staff is unstructured. With today’s data visualisation techniques, a picture emerges from different data sets without time being wasted on wading through information. Here are a few of them:Interpreting visual data: The human brain can process visual data 60,000 times faster than it does text. Data visualisation is core to this because it provides an easy-to-understand translation of machine learning models and presents actionable intelligence. Sharing critical data: Data visualisation is not just of academic use to data scientists.

Find him on Twitter: @Gadget_RyA deepfake of President Zelenskyy calling on citizens to “lay down arms” was posted to a hacked Ukrainian news website and shared across social networks. The deepfake purports to show Zelenskyy declaring that Ukraine has “decided to return Donbas” to Russia and that his nation’s efforts had failed. It shouldn’t have fooled anyone, but Zelenskyy posted a video to his Instagram to call out the fake anyway. “I only advise that the troops of the Russian Federation lay down their arms and return home,” Zelenskyy said in his official video. TV24's hacked website still has a screenshot of the fake video along with a transcript of it.

But with theirs, researchers at MIT claim to have broken the record for the fastest robot run recorded. Last year, researchers at the University of California, Berkeley trained a bipedal robot called Cassie to walk in simulation and then translated those skills to a real-world replica robot. Also, last year, Meta (formerly Facebook) data scientists trained a four-legged robot in simulation on different surfaces so that an identical, real-world robot could recover when it stumbled. The MIT researchers, too, trained their system entirely in simulation. But the researchers say their approach eliminates the need to program how a robot — Mini Cheetah or otherwise — should act in every possible situation.

Companies that have felt locked in to on-premises solutions now have the security, power, and scale they’ve been waiting for. Learn more at this year’s spring NVIDIA GTC conference, taking place virtually from March 21 – 24. Tech leaders from Microsoft are offering twenty-one different sessions spanning eleven topics focused on eight key industry verticals. Azure executives will take attendees through an overview of the Azure HPC + AI and Confidential Computing platforms. Check out Azure’s three new GPU virtual machines during the talk “Azure AI Supercomputing VMs: AI at Any Scale.

Crashes and unsafe driving incidents are ever-present concerns in the trucking industry. KeepTruckin today released the results of a two-year-long study on the use of AI technology for driver safety and coaching in commercial vehicles. The key findings: active use of AI and coaching techniques led to 22% fewer accidents and 56% fewer unsafe driving incidents. “They can take this information and start coaching the driver,” Gupta said. We’ve been able to collect a wealth of really unique data and turn that data into actionable insights.”

A plurality of Americans support the widespread use of facial recognition by law enforcement to monitor crowds and track down people who might have committed a crime. Facial recognition, which has long been a flash point for controversy, reentered the public debate after the killing of George Floyd in May 2020. As Wired’s Khari Johnson reports, some police departments have adopted policies governing their respective uses of facial recognition. In Detroit and New York, two analysts must review the results of a facial recognition scan before the results are turned over to detectives, and facial recognition alone can’t be used to justify an arrest. Government Accountability Office reports that 10 branches including the Departments of Agriculture, Commerce, Defense, and Homeland Security plan to expand their use of facial recognition between 2020 and 2023 as they implement as many as 17 different facial recognition systems.

Edge technologies are projected to experience the highest investment increase in 2022, growing 76% to $462,000 according to a new Gartner, Inc. survey. Looking back to 2021, 5G drew the highest average investment in 2021, with survey respondents reporting an average of $465,000 invested in the technology. This was followed by IoT at $417,000 and edge technologies (i.e., edge AI and edge computing) at $262,000. It’s interesting to note that the decisions to invest in these emerging technologies (ET) no longer solely rest with IT. In fact, counter to conventional thinking, the vast majority of survey respondents reported that ET investments are either meeting or exceeding user expectations.

Cloudera, in partnership with Sapio Research, surveyed 2,213 business decision-makers who work at worldwide organizations with more than 1,000 employees. Over 10,000 knowledge workers — people who access technology as part of their jobs — also participated in the August 2021 study. A vast majority of decision-makers (95%) and knowledge workers (87%) believe AI can be used to create more sustainable business practices. In reality, business decision-makers say that AI (61%), ML (49%), and data analytics (57%) are already being used to make sustainable business decisions at their organization. The research confirms a growing need for AI, ML and data analytics in the enterprise.

