Genius Makers: The Mavericks Who Brought A. I. To Google, Facebook, and the World
by Cade Metz · 15 Mar 2021 · 414pp · 109,622 words
two-year-old start-up most of the world had never heard of. This was DeepMind, a London company founded by a young neuroscientist named Demis Hassabis that would grow to become the most celebrated and influential AI lab of the decade. The week of the auction, Alan Eustace, Google’s head
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changed the way that I looked at technology,” Alan Eustace says. “It changed the way many others looked at it, too.” Some researchers, most notably Demis Hassabis, the young neuroscientist behind DeepMind, even believed they were on their way to building a machine that could do anything the human brain could do
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. “Best not to go into that.” He did not mention DeepMind. But that was another story. In the wake of the auction in Lake Tahoe, Demis Hassabis, the founder of the London lab, imprinted his own views on the world. In some ways, they echoed Hinton’s. In other ways, they looked
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in London that evening, and the following morning, Hinton walked into DeepMind. * * * — DEEPMIND was led by an eclectic array of powerful minds. Two of them, Demis Hassabis and David Silver, had met as undergraduates at Cambridge, but they originally crossed paths at a youth chess tournament near Silver’s hometown on the
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explore what he called the “connections between the brain and machine learning.” Years later, when asked to describe Shane Legg, Geoff Hinton compared him to Demis Hassabis: “He’s not as bright, not as competitive, and not as good at social interactions. But then, that applies to almost everybody.” Even so, in
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being accused of sexual harassment, and he was replaced as the head of Google Search by the head of artificial intelligence: John Giannandrea. In London, Demis Hassabis soon revealed that DeepMind had built a system that reduced power consumption across Google’s network of data centers, drawing on the same techniques the
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Sutskever. * * * — IN 2011, while he was still at the University of Toronto, Sutskever flew to London for a job interview at DeepMind. He met with Demis Hassabis and Shane Legg near Russell Square, and as the three men talked, Hassabis and Legg explained what they were trying to do. They were building
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future of artificial intelligence without waking up to headlines about The Terminator”). It called this closed-door gathering “The Future of AI: Opportunities and Challenges.” Demis Hassabis and Shane Legg attended. So did Elon Musk. On the first Sunday of 2015, six weeks after his dinner with Mark Zuckerberg, Musk took the
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that others were moving down the same path. Days after news stories appeared describing Facebook’s efforts to crack Go, one of those companies responded. Demis Hassabis appeared in an online video, looking straight into the camera, dominating the frame. It was a rare appearance from the founder of DeepMind. The London
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Hui. Several weeks later, in Seoul, it would challenge Lee Sedol, the world’s best player of the last decade. * * * — WEEKS after Google acquired DeepMind, Demis Hassabis and several other DeepMind researchers flew to Northern California for a powwow with the leaders of their new parent company and a demo of the
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system capable of beating the world champion. “I thought that was impossible,” Brin said. In that moment, Hassabis resolved to do it. Geoff Hinton compared Demis Hassabis to Robert Oppenheimer, the man whose stewardship of the Manhattan Project during the Second World War led to the first atomic bomb. Oppenheimer was a
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better outcome, a better contest.” Minutes later, after apparently realizing he should be gracious in technical defeat, Mark Zuckerberg posted a message to Facebook congratulating Demis Hassabis and DeepMind. Yann LeCun did the same. But sitting next to Lee Sedol, Hassabis found himself hoping the Korean would win at least one of
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morning of the first game, inside a private room down a side hall from the cavernous auditorium where the match was due to be played, Demis Hassabis sat in a plush, oversized, cream-colored chair in front of a wall painted like an afternoon sky. This was the theme across the building
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during this one-day mini-conference. Dozens of Chinese journalists descended on Wuzhen for the match, and many more traveled from around the world. When Demis Hassabis walked through the conference center before the first game, they photographed him like he was a pop star. Later that morning, as Hassabis described the
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not what anyone at Google had envisioned. On the morning of the first game with Ke Jie, sitting in front of that painted afternoon sky, Demis Hassabis said AlphaGo would soon grow even more powerful. His researchers were building a version that could master the game entirely on its own. Unlike the
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a hundred people in the field. They included Elon Musk, who had so frequently warned against the threat of superintelligence, as well as Geoff Hinton, Demis Hassabis, and Mustafa Suleyman. For Suleyman, these were technologies that required a new kind of oversight. “Who’s making the decisions that will one day affect
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’s Go milestone several weeks later. But LeCun, the head of the Facebook lab, was not someone who chased the moonshot moments. He was not Demis Hassabis or Elon Musk. Having worked in the field for decades, he saw AI research as a much longer and much slower endeavor. As it turned
