The Internet Is Not the Answer
by Andrew Keen · 5 Jan 2015 · 361pp · 81,068 words
system, Gmail, Google+, Blogger, the Chrome browser, Google self-driving cars, Google Glass, Waze, and its most recent roll-up of artificial intelligence companies including DeepMind, Boston Dynamics, and Nest Labs.70 More than just cracking the code on Internet profits, Google had discovered the holy grail of the information economy
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,26 Google also made two important purchases at the beginning of 2014 to consolidate its lead in this market. It acquired the secretive British company DeepMind, “the last large independent company with a strong focus on artificial intelligence,” according to one inside source, for $500 million; and it bought Nest Labs
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next quarter century? If we “win” this race, won’t that mean Google—having invested in artificial intelligence companies like Boston Dynamics, Nest Labs, and Deep Mind—will have lost? Rather than focusing on “winning,” our networked automation anxiety is really all about identifying the losers, the people who will lose their
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less know what you’re thinking about.”59 This is the real reason why Google spent $500 million in 2014 on the artificial intelligence startup DeepMind—a technology that, according to The Information’s Amir Efrati, wants to “make computers think like humans.”60 By thinking like us, by being able
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of one tech startup wrote on Facebook.60 Indeed, much of the “work” being done by Google-acquired robotic companies like Nest, Boston Dynamics, and DeepMind is focused on figuring out how to automate the jobs of traditional workers such as BART drivers. “Coming to an office near you,” we’ve
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22, 2007. 59 Derek Thompson, “Google’s CEO: The Laws Are Written by Lobbyists,” Atlantic, October 1, 2010. 60 Amir Efrati, “Google Beat Facebook for DeepMind, Creates Ethics,” The Information, January 26, 2014. 61 Jaron Lanier, Who Owns the Future? (New York: Simon & Schuster, 2013), p. 366. Chapter Five 1 Kunal
How to Fix the Future: Staying Human in the Digital Age
by Andrew Keen · 1 Mar 2018 · 308pp · 85,880 words
isn’t entirely pessimistic about the future. He is encouraged, for example, by what he describes as the ethical maturity of the three cofounders of DeepMind, particularly Demis Hassabis, its young Cambridge-educated CEO. This is the London-based tech company whose investors include Jaan Tallinn and Elon Musk, a start
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-up founded in 2011 and then acquired by Google for $500 million in 2014. DeepMind made the headlines in March 2016 when AlphaGo, its specially designed algorithm, defeated a South Korean world champion Go player in this 5,500-year
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oldest and one of the most complex games ever invented by humans. But in addition to the commercial development of artificial intelligence, Price explains, the DeepMind founders—with other Big Tech companies like Microsoft, Facebook, IBM, and Amazon—are helping engineer an industrywide moral code about smart technology. This self-policing
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us with your future, they are saying. Trust us is, indeed, becoming a familiar promise from the tech community. The self-policing strategy of the DeepMind coalition sounds similar to the goals of another idealistic Elon Musk start-up—OpenAI, a Silicon Valley–based nonprofit research company focused on the promotion
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as if it’s the plotline of a Star Trek episode.5 I ask Price what these young entrepreneurs, fabulously wealthy and gifted technologists like Deep Mind’s Demis Hassabis, or Y Combinator’s Sam Altman, need to incorporate into their self-prescribed moral code. What, I wonder, should the new men
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at Cambridge—to foster a combinatorial network of researchers, ethicists, and technologists focused on studying the impact of AI on society. In contrast with the Deep Mind or OpenAI coalition, this Knight Foundation initiative doesn’t just rely on technologists to make ethical decisions. “My point of view is that it is
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; regulation; social responsibility; worker and consumer choice) “age of acceleration,” 13–14 Ahuja, Anjana, 207 “AI Control Problem, The” (Tallinn), 54 AirBnB, 145, 254 AlphaGo (DeepMind), 199 Alter, Adam, 67, 213, 281–282 Altman, Sam, 199, 260 Amazon Bezos and, 205, 211–213, 223 centralized power of, 64–70 regulation issues
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, 150–151 decentralized marketplaces and decentralized autonomous organizations (DAOs), 169 informational freedom and, 267 re-decentralization, 165–172 “Decentralized Web Summit” (Internet Archive), 59, 166 DeepMind, 198 democratization of media concept, 66 Denmark, regulation issues, 141–142 digital competency, Estonian education system on, 77–78, 90–91 Digital Life Design (DLD
Rule of the Robots: How Artificial Intelligence Will Transform Everything
by Martin Ford · 13 Sep 2021 · 288pp · 86,995 words
my mother, Sheila Explore book giveaways, sneak peeks, deals, and more. Tap here to learn more. CHAPTER 1 THE EMERGING DISRUPTION ON NOVEMBER 30, 2020, DEEPMIND, A LONDON-BASED ARTIFICIAL intelligence company owned by Google parent Alphabet, announced a stunning, and likely historic, breakthrough in computational biology, an innovation with the
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in science. The number of possible shapes is virtually infinite. Scientists have devoted entire careers to the problem, but have collectively achieved only modest success. DeepMind’s system uses AI techniques that the company pioneered in the AlphaGo and AlphaZero systems that had famously triumphed over the world’s best human
