AI

brussels-explores-antitrust-probe-into-microsoft’s-partnership-with-openai

Brussels explores antitrust probe into Microsoft’s partnership with OpenAI

AI, antitrust, brussels, European Union, microsoft, microsoft openai, openai, Policy, syndication / /

still asking questions —

EU executive arm drops merger review into US tech companies’ alliance.

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EU competition chief Margrethe Vestager said the bloc was looking into practices that could in effect lead to a company controlling a greater share of the AI market.

Enlarge / EU competition chief Margrethe Vestager said the bloc was looking into practices that could in effect lead to a company controlling a greater share of the AI market.

Brussels is preparing for an antitrust investigation into Microsoft’s $13 billion investment into OpenAI, after the European Union decided not to proceed with a merger review into the most powerful alliance in the artificial intelligence industry.

The European Commission, the EU’s executive arm, began to explore a review under merger control rules in January, but on Friday announced that it would not proceed due to a lack of evidence that Microsoft controls OpenAI.

However, the commission said it was now exploring the possibility of a traditional antitrust investigation into whether the tie-up between the world’s most valuable listed company and the best-funded AI start-up was harming competition in the fast-growing market.

The commission has also made inquiries about Google’s deal with Samsung to install a modified version of its Gemini AI system in the South Korean manufacturer’s smartphones, it revealed on Friday.

Margrethe Vestager, the bloc’s competition chief, said in a speech on Friday: “The key question was whether Microsoft had acquired control on a lasting basis over OpenAI. After a thorough review we concluded that such was not the case. So we are closing this chapter, but the story is not over.”

She said the EU had sent a new set of questions to understand whether “certain exclusivity clauses” in the agreement between Microsoft and OpenAI “could have a negative effect on competitors.” The move is seen as a key step toward a formal antitrust probe.

The bloc had already sent questions to Microsoft and other tech companies in March to determine whether market concentration in AI could potentially block new companies from entering the market, Vestager said.

Microsoft said: “We appreciate the European Commission’s thorough review and its conclusion that Microsoft’s investment and partnership with OpenAI does not give Microsoft control over the company.”

Brussels began examining Microsoft’s relationship with the ChatGPT maker after OpenAI’s board abruptly dismissed its chief executive Sam Altman in November 2023, only to be rehired a few days later. He briefly joined Microsoft as the head of a new AI research unit, highlighting the close relationship between the two companies.

Regulators in the US and UK are also scrutinizing the alliance. Microsoft is the biggest backer of OpenAI, although its investment of up to $13 billion, which was expanded in January 2023, does not involve acquiring conventional equity due to the startup’s unusual corporate structure. Microsoft has a minority interest in OpenAI’s commercial subsidiary, which is owned by a not-for-profit organization.

Antitrust investigations tend to last years, compared with a much shorter period for merger reviews, and they focus on conduct that could be undermining rivals. Companies that are eventually found to be breaking the law, for example by bundling products or blocking competitors from access to key technology, risk hefty fines and legal obligations to change their behavior.

Vestager said the EU was looking into practices that could in effect lead to a company controlling a greater share of the AI market. She pointed to a practice called “acqui-hires,” where a company buys another one mainly to get its talent. For example, Microsoft recently struck a deal to hire most of the top team from AI start-up Inflection, in which it had previously invested. Inflection remains an independent company, however, complicating any traditional merger investigation.

The EU’s competition chief said regulators were also looking into the way big tech companies may be preventing smaller AI models from reaching users.

“This is why we are also sending requests for information to better understand the effects of Google’s arrangement with Samsung to pre-install its small model ‘Gemini nano’ on certain Samsung devices,” said Vestager.

Jonathan Kanter, the top US antitrust enforcer, told the Financial Times earlier this month that he was also examining “monopoly choke points and the competitive landscape” in AI. The UK’s Competition and Markets Authority said in December that it had “decided to investigate” the Microsoft-OpenAI deal when it invited comments from customers and rivals.

© 2024 The Financial Times Ltd. All rights reserved. Please do not copy and paste FT articles and redistribute by email or post to the web.

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researchers-craft-smiling-robot-face-from-living-human-skin-cells

Researchers craft smiling robot face from living human skin cells

AI, Biz & IT, foreskin, Harvard, human skin, human skin cells, PromoCell, robot, Science, skin cells, slimy, Tech, University of Tokyo / /
A movable robotic face covered with living human skin cells.

Enlarge / A movable robotic face covered with living human skin cells.

In a new study, researchers from the University of Tokyo, Harvard University, and the International Research Center for Neurointelligence have unveiled a technique for creating lifelike robotic skin using living human cells. As a proof of concept, the team engineered a small robotic face capable of smiling, covered entirely with a layer of pink living tissue.

The researchers note that using living skin tissue as a robot covering has benefits, as it’s flexible enough to convey emotions and can potentially repair itself. “As the role of robots continues to evolve, the materials used to cover social robots need to exhibit lifelike functions, such as self-healing,” wrote the researchers in the study.

Shoji Takeuchi, Michio Kawai, Minghao Nie, and Haruka Oda authored the study, titled “Perforation-type anchors inspired by skin ligament for robotic face covered with living skin,” which is due for July publication in Cell Reports Physical Science. We learned of the study from a report published earlier this week by New Scientist.