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the next few years, such ideas did become more mainstream, and this was largely thanks to Shane Legg, who went on to build DeepMind alongside Demis Hassabis, and who, with Hassabis, convinced three significant figures (Peter Thiel, Elon Musk, and Larry Page) that the research was worth investing in. After Google acquired
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would be enormous pressure, he said, to keep building more. Across the Atlantic, inside the new Google building near St. Pancras station, Shane Legg and Demis Hassabis described the future in simpler terms. But their message wasn’t all that different. As Legg explained it, DeepMind was on the same trajectory that
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revealed to anyone outside the company, he, too, left DeepMind for Google. His philosophy had always seemed more aligned with Jeff Dean’s than with Demis Hassabis’s, and now he and his pet project, the most practical and near-term part of DeepMind, had parted ways with Hassabis and Legg. More
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a wonderful reductio ad absurdum of reinforcement learning,” he told the crowd. “It is called DeepMind.” Hinton did not believe in reinforcement learning, the method Demis Hassabis and DeepMind saw as the path to AGI. It required too much data and too much processing power to succeed with practical tasks in the
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Research lab in Seattle to explore deep learning for speech recognition. 2010—Abdel-rahman Mohamed and George Dahl, two of Hinton’s students, visit Microsoft. Demis Hassabis, Shane Legg, and Mustafa Suleyman found DeepMind. Stanford professor Andrew Ng pitches Project Marvin to Google chief executive Larry Page. 2011—University of Toronto researcher
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at the Google lab in Toronto. ERIC SCHMIDT, chairman. AT DEEPMIND ALEX GRAVES, the Scottish researcher who built a system that could write in longhand. DEMIS HASSABIS, the British chess prodigy, game designer, and neuroscientist who founded DeepMind, a London AI start-up that would grow into the world’s most celebrated
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AI lab. KORAY KAVUKCUOGLU, the Turkish researcher who oversaw the lab’s software code. SHANE LEGG, the New Zealander who founded DeepMind alongside Demis Hassabis, intent on building machines that could do anything the brain could do—even as he worried about the dangers this could bring. VLAD MNIH, the
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led the DeepMind team that built AlphaGo, the machine that marked a turning point in the progress of AI. MUSTAFA SULEYMAN, the childhood acquaintance of Demis Hassabis who helped launch DeepMind and led the lab’s efforts in ethics and healthcare. AT FACEBOOK LUBOMIR BOURDEV, the computer vision researcher who helped create
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set a world record: John Markoff, “Parachutist’s Record Fall: Over 25 Miles in 15 Minutes,” New York Times, October 24, 2014. Two of them, Demis Hassabis and David Silver: Cade Metz, “What the AI Behind AlphaGo Can Teach Us About Being Human,” Wired, May 19, 2016, https://www.wired.com/2016
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also returned to academia: Metz, “What the AI Behind AlphaGo Can Teach Us About Being Human.” With one paper, he studied people who developed amnesia: Demis Hassabis, Dharshan Kumaran, Seralynne D. Vann, and Eleanor A. Maguire, “Patients with Hippocampal Amnesia Cannot Imagine New Experiences,” Proceedings of the National Academy of Sciences 104
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.1843magazine.com/features/deepmind-and-google-the-battle-to-control-artificial-intelligence. He called this “the biological approach”: “A Systems Neuroscience Approach to Building AGI—Demis Hassabis, Singularity Summit 2010,” YouTube, https://www.youtube.com/watch?v=Qgd3OK5DZWI. “We should be focusing on the algorithmic level”: Ibid. He told his audience that
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.html. he was replaced as the head of Google Search: Metz, “AI Is Transforming Google Search. The Rest of the Web Is Next.” In London, Demis Hassabis soon revealed: Jack Clarke, “Google Cuts Its Giant Electricity Bill with DeepMind-Powered AI,” Bloomberg News, July 19, 2016, https://www.bloomberg.com/news/articles
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that could read passages from The Lord of the Rings: Metz, “Facebook Aims Its AI at the Game No Computer Can Crack.” Demis Hassabis appeared in an online video: “Interview with Demis Hassabis,” YouTube, https://www.youtube.com/watch?v=EhAjLnT9aL4. “I can’t talk about it yet”: Ibid. Hassabis and DeepMind revealed that
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,” Wired, January 27, 2016, https://www.wired.com/2016/01/in-a-huge-breakthrough-googles-ai-beats-a-top-player-at-the-game-of-go/. Demis Hassabis and several other DeepMind researchers: Cade Metz, “What the AI Behind AlphaGo Can Teach Us About Being Human,” Wired, May 19, 2016, https://www.wired
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/business/2016-11/15/content_27381349.htm. Its roof spans more than 2.5 trillion tiles: Ibid. Built to host the World Internet Conference: Ibid. Demis Hassabis sat in a plush: Cade Metz, “Google’s AlphaGo Levels Up from Board Games to Power Grids,” Wired, May 24, 2017, https://www.wired.com
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as a technical advisor to DeepMind, 110, 169–70 Turing Award, 305–07, 308–09 Lee, Peter, 73, 132, 193–94, 195 Legg, Shane and Demis Hassabis, 105–07, 186–87, 300 negotiation of Google’s acquisition of DeepMind, 115–16, 123–24 research on a computer’s ability to learn, 113
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venture capital firms, 160–61 Silver, David AlphaGo project, 171, 173–74, 175, 198 artificial intelligence research, 104–05 as cofounder of Elixir, 103 and Demis Hassabis, 101–02, 103, 104–05 Simon, Herbert, 22, 288 Singhal, Amit, 83–84, 139 the Singularity Summit, 107–09, 325–26 SNARC machine, 21 speech