One broad domain is health care, where Watkins says inequity shows up in both quality of and access to care. The demand for mental health care, for example, far outstrips the capacity for services in the United States. In the case of mental health access, this entails developing a tool to help mental health providers deliver care more efficiently. The ICSR collaboration has separate project teams researching systemic racism in different sectors of society, including health care. “Systemic change requires a collaborative model and different expertise,” says Watkins.

That early love of outer space and building things led her to a bachelor's degree in astronautical engineering and the Air Force. Learning how to learnWorking outside of academia for eight years proved to be a tough transition. Again she turned to MIT OCW to supplement her studies. But her relationship with online learning is far from over; again she has turned to OCW and MITx resources for the foundation to succeed in subjects such as controls, machine learning, quantum mechanics, and quantum computation. When the pandemic struck the East Coast, Henshall was only nine months into her PhD program at MIT.

Find him on Twitter: @Gadget_RyArm is cutting up to 1,000 jobs from its global workforce after the collapse of Nvidia’s acquisition. Nvidia proposed to acquire Arm for $40 billion and ensure the company is able to continue its pioneering work. Despite Nvidia offering legal assurances to counter the concerns, the deal looked set to be blocked by regulators. “Though we won’t be one company, we will partner closely with Arm,” said Nvidia CEO Jensen Huang at the time. Whatever happens, we hope Arm finds a way to continue delivering the innovation that it has for the past three decades.

According to AIMultiple, 71% of B2B marketers are interested in using AI for personalization, 63% are interested in using AI to identify trends and 59% expect AI to help identify prospective customers. “It comes down to, how do you get a better idea of who is going to become a customer?” Glover said. Adobe’s expanded predictive lead and account scoring helps brands determine account context by identifying the people within their account and their behaviors, he explained. This tool can be used within the Adobe Real-Time Customer Data Platform (CDP) and will also be available with the company’s B2B marketing automation app Adobe Marketo Engage later this year, Glover said. Through Adobe’s Real-Time CDP, marketers can then identify account leads and shift dollars to the most appropriate online and offline channels.

To help with their hiring efforts, seven out of 10 companies plan to boost spending on HR tech this year, according to a PwC survey. HR tech vendors assert their solutions can solve organizational challenges, however, if implemented with a clear plan. For example, Talent.com, a recruitment platform based in Montreal, claims to send more than 80 million job candidates to employers each month and provide 30 million jobs to those candidates. Among them is HireEZ, which provides AI-powered tools for job candidate recruitment and profiling. From January 2021 to October 2021, venture capitalists put $9.2 million toward HR tech startups, a 130% jump from 2020’s total, according to PitchBook data.

At the same time, AI systems are becoming more affordable to train — at least when it comes to certain tasks, like object classification. Those are the top-level findings of the 2022 AI Index report out of Stanford’s Institute for Human-Centered AI (HAI), an academic research center focused on the human impact of AI technologies. Decreasing training costsThis year’s AI Index pushes back against the notion that AI systems remain expensive to train — at least depending on the domain. As of 2021, nine out of 10 state-of-the-art AI systems in the 2022 AI Index were trained with extra data. This bodes poorly for efforts to address the growing bias problems with AI systems.

The market for edge AI chips, designed to accelerate AI workloads offline, often in self-contained hardware, is maturing at a rapid clip. Valuates cites the increased demand for low latency and real-time processing, as well as reductions in storage and operations costs, as factors driving the use of edge AI chips. But edge AI chips are fettered in other ways: For example, because they lack the computing power of, say, a cloud datacenter, only select tasks can be performed on an edge device. Quadric is one of the many startups diving into the AI edge market with gusto, promising to eliminate the historical bottlenecks of edge hardware. Expanding edge marketDeloitte estimates more than 750 million edge AI chips that perform tasks on-device have been sold to date, representing $2.6 billion in revenue.

After years of frying and flipping burgers at White Castle locations, Miso Robotics’ autonomous kitchen assistant has a new gig: making tortilla chips for Chipotle. Later this year, the companies say, Chippy will be deployed at a Chipotle restaurant in Southern California. While making tortilla chips might not be the pinnacle of achievement in robotics, the partnership between Miso and Chipotle reflects the restaurant industry’s eagerness to embrace automation technologies. Making tortilla chipsThere’s nothing particularly complicated about the recipe for Chipotle’s tortilla chips, which the brand shared on TikTok in 2020. Miso has previously said that it plans to take its kitchen robots to markets outside of the U.S. in the future, including the U.K., Canada, and Australia.