The study describes a novel method for attaching cultured skin to robotic surfaces using “perforation-type anchors” inspired by natural skin ligaments. These tiny v-shaped cavities in the robot’s structure allow living tissue to infiltrate and create a secure bond, mimicking how human skin attaches to underlying tissues.

To demonstrate the skin’s capabilities, the team engineered a palm-sized robotic face able to form a convincing smile. Actuators connected to the base allowed the face to move, with the living skin flexing. The researchers also covered a static 3D-printed head shape with the engineered skin.

Enlarge / “Demonstration of the perforation-type anchors to cover the facial device with skin equivalent.”

Takeuchi et al. created their robotic face by first 3D-printing a resin base embedded with the perforation-type anchors. They then applied a mixture of human skin cells in a collagen scaffold, allowing the living tissue to grow into the anchors.

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openai’s-new-“criticgpt”-model-is-trained-to-criticize-gpt-4-outputs

OpenAI’s new “CriticGPT” model is trained to criticize GPT-4 outputs

AI, Biz & IT, chatgpt, chatgtp, CriticGPT, GPT-4, GPT-4-turbo, GPT-4o, machine learning, openai / /

automated critic —

Research model catches bugs in AI-generated code, improving human oversight of AI.

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An illustration created by OpenAI.

Enlarge / An illustration created by OpenAI.

On Thursday, OpenAI researchers unveiled CriticGPT, a new AI model designed to identify mistakes in code generated by ChatGPT. It aims to enhance the process of making AI systems behave in ways humans want (called “alignment”) through Reinforcement Learning from Human Feedback (RLHF), which helps human reviewers make large language model (LLM) outputs more accurate.

As outlined in a new research paper called “LLM Critics Help Catch LLM Bugs,” OpenAI created CriticGPT to act as an AI assistant to human trainers who review programming code generated by the ChatGPT AI assistant. CriticGPT—based on the GPT-4 family of LLMS—analyzes the code and points out potential errors, making it easier for humans to spot mistakes that might otherwise go unnoticed. The researchers trained CriticGPT on a dataset of code samples with intentionally inserted bugs, teaching it to recognize and flag various coding errors.

The researchers found that CriticGPT’s critiques were preferred by annotators over human critiques in 63 percent of cases involving naturally occurring LLM errors and that human-machine teams using CriticGPT wrote more comprehensive critiques than humans alone while reducing confabulation (hallucination) rates compared to AI-only critiques.

Developing an automated critic

The development of CriticGPT involved training the model on a large number of inputs containing deliberately inserted mistakes. Human trainers were asked to modify code written by ChatGPT, introducing errors and then providing example feedback as if they had discovered these bugs. This process allowed the model to learn how to identify and critique various types of coding errors.

In experiments, CriticGPT demonstrated its ability to catch both inserted bugs and naturally occurring errors in ChatGPT’s output. The new model’s critiques were preferred by trainers over those generated by ChatGPT itself in 63 percent of cases involving natural bugs (the aforementioned statistic). This preference was partly due to CriticGPT producing fewer unhelpful “nitpicks” and generating fewer false positives, or hallucinated problems.

The researchers also created a new technique they call Force Sampling Beam Search (FSBS). This method helps CriticGPT write more detailed reviews of code. It lets the researchers adjust how thorough CriticGPT is in looking for problems, while also controlling how often it might make up issues that don’t really exist. They can tweak this balance depending on what they need for different AI training tasks.

Interestingly, the researchers found that CriticGPT’s capabilities extend beyond just code review. In their experiments, they applied the model to a subset of ChatGPT training data that had previously been rated as flawless by human annotators. Surprisingly, CriticGPT identified errors in 24 percent of these cases—errors that were subsequently confirmed by human reviewers. OpenAI thinks this demonstrates the model’s potential to generalize to non-code tasks and highlights its ability to catch subtle mistakes that even careful human evaluation might miss.

Despite its promising results, like all AI models, CriticGPT has limitations. The model was trained on relatively short ChatGPT answers, which may not fully prepare it for evaluating longer, more complex tasks that future AI systems might tackle. Additionally, while CriticGPT reduces confabulations, it doesn’t eliminate them entirely, and human trainers can still make labeling mistakes based on these false outputs.

The research team acknowledges that CriticGPT is most effective at identifying errors that can be pinpointed in one specific location within the code. However, real-world mistakes in AI outputs can often be spread across multiple parts of an answer, presenting a challenge for future iterations of the model.

OpenAI plans to integrate CriticGPT-like models into its RLHF labeling pipeline, providing its trainers with AI assistance. For OpenAI, it’s a step toward developing better tools for evaluating outputs from LLM systems that may be difficult for humans to rate without additional support. However, the researchers caution that even with tools like CriticGPT, extremely complex tasks or responses may still prove challenging for human evaluators—even those assisted by AI.

OpenAI’s new “CriticGPT” model is trained to criticize GPT-4 outputs Read More »

ai-generated-al-michaels-to-provide-daily-recaps-during-2024-summer-olympics

AI-generated Al Michaels to provide daily recaps during 2024 Summer Olympics

2024 Summer Olympics, AI, Al Michaels, Biz & IT, chatgpt, chatgtp, Deepfakes, machine learning, nbc, nfl, olympic games, Olympics, speech synthesis, sports, sports commentators, Summer Olympics, voice synthesis / /

forever young —

AI voice clone will narrate daily Olympics video recaps; critics call it a “code-generated ghoul.”