Supremacy: AI, ChatGPT, and the Race That Will Change the World
by Parmy Olson · 284pp · 96,087 words
the ideas of others—which is why, when you boil everything down, our AI future has been written by just two men: Sam Altman and Demis Hassabis. One is a scrawny but placid entrepreneur in his late thirties who wears sneakers to the office. The other is a former chess champion in
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wasn’t made by middle-aged game designers eager to teach business principles to kids, but by a dark-haired teenager from North London named Demis Hassabis (pronounced hah-SAH-bis). He had the work ethic of a Silicon Valley entrepreneur and was obsessed with playing games. Years before Hassabis would become
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enigmatic billionaire whose ideas border on science fiction, would become a kingmaker in the quest to build powerful AI, helping to fund both Altman and Demis Hassabis in London. He was part of the so-called PayPal Mafia, an elite group of cofounders and executives from the online payment giant who invested
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or hell, Bostrom sparked a prevailing wisdom that would eventually drive the Silicon Valley AI builders like Sam Altman to race to build AGI before Demis Hassabis did in London: they had to build AGI first because only they could do so safely. If not, someone else might build AGI that was
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had been spun up by his old investor, Elon Musk. It was called OpenAI. CHAPTER 6 The Mission It was 2015, and for five years, Demis Hassabis had been growing his team and hitting research milestones on a slow but steady path to AGI, operating in an open field with virtually no
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models underpinning his upcoming brain-computer interface company Neuralink. For all of Musk’s apocalyptic views and moral convictions that he should reach AGI before Demis Hassabis, building AI that was as capable as Google’s would also boost his businesses. It was a profitable endeavor. Only that could explain why he
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with about forty of his new artificial intelligence researchers. At one point, Musk started talking about why he’d funded OpenAI, and the reason was Demis Hassabis. “I was one of the investors in DeepMind, and I was very concerned that Larry [Page] thinks Demis works for him. Actually, Demis just works
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, Alphabet. It had been more than two years since the acquisition, and the tech giant’s executives were dangling a new prospect in front of Demis Hassabis, Mustafa Suleyman, and Shane Legg. Instead of being an “autonomous unit,” DeepMind could become an “Alphabet company” with its own profit-and-loss statements. Being
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employees and Microsoft had 124,000, while start-up founders dreamed of running their own corporate campuses complete with gyms and free ice cream stalls. Demis Hassabis was one exception to the rule, perhaps because he was stationed an ocean away. He didn’t want DeepMind to get sucked into the distracting
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it, more impressive than moving some black and white stones around on a board. A mini cold war was also brewing between Sam Altman and Demis Hassabis, and OpenAI’s convivial board member Reid Hoffman was looking for ways to get the two of them to “smoke the peace pipe,” according to
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. Its title was “Better Language Models and Their Implications.” The release barely seemed to register among the leadership of DeepMind over in the UK. Though Demis Hassabis quietly resented what Sam Altman was doing, he didn’t give much credence to OpenAI’s strategy of focusing on language. He saw that as
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hip to a powerful corporation, they might come under greater pressure to release the technology before testing them properly. Amodei’s concerns were shared by Demis Hassabis in London. Around the time OpenAI was preparing to release GPT-3, Sam Altman, Greg Brockman, and Ilya Sutskever had dinner with the founders of
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DeepMind as part of the ongoing effort to smooth relations between the two rival companies. The meeting was tense. Demis Hassabis made a point of asking Altman why OpenAI was releasing its AI models to the world for anyone to access when dangerous people could misuse
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bottomless resources, people didn’t say no to the tech conglomerates. Across the sea in London, that association was turning into a liability for DeepMind. Demis Hassabis was looking for new scientific milestones that the company could hit to show it was ahead of OpenAI and wow the world after AlphaGo. But
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AI could be used for good. For years now, he’d been grappling with a sense of unease about the direction in which his friend Demis Hassabis was steering the company. The chess genius seemed preoccupied with using games and simulations to develop AI, but Suleyman thought they should study the real
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long-running, painful efforts to break away from Google finally died too. On a cloudy April morning in London in 2021, the round face of Demis Hassabis crinkled into a smile on a video conference call with all of his staff as he prepared to do what he did best: spin bad
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and other games, but OpenAI’s ability to create a system that could simply write an email was somehow more impressive. The scientific strategy that Demis Hassabis had been chasing was starting to look insular. Hassabis had sought to build AGI through games and simulations and measured the success of his company
