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Tag: Yann LeCun

  • Who’s behind AMI Labs, Yann LeCun’s ‘world model’ startup | TechCrunch

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    Yann LeCun’s new venture, AMI Labs, has drawn intense attention since the AI scientist left Meta to found it. This week, the startup finally confirmed what it’s building — and several key details have been hiding in plain sight.

    On its newly launched website, the startup disclosed its plans to develop “world models” in order to “build intelligent systems that understand the real world.” The focus on world models was already hinted at by AMI’s name, which stands for Advanced Machine Intelligence, but it has now officially joined the ranks of the hottest AI research startups.

    Building foundational models that bridge AI and the real world has become one of the field’s most exciting pursuits, attracting top scientists and deep-pocketed investors alike — product or no product.

    World Labs, a direct rival founded by AI pioneer Fei-Fei Li, became a unicorn shortly after coming out of stealth. After launching its first product, Marble, which generates physically sound 3D worlds, World Labs is now reportedly in talks to raise fresh funding at a valuation of $5 billion. 

    There’s little doubt that VCs would be equally eager to invest in LeCun, adding credibility to rumors that AMI Labs might be raising funding at a $3.5 billion valuation. According to Bloomberg, VCs in talks with the startup include Cathay Innovation, Greycroft, and Hiro Capital, to which LeCun is an advisor. Other potential investors reportedly include 20VC, Bpifrance, Daphni, and HV Capital. 

    Regardless of who writes the checks, investors may want to note an important detail: As LeCun has made clear, he is AMI’s executive chairman, not its CEO. Instead, that role belongs to Alex LeBrun, previously co-founder and CEO at Nabla, a health AI startup with offices in Paris and New York.

    LeBrun’s transition from Nabla to AMI is part of a partnership announced last December by Nabla, which develops AI assistants for clinical care and to which LeCun has been an advisor. In exchange for “privileged access” to AMI’s world models, Nabla’s board supported LeBrun’s shift from CEO to chief AI scientist and chairman, clearing the way for his new role.

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    As AMI Labs’ CEO, LeBrun will be surrounded by familiar faces. After Facebook acquired his previous startup, Wit.ai, the serial entrepreneur and AI engineer worked under LeCun’s leadership at Meta’s AI research laboratory, FAIR. According to reports, the duo will also be joined by Laurent Solly, who stepped down as Meta’s vice president for Europe last December.

    The talent overlap between AMI and Meta likely won’t stop there. LeCun told the MIT Technology Review that his former employer could well be AMI’s first client. But he has also been publicly critical of some of Meta’s strategic choices made under Mark Zuckerberg’s direction. More broadly, the Review interprets AMI Labs as a contrarian bet against large language models (LLMs).

    The limitations of LLMs that LeCun has pointed out include hallucinations, which are a serious concern in contexts like medicine, as LeBrun also knows firsthand. AMI Labs’ CEO told Forbes that a big reason he took the role was the prospect of applying its world models to healthcare. But the startup will also target other high-stakes applied fields.

    “AMI Labs will advance AI research and develop applications where reliability, controllability, and safety really matter, especially for industrial process control, automation, wearable devices, robotics, healthcare, and beyond,” it wrote in its mission statement. “We share one belief: real intelligence does not start in language. It starts in the world.”

    Unlike generative approaches, which LeCun and his team see as poorly suited for unpredictable data such as sensor input, the startup promises that its AI systems will not only understand the real world, but also have persistent memory, the ability to reason and plan, and be controllable and safe.

    The startup plans to license its technology to industry partners for real-life applications, but says it also plans to contribute to building the future of AI “with the global academic research community via open publications and open source.” LeCun said he plans to keep his professor position at NYU, where he teaches one class per year and supervises PhD and postdoctoral students.

    This means that the French-born researcher will remain based in New York, but he told the MIT Technology Review that AMI Labs “is going to be a global company [that’s] headquartered in Paris.” The news was welcomed by French President Emmanuel Macron, who expressed his pride that LeCun, who is also a Turing Prize winner, chose Paris. “We will do everything we can to ensure his success from France,” he said.