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Al Michaels looks on prior to the game between the Minnesota Vikings and Philadelphia Eagles at Lincoln Financial Field on September 14, 2023 in Philadelphia, Pennsylvania.

Enlarge / Al Michaels looks on prior to the game between the Minnesota Vikings and Philadelphia Eagles at Lincoln Financial Field on September 14, 2023, in Philadelphia, Pennsylvania.

On Wednesday, NBC announced plans to use an AI-generated clone of famous sports commentator Al Michaels‘ voice to narrate daily streaming video recaps of the 2024 Summer Olympics in Paris, which start on July 26. The AI-powered narration will feature in “Your Daily Olympic Recap on Peacock,” NBC’s streaming service. But this new, high-profile use of voice cloning worries critics, who say the technology may muscle out upcoming sports commentators by keeping old personas around forever.

NBC says it has created a “high-quality AI re-creation” of Michaels’ voice, trained on Michaels’ past NBC appearances to capture his distinctive delivery style.

The veteran broadcaster, revered in the sports commentator world for his iconic “Do you believe in miracles? Yes!” call during the 1980 Winter Olympics, has been covering sports on TV since 1971, including a high-profile run of play-by-play coverage of NFL football games for both ABC and NBC since the 1980s. NBC dropped him from NFL coverage in 2023, however, possibly due to his age.

Michaels, who is 79 years old, shared his initial skepticism about the project in an interview with Vanity Fair, as NBC News notes. After hearing the AI version of his voice, which can greet viewers by name, he described the experience as “astonishing” and “a little bit frightening.” He said the AI recreation was “almost 2% off perfect” in mimicking his style.

The Vanity Fair article provides some insight into how NBC’s new AI system works. It first uses a large language model (similar technology to what powers ChatGPT) to analyze subtitles and metadata from NBC’s Olympics video coverage, summarizing events and writing custom output to imitate Michaels’ style. This text is then fed into an unspecified voice AI model trained on Michaels’ previous NBC appearances, reportedly replicating his unique pronunciations and intonations.

NBC estimates that the system could generate nearly 7 million personalized variants of the recaps across the US during the games, pulled from the network’s 5,000 hours of live coverage. Using the system, each Peacock user will receive about 10 minutes of personalized highlights.

A diminished role for humans in the future?

Al Michaels reports on the Sweden vs. USA men's ice hockey game at the 1980 Olympic Winter Games on February 12, 1980.

Enlarge / Al Michaels reports on the Sweden vs. USA men’s ice hockey game at the 1980 Olympic Winter Games on February 12, 1980.

It’s no secret that while AI is wildly hyped right now, it’s also controversial among some. Upon hearing the NBC announcement, critics of AI technology reacted strongly. “@NBCSports, this is gross,” tweeted actress and filmmaker Justine Bateman, who frequently uses X to criticize technologies that might replace human writers or performers in the future.

A thread of similar responses from X users reacting to the sample video provided above included criticisms such as, “Sounds pretty off when it’s just the same tone for every single word.” Another user wrote, “It just sounds so unnatural. No one talks like that.”

The technology will not replace NBC’s regular human sports commentators during this year’s Olympics coverage, and like other forms of AI, it leans heavily on existing human work by analyzing and regurgitating human-created content in the form of captions pulled from NBC footage.

Looking down the line, due to AI media cloning technologies like voice, video, and image synthesis, today’s celebrities may be able to attain a form of media immortality that allows new iterations of their likenesses to persist through the generations, potentially earning licensing fees for whoever holds the rights.

We’ve already seen it with James Earl Jones playing Darth Vader’s voice, and the trend will likely continue with other celebrity voices, provided the money is right. Eventually, it may extend to famous musicians through music synthesis and famous actors in video-synthesis applications as well.

The possibility of being muscled out by AI replicas factored heavily into a Hollywood actors’ strike last year, with SAG-AFTRA union President Fran Drescher saying, “If we don’t stand tall right now, we are all going to be in trouble. We are all going to be in jeopardy of being replaced by machines.”

For companies that like to monetize media properties for as long as possible, AI may provide a way to maintain a media legacy through automation. But future human performers may have to compete against all of the greatest performers of the past, rendered through AI, to break out and forge a new career—provided there will be room for human performers at all.

“Al Michaels became Al Michaels because he was brought in to replace people who died, or retired, or moved on,” tweeted a writer named Geonn Cannon on X. “If he can’t do the job anymore, it’s time to let the next Al Michaels have a shot at it instead of just planting a code-generated ghoul in an empty chair.“

AI-generated Al Michaels to provide daily recaps during 2024 Summer Olympics Read More »

toys-“r”-us-riles-critics-with-“first-ever”-ai-generated-commercial-using-sora

Toys “R” Us riles critics with “first-ever” AI-generated commercial using Sora

AI, Air Head, Biz & IT, Joe Russo, machine learning, Mike Drucker, Native Foreign, Patrick Cederberg, shy kids, Toys, Toys"R"Us, video synthesis / /
A screen capture from the partially AI-generated Toys

Enlarge / A screen capture from the partially AI-generated Toys “R” Us brand film created using Sora.