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threshold for humanlike AI in the next ten to fifty years, and more of the general public believe in the once-fringe ideas that drove Demis Hassabis and Sam Altman. Thanks to their persistence and rivalry, it is no longer science fiction. But AGI’s blurry definition has also made it easier
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’re building, it’s just a language model.” Altman’s ability to create excitement about AI and his vision for prosperity had, like that of Demis Hassabis, meant he could spin a narrative that took on a life of its own. The hazy goals of AGI also made its ethical boundaries harder
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intelligence systems that were smarter than humans? The two innovators at the forefront grappled with the answer as their quests turned into a heated rivalry. Demis Hassabis believed that AGI could help us better understand the universe and drive forward scientific discovery, while Sam Altman thought it could create an abundance of
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designs of a handful of people and the systemic forces they operated in. When the question arises of whether we can trust Sam Altman and Demis Hassabis, along with Microsoft and Google, to build our AI future, the answer is that we have little choice in the matter. Both men hitched their
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present tense and cited with words such as “says,” “remembers,” or “recalls,” these are direct interviews that I conducted with those individuals, and they include Demis Hassabis and Sam Altman. Many others whom I spoke to are referred to as former employees or individuals familiar with the matter, and who remain anonymous
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not include in the story due to constraints on space, but that were valuable in giving me context on the lives of Sam Altman and Demis Hassabis and their work and the field of AI, as well as experts who helped me understand and translate the workings of machine-learning systems, neural
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Journal, and Forbes also greatly informed my research. I exploited my love of running to listen to countless hours of podcast interviews with Sam Altman, Demis Hassabis, Ilya Sutskever, Greg Brockman, and many other individuals who were involved in the creation of OpenAI and DeepMind, or who witnessed the evolution of AI
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.” The Ezra Klein Show, July 11, 2023. Burton-Hill, Clemency. “The Superhero of Artificial Intelligence.” The Guardian, February 16, 2016. “Demis Hassabis.” Desert Island Discs (podcast), BBC Radio 4, May 21, 2017. “Demis Hassabis, Ph.D.” What It Takes (podcast), American Academy of Achievement, April 23, 2018. “Genius Entrepreneur.” The Bridge, Queens College Cambridge
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Magazine, September 2014. “Google DeepMind’s Demis Hassabis.” The Bottom Line (podcast), BBC Radio 4, October 16, 2023. Hassabis, Demis. The Elixir Diaries, columns in Edge magazine, also available at https://archive.kontek
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. “Thinking about God Increases Acceptance of Artificial Intelligence in Decision-Making.” Proceedings of the National Academy of Sciences (PNAS) 120, no. 33 (2023): e2218961120–e2218961120. “Demis Hassabis, Ph.D.” What It Takes (podcast), American Academy of Achievement, April 23, 2018. Dowd, Maureen. “Elon Musk’s Billion-Dollar Crusade to Stop the A
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/index.php/aimagazine/article/view/1904. Penrose, Roger. Shadows of the Mind: A Search for the Missing Science of Consciousness. London: Vintage, 1995. Syed, Matthew. “Demis Hassabis Interview: The Kid from the Comp Who Founded DeepMind and Cracked a Mighty Riddle of Science.” The Sunday Times, December 5, 2020. “A Systems Neuroscience
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Approach to Building AGI—Demis Hassabis, Singularity Summit 2010.” Google DeepMind’s YouTube channel, March 7, 2018. Thiel, Peter, with Blake Masters. Zero to One: Notes on Start Ups, or How
Artificial Intelligence: A Guide for Thinking Humans
by Melanie Mitchell · 14 Oct 2019 · 350pp · 98,077 words
science but a kind of alchemy.”5 And the people who can do this kind of “network whispering” form a small, exclusive club: according to Demis Hassabis, cofounder of Google DeepMind, “It’s almost like an art form to get the best out of these systems.… There’s only a few hundred
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climate change, population growth and demographic change, ecological and food science, and other major issues that society will be facing over the next century. For Demis Hassabis, the cofounder of Google’s DeepMind group, this is the most important potential benefit of AI: We might have to come to the sobering realisation
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favorite game, whether it be checkers, chess, backgammon, Go, poker, or, more recently, video games. In 2010, a young British scientist and game enthusiast named Demis Hassabis, along with two close friends, launched a company in London called DeepMind Technologies. Hassabis is a colorful and storied figure in the modern AI world
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), mastering the game of checkers was never seen as a proxy for general intelligence. Chess is a different story. In the words of DeepMind’s Demis Hassabis, “For decades, leading computer scientists believed that, given the traditional status of chess as an exemplary demonstration of human intellect, a competent computer chess player
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than chess and had done so in a much more impressive fashion. Unlike Deep Blue, AlphaGo acquired its abilities by reinforcement learning via self-play. Demis Hassabis noted that “the thing that separates out top Go players [is] their intuition” and that “what we’ve done with AlphaGo is to introduce with