    The startup will also have offices in Montreal, New York, and Singapore, but its decision to pick Paris for its headquarters will help consolidate Paris’ reputation as an AI hub, where it will join the ranks of H, Mistral AI and several international labs, including FAIR. It’s fitting, perhaps, that AMI is pronounced a-mee — like “ami” in French, which means “friend,” LeCun has pointed out.

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    Anna Heim

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  • 4 A.I. Themes That Defined 2025 and Are Shaping What Comes Next

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    From infrastructure battles to physical-world intelligence, A.I.’s next chapter is already taking shape. Unsplash

    In November, ChatGPT turned three, with a global user base rapidly approaching one billion. At this point, A.I. is no longer an esoteric acronym that needs explaining in news stories. It has become a daily utility, woven into how we work, learn, shop and even love. The field is also far more crowded than it was just a few years ago, with competitors emerging at every layer of the stack.

    Over the past year, conversation around A.I. has taken on a more complicated tone. Some argue that consumer chatbots are nearing a plateau. Others warn that startup valuations are inflating into a bubble. And, as always, there’s the persistent anxiety that A.I. may one day outgrow human control altogether.

    So what comes next? Much of the industry’s energy is now focused on the infrastructure side of A.I. Big Tech companies are racing to solve the hardware bottlenecks that limit today’s systems, while startups experiment with applications far beyond chatbots. At the same time, researchers are beginning to look past language models altogether, toward models that can reason about the physical world.

    Below are the key themes Observer has identified over the past year of covering this space. Many of these developments are still unfolding and are likely to shape the field well into 2026 and beyond.

    A.I. chips

    Even as OpenAI faces growing competition at the model level, its primary chip supplier, Nvidia, remains in a league of its own. Demand for its GPUs continues to outstrip supply, and no rival has yet meaningfully disrupted its dominance. Traditional semiconductor companies such as AMD and Intel are racing to claw back market share, while some of Nvidia’s largest customers are designing their own chips to reduce dependence on a single supplier.

    Google’s long-in-the-making Tensor Processing Unit, or TPU, has reportedly found its first major customer, Meta, marking a milestone after years of internal use. Meta, Microsoft and Amazon are also deep into developing in-house chips of their own—Meta’s Artemis, Microsoft’s Maia and Amazon’s Trainium.

    World models

    To borrow from philosopher Ludwig Wittgenstein, the limits of language are the limits of our world. Today’s A.I. systems have grown remarkably fluent in human language—especially English—but language captures only a narrow slice of intelligence. That limitation has prompted some researchers to argue that large language models alone can never reach human-level understanding.

    Meta’s longtime chief A.I. scientist, Yann LeCun, has been among the most vocal critics. “We’re never going to get to human-level A.I. by just training on text,” he said during a Harvard talk in September.

    That belief is fueling a push toward so-called “world models,” which aim to teach machines how the physical world works—how objects move, how space is structured, and how cause and effect unfold. LeCun is now leaving Meta to build such a system himself. Fei-Fei Li’s startup, World Labs, unveiled its first model in November after nearly two years of development. Google DeepMind has released early versions through its Genie projects, and Nvidia is betting heavily on physical A.I. with its Cosmos models.

    Language-specific A.I.

    While pioneering researchers look beyond language, linguistic barriers remain one of A.I.’s most practical challenges. More than half of the internet’s content is written in English, skewing training data and limiting performance in other languages.

    In response, developers around the world are building models rooted in local cultures and linguistic norms. In Japan, companies such as Sanaka and NTT are developing LLMs tailored to Japanese language and values. In India, Krutrim is working to support the country’s vast linguistic diversity. France’s Mistral AI has positioned its Le Chat assistant as a European alternative to ChatGPT. Earlier this year, Microsoft also issued a call for proposals to expand training data across European languages.

    A.I. wearables

    It’s only natural that there’s a consumer hardware angle of A.I. This year brought a wave of experiments in wearable A.I.—some met with curiosity, others with discomfort.