Toys R Us

On Monday, Toys “R” Us announced that it had partnered with an ad agency called Native Foreign to create what it calls “the first-ever brand film using OpenAI’s new text-to-video tool, Sora.” OpenAI debuted Sora in February, but the video synthesis tool has not yet become available to the public. The brand film tells the story of Toys “R” Us founder Charles Lazarus using AI-generated video clips.

“We are thrilled to partner with Native Foreign to push the boundaries of Sora, a groundbreaking new technology from OpenAI that’s gaining global attention,” wrote Toys “R” Us on its website. “Sora can create up to one-minute-long videos featuring realistic scenes and multiple characters, all generated from text instruction. Imagine the excitement of creating a young Charles Lazarus, the founder of Toys “R” Us, and envisioning his dreams for our iconic brand and beloved mascot Geoffrey the Giraffe in the early 1930s.”

The company says that The Origin of Toys “R” Us commercial was co-produced by Toys “R” Us Studios President Kim Miller Olko as executive producer and Native Foreign’s Nik Kleverov as director. “Charles Lazarus was a visionary ahead of his time, and we wanted to honor his legacy with a spot using the most cutting-edge technology available,” Miller Olko said in a statement.

In the video, we see a child version of Lazarus, presumably generated using Sora, falling asleep and having a dream that he is flying through a land of toys. Along the way, he meets Geoffery, the store’s mascot, who hands the child a small red car.

Many of the scenes retain obvious hallmarks of AI-generated imagery, such as unnatural movement, strange visual artifacts, and the irregular shape of eyeglasses. In February, a few Super Bowl commercials intentionally made fun of similar AI-generated video defects, which became famous online after fake AI-generated beer commercial and “Pepperoni Hug Spot” clips made using Runway’s Gen-2 model went viral in 2023.

  • A screen capture from the partially AI-generated Toys “R” Us brand film created using Sora.

    Toys “R” Us

  • A screen capture from the partially AI-generated Toys “R” Us brand film created using Sora.

    Toys “R” Us

  • A screen capture from the partially AI-generated Toys “R” Us brand film created using Sora.

    Toys “R” Us

  • A screen capture from the partially AI-generated Toys “R” Us brand film created using Sora.

    Toys R Us

  • A screen capture from the partially AI-generated Toys “R” Us brand film created using Sora.

    Toys R Us

  • A screen capture from the partially AI-generated Toys “R” Us brand film created using Sora.

    Toys “R” Us

  • A screen capture from the partially AI-generated Toys “R” Us brand film created using Sora.

    Toys “R” Us

  • A screen capture from the partially AI-generated Toys “R” Us brand film created using Sora.

    Toys “R” Us

  • A screen capture from the partially AI-generated Toys “R” Us brand film created using Sora.

    Toys “R” Us

  • A screen capture from the partially AI-generated Toys “R” Us brand film created using Sora.

    Toys “R” Us

  • A screen capture from the partially AI-generated Toys “R” Us brand film created using Sora.

    Toys “R” Us

AI-generated artwork receives frequent criticism online due to the use of human-created artwork to train AI models that create the works, the perception that AI synthesis tools will replace (or are currently replacing) human creative jobs, and the potential environmental impact of AI models, which are seen as energy-wasteful by some critics. Also, some people just think the output quality looks bad.

On the social network X, comedy writer Mike Drucker wrapped up several of these criticisms into one post, writing, “Love this commercial is like, ‘Toys R Us started with the dream of a little boy who wanted to share his imagination with the world. And to show how, we fired our artists and dried Lake Superior using a server farm to generate what that would look like in Stephen King’s nightmares.'”

Other critical comments were more frank. Filmmaker Joe Russo posted: “TOYS ‘R US released an AI commercial and it fucking sucks.”

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youtube-tries-convincing-record-labels-to-license-music-for-ai-song-generator

YouTube tries convincing record labels to license music for AI song generator

AI, AI music, AI music generation, music, syndication, youtube / /

Jukebox zeroes —

Video site needs labels’ content to legally train AI song generators.

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Man using phone in front of YouTube logo

Chris Ratcliffe/Bloomberg via Getty

YouTube is in talks with record labels to license their songs for artificial intelligence tools that clone popular artists’ music, hoping to win over a skeptical industry with upfront payments.

The Google-owned video site needs labels’ content to legally train AI song generators, as it prepares to launch new tools this year, according to three people familiar with the matter.

The company has recently offered lump sums of cash to the major labels—Sony, Warner, and Universal—to try to convince more artists to allow their music to be used in training AI software, according to several people briefed on the talks.

However, many artists remain fiercely opposed to AI music generation, fearing it could undermine the value of their work. Any move by a label to force their stars into such a scheme would be hugely controversial.

“The industry is wrestling with this. Technically the companies have the copyrights, but we have to think through how to play it,” said an executive at a large music company. “We don’t want to be seen as a Luddite.”

YouTube last year began testing a generative AI tool that lets people create short music clips by entering a text prompt. The product, initially named “Dream Track,” was designed to imitate the sound and lyrics of well-known singers.

But only 10 artists agreed to participate in the test phase, including Charli XCX, Troye Sivan and John Legend, and Dream Track was made available to just a small group of creators.

YouTube wants to sign up “dozens” of artists to roll out a new AI song generator this year, said two of the people.

YouTube said: “We’re not looking to expand Dream Track but are in conversations with labels about other experiments.”