In ocean shipping, ships leave port at high speeds only to slow down before reaching their destination, resulting in fuel waste and excess emissions as well as lost capital for shipowners and charterers. Aiming to tackle the problem with technology, shippers are investing in startups offering software that’s designed to optimize routes in a way that minimizes carbon emissions. Another startup, Windward, has shipping customers that use its AI platform to monitor fuel and emissions. Optimizing ship routingNew York-based Nautilus was founded in 2016 by Anthony DiMare and Brian O’Clair. Meanwhile, the European Union (EU) Emissions Trading Scheme — which aims to incentivize firms to reduce their emissions — will cover ocean commerce starting next year.

The Bloom filter provides a solution, allowing you to quickly and accurately check whether any particular site’s address, or URL, is on the blacklist. Bloom filters never produce false negatives — if they say the site is bad, it’s bad. A benign website that could have been blocked by a larger Bloom filter can now get past the more accurate learned Bloom filter. Mitzenmacher also improved the learned Bloom filters. Like the learned Bloom filter, most of these new structures only incorporate a single machine learning element.

This special machine-learning model, known as a generative model, requires far less memory to store or share than a dataset. Using synthetic data also has the potential to sidestep some concerns around privacy and usage rights that limit how some real data can be distributed. A generative model could also be edited to remove certain attributes, like race or gender, which could address some biases that exist in traditional datasets. One advantage of using a generative model is that it can, in theory, create an infinite number of samples. But he cautions that there are some limitations to using generative models.

Data scientists are increasingly using synthetic data to develop their AI systems. In January, Mostly AI, a company that uses AI to create synthetic data for enterprises, raised $25 million in venture capital. Synthetic data company Synthesis AI emerged from stealth in April. Another company, Synthetaic, goes a step beyond most synthetic data startups in claiming that its platform can eliminate the need for data labeling. “While it is true that synthetic data can introduce bias in AI models, at Synthetaic, we use synthetic data in a novel manner in that we don’t actually create synthetic data for the purpose of training.

Entrepreneurs Ran Oelgiesser and Rotem Dafni sought to address the challenge of infusing sales with AI in RightBound, a platform designed to automate manual business-to-business (B2B) sales processes. RightBound does data collection and outreach on behalf of sales reps, involving them when prospective customers express interest. (In sales, “development” refers to identifying, connecting with and vetting prospective buyers.) Proton.ai is developing AI-powered technologies to support sales operations teams, while Highspot is leveraging AI to promote sales enablement playbook and workflows. “One challenge that we see with sales across organizations today is that the teams of sales development reps are at velocity growth and implementing many software-as-a-service tools simultaneously.

All the same, forward-looking companies are testing driverless trucks outside their usual warehouse and terminal locations. In addition, autonomous truck developer Aurora recently partnered with Volvo to create driverless truck and automate processes for US Xpress. Companies must develop software that leverages machine learning and classic robotics to control and service the driverless trucks, even outside a controlled environment. The main technology driver will be the lidar devices that help autonomous vehicles measure the distance to objects in real time. One thing is certain: people need to see driverless trucks on highways before they can get used to them.

The high upfront costs of hardware drive many companies to the cloud, which is often expensive and difficult to monitor. Meanwhile, data quality issues like a lack of data curation, data governance, and data literacy are introducing compliance risks such as biased algorithms. Run:AI attempts to allocate workloads such that available hardware resources are maximized, considering factors like network bandwidth, compute resources, cost, and data pipeline and size. Policies in Run:AI create quotas for different projects. Nvidia, for its part, sells AI Enterprise, a software suite of tools and frameworks that enable companies to virtualize AI workloads on Nvidia-certified servers.

“[It’s perfect for] if you have writer’s block.”You.com’s new tool is powered by the same technology behind OpenAI’s GPT-3, an AI language system that can generate human-like poetry, emails, recipes, short stories, movie scripts, and more. (When contacted for comment, an OpenAI spokesperson said that YouWrite is powered by GPT-3 — specifically the recently-released InstructGPT models — through its API.) “We want to, basically, create this AI-powered writing system to help people be more productive, but also being controlled — you can decide what it should write,” Socher said. Of course, with any AI-powered language system, there’s a risk that the system might become susceptible to bias and toxicity. “I think that ultimately, if you want to be a writer and have a search engine that helps you do research, summarize the web, and also get something on the page, You.com is going to be your best search engine.”