    Friend, a startup selling an A.I. pendant, sparked backlash after a New York City subway campaign framed its product as a substitute for human companionship. In December, Meta acquired Limitless, the maker of a $99 wearable that records and summarizes conversations. Earlier in the year, Amazon bought Bee, which produces a $50 bracelet designed to transcribe daily activity and generate summaries.

    Meta is also developing a new line of smart glasses with EssilorLuxottica, the company behind Ray-Ban and Oakley. In July, Mark Zuckerberg went so far as to suggest that people without A.I.-enhanced glasses could eventually face a “significant cognitive disadvantage.” Meanwhile, OpenAI is quietly collaborating with former Apple design chief Jony Ive on a mysterious hardware project of its own. This all suggests the next phase of A.I. may be something we wear, not just something we type into.

    4 A.I. Themes That Defined 2025 and Are Shaping What Comes Next

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    Sissi Cao

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  • A.I. Degrees Boom as Students Prepare for an Uncertain Job Market

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    Universities are rapidly expanding A.I. programs as students seek skills that can withstand an increasingly automated future. Photo by: Jumping Rocks/Universal Images Group via Getty Images

    When Chris Callison-Burch first started teaching an A.I. course at the University of Pennsylvania in2018, his inaugural class had about 100 students. Seven years later, enrollment has swelled to roughly 400—excluding another 250 students attending remotely and an additional 100 to 200 on the waiting list. The professor now teaches in the largest classroom on campus. If his course grew any bigger, he’d need to move into the school’s sports stadium.

    “I would love to think that’s all because I’m a dynamic lecturer,” Callison-Burch told Observer. “But it’s really a testament to the popularity of the field.”

    Demand for A.I. courses and degrees has soared across higher education as the technology plays an increasingly central role in daily life and begins to encroach on once-popular fields like computer science. Amid uncertainty about the future of the labor market, students are seeking to prepare for an A.I.-dominated economy by immersing themselves in the field.

    Universities have followed suit. Schools like Carnegie Mellon and Purdue University are among a number offering undergraduate or graduate degrees in A.I., a trend expected to accelerate in the coming years. The University of Pennsylvania recently became the first Ivy League school to offer both undergraduate and graduate A.I. programs. Its graduate curriculum includes courses in natural language processing and machine learning, in addition to required classes on technology ethics and the broader legal landscape.

    The demand is widespread. The University of Buffalo’s A.I. master’s program enrolled 103 students last year, up from just five in its inaugural 2020 cohort. At the Massachusetts Institute of Technology, undergraduate enrollment in A.I. has jumped from 37 students in 2022 to more than 300. Miami Dade College has seen a 75 percent increase in enrollment in its A.I. programs since 2022, while its other programs have remained relatively steady aside from a “slight decrease in computer science,” the school told Observer.

    Callison-Burch, who also serves as faculty director of Penn’s online A.I. master’s program, has noticed a similar decline. “There’s an interesting trend at the moment where it looks like computer science enrollment is dipping,” he said, pointing to increased A.I.-powered automation across the field. More than 60 percent of undergraduate computing programs saw a decline in employment for the 2025-2026 year compared to the year prior, according to a recent report from the Computing Research Association.

    That decline comes as A.I. reshapes some of the professions most exposed to its advances. In fields like coding, early-career workers have already experienced a 13 percent relative decline in employment, according to an August research paper from Stanford.

    A.I. leaders’ advice for students

    Experts have offered a range of advice as the technology they helped develop begins to reshape the labor market. Demis Hassabis, CEO of Google DeepMind, has advocated for an immersion in A.I. tools, while acclaimed researcher Geoffrey Hinton suggests prospective students focus on a well-rounded education that pairs mathematics and science with liberal arts.

    Yann LeCun, Meta’s former chief A.I. scientist, advises young people to become adept at learning itself, as their job is “almost certainly going to change” over time. “My suggestion is to take courses on topics that are fundamental and have a long shelf life,” he told Observer via email, pointing to mathematics, physics and engineering as core areas of focus.

    It’s not just students grappling with these shifts. Callison-Burch noted that professors, too, are trying to adapt and determine how best to integrate A.I. into their classrooms. One thing, he said, is certain: the technology will only become more pervasive. That makes it all the more important for young people to familiarize themselves with its tools.