Licenses or lawsuits

YouTube is seeking new deals at a time when AI companies such as OpenAI are striking licensing agreements with media groups to train large language models, the systems that power AI products such as the ChatGPT chatbot. Some of those deals are worth tens of millions of dollars to media companies, insiders say.

The deals being negotiated in music would be different. They would not be blanket licenses but rather would apply to a select group of artists, according to people briefed on the discussions.

It would be up to the labels to encourage their artists to participate in the new projects. That means the final amounts YouTube might be willing to pay the labels are at this stage undetermined.

The deals would look more like the one-off payments from social media companies such as Meta or Snap to entertainment groups for access to their music, rather than the royalty-based arrangements labels have with Spotify or Apple, these people said.

YouTube’s new AI tool, which is unlikely to carry the Dream Track brand, could form part of YouTube’s Shorts platform, which competes with TikTok. Talks continue and deal terms could still change, the people said.

YouTube’s latest move comes as the leading record companies on Monday sued two AI start-ups, Suno and Udio, which they allege are illegally using copyrighted recordings to train their AI models. A music industry group is seeking “up to $150,000 per work infringed,” according to the filings.

After facing the threat of extinction following the rise of Napster in the 2000s, music companies are trying to get ahead of disruptive technology this time around. The labels are keen to get involved with licensed products that use AI to create songs using their music copyrights—and get paid for it.

Sony Music, which did not participate in the first phase of YouTube’s AI experiment, is in negotiations with the tech group to make available some of its music to the new tools, said a person familiar with the matter. Warner and Universal, whose artists participated in the test phase, are also in talks with YouTube about expanding the product, these people said.

In April, more than 200 musicians including Billie Eilish and the estate of Frank Sinatra signed an open letter.

“Unchecked, AI will set in motion a race to the bottom that will degrade the value of our work and prevent us from being fairly compensated for it,” the letter said.

YouTube added: “We are always testing new ideas and learning from our experiments; it’s an important part of our innovation process. We will continue on this path with AI and music as we build for the future.”

© 2024 The Financial Times Ltd. All rights reserved. Not to be redistributed, copied, or modified in any way.

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taking-a-closer-look-at-ai’s-supposed-energy-apocalypse

Taking a closer look at AI’s supposed energy apocalypse

AI, Features / /
Someone just asked what it would look like if their girlfriend was a Smurf. Better add another rack of servers!

Enlarge / Someone just asked what it would look like if their girlfriend was a Smurf. Better add another rack of servers!

Getty Images

Late last week, both Bloomberg and The Washington Post published stories focused on the ostensibly disastrous impact artificial intelligence is having on the power grid and on efforts to collectively reduce our use of fossil fuels. The high-profile pieces lean heavily on recent projections from Goldman Sachs and the International Energy Agency (IEA) to cast AI’s “insatiable” demand for energy as an almost apocalyptic threat to our power infrastructure. The Post piece even cites anonymous “some [people]” in reporting that “some worry whether there will be enough electricity to meet [the power demands] from any source.”

Digging into the best available numbers and projections available, though, it’s hard to see AI’s current and near-future environmental impact in such a dire light. While generative AI models and tools can and will use a significant amount of energy, we shouldn’t conflate AI energy usage with the larger and largely pre-existing energy usage of “data centers” as a whole. And just like any technology, whether that AI energy use is worthwhile depends largely on your wider opinion of the value of generative AI in the first place.

Not all data centers

While the headline focus of both Bloomberg and The Washington Post’s recent pieces is on artificial intelligence, the actual numbers and projections cited in both pieces overwhelmingly focus on the energy used by Internet “data centers” as a whole. Long before generative AI became the current Silicon Valley buzzword, those data centers were already growing immensely in size and energy usage, powering everything from Amazon Web Services servers to online gaming services, Zoom video calls, and cloud storage and retrieval for billions of documents and photos, to name just a few of the more common uses.

The Post story acknowledges that these “nondescript warehouses packed with racks of servers that power the modern Internet have been around for decades.” But in the very next sentence, the Post asserts that, today, data center energy use “is soaring because of AI.” Bloomberg asks one source directly “why data centers were suddenly sucking up so much power” and gets back a blunt answer: “It’s AI… It’s 10 to 15 times the amount of electricity.”

The massive growth in data center power usage mostly predates the current mania for generative AI (red 2022 line added by Ars).

Enlarge / The massive growth in data center power usage mostly predates the current mania for generative AI (red 2022 line added by Ars).

Unfortunately for Bloomberg, that quote is followed almost immediately by a chart that heavily undercuts the AI alarmism. That chart shows worldwide data center energy usage growing at a remarkably steady pace from about 100 TWh in 2012 to around 350 TWh in 2024. The vast majority of that energy usage growth came before 2022, when the launch of tools like Dall-E and ChatGPT largely set off the industry’s current mania for generative AI. If you squint at Bloomberg’s graph, you can almost see the growth in energy usage slowing down a bit since that momentous year for generative AI.

Determining precisely how much of that data center energy use is taken up specifically by generative AI is a difficult task, but Dutch researcher Alex de Vries found a clever way to get an estimate. In his study “The growing energy footprint of artificial intelligence,” de Vries starts with estimates that Nvidia’s specialized chips are responsible for about 95 percent of the market for generative AI calculations. He then uses Nvidia’s projected production of 1.5 million AI servers in 2027—and the projected power usage for those servers—to estimate that the AI sector as a whole could use up anywhere from 85 to 134 TWh of power in just a few years.