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be most effective against a newly emergent virus, putting powerful treatments in the hands of doctors in the earliest stages of an outbreak. Beyond this, DeepMind’s technology is poised to lead to a variety of advances, including the design of entirely new drugs and a better understanding of the ways
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of this progress has been “deep learning”—a machine learning technique based on the use of multilayered artificial neural networks of the kind employed by DeepMind. The basic principles of deep neural networks have been understood for decades, but recent dramatic advances have been enabled by the confluence of two relentless
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alone to one of the world’s best medical specialists. Now imagine taking a single, extremely specific innovation—an AI-based diagnostic tool or perhaps DeepMind’s breakthrough in protein folding—and multiplying it by a virtually limitless number of possibilities in other areas from medicine to science, industry, transportation, energy
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from the ground up to deliver artificial intelligence. Microsoft’s 2019 billion-dollar investment in the AI research company OpenAI—which along with Google’s DeepMind is a leader in pushing the frontiers of deep learning—offers a case study in the natural synergy between cloud computing and artificial intelligence. OpenAI
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research organizations and better position Microsoft to compete with Google, which enjoys a strong reputation for AI leadership, in part because of its ownership of DeepMind.14 This synergy extends far beyond this single example. Virtually every important initiative in AI, ranging from university research labs to AI startups to practical
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open-source form; in other words, they give it away for free. This is also true of the most advanced research conducted by organizations like DeepMind and OpenAI. Both publish openly in leading scientific journals and make the details of their deep learning systems available to everyone. There is one thing
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artificial general intelligence. In other words, the company is, in a sense, competing directly with higher-profile and far better funded initiatives like those at DeepMind and OpenAI. We’ll delve into the paths being forged by those two companies and the general quest for human-level AI in Chapter 5
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that in some cases exceeded that of doctors. Though progress was promising, the arrangement exploded into controversy in 2019 when the program was transferred to DeepMind’s parent company, Google. There was an immediate backlash against the specter of the tech giant having access to NHS patient data despite the fact
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promising near-term application of artificial intelligence, and especially deep learning, in scientific research may be in the discovery of new chemical compounds. Just as DeepMind’s AlphaGo system confronts a virtually infinite game space—where the number of possible configurations of the Go board exceeds the number of atoms in
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correctly. In other words, this preliminary version of AlphaFold was not yet accurate enough to be a truly useful research tool.80 The fact that DeepMind was able to refine its technology to the point where a number of scientists declared the protein folding problem to be “solved” just two years
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process enough times and the dog will learn to reliably sit. The leader in reinforcement learning is the London-based company DeepMind, which is now owned by Google’s parent, Alphabet. DeepMind has made massive investments in research based on the technique, merging it with powerful convolutional neural networks to develop what
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the company calls “deep reinforcement learning.” DeepMind began working on applying reinforcement learning to build AI systems that could play video games shortly after its founding in 2010. In January 2013, the
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company announced that it had created a system called DQN that was capable of playing classic Atari games, including Space Invaders, Pong and Breakout. DeepMind’s system was able to teach itself to play the games by using only raw pixels and the game score as the learning inputs. After
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the games, and was able to defeat the best human players in three.4 By 2015, the system had conquered forty-nine Atari games, and DeepMind declared that it had developed the first AI system that bridged “the divide between high-dimensional sensory inputs and actions” and that the DQN was
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of the 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
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. Nonetheless, the game of Go fell to the machines at least a decade before most computer scientists believed such a feat would be possible. The DeepMind team began by using a supervised learning technique to train AlphaGo’s neural networks on thirty million moves extracted from detailed records of games played
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at Vicarious—the small company focused on building dexterous robots that we met in Chapter 3—performed an analysis of the neural network used in DeepMind’s DQN, the system that had learned to dominate Atari video games.13 One test was performed on Breakout, a game in which the player
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the screen—a change that might not even be noticed by a human player—the system’s previously superhuman performance immediately took a nose dive. DeepMind’s software had no ability to adapt to even this small alteration. The only way to get back to top-level performance would have been