    Even so, he acknowledged that predicting how A.I. will reshape the labor market remains extraordinarily difficult, making it hard for students to bet confidently on any one path. “I don’t think there’s an easy way of picking something that’s going to be future-proof, when we can’t yet see that future,” he said.

    A.I. Degrees Boom as Students Prepare for an Uncertain Job Market

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    Alexandra Tremayne-Pengelly

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  • Yann LeCun Leaves Meta to Create ‘Independent Entity’

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    A Meta spokesperson confirmed to Bloomberg Wednesday that AI legend Yann LeCun is exiting Zuckland and striking out on his own. According to a Memo from LeCun himself that Bloomberg claims to have read, LeCun’s new endeavor is meant to “bring about the next big revolution in AI: systems that understand the physical world, have persistent memory, can reason, and can plan complex action sequences.”

    Sources apparently told Bloomberg that LeCun “clashed with others internally.” Meta had recently constructed a fully separate AI research department focused on generative AI, and in its latest story, Bloomberg now claims that Meta had begun to hide LeCun from view in favor of high-profile recent hires. Recent hires have invluded ChatGPT co-creator Shengjia Zhao.

    As previously discussed here at Gizmodo, LeCun is fascinated by an area of AI called “world models.” He has spent more than a year saying he thinks LLM research, the backbone of systems like ChatGPT, is no longer a worthy area of pursuit—at least as far as hypothetical advanced AI functions with terms like “AGI” and “superintelligence” are concerned.

    LeCun, who was born and raised in France, is among the handful of researchers often referred to as the “godfathers of AI,” or more specifically the godfathers of deep learning, and shared a Turing Award in 2019 with fellow godfathers Geoffrey Hinton and Yoshua Bengio. The influential cognitive scientist and AI researcher Gary Marcus is a longtime critic of LeCun, and their public disagreements go back years.

    LeCun joined Meta in 2013, when it was still called Facebook, as the head of what at the time was a research operation with a location in New York that LeCun could walk to from his office at NYU, where he works as a professor. At the time, it wasn’t totally clear what a company like Facebook even wanted from a scientist who worked with deep neural networks. Another major AI researcher, Andrew Ng, explained Facebook’s hiring decision to Wired in terms that now seem sort of quaint and social media-centric:

    “Machine learning is already used in hundreds of places throughout Facebook, ranging from photo tagging to ranking articles to your news feed. Better machine learning will be able to help improve all of these features, as well as help Facebook create new applications that none of us have dreamed of yet.”

    After the 2022 release of ChatGPT led to AI’s rise to domination of all priorities in the tech world, LeCun became notable for his skepticism about the need for AI safety. He told the Wall Street Journal last year that the idea that AI poses a threat to humanity is “complete B.S.”

    But LLMs aren’t LeCun’s cup of tea anyway. He clarified last month that he had almost no involvement with Meta’s Llama models, and that such generative AI-related work happened way off in another department at Meta. LeCun worked, he explained, in Meta’s Fundamental AI Research (FAIR) department, and was attempting to go “beyond LLMs.” 

    LeCun believes AI models are needed that can comprehensively understand the physical world through sensory inputs like vision, and how to reason its way through interactions with, and changes to, that world. He thinks the current crop of AI systems can’t do anything even close to this, and that they are in fact dumber than cats. 

    You can already see the start of LeCun’s world model research under the aegis of Meta in V-JEPA-2. That model is trained not on text, but on videos of the physical world, and designed not simply to replicate all that video, like Sora, but to model the causes and effects of actions in the world when things move around and interact. That’s the theory anyway.

    Bloomberg writes that Meta “plans to partner with LeCun on his startup, though details are still being finalized.” In LeCun’s memo, he wrote that his former company “will be a partner of the new company and will have access to its innovations.”

    It’s not at all clear yet how the partnership between LeCun’s new company and Meta will be structured, but tech companies are famous for being near inextricable from one another where AI is concerned. Microsoft owns about 27% of OpenAI, and has special rights to use its technology. Google similarly owns 14% of Anthropic. The way interdependent investments in the AI world lead to higher valuations has been compared to “circular dealmaking.“

    LeCun’s memo says his new technology “will have far-ranging applications in many sectors of the economy, some of which overlap with Meta’s commercial interests, but many of which do not.”