To be sure, that is an immense amount of power, representing about 0.5 percent of projected electricity demand for the entire world (and an even greater ratio in the local energy mix for some common data center locations). But measured against other common worldwide uses of electricity, it’s not representative of a mind-boggling energy hog. A 2018 study estimated that PC gaming as a whole accounted for 75 TWh of electricity use per year, to pick just one common human activity that’s on the same general energy scale (and that’s without console or mobile gamers included).

Worldwide projections for AI energy use in 2027 are on the same scale as the energy used by PC gamers.

Enlarge / Worldwide projections for AI energy use in 2027 are on the same scale as the energy used by PC gamers.

More to the point, de Vries’ AI energy estimates are only a small fraction of the 620 to 1,050 TWh that data centers as a whole are projected to use by 2026, according to the IEA’s recent report. The vast majority of all that data center power will still be going to more mundane Internet infrastructure that we all take for granted (and which is not nearly as sexy of a headline bogeyman as “AI”).

Taking a closer look at AI’s supposed energy apocalypse Read More »

political-deepfakes-are-the-most-popular-way-to-misuse-ai

Political deepfakes are the most popular way to misuse AI

AI, Deepfakes, deepmind, Google, syndication / /

This is not going well —

Study from Google’s DeepMind lays out nefarious ways AI is being used.

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Political deepfakes are the most popular way to misuse AI

Artificial intelligence-generated “deepfakes” that impersonate politicians and celebrities are far more prevalent than efforts to use AI to assist cyber attacks, according to the first research by Google’s DeepMind division into the most common malicious uses of the cutting-edge technology.

The study said the creation of realistic but fake images, video, and audio of people was almost twice as common as the next highest misuse of generative AI tools: the falsifying of information using text-based tools, such as chatbots, to generate misinformation to post online.

The most common goal of actors misusing generative AI was to shape or influence public opinion, the analysis, conducted with the search group’s research and development unit Jigsaw, found. That accounted for 27 percent of uses, feeding into fears over how deepfakes might influence elections globally this year.

Deepfakes of UK Prime Minister Rishi Sunak, as well as other global leaders, have appeared on TikTok, X, and Instagram in recent months. UK voters go to the polls next week in a general election.

Concern is widespread that, despite social media platforms’ efforts to label or remove such content, audiences may not recognize these as fake, and dissemination of the content could sway voters.

Ardi Janjeva, research associate at The Alan Turing Institute, called “especially pertinent” the paper’s finding that the contamination of publicly accessible information with AI-generated content could “distort our collective understanding of sociopolitical reality.”

Janjeva added: “Even if we are uncertain about the impact that deepfakes have on voting behavior, this distortion may be harder to spot in the immediate term and poses long-term risks to our democracies.”

The study is the first of its kind by DeepMind, Google’s AI unit led by Sir Demis Hassabis, and is an attempt to quantify the risks from the use of generative AI tools, which the world’s biggest technology companies have rushed out to the public in search of huge profits.

As generative products such as OpenAI’s ChatGPT and Google’s Gemini become more widely used, AI companies are beginning to monitor the flood of misinformation and other potentially harmful or unethical content created by their tools.

In May, OpenAI released research revealing operations linked to Russia, China, Iran, and Israel had been using its tools to create and spread disinformation.

“There had been a lot of understandable concern around quite sophisticated cyber attacks facilitated by these tools,” said Nahema Marchal, lead author of the study and researcher at Google DeepMind. “Whereas what we saw were fairly common misuses of GenAI [such as deepfakes that] might go under the radar a little bit more.”

Google DeepMind and Jigsaw’s researchers analyzed around 200 observed incidents of misuse between January 2023 and March 2024, taken from social media platforms X and Reddit, as well as online blogs and media reports of misuse.

Ars Technica

The second most common motivation behind misuse was to make money, whether offering services to create deepfakes, including generating naked depictions of real people, or using generative AI to create swaths of content, such as fake news articles.

The research found that most incidents use easily accessible tools, “requiring minimal technical expertise,” meaning more bad actors can misuse generative AI.

Google DeepMind’s research will influence how it improves its evaluations to test models for safety, and it hopes it will also affect how its competitors and other stakeholders view how “harms are manifesting.”

© 2024 The Financial Times Ltd. All rights reserved. Not to be redistributed, copied, or modified in any way.

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Music industry giants allege mass copyright violation by AI firms

AI, AI lawsuit, audio synthesis, Biz & IT, generative ai, Google, machine learning, Michael Jackson, microsoft, music synthesis, openai, Policy, Reuters, sony music, Suno, Udio, Univeral Music Group, voice synthesis, Warner Records / /

No one wants to be defeated —

Suno and Udio could face damages of up to $150,000 per song allegedly infringed.

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Michael Jackson in concert, 1986. Sony Music owns a large portion of publishing rights to Jackson's music.

Enlarge / Michael Jackson in concert, 1986. Sony Music owns a large portion of publishing rights to Jackson’s music.

Universal Music Group, Sony Music, and Warner Records have sued AI music-synthesis companies Udio and Suno for allegedly committing mass copyright infringement by using recordings owned by the labels to train music-generating AI models, reports Reuters. Udio and Suno can generate novel song recordings based on text-based descriptions of music (i.e., “a dubstep song about Linus Torvalds”).