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researchers, not an end in and of itself. Let’s step back and ask about the implications of these successes for broader progress in AI. Demis Hassabis has something to say about this: Games are just our development platform.… It’s the fastest way to develop these AI algorithms and test them
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in this way the lowliest kindergartner in the school chess club is smarter than AlphaGo. From Games to the Real World Finally, let’s consider Demis Hassabis’s statement that the ultimate goal of these demonstrations on games is to “use them so they apply to real-world problems and have a
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. How, “Real-World Reinforcement Learning via Multifidelity Simulators,” IEEE Transactions on Robotics 31, no. 3 (2015): 655–71. 9: Game On 1. Demis Hassabis, quoted in P. Iwaniuk, “A Conversation with Demis Hassabis, the Bullfrog AI Prodigy Now Finding Solutions to the World’s Big Problems,” PCGamesN, accessed Dec. 7, 2018, www.pcgamesn.com
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/demis-hassabis-interview. 2. Quoted in “From Not Working to Neural Networking,” Economist, June 25, 2016. 3. M. G. Bellemare et al., “The Arcade Learning Environment: An
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‘Cat Face,’” London Review of Books, Aug. 11, 2016. 26. Quoted in S. Byford, “DeepMind Founder Demis Hassabis on How AI Will Shape the Future,” Verge, March 10, 2016, www.theverge.com/2016/3/10/11192774/demis-hassabis-interview-alphago-google-deepmind-ai. 27. D. Silver et al., “Mastering the Game of Go Without
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Chess, Shogi, and Go Through Self-Play,” Science 362, no. 6419 (2018): 1140–44. 10: Beyond Games 1. Quoted in P. Iwaniuk, “A Conversation with Demis Hassabis, the Bullfrog AI Prodigy Now Finding Solutions to the World’s Big Problems,” PCGamesN, accessed Dec. 7, 2018, www.pcgamesn.com
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/demis-hassabis-interview. 2. E. David, “DeepMind’s AlphaGo Mastered Chess in Its Spare Time,” Silicon Angle, Dec. 6, 2017, siliconangle.com/blog/2017/12/06/deepminds-
The Industries of the Future
by Alec Ross · 2 Feb 2016 · 364pp · 99,897 words
design company with Pentagon contracts, for an untold sum in December 2013. It also bought DeepMind, a London-based artificial intelligence company founded by wunderkind Demis Hassabis. As a kid, Hassabis was the second-highest-ranked chess player in the world under the age of 14, and while he was getting his
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, http://www.nytimes.com/2013/12/14/technology/google-adds-to-its-menagerie-of-robots.html?_r=1&. As a kid, Hassabis was: Samuel Gibbs, “Demis Hassabis: 15 Facts about the DeepMind Technologies Founder,” Guardian, January 28, 2014, http://www.theguardian.com/technology/shortcuts/2014/jan/28
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/demis-hassabis-15-facts-deepmind-technologies-founder-google; “Breakthrough of the Year: The Runners-Up,” Science 318, no. 5858 (2007): 1844–49, doi:10.1126/science.318.
Architects of Intelligence
by Martin Ford · 16 Nov 2018 · 586pp · 186,548 words
How AI Systems Learn 2. YOSHUA BENGIO 3. STUART J. RUSSELL 4. GEOFFREY HINTON 5. NICK BOSTROM 6. YANN LECUN 7. FEI-FEI LI 8. DEMIS HASSABIS 9. ANDREW NG 10. RANA EL KALIOUBY 11. RAY KURZWEIL 12. DANIELA RUS 13. JAMES MANYIKA 14. GARY MARCUS 15. BARBARA J. GROSZ 16. JUDEA
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and Bengio, as well as with several other very prominent researchers at the forefront of the technology. Andrew Ng, Fei-Fei Li, Jeff Dean and Demis Hassabis have all advanced neural networks in areas like web search, computer vision, self-driving cars and more general intelligence. They are also recognized leaders in
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to be surmounted and the timeframe for when it might be achieved. Everyone had important insights, but I found three conversations to be especially interesting: Demis Hassabis discussed efforts underway at DeepMind, which is the largest and best funded initiative geared specifically toward AGI. David Ferrucci, who led the team that created
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a treat. Reinforcement learning has been an especially powerful way to build AI systems that play games. As you will learn from the interview with Demis Hassabis in this book, DeepMind is a strong proponent of reinforcement learning and relied on it to create the AlphaGo system. The problem with reinforcement learning
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a very long incubation time. I think that’s what people should be worried about, not ultra-intelligent systems. MARTIN FORD: Some people, such as Demis Hassabis at DeepMind, do believe that they can build the kind of system that you’re saying you don’t think is going to come into
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from underrepresented groups into the field of AI, which began at Stanford and has now scaled up to universities across the United States. Chapter 8. DEMIS HASSABIS Games are just our training domain. We’re not doing all this work just to solve games; we want to build these general algorithms that
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we can apply to real-world problems. CO-FOUNDER & CEO OF DEEPMIND AI RESEARCHER AND NEUROSCIENTIST Demis Hassabis is a former child chess prodigy, who started coding and designing video games professionally at age 16. After graduating from Cambridge University, Demis spent a
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interest in chess and video games when you were younger. How has that influenced your career in AI research and your decision to found DeepMind? DEMIS HASSABIS: I was a professional chess player in my childhood with aspirations of becoming the world chess champion. I was an introspective kid and I wanted
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those different strands then came together into DeepMind. MARTIN FORD: Your focus then, right from the beginning, has been on machine intelligence and especially AGI? DEMIS HASSABIS: Exactly. I’ve known I wanted to do this as a career since my early teens. That journey started with my first computer. I realized