    LeCun famously favors the term Advanced Machine Intelligence (AMI) in place of something like AGI (nota bene: “ami” is French for “friend”). In his memo, he reportedly wrote that “Pursuing the goal of AMI in an independent entity is a way to maximize its broad impact.” It’s an appropriately ambiguous turn of phrase. Presumably the “independent entity” is the new, non-Meta company, not an intelligent entity. Though he may mean that too. 

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    Mike Pearl

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  • Anthropic warns of AI-driven hacking campaign linked to China

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    WASHINGTON (AP) — A team of researchers has uncovered what they say is the first reported use of artificial intelligence to direct a hacking campaign in a largely automated fashion.

    The AI company Anthropic said this week that it disrupted a cyber operation that its researchers linked to the Chinese government. The operation involved the use of an artificial intelligence system to direct the hacking campaigns, which researchers called a disturbing development that could greatly expand the reach of AI-equipped hackers.

    While concerns about the use of AI to drive cyber operations are not new, what is concerning about the new operation is the degree to which AI was able to automate some of the work, the researchers said.

    “While we predicted these capabilities would continue to evolve, what has stood out to us is how quickly they have done so at scale,” they wrote in their report.

    The operation targeted tech companies, financial institutions, chemical companies and government agencies. The researchers wrote that the hackers attacked “roughly thirty global targets and succeeded in a small number of cases.” Anthropic detected the operation in September and took steps to shut it down and notify the affected parties.

    Anthropic noted that while AI systems are increasingly being used in a variety of settings for work and leisure, they can also be weaponized by hacking groups working for foreign adversaries. The San Francisco-based company, maker of the generative AI chatbot Claude, is one of many tech developers pitching AI “agents” that go beyond a chatbot’s capability to access computer tools and take actions on a person’s behalf.

    “Agents are valuable for everyday work and productivity — but in the wrong hands, they can substantially increase the viability of large-scale cyberattacks,” the researchers concluded. “These attacks are likely to only grow in their effectiveness.”

    A spokesperson for China’s embassy in Washington did not immediately return a message seeking comment on the report.

    Microsoft warned earlier this year that foreign adversaries were increasingly embracing AI to make their cyber campaigns more efficient and less labor-intensive. The head of OpenAI’s safety panel, which has the authority to halt the ChatGPT maker’s AI development, recently told The Associated Press he’s watching out for new AI systems that give malicious hackers “much higher capabilities.”

    America’s adversaries, as well as criminal gangs and hacking companies, have exploited AI’s potential, using it to automate and improve cyberattacks, to spread inflammatory disinformation and to penetrate sensitive systems. AI can translate poorly worded phishing emails into fluent English, for example, as well as generate digital clones of senior government officials.

    Anthropic said the hackers were able to manipulate Claude, using “jailbreaking” techniques that involve tricking an AI system to bypass its guardrails against harmful behavior, in this case by claiming they were employees of a legitimate cybersecurity firm.

    “This points to a big challenge with AI models, and it’s not limited to Claude, which is that the models have to be able to distinguish between what’s actually going on with the ethics of a situation and the kinds of role-play scenarios that hackers and others may want to cook up,” said John Scott-Railton, senior researcher at Citizen Lab.

    The use of AI to automate or direct cyberattacks will also appeal to smaller hacking groups and lone wolf hackers, who could use AI to expand the scale of their attacks, according to Adam Arellano, field CTO at Harness, a tech company that uses AI to help customers automate software development.

    “The speed and automation provided by the AI is what is a bit scary,” Arellano said. “Instead of a human with well-honed skills attempting to hack into hardened systems, the AI is speeding those processes and more consistently getting past obstacles.”

    AI programs will also play an increasingly important role in defending against these kinds of attacks, Arellano said, demonstrating how AI and the automation it allows will benefit both sides.

    Reaction to Anthropic’s disclosure was mixed, with some seeing it as a marketing ploy for Anthropic’s approach to defending cybersecurity and others who welcomed its wake-up call.