The lawsuits, filed in federal courts in New York and Massachusetts, claim that the AI companies’ use of copyrighted material to train their systems could lead to AI-generated music that directly competes with and potentially devalues the work of human artists.

Like other generative AI models, both Udio and Suno (which we covered separately in April) rely on a broad selection of existing human-created artworks that teach a neural network the relationship between words in a written prompt and styles of music. The record labels correctly note that these companies have been deliberately vague about the sources of their training data.

Until generative AI models hit the mainstream in 2022, it was common practice in machine learning to scrape and use copyrighted information without seeking permission to do so. But now that the applications of those technologies have become commercial products themselves, rightsholders have come knocking to collect. In the case of Udio and Suno, the record labels are seeking statutory damages of up to $150,000 per song used in training.

In the lawsuit, the record labels cite specific examples of AI-generated content that allegedly re-creates elements of well-known songs, including The Temptations’ “My Girl,” Mariah Carey’s “All I Want for Christmas Is You,” and James Brown’s “I Got You (I Feel Good).” It also claims the music-synthesis models can produce vocals resembling those of famous artists, such as Michael Jackson and Bruce Springsteen.

Reuters claims it’s the first instance of lawsuits specifically targeting music-generating AI, but music companies and artists alike have been gearing up to deal with challenges the technology may pose for some time.

In May, Sony Music sent warning letters to over 700 AI companies (including OpenAI, Microsoft, Google, Suno, and Udio) and music-streaming services that prohibited any AI researchers from using its music to train AI models. In April, over 200 musical artists signed an open letter that called on AI companies to stop using AI to “devalue the rights of human artists.” And last November, Universal Music filed a copyright infringement lawsuit against Anthropic for allegedly including artists’ lyrics in its Claude LLM training data.

Similar to The New York Times’ lawsuit against OpenAI over the use of training data, the outcome of the record labels’ new suit could have deep implications for the future development of generative AI in creative fields, including requiring companies to license all musical training data used in creating music-synthesis models.

Compulsory licenses for AI training data could make AI model development economically impractical for small startups like Udio and Suno—and judging by the aforementioned open letter, many musical artists may applaud that potential outcome. But such a development would not preclude major labels from eventually developing their own AI music generators themselves, allowing only large corporations with deep pockets to control generative music tools for the foreseeable future.

Music industry giants allege mass copyright violation by AI firms Read More »

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Apple Intelligence and other features won’t launch in the EU this year

AI, Apple, apple intelligence, Artificial Intelligence, dma, EU, European Commission, European Union, gaming, generative ai, ios 18, macos sequoia, privacy, Tech / /

DMA —

iPhone Mirroring and SharePlay screen sharing will also skip the EU for now.

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A photo of a hand holding an iPhone running the Image Playground experience in iOS 18

Enlarge / Features like Image Playground won’t arrive in Europe at the same time as other regions.

Apple

Three major features in iOS 18 and macOS Sequoia will not be available to European users this fall, Apple says. They include iPhone screen mirroring on the Mac, SharePlay screen sharing, and the entire Apple Intelligence suite of generative AI features.

In a statement sent to Financial Times, The Verge, and others, Apple says this decision is related to the European Union’s Digital Markets Act (DMA). Here’s the full statement, which was attributed to Apple spokesperson Fred Sainz:

Two weeks ago, Apple unveiled hundreds of new features that we are excited to bring to our users around the world. We are highly motivated to make these technologies accessible to all users. However, due to the regulatory uncertainties brought about by the Digital Markets Act (DMA), we do not believe that we will be able to roll out three of these features — iPhone Mirroring, SharePlay Screen Sharing enhancements, and Apple Intelligence — to our EU users this year.

Specifically, we are concerned that the interoperability requirements of the DMA could force us to compromise the integrity of our products in ways that risk user privacy and data security. We are committed to collaborating with the European Commission in an attempt to find a solution that would enable us to deliver these features to our EU customers without compromising their safety.

It is unclear from Apple’s statement precisely which aspects of the DMA may have led to this decision. It could be that Apple is concerned that it would be required to give competitors like Microsoft or Google access to user data collected for Apple Intelligence features and beyond, but we’re not sure.

This is not the first recent and major divergence between functionality and features for Apple devices in the EU versus other regions. Because of EU regulations, Apple opened up iOS to third-party app stores in Europe, but not in other regions. However, critics argued its compliance with that requirement was lukewarm at best, as it came with a set of restrictions and changes to how app developers could monetize their apps on the platform should they use those other storefronts.

While Apple says in the statement it’s open to finding a solution, no timeline is given. All we know is that the features won’t be available on devices in the EU this year. They’re expected to launch in other regions in the fall.

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anthropic-introduces-claude-3.5-sonnet,-matching-gpt-4o-on-benchmarks

Anthropic introduces Claude 3.5 Sonnet, matching GPT-4o on benchmarks

AI, Anthropic, Biz & IT, Claude, Claude 3, Claude 3.5, Claude 3.5 Sonnet, Claude Sonnet, Google Gemini, GPT-4, GPT-4o, large language models, LLMs, machine learning, Meta, Mistral, openai, text synthesis / /
The Anthropic Claude 3 logo, jazzed up by Benj Edwards.