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there’s not really a business model for doing that; it’s hard to generate revenue in the short term. How did DeepMind overcome that? DEMIS HASSABIS: From the beginning, we were an AGI company, and we were very clear about that. Our mission statement of solving intelligence was there from the
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independent company. But then you decided to let Google acquire DeepMind. Can you tell me about the rationale behind the acquisition and how that happened? DEMIS HASSABIS: It’s worth noting that we had no plans to sell, partly because we figured no big corporate would understand our value until DeepMind started
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unusual but very prescient of them. MARTIN FORD: Why did you choose to be in London, and not Silicon Valley? Is that a Demis Hassabis or a DeepMind thing? DEMIS HASSABIS: Both really. I’m a born-and-bred Londoner, and I love London, but at the same time, I thought it was a
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use it for, and how to distribute the proceeds, is important. MARTIN FORD: I believe you’re also opening up labs in other European cities? DEMIS HASSABIS: We’ve opened a small research lab in Paris, which is our first continental European office. We’ve also opened two labs in Canada in
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are right next to the Google teams that we work with. MARTIN FORD: How closely do you work with the other AI teams at Google? DEMIS HASSABIS: Google’s a huge place, and there are thousands of people working on every aspect of machine learning and AI, from both a very applied
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forward, are you finished with that type of game? Are you planning to move on to more complex games with hidden information, and so forth? DEMIS HASSABIS: There’s a new version of AlphaZero that we’re going to publish soon that’s even more improved, and as you’ve said, you
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that reinforcement learning offers a viable path to general intelligence, that it might be sufficient to get there. Is that your primary focus going forward? DEMIS HASSABIS: Going forward, yes, it is. I think that technique is extremely powerful, but you need to combine it with other things to scale it. Reinforcement
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it seems to be more driven by observation or random interaction with the environment rather than learning by practice with a specific goal in mind. DEMIS HASSABIS: A child learns with many mechanisms, it’s not like the brain only uses one. The child gets supervised learning from their parents, teachers, or
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and computer science. Is that combined approach true for DeepMind as a whole? How does the company integrate knowledge and talent from those two areas? DEMIS HASSABIS: I’m definitely right in the middle for both those fields, as I’m equally trained in both. I would say DeepMind is clearly more
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-engineer the brain on a cortical level, a prime example being the Blue Brain Project. MARTIN FORD: That’s being directed by Henry Markram, right? DEMIS HASSABIS: Right, and he’s literally trying to reverse-engineer cortical columns. It may be interesting neuroscience but, in my view, that is not the most
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. MARTIN FORD: You often hear the analogy that airplanes don’t flap their wings. Airplanes achieve flight, but don’t precisely mimic what birds do. DEMIS HASSABIS: That’s a great example. At DeepMind, we’re trying to understand aerodynamics by looking at birds, and then abstracting the principles of aerodynamics and
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neural network. In other words, the same basic structure naturally arises in both the biological brain and in artificial neural networks, which seems pretty remarkable. DEMIS HASSABIS: I’m very excited about that because it’s one of our biggest breakthroughs in the last year. Edvard Moser and May-Britt Moser, who
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be some discoverable general principles of intelligence that are substrate-independent. To return to the flight analogy, you might call it “the aerodynamics of intelligence.” DEMIS HASSABIS: That’s right, and if you extract that general principle, then it must be useful for understanding the particular instance of the human brain. MARTIN
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you imagine happening within the next 10 years? How are your breakthroughs going to be applied in the real world in the relatively near future? DEMIS HASSABIS: We’re already seeing lots of things in practice. All over the world people are interacting with AI today through machine translation, image analysis, and
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path to AGI look like? What would you say are the main hurdles that will have to be surmounted before we have human-level AI? DEMIS HASSABIS: From the beginning of DeepMind we identified some big milestones, such as the learning of abstract, conceptual knowledge, and then using that for transfer learning
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do achieve AGI, do you imagine intelligence being coupled with consciousness? Is it something that would automatically emerge, or is consciousness a completely separate thing? DEMIS HASSABIS: That’s one of the interesting questions that this journey will address. I don’t know the answer to it at the moment, but that
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measures but would not feel conscious to us in any way at all. MARTIN FORD: Like an intelligent zombie, something that has no inner experience. DEMIS HASSABIS: Something that wouldn’t feel sentient in the way we feel about other humans. Now that’s a philosophical question, because the problem is, as