    “This is going to destroy us – sooner than we think – if we don’t make AI regulation a national priority tomorrow,” wrote U.S. Sen. Chris Murphy, a Connecticut Democrat, on social media.

    That led to criticism from Meta’s chief AI scientist Yann LeCun, an advocate of the Facebook parent company’s open-source AI systems that, unlike Anthropic’s, make their key components publicly accessible in a way that some AI safety advocates deem too risky.

    “You’re being played by people who want regulatory capture,” LeCun wrote in a reply to Murphy. “They are scaring everyone with dubious studies so that open source models are regulated out of existence.”

    __

    O’Brien reported from Providence, Rhode Island.

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  • ‘Imagine a Cube Floating in the Air’: The New AI Dream Allegedly Driving Yann LeCun Away from Meta

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    One of the most important AI scientists in Big Tech wants to scrap the current approach to building human-level AI. What we need, Yann LeCun has indicated, are not large language models, but “world models.”

    LeCun, chief AI scientist of “fundamental AI research” at Meta, is expected to resign from Meta soon according to multiple reports from credible outlets. LeCun is a 65-year-old elder statesman in the world of AI science, and he has had seemingly limitless resources at his disposal working as the big AI brain at one of the world’s largest tech companies.

    Why is he leaving a company that’s been spending lavishly, poaching the most highly-skilled AI experts from other firms, and, according to a July blog post by CEO Mark Zuckerburg, making such astonishing leaps in-house that supposedly the development of “superintelligence is now in sight”?

    He’s actually been hinting at the answer for a long time. When it comes to human-level intelligence, LeCun has become notorious lately for saying LLMs as we currently understand them are duds—no longer worth pursuing, no matter how much Big Tech scales them up. He said in April of last year that “an LLM is basically an off-ramp, a distraction, a dead end.” (The arch AI critic Gary Marcus has ripped into LeCun for “belligerently” defending LLMs from Marcus’ own critiques and then flip-flopping.)

    A Wall Street Journal analysis of LeCun’s career published Friday points to some other possibilities about the reasons for his departure in light of this belief. This past summer, a 28-year-old named Alexandr Wang—the co-creator of the LLM-based sensation ChatGPT—became the head of AI at Meta, making an upstart LLM fanatic LeCun’s boss. And Meta brought in another relatively young chief scientist to work above LeCun this year, Shengjia Zhao. Meta’s announcement of Zhao’s new role touts a scaling “breakthrough” he apparently delivered. LeCun says he has lost faith in scaling.

    If you’re wondering how LeCun can be a chief scientist if Zhao is also a chief scientist, it’s because Meta’s AI operation sounds like it has an eccentric org chart, split into multiple, separate groups. Hundreds of people were laid off last month, apparently in an effort to straighten all this out.

    The Financial Times’ report on LeCun from earlier this week suggests that LeCun will now found a startup focused on “world models.” 

    Again, LeCun has not been shy about why he thinks world models have the answers AI needs. He gave a detailed speech about this at the AI Action Summit in Paris back in February, but it got kind of overshadowed by the U.S. representative, Vice President J.D. Vance, giving a bellicose speech about how everyone had better get out of America’s way on AI. 

    Why Is Yann LeCun fascinated by world models?

    As spelled out in his speech—LeCun, who worked on the Meta AI smart glasses, but not to a significant degree on Meta’s Llama LLM—is a huge believer in wearables.

    We’ll need to interact with future wearables as if they are people, he thinks, and LLMs simply don’t understand the world like people do. With LLMs, he says, “we can’t even reproduce cat intelligence or rat intelligence, let alone dog intelligence. They can do amazing feats. They understand the physical world. Any housecat can plan very highly complex actions. And they have causal models of the world.” 

    LeCun provides a thought experiment to illustrate what he thinks might prompt—if you will—a world model, and it’s something he thinks any human can easily do that an LLM simply cannot: 

    “If I tell you ‘imagine a cube floating in the air in front of you. Okay now rotate this cube by 90 degrees around a vertical axis. What does it look like?’ It’s very easy for you to kind of have this mental model of a cube rotating.”  