Spurred by the challenge, Kannan Kothandaraman and Nitin Kumar — both networking industry veterans — in 2019 launched Selector, an AIops platform for network, cloud and app delivery workflows. “We saw that cloud providers can build large and complex cloud infrastructures while enterprises, service providers, financial institutions and retailers are struggling to manage their own infrastructure. The key insight was that cloud builders have built in-house observability tools not available to enterprises and service providers,” Kothandaraman told VentureBeat via email. Network World’s Shamus McGillicuddy, reporting on an EMA study, notes that successful users of AIops are focused on transforming network engineering and operations rather than addressing challenges with existing network management tools. “AIops-driven network management can make a business run better.

A multi-disciplinary team of researchers from Technische Universität Berlin recently created a neural ‘network’ that could one day surpass human brain power with a single neuron. The Berlin team decided to challenge the idea that bigger is better by building a neural network that uses a single neuron. In this case however, the single neuron is able to network with itself by spreading out over time instead of space. This Fit-DNN approach requires only a single neuron with feedback-modulated delay loops. It’s feasible such a system could surpass the human brain and become the world’s most powerful neural network, something AI experts refer to as a “superintelligence.”

In this series, titled Achieving ROI with Early AI Projects, we draw on the expertise of five AI leaders and explore their journeys through early AI adoption in their respective enterprises, from large multinationals to smaller startups. In these next five episodes, we will center our discussions on their advice regarding how to achieve a return on the funds and resources we invest into early AI projects. Every episode centers on one unifying theme: Achieving ROI with Early AI Projects. EPISODE 2 – Quantifying AI RiskGuest: Michael Berger – Head of AI Insurance – Munich RePodcast Release Date: March 15, 2022Michael Berger of Munich Re advises leaders to screen AI project candidates for risk immediately. EPISODE 3 – Crawl, Walk, RunGuest: Raefer Gabriel – Vice President, Engineering – OraclePodcast Release Date: March 16, 2022Raefer Gabriel, Vice President of Engineering at Oracle, compares AI projects to investment projects.

According to recent research, over a quarter of 9- to 12-year-olds report having experienced sexual solicitation online. One in eight report having been asked to send a nude photo or video; one in 10 report having been asked to join a sexually explicit livestream. Smartphones, internet access, and Facebook together now reach into children’s hands and homes and create new spaces for active predation. So I tweeted at Facebook and the Facebook newsroom. When I searched for a translation of “hanap jowa,” the name of a series of groups, it led me to an article from the Philippines reporting on efforts by Reddit users to get child-endangering Facebook groups removed there.

But Luckey quickly founded Anduril, a Founders Fund-backed startup devoted to cutting-edge military tech. Anduril has a valuation of nearly $5 billion, making Luckey a rare founder of two unicorns. Palmer Luckey: I met with President Zelensky shortly after we started, and I last saw him about two years ago. I'm really devastated that he wasn't able to stop it, that the whole world wasn't able to stop it. If Anduril technology had been deployed, would that conflict be playing out differently?

To these critics, this gift was an exercise in narcissism, evidence of a self-involved ego playing at god. To understand the lineage of these tools, we need to go back to another notable period of death in the US: the Civil War. Early photographs memorializing family members weren’t uncommon, but as the nation reeled in the aftermath of the war, a peculiar practice started to gain traction. Dubbed “spirit photographs,” these images showcased living relatives flanked by ghostly apparitions. Rather than betray vanity or excess, material objects like these photographs helped people keep their loved ones near in a culture that expected them to move on.

RT @AndrewLampinen: In our paper "Symbolic Behaviour in AI" (https://t.co/BW3yf6clt5) we discuss a perspective on symbols, that we suggest 1) better describes human capacities 2) provides a more promising direction for AI. Briefly, we focus on symbols as subjective, following C.S. https://t.co/ROJwqXXFZGThe ability to use symbols is the pinnacle of human intelligence, but has yet to be fully replicated in machines. Here we argue that the path towards symbolically fluent artificial ...