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different to your substrate? MARTIN FORD: Do you believe machine consciousness is possible? There are some people that argue consciousness is fundamentally a biological phenomenon. DEMIS HASSABIS: I am actually open-minded about that, in the sense that I don’t think we know. It could well turn out that there’s
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is on DeepMind’s advisory board and has written a lot on this idea. What do you think about these fears? Should we be worried? DEMIS HASSABIS: I’ve talked to them a lot about these things. As always, the soundbites seem extreme but it’s a lot more nuanced when you
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how we interpret their behavior. MARTIN FORD: Are you confident that we’ll be able to manage the risks that come along with advanced AI? DEMIS HASSABIS: Yes, I’m very confident, and the reason is that we’re at the inflection point where we’ve just got these things working, and
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will arise long before AGI is achieved? For example, autonomous weapons. I know you’ve been very outspoken about AI being used in military applications. DEMIS HASSABIS: These are very important questions. At DeepMind, we start from the premise that AI applications should remain under meaningful human control, and be used for
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AI race with China. They do have a much more authoritarian system of government. Should we worry that they will gain an advantage in AI? DEMIS HASSABIS: I don’t think it’s a race in that sense because we know all the researchers and there’s a lot of collaboration. We
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the economic impact of all of this? Is there going to be a big disruption of the job market and perhaps rising unemployment and inequality? DEMIS HASSABIS: I think there’s been very minimal disruption so far from AI, it’s just been part of the technology disruption in general. AI is
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left behind. But it is a staggering political challenge to come up with a new paradigm that will create an economy that works for everyone. DEMIS HASSABIS: Right. Whenever I meet an economist, I think they should be working quite hard on this problem, but it’s difficult to because they can
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it’s arguably going to be one of the best things that’s ever happened to humanity. Assuming, of course, that we manage it wisely? DEMIS HASSABIS: Definitely, and that’s why I’ve worked towards it my whole life. All of the things I’ve been doing that we covered in
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the excess resources or activity to solve them. But ultimately, I’m actually optimistic about the world because a transformative technology like AI is coming. DEMIS HASSABIS is a former child chess prodigy who finished his high school exams two years early before coding the multi-million selling simulation game Theme Park
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, and tracking the progress of AI, and I’ve stayed in constant dialogues as well as collaborated with AI friends like Eric Horvitz, Jeff Dean, Demis Hassabis, and Fei-Fei Li, and also learning from legends like Barbara Grosz. While I’ve tried to stay close to the technology and the science
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and people in order to do that instead of working on something else important. MARTIN FORD: There is definitely a lot of interest. People like Demis Hassabis at DeepMind, are definitely interested in building AGI, or at least getting much closer to it. It’s their stated goal. CYNTHIA BREAZEAL: People may
Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again
by Eric Topol · 1 Jan 2019 · 424pp · 114,905 words
Brain, Andrew Ng, Jeff Dean) 2014—DeepFace facial recognition (Facebook) 2015—DeepMind vs. Atari (David Silver, Demis Hassabis) 2015—First AI risk conference (Max Tegmark) 2016—AlphaGo vs. Go (Silver, Demis Hassabis) 2017—AlphaGo Zero vs. Go (Silver, Demis Hassabis) 2017—Libratus vs. poker (Noam Brown, Tuomas Sandholm) 2017—AI Now Institute launched TABLE 4.2
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outputting, at regular intervals, numbers which we (but not the AI) would recognize as codes for which keys to press.” According to DeepMind’s leader, Demis Hassabis, the strategy DeepMind learned to play was unknown to any human “until they learned it from the AI they’d built.” You could therefore interpret
The Optimist: Sam Altman, OpenAI, and the Race to Invent the Future
by Keach Hagey · 19 May 2025 · 439pp · 125,379 words
College London’s Gatsby Computational Neuroscience Unit, a lab that encompassed neuroscience, machine learning, and AI. There, he met a gaming savant from London named Demis Hassabis, the son of a Singaporean mother and Greek Cypriot father. Hassabis had once been the second-ranked chess player in the world under the age
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existed was a huge leap toward the mainstream for a set of beliefs long considered fringe. Among the AI practitioners who signed were DeepMind’s Demis Hassabis and two AI researchers, Ilya Sutskever and Dario Amodei.3 Tegmark was giddy about the conference’s impact, quipping, “Perhaps it was a combination of
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decade. But in late January 2016, before the end of OpenAI’s first month of work, DeepMind published a paper in Nature—prestigious journals were Demis Hassabis’s preferred medium—announcing that their AI system, AlphaGo, had defeated a former European Go champion in a closed-door match the previous October. The
Rule of the Robots: How Artificial Intelligence Will Transform Everything
by Martin Ford · 13 Sep 2021 · 288pp · 86,995 words
game, the board, which consists of a nineteen-by-nineteen grid, is largely filled with black and white game pieces called “stones.” As DeepMind CEO Demis Hassabis often likes to point out when he discusses AlphaGo’s accomplishment, the number of possible arrangements of the stones on the board exceeds the estimated