    With very little effort, an LLM can write a dirty limerick about a hovering, rotating cube, sure, but it can’t really help you interact with one. LeCun avers that this is because of a difference between text data and data derived from processing the many parts of the world that aren’t text. While LLMs are trained on an amount of text it would take 450,000 years to read, LeCun says, a four-year-old child who has been awake for 16,000 hours has processed, with their eyes or by touching, 1.4 x 10^14bytes of sensory data about the world, which he says is more than an LLM.

    These, by the way, are just the estimates LeCun gives in his speech, and it should be noted that he has given others. The abstraction the numbers are pointing to, however, is that LLMs are limited in ways that LeCun thinks world models would not be. 

    What model does LeCun want to build, and how will he build it?

    LeCun has already begun working on world models at Meta—including making an introductory video that implores you to imagine a rotating cube.

    The model of LeCun’s dreams as described in his AI Action Summit speech contains a current “estimate of the state of the world,” in the form of some sort of abstract representation of, well, everything, or at least everything that’s relevant in the current context, and rather than sequential, tokenized prediction, it “predicts the resulting state of the world that will occur after you take that sequence of actions.” 

    World models will allow future computer scientists to build, he says, “systems that can plan actions—possibly hierarchically—so as to fulfill an objective, and systems that can reason.” LeCun also insists that such systems will have more robust safety features, because the ways we control them will be built into them, rather than being mysterious black boxes that spit out text, and which have to be refined by fine tuning. 

    In what LeCun says is classical AI—such as the software used in a search engine—all problems are reducible to optimization. His world model, he suggests, will look at the current state of the world, and seek compatibility with some different state by finding efficient solutions. “You want an energy function that measures incompatibility, and given an x, find a y that has low energy for that x,” LeCun says in his speech.  

    Again, these are just credible reports from leaked information about LeCun’s plans, and he hasn’t even confirmed that he’s founding something new. If everything we can cobble together from LeCun’s public statements sounds tentative and a bit fuzzy at the current phase, it should. LeCun sounds like he has a moonshot in mind, and he’s pushing for another ChatGPT-like explosion of uncanny abilities. It could take ages—or literally forever—not to mention billions of investor dollars, for anything truly remarkable to materialize. 

    Gizmodo reached out to Meta for comment on how LeCun’s work fits into the company’s AI mission, and will update if we hear back. 

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    Mike Pearl

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  • A.I. Pioneer Yoshua Bengio Becomes 1st Living Scientist With 1M Google Scholar Citations

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    Yoshua Bengio was also a recipient of the 2018 Turing Award. Andrej Ivanov/AFP via Getty Images

    Michel Foucault, the late French philosopher and historian, long held the distinction as the only researcher to surpass more than one million citations on Google Scholar. These days, however, Foucault has company: A.I. pioneer Yoshua Bengio.

    Last month, Bengio became the first living scientist to have his work cited more than one million times on Google Scholar. Citations to his research have surged in recent years, with more than 730,000 recorded since 2020 and roughly 135,000 in 2024 alone.

    Often dubbed one of the “Godfathers of A.I.,” Bengio’s work in deep learning helped lay the foundations for much of today’s A.I. revolution. A founder of the Mila-Quebec AI Institute and a professor of computer science at the University of Montreal, Bengio recently launched LawZero, a nonprofit focused on developing safety-centered A.I. systems to assist in scientific research.

    “This Google Scholar citation count reflects the extensive impact of Professor Bengio’s research in deep learning, which serves as a foundation for countless other scientific and technological advancements worldwide,” said Hugo Larochelle, who earlier this year succeeded Bengio as scientific director of Mila, in a statement.

    Bengio, alongside fellow A.I. researchers Geoffrey Hinton and Yann LeCun, received the 2018 Turing Award—often referred to as the “Nobel Prize of Computing”— for their breakthroughs in neural networks. The trio also co-authored Bengio’s second most-cited paper. Hinton, who currently has nearly 980,000 citations on Google Scholar, is also on track to soon join Bengio in the million-citation club, according to Mila.