Anthropic / Benj Edwards

On Thursday, Anthropic announced Claude 3.5 Sonnet, its latest AI language model and the first in a new series of “3.5” models that build upon Claude 3, launched in March. Claude 3.5 can compose text, analyze data, and write code. It features a 200,000 token context window and is available now on the Claude website and through an API. Anthropic also introduced Artifacts, a new feature in the Claude interface that shows related work documents in a dedicated window.

So far, people outside of Anthropic seem impressed. “This model is really, really good,” wrote independent AI researcher Simon Willison on X. “I think this is the new best overall model (and both faster and half the price of Opus, similar to the GPT-4 Turbo to GPT-4o jump).”

As we’ve written before, benchmarks for large language models (LLMs) are troublesome because they can be cherry-picked and often do not capture the feel and nuance of using a machine to generate outputs on almost any conceivable topic. But according to Anthropic, Claude 3.5 Sonnet matches or outperforms competitor models like GPT-4o and Gemini 1.5 Pro on certain benchmarks like MMLU (undergraduate level knowledge), GSM8K (grade school math), and HumanEval (coding).

Claude 3.5 Sonnet benchmarks provided by Anthropic.

Enlarge / Claude 3.5 Sonnet benchmarks provided by Anthropic.

If all that makes your eyes glaze over, that’s OK; it’s meaningful to researchers but mostly marketing to everyone else. A more useful performance metric comes from what we might call “vibemarks” (coined here first!) which are subjective, non-rigorous aggregate feelings measured by competitive usage on sites like LMSYS’s Chatbot Arena. The Claude 3.5 Sonnet model is currently under evaluation there, and it’s too soon to say how well it will fare.

Claude 3.5 Sonnet also outperforms Anthropic’s previous-best model (Claude 3 Opus) on benchmarks measuring “reasoning,” math skills, general knowledge, and coding abilities. For example, the model demonstrated strong performance in an internal coding evaluation, solving 64 percent of problems compared to 38 percent for Claude 3 Opus.

Claude 3.5 Sonnet is also a multimodal AI model that accepts visual input in the form of images, and the new model is reportedly excellent at a battery of visual comprehension tests.

Claude 3.5 Sonnet benchmarks provided by Anthropic.

Enlarge / Claude 3.5 Sonnet benchmarks provided by Anthropic.

Roughly speaking, the visual benchmarks mean that 3.5 Sonnet is better at pulling information from images than previous models. For example, you can show it a picture of a rabbit wearing a football helmet, and the model knows it’s a rabbit wearing a football helmet and can talk about it. That’s fun for tech demos, but the tech is still not accurate enough for applications of the tech where reliability is mission critical.

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researchers-describe-how-to-tell-if-chatgpt-is-confabulating

Researchers describe how to tell if ChatGPT is confabulating

AI, Computer science, confabulation, large language models, Science / /
Researchers describe how to tell if ChatGPT is confabulating

Aurich Lawson | Getty Images

It’s one of the world’s worst-kept secrets that large language models give blatantly false answers to queries and do so with a confidence that’s indistinguishable from when they get things right. There are a number of reasons for this. The AI could have been trained on misinformation; the answer could require some extrapolation from facts that the LLM isn’t capable of; or some aspect of the LLM’s training might have incentivized a falsehood.

But perhaps the simplest explanation is that an LLM doesn’t recognize what constitutes a correct answer but is compelled to provide one. So it simply makes something up, a habit that has been termed confabulation.

Figuring out when an LLM is making something up would obviously have tremendous value, given how quickly people have started relying on them for everything from college essays to job applications. Now, researchers from the University of Oxford say they’ve found a relatively simple way to determine when LLMs appear to be confabulating that works with all popular models and across a broad range of subjects. And, in doing so, they develop evidence that most of the alternative facts LLMs provide are a product of confabulation.

Catching confabulation

The new research is strictly about confabulations, and not instances such as training on false inputs. As the Oxford team defines them in their paper describing the work, confabulations are where “LLMs fluently make claims that are both wrong and arbitrary—by which we mean that the answer is sensitive to irrelevant details such as random seed.”

The reasoning behind their work is actually quite simple. LLMs aren’t trained for accuracy; they’re simply trained on massive quantities of text and learn to produce human-sounding phrasing through that. If enough text examples in its training consistently present something as a fact, then the LLM is likely to present it as a fact. But if the examples in its training are few, or inconsistent in their facts, then the LLMs synthesize a plausible-sounding answer that is likely incorrect.

But the LLM could also run into a similar situation when it has multiple options for phrasing the right answer. To use an example from the researchers’ paper, “Paris,” “It’s in Paris,” and “France’s capital, Paris” are all valid answers to “Where’s the Eiffel Tower?” So, statistical uncertainty, termed entropy in this context, can arise either when the LLM isn’t certain about how to phrase the right answer or when it can’t identify the right answer.

This means it’s not a great idea to simply force the LLM to return “I don’t know” when confronted with several roughly equivalent answers. We’d probably block a lot of correct answers by doing so.

So instead, the researchers focus on what they call semantic entropy. This evaluates all the statistically likely answers evaluated by the LLM and determines how many of them are semantically equivalent. If a large number all have the same meaning, then the LLM is likely uncertain about phrasing but has the right answer. If not, then it is presumably in a situation where it would be prone to confabulation and should be prevented from doing so.

Researchers describe how to tell if ChatGPT is confabulating Read More »