Find him on Twitter: @Gadget_RyUkraine is using Clearview AI’s facial recognition software to uncover Russian assailants and identify Ukrainians who’ve sadly lost their lives in the conflict. Clearview AI’s facial recognition system is controversial but indisputably powerful—using billions of images scraped from the web to identify just about anyone. Ukraine is now reportedly using the facial recognition software for free, but the same offer has not been extended to Russia. “Common law has never recognised a right to privacy for your face,” Clearview AI lawyer Tor Ekeland once argued. However, Clearview AI has boasted that police use of its facial recognition system increased 26 percent in the wake of the US Capitol raid.

Last year, the Intuit Prosperity Accelerator was launched to support promising startups who could help small businesses and Canadians thrive in the wake of the pandemic. This year’s startup cohort was selected based on their ability to leverage artificial intelligence to create solutions that help consumers and small businesses overcome financial challenges. Another participating startup, Beam.city, is developing an advertising automation platform that helps small businesses optimize their digital advertising performance. Small businesses don’t have the same access to online advertising ecosystems, or the knowledge to leverage them effectively. In the health industry, AI doesn’t just offer business results, but can have a dramatic impact on social well-being.

What is #ComputerVision? See this comprehensive guide — Basic to Advanced CV & How Does it Work: https://t.co/deZ8F9vASJ by @GreatLearning HT @ipfconline1 ————— #BigData #DataScience #AI #DeepLearning #MachineLearning #NeuralNetworks ——— ➕see this book: https://t.co/i6XcLltUBI https://t.co/kDoyZxNttvWhat is Computer Vision and How Does it Work: Learn about the challenges we face in this and how to solve them and future of this.

Perhaps more disturbing is the fact that we train large-scale AI language models with data from often toxic online conversations. Right now, researchers are doing a lot with AI language models to tune their accuracy. In multilingual translation models, for example, a human in the loop can make a huge difference. It’s relatively easy to see where the statistical properties of a certain dataset are out of whack and thus where synthetic data could best be deployed. All of this begs the question: Is virtual data going to be a critical part of making virtual worlds fair and equitable?

Decision intelligence (DI) is now helping companies bridge the gap between theoretical AI and commercial AI, with Gartner predicting that more than a third of large organizations will be using DI within the next two years. The impact of DI for technical teamsWhile AI can be a somewhat nebulous concept, decision intelligence is more concrete. Today, commercial AI strategies are plagued by a host of problems that limit their effectiveness. Every business is now a technology business, and the expectations on CTOs and technical teams to drive commercial growth are mounting. With departments throughout the business relying on it, DI will put AI at the center of every business.

According to IDC’s 2022 AI InfrastructureView survey, 31% of companies say that they now have AI in production while the majority are actively piloting AI technologies. But commonly, companies fail to establish systems that would allow their data science teams — the teams responsible for deploying AI technologies — to properly version and share AI models, code, and datasets, he says. By simplifying AI management processes, the goal of MLOps is to automate the deployment of AI models into the core software systems of an organization. Also, AI systems work best when experts securely use real business data to train, test, and deploy the AI services,” Xu said. Torch.AI, for instance, connects apps, systems, services, and databases to enable reconciliation and processing of both unstructured and structured data for AI applications.

Latvia headquartered Enot, an AI startup backed by New Nordic Ventures, has announced the launch of a dedicated framework to optimize deep neural networks. The AI-driven offering, as the company explains, comes with a Python API that can be integrated within various neural network training pipelines. The whole process makes the neural network faster, smaller, and more energy-efficient, solving the challenges commonly faced by developers worldwide. Enot’s ImpactEnot claims that its solution has the potential to help AI developers and companies achieve up to 20 times neural network acceleration and up to 25 times network compression. In one case, it accelerated an image enhancement neural network by 13.3 times for a smartphone manufacturer.

This week in AI, DARPA, the emerging technologies R&D agency of the U.S. Defense Department, launched a new program that aims to “align” AI systems with human decision-makers in domains where there isn’t an agreed-upon right answer. “ITM is different from typical AI development approaches that require human agreement on the right outcomes,” ITM program manager Matt Turek said in a statement. “Although [AI systems are] quite smart today, they don’t always do what we want them to do. The goal of alignment is to produce AI systems that do [achieve] what we want them to,” OpenAI cofounder and chief scientist Ilya Sutskever told VentureBeat in a phone interview earlier this year. For AI coverage, send news tips to Kyle Wiggers — and be sure to subscribe to the AI Weekly newsletter and bookmark our AI channel, The Machine.