    Researchers in fields like A.I., machine learning and cancer research are more likely to accumulate high citation counts due to widespread interest and rapid publication cycles, said Daniel Sage, a mathematics professor at the University of Buffalo who studies citation metrics.

    Top-cited scholars tend to work “in certain fields which have a lot of people working in them, and a lot of papers being produced,” he told Observer.

    The growing fascination with A.I. has even boosted citation counts of researchers outside the field. For example, Terence Tao, a renowned mathematician and Fields medalist, has earned more than 100,000 Google Scholar citations. Many of his top-cited papers, however, were actually published in electrical engineering or computer science journals, rather than pure mathematics, said Sage.

    “It’s apples and oranges comparisons if you try to compare people in A.I. vs. people in various other fields,” he added, noting that Google Scholar generally reports higher citation counts than other data providers such as Web of Science due to its broader indexing criteria.

    That said, reaching one million citations remains a remarkable achievement. “It’s still incredibly impressive,” said Sage. “One has to take these kinds of things with a grain of salt, but it is a sign both of the hotness of the field and the quality of the work within the field.”

    A.I. Pioneer Yoshua Bengio Becomes 1st Living Scientist With 1M Google Scholar Citations

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    Alexandra Tremayne-Pengelly

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  • Hugo Larochelle Succeeds Yoshua Bengio to Lead Canada’s Top A.I. Lab: Interview

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    Hugo Larochelle assumed his new role as head of Mila on Sept. 2. BENEDICTE BROCARD

    Hugo Larochelle first caught the A.I. research bug after interning in the lab of Yoshua Bengio, a pioneering A.I. academic, during his undergraduate studies at the University of Montreal. Decades later, Larochelle is now succeeding his former mentor as the scientific director of Quebec’s Mila A.I. Institute, an organization known in the A.I. field for its deep learning research.

    “My first mission is to maintain the caliber of our research and make sure we continue being a leading research institute,” Larochelle, who began his new role yesterday (Sept. 2), told Observer.

    Larochelle will oversee some 1,500 machine learning researchers at Mila, which Bengio founded in 1993 as a small research lab. Today, the institute is a cornerstone of Canada’s national A.I. strategy alongside two other research hubs in Ontario and Alberta.

    Larochelle “has the rigor, creativity and vision needed to meet Mila’s scientific ambitions and accompany its growth,” said Bengio, who left the institute to focus on a new A.I. safety venture he launched in June, in a statement. “Our collaboration goes back more than 20 years, and I am delighted to see it continue in a new form.”

    After his early work with Bengio, Larochelle completed a postdoctoral fellowship under Geoffrey Hinton at the University of Montreal. Bengio, Hinton and Yann LeCun went on to win the 2018 Turing Award for their contributions to neural networks—a field once overlooked but now central to the A.I. revolution.

    Larochelle’s own career reflects that shift. His first paper was rejected for relying on neural networks, but as their applications became clear, the field’s importance skyrocketed. “We felt like we were at the center of what’s important in the field, and that was exhilarating,” said the Larochelle.

    He went on to co-found Whetlab, a machine learning startup later acquired by Twitter (now X), before leading A.I. research at Google’s Montreal office in 2016. While most of his eight years at Google were highly productive, Larochelle noted that growing competition and a stronger focus on consumer products made publishing more difficult—a key factor in his decision to leave for Mila. “My passion was really scientific discovery, and simultaneously, I heard that Yoshua was going to find a successor,” he said.

    In his new role, Larochelle wants to build on Montreal’s tradition of scientific discovery. “I want to set the condition that we make the next one in the next five years, and that’s really the foundation of everything else we do,” he said. He also highlighted interests in advancing A.I. literacy, developing tools for biodiversity and accelerating scientific research.

    More broadly, Larochelle hopes to ensure that innovation moves faster—both across the industry and within Mila. “There’s definitely an interest in also making sure that our researchers, who might be interested in taking their own research and doing a startup based on what they’ve discovered, are well equipped in doing that,” he said.

    Hugo Larochelle Succeeds Yoshua Bengio to Lead Canada’s Top A.I. Lab: Interview

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    Alexandra Tremayne-Pengelly

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