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to start from scratch and completely retrain the system with data based on the new screen configuration. What this tells us is that while DeepMind’s powerful neural networks do instantiate a representation of the Breakout screen, this representation remains firmly anchored to raw pixels even at the higher levels
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the research philosophies of the various teams working on AI at Google. Jeff Dean, Google’s overall director of artificial intelligence, told me that while DeepMind, the independent company Google acquired in 2014, is specifically oriented toward general machine intelligence with a “structured plan” to solve specific issues in the hope
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workings of the human brain for inspiration. 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
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PhD in the field from University College, London. Hassabis told me that the single largest research group at DeepMind consists of neuroscientists who are focused on finding ways to apply the latest insights from brain science to artificial intelligence.17 Their objective is not
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“internal GPS,” a neural representation of a mapping system that allows animals to remain oriented as they find their way through complex and unpredictable environments. DeepMind conducted a computational experiment in which the company’s researchers trained a powerful neural network on data that simulated the kind of movement-based information
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grid cells may simply be the most computationally efficient way to represent navigation information in any system, regardless of the details of its implementation.19 DeepMind’s scientific paper describing the research, published in the journal Nature,20 resonated widely within the field of neuroscience, and insights like this suggest the
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turn out to be a two-way street, with AI research not only drawing upon lessons from the brain but also contributing to its understanding. DeepMind once again made an important contribution to neuroscience when the company leveraged its expertise in reinforcement learning in early 2020 by exploring the operation of
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the same way; the algorithm makes a prediction and then adjusts the reward based on the difference between the predicted and actual results. Researchers at DeepMind were able to greatly improve a reinforcement learning algorithm by generating a distribution of predictions, rather than a single average prediction, and then adjusting the
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often says that if intelligence were a black forest cake, then reinforcement learning would amount to only the cherry on top.22 The team at DeepMind believes it is far more central—and that it possibly provides a viable path to achieving AGI. We generally describe reinforcement learning in terms of
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, sounds very close to human-level intelligence. Existing AI systems that process natural language suffer from a similar limitation to the one we saw with DeepMind’s Atari-playing DQN when the game paddle was shifted a few pixels higher. Just as DQN has no understanding that the pixels on the
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the language understanding problem represents the clearest path to more general intelligence. Rather than delving into the physiology of the brain in the way that DeepMind’s team is attempting, Ferrucci argues that it is possible to directly engineer a system that can approach human level in its comprehension of language
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incorporating symbolic AI capabilities into a system built entirely from neural networks. As Marcus points out, many of deep learning’s most prominent accomplishments, including Deep Mind’s AlphaGo system, are in fact hybrid systems because they succeeded only by relying on traditional search algorithms in addition to deep neural networks. As
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consistently attach a name to it. Unsupervised learning is currently one of the hottest research topics in the field of artificial intelligence. Google, Facebook and DeepMind all have teams focused in this area. Progress, however, has been slow, and few if any truly practical applications have so far emerged. The truth
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take us there, and the time of arrival, remain shrouded in deep uncertainty. So far, progress has largely been incremental. For example, in late 2017, DeepMind released AlphaZero, an update to its Go-playing AlphaGo system. AlphaZero dispensed with the need for a supervised learning regimen on data from thousands of
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require the ability to operate under uncertainty and to deal with situations where vast amounts of information are hidden or simply unattainable. In January 2019, DeepMind again demonstrated progress with its release of AlphaStar, a system designed to play the strategy video game StarCraft. StarCraft simulates a galactic struggle for resources
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information about their opponents’ activities. The game also requires long-term planning and management of resources across a vast game space. In another first for DeepMind’s team, AlphaStar defeated a top professional StarCraft player 5-0 in a match conducted in December 2018.60 Though these achievements are impressive, they
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on creative work as well. Already smart algorithms can paint original works of art, formulate scientific hypotheses, compose classical music and generate innovative electronic designs. DeepMind’s AlphaGo and AlphaZero have injected new energy and creativity into professional Go and chess competitions because the systems represent truly alien intelligences, often adopting