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. These researchers believe that artificial intelligence should be directly informed by neuroscience. The leader in this area is DeepMind. The company’s founder and CEO, Demis Hassabis—unusually for an AI researcher—received his graduate training in neuroscience, rather than computing, and holds a PhD in the field from University College, London
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planet by defeating the very best dedicated chess-playing algorithms—which, of course, were already able to easily dispense with the most capable human players. Demis Hassabis told me that AlphaZero probably represents a general solution to “information complete” games, or in other words the type of challenges in which all the
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competition; they believe strongly in a global system that emphasizes open publication of research and a free exchange of ideas. When I asked DeepMind CEO Demis Hassabis about a perceived “AI race with China,” he told me that DeepMind publishes openly and that he knows “Tencent has created an AlphaGo clone,” but
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’: DeepMind’s AI makes gigantic leap in solving protein structures,” Nature, November 30, 2020, www.nature.com/articles/d41586-020-03348-4. 2. Andrew Senior, Demis Hassabis, John Jumper and Pushmeet Kohli, “AlphaFold: Using AI for scientific discovery,” DeepMind Research Blog, January 15, 2020, deepmind.com/blog/article/AlphaFold-Using-AI-for
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Yuanyuan, “The game of Go: Ancient wisdom,” Confucius Institute Magazine, volume 17, pp. 46–51 (November 2011), confuciusmag.com/go-game. 7. David Silver and Demis Hassabis, “AlphaGo: Mastering the ancient game of Go with machine learning,” Google AI Blog, January 27, 2016, ai.googleblog.com/2016/01/alphago-mastering-ancient-game
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/05/28/ai-winter-is-well-on-its-way/. 16. Ford, Interview with Jeffery Dean, in Architects of Intelligence, p. 377. 17. Ford, Interview with Demis Hassabis, in Architects of Intelligence, p. 171. 18. Andrea Banino, Caswell Barry, Dharshan Kumaran and Benigno Uria, “Navigating with grid-like representations in artificial agents,” DeepMind
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Research Blog, May 9, 2018, deepmind.com/blog/article/grid-cells. 19. Ford, Interview with Demis Hassabis, in Architects of Intelligence, p. 173. 20. Andrea Banino, Caswell Barry, Benigno Uria et al., “Vector-based navigation using grid-like representations in artificial agents
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, “Yann LeCun Cake Analogy 2.0,” Synced Review, February 22, 2019, medium.com/syncedreview/yann-lecun-cake-analogy-2-0-a361da560dae. 23. Ford, Interview with Demis Hassabis, in Architects of Intelligence, pp. 172–173. 24. Jeremy Kahn, “A.I. breakthroughs in natural-language processing are big for business,” Fortune, January 20, 2020
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Lu joins Baidu as COO,” TechCrunch, January 17, 2017, techcrunch.com/2017/01/16/qi-lu-joins-baidu-as-coo/. 13. Martin Ford, Interview with Demis Hassabis, in Architects of Intelligence: The Truth about AI from the People Building It, Packt Publishing, 2018, p. 179. 14. Field Cady and Oren Etzioni, “China
Human Compatible: Artificial Intelligence and the Problem of Control
by Stuart Russell · 7 Oct 2019 · 416pp · 112,268 words
ways, then first-order logic is going to be relevant, because it provides the basic mathematics of objects and relations. This view is shared by Demis Hassabis, CEO of Google DeepMind:11 You can think about deep learning as it currently is today as the equivalent in the brain to our sensory
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networks and larger data sets and bigger machines is not enough to create human-level AI. We have already seen (in Appendix B) DeepMind CEO Demis Hassabis’s view that “higher-level thinking and symbolic reasoning” are essential for AI. Another prominent deep learning expert, François Chollet, put it this way:10
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origin of the term GOFAI: John Haugeland, Artificial Intelligence: The Very Idea (MIT Press, 1985). 11. Interview with Demis Hassabis on the future of AI and deep learning: Nick Heath, “Google DeepMind founder Demis Hassabis: Three truths about AI,” TechRepublic, September 24, 2018. APPENDIX C 1. Pearl’s work was recognized by the Turing
Spike: The Virus vs The People - The Inside Story
by Jeremy Farrar and Anjana Ahuja · 15 Jan 2021 · 245pp · 71,886 words
ropey lines). I do not recall Treasury officials at the meetings I attended. Outsiders were occasionally invited in; at one meeting I sat next to Demis Hassabis, a researcher who cofounded artificial intelligence start-up DeepMind. The SAGE meetings mostly took place in a basement at 10 Victoria Street in Westminster, which
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of three scientists beyond SAGE, to whom he was occasionally sending SAGE papers: Venki Ramakrishnan, president of the Royal Society and a chemistry Nobel laureate; Demis Hassabis; and Timothy Gowers, the British mathematician and Fields medallist. I saw both Demis and Venki at the odd SAGE meeting but not Timothy. Timothy told
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: act now. SAGE met again on Wednesday 18 March, a day on which 999 fresh coronavirus cases were reported across the UK. I sat between Demis Hassabis and Ian Diamond, the brilliant chief statistician at the Office for National Statistics. There were around a dozen advisers in the room, and as many
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‘test, test, test’) because it did not apply to high-income countries. In 2021, she was appointed chief executive of the UK Health Security Agency. Demis Hassabis A former child chess prodigy, neuroscientist, games designer and entrepreneur, and co-founder of artificial intelligence start-up DeepMind. Hassabis attended the SAGE meeting on
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