To help facilitate this process for modern enterprise, real-time data platform Redis and enterprise feature store company Tecton announced a partnership and product integration. He highlighted the combination of Tecton’s data orchestration capabilities with the Redis Enterprise Cloud’s speed and low cost. The Redis Enterprise Cloud is a database-as-a-service (DBaaS) available as both hybrid and multicloud. Competing offerings include DataStax Enterprise, Cloudera Enterprise Data Hub, MarkLogic, Couchbase and Databricks Lakehouse Platform. The new Tecton-Redis integration enables Tecton customers to use Redis Enterprise Cloud as an online store.

If data is the new oil of the digital economy, artificial intelligence (AI) is the steam engine. In 2022, data and AI have set the stage for the next chapter of the digital revolution, increasingly powering companies across the globe. “Responsible AI is a very important topic, but it is only as good as it is actionable,” said Olga Megorskaya, Data Summit panelist and CEO of a global data labeling platform Toloka AI. Secondly, responsible AI means responsible treatment of people who are actually acting behind the scene of training AI models. Oftentimes, companies that outsource labeling and training their AI models don’t have the option to interact directly with the individuals who are actually labeling the data.

Despite the challenges, including data quality issues, Gap, which is one of the biggest clothing and accessories retailers in the world, has come out with the successful implementation of data and AI to solve key business problems. The Gap data journeyInitially, Gap was leveraging traditional BI (business intelligence) capabilities and operational reports to take day-to-day decisions. And, after that, we moved to our next evolution what we have today – the Gap data platform,” she said. “There are many nuances in terms of how you make that happen, but that needs to be intentional across each of the datasets,” Mickman said. “We use different attributes that you might imagine like sales and inventory data to determine the size selling for a specific item in a store,” she said.

Before transformers came along, progress on AI language tasks largely lagged behind developments in other areas. “So NLP was, in a sense, behind computer vision. “Transformers seem to really be quite transformational across many problems in machine learning, including computer vision,” said Vladimir Haltakov, who works on computer vision related to self-driving cars at BMW in Munich. The eventual result was a network dubbed the Vision Transformer, or ViT, which the researchers presented at a conference in May 2021. ViT’s success suggested that maybe convolutions aren’t as fundamental to computer vision as researchers believed.

Someone at a robot company once told me a story about one of its bomb disposal machines. The soldiers who had been using the robot in Afghanistan were dismayed after it returned from repairs. But for anyone who’s seen After Yang, the beautiful and strange new movie by the South Korean filmmaker Kogonada, it won’t. Caught in an infinite loop, Yang repeats the same movement over and over, an act that appears comical at first but quickly turns grotesque. And since Jake bought the robot secondhand, to save a little money, Yang cannot be fixed by the unhelpful and animatronic guy at the future equivalent of Geek Squad.

Naively, they can disagree with one another with probability anywhere between zero and twice the error rate. Estimating the generalization error of a model — how well the model performs on unseen data — is a fundamental component in any machine learning system. This approach is particularly useful because we can use the same training data to train the two copies of the model. Since the disagreement rate 2q(1-q) equals the expected error 2q(1-q), we conclude that the expected error equals the disagreement rate. Since the disagreement rate 2q(1-q) equals the expected error 2q(1-q), we conclude that the expected error equals the disagreement rate.

When the stakes are higher, though, as in radiology or driverless cars, we need to be much more cautious about adopting deep learning. The deep learning hope—seemingly grounded not so much in science, but in a sort of historical grudge—is that intelligent behavior will emerge purely from the confluence of massive data and deep learning. Deep learning wasn’t so deep then, but it was again on the move. Hybrids that allow us to connect the learning prowess of deep learning, with the explicit, semantic richness of symbols, could be transformative. AlphaGo used symbolic-tree search, an idea from the late 1950s (and souped up with a much richer statistical basis in the 1990s) side by side with deep learning; classical tree search on its own wouldn’t suffice for Go, and nor would deep learning alone.

In The NewsMeta’s Yann LeCun on his vision for human-level AI What is the next step toward bridging the gap between natural and artificial intelligence? Scientists and researchers are divided on the answer. Yann LeCun, Chief AI Scientist at Meta and the recipient of the 2018 Turing Award, is betting on self-supervised learning, machine learning models that can be trained without the need for human-labeled examples. Looking at 2030: The Future of Artificial Intelligence and Metaverse An analytic predictive study for the future of artificial intelligence and metaverse in 2030 With the pace artificial intelligence is intertwining within our lives, there is no doubt that it will not end anytime soon. Predictions for AI in 2030 A report published from Harvard University presents the...

But conservationists are increasingly turning to AI as an innovative tech solution to tackle the biodiversity crisis and mitigate climate change. Without AI, researchers could not have analysed water changes across the country at the scale and level of detail needed. “AI technology provided us with a shockingly clear picture,” says Cássio Bernardino, WWF-Brasil’s MapBiomas water project lead. “We will increasingly see AI used in conservation,” he adds. “Appsilon’s work on the Mbaza AI app enables conservationists to rapidly identify and respond to threats to biodiversity.

Previous workIn recent years, several works have proposed traditional machine learning approaches to the study of ancient texts. The closest work to Ithaca is our 2019 research on ancient text restoration: Pythia15. 2), and includes an additional metadata string referring to the date proposed by epigraphers for the text (Extended Data Fig. For the task of chronological attribution, Ithaca discretizes all dates between 800 bc and ad 800 with a bin size of 10 years. Ancient historian baselineThe evaluators for ancient text restoration were two graduate students of ancient history, with 7 years of historical and linguistic training and specializing in Greek history and epigraphic documents.

The adoption and integration of these technologies within industry and manufacturing spaces is a fundamental part of what is called Industry 4.0, or the Fourth Industrial Revolution. The impacts of this paradigm shift on the human operators who continue to work alongside and symbiotically with these technologies in the industry bring with it novel ethical issues. Therefore, how to design these technologies for human values becomes the critical area of intervention. This paper takes up the case study of robotic AI-based assistance systems to explore the potential value implications that emerge due to current design practices and use. The design methodology known as Value Sensitive Design (VSD) is proposed as a sufficient starting point for designing these technologies for human values to address these issues.

Looking at 2030: The Future of Artificial Intelligence and MetaverseAn analytic predictive study for the future of artificial intelligence and metaverse in 2030With the pace artificial intelligence is intertwining within our lives, there is no doubt that it will not end anytime soon. Rather, the future looks like a society that would breathe and thrive through artificial intelligence only. But oftentimes, people confuse that leap with machine intelligence and the way that we think about intelligence for humans and it’s simply not true. So, it can be said that the wave of the future is the collaboration of humans and these artificial intelligence technologies. For example, whether a marriage in the metaverse will be legal or if someone is assaulted in the metaverse, how the convict will be penalized.

In a similar vein to other robotics related dataset papers (Celiktutan et al., 2017; Lemaignan et al., 2018; Yang et al., 2021), we do not take a hypothesis formulation and testing approach. Finally, we formulate learning social appropriateness of actions as a continual learning problem, more precisely task-incremental learning, and propose a Bayesian continual learning model that can incrementally learn social appropriateness of new actions. This can be achieved by adding new neurons (Parisi et al., 2017; Part and Lemon, 2017; Doğan et al., 2018a), layers (Rusu et al., 2016; Fernando et al., 2017) or both (Doğan et al., 2018b). 4 A Continual Learning Model for Social AppropriatenessIn this section, we propose a continual learning model for learning social appropriateness of robot actions. For example, other environments, social settings and robot actions can be considered to study the social appropriateness of robot actions at large.

In a recent event held by Meta AI, LeCun discussed possible paths toward human-level AI, challenges that remain, and the impact of advances in AI. Scientists have also managed to apply self-supervised learning to computer vision tasks such as medical imaging. Example of self-supervised learning in medical imaging (source: arXiv)High-level abstractionsMore recently, scientists have experimented with pure self-supervised learning on computer vision tasks. LeCun believes that self-supervised learning with these types of high-level abstractions will be key to developing the kind of robust world models required for human-level AI. Joint Embedding Predictive Architecture (JEPA) (source: Meta)Modular architectureAt the Meta AI event, LeCun also talked about a modular architecture for human-level AI.

RT @MattFenech83: Will Hinton's 2016 Twain-like prediction ("Rumours of my demise etc") go down as the most egregious example of #AI hype ever (in a crowded field)? Great piece by @GaryMarcus saying what many in DL don't want to hear: it just won't work without a rethink. https://t.co/fc1CZuF8ZS https://t.co/3Pat74CiTp

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