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that the catalyst for the sudden surge of interest in AI on the part of the Chinese Communist Party was the highly touted contest between DeepMind’s AlphaGo system and Go champion Lee Sedol that took place in March 2016. The game of Go originated in China at least 2,500
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outrage in democratic societies—are nonexistent or barely cause a ripple in China. While Google’s access to NHS data that was originally contracted to DeepMind immediately led to an outcry in the United Kingdom, Chinese tech companies generally benefit from a smoother path to implementation and profitability when it comes
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, Fine Art, had also succeeded in defeating the Go master. Tencent’s system, however, was likely heavily inspired by, or perhaps even directly copied from, DeepMind’s published work. Most of the Western AI researchers with whom I spoke don’t seem particularly concerned about this kind of knowledge transfer or
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recommendations, and news about your favorite authors. Tap here to learn more. NOTES CHAPTER 1. THE EMERGING DISRUPTION 1. Ewen Callaway, “‘It will change everything’: 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
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-amazon-microsoft-and-google-big-data-is-the-proverbial-big-deal/. 6. Richard Evans and Jim Gao, “DeepMind AI reduces Google data centre cooling bill by 40%,” DeepMind Research Blog, July 20, 2016, deepmind.com/blog/article/deepmind-ai-reduces-google-data-centre-cooling-bill-40. 7. Urs Hölzle, “Data centers are more energy
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/scale-ai-is-silicon-valley-s-latest-unicorn. 4. Volodymyr Mnih, Koray Kavukcuoglu, David Silver et al. “Playing Atari with deep reinforcement learning,” DeepMind Research, January 1, 2013, deepmind.com/research/publications/playing-atari-deep-reinforcement-learning. 5. Volodymyr Mnih, Koray Kavukcuoglu, David Silver et al., “Human-level control through deep reinforcement
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Architects of Intelligence, p. 171. 18. Andrea Banino, Caswell Barry, Dharshan Kumaran and Benigno Uria, “Navigating with grid-like representations in artificial agents,” DeepMind 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
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-018-0102-6. 21. Will Dabney and Zeb Kurth-Nelson, “Dopamine and temporal difference learning: A fruitful relationship between neuroscience and AI,” DeepMind Research Blog, January 15, 2020, deepmind.com/blog/article/Dopamine-and-temporal-difference-learning-A-fruitful-relationship-between-neuroscience-and-AI. 22. Tony Peng, “Yann LeCun Cake Analogy 2
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China Escape Thucydides’s Trap?, Houghton Mifflin Harcourt, 2017. 60. The AlphaStar team, “AlphaStar: Mastering the real-time strategy game StarCraft II,” DeepMind Research Blog, January 24, 2019, deepmind.com/blog/article/alphastar-mastering-real-time-strategy-game-starcraft-ii. 61. Ford, Interview with Oren Etzioni, in Architects of Intelligence, p. 494
The Simulation Hypothesis
by Rizwan Virk · 31 Mar 2019 · 315pp · 89,861 words
accomplishing other tasks that only humans would be capable of at the time. Deep Mind, Alpha Go and Video Games Not only is the history of AI and games intertwined, it continues to be in the near future. Google’s DeepMind group created AlphaGo, the first computer program to beat a professional Go
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also beat the South Korean Go champion Lee Sedol in 2016. An interesting twist on the “AI learns to play games” mechanic was when the DeepMind team trained the AI to play video games. This was done not through rules-based AI for a specific game, like the Tic Tac Toe
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languages they had never been taught, this might be an interesting clue that we are in some kind of simulation. Spatial Awareness. As Google’s DeepMind and Musk’s OpenAI showed, AI can learn to play video games. This means that they can become aware of a 2D space and examine
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autonomous lethal weapons that could kill humans while completely under the control of computer programs. The signers included one of the co-founders of Google DeepMind and Elon Musk. While today these researchers grapple with the ethical challenges presented by AI, noted sci-fi writer Isaac Asimov anticipated these challenges back
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NPCs, 82–84 super-intelligence, 100–101 and virtual reality and simulated consciousness, 16–18 AI (artificial intelligence), history of AI and games, 85–86 DeepMind, AlphaGo and video games, 86–88 digital psychiatrist, 88–89 NLP, AI and quest to pass the Turing Test, 89–92 Turing Test, 84–85
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, Adam, 76 D Dalai Lama, 207 Data, Star Trek: The Next Generation, 95–96, 115 Davoudi, Zohreh, 255 deathmatch mode, 43–44 Deep Blue, 86 DeepMind, 86–88, 94, 98 déjà vu, 240–41 delayed-choice double slit experiment, 145f delayed-choice experiment, 143–46 delayed-measurement experiment, 146 DELTA t
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.wikimedia.org/wiki/File:Turing_test_diagram.png (Source: Juan Alberto Sánchez Margallo) [←13] Minh, Kavukcuoglu, Silver, et al., “Playing Atari with Deep Reinforcement Learning,” Deepmind Technologies (2013). [←14] https://commons.wikimedia.org/wiki/File:Sophia_at_the_AI_for_Good_Global_Summit_2018_(27254369347).jpg [←15] https://www.theverge.com
Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI
by Karen Hao · 19 May 2025 · 660pp · 179,531 words
OpenAI’s model development practices. Another share of the interviews was with some 40 current and former executives and employees at Microsoft, Anthropic, Meta, Google, DeepMind, and Scale, as well as people close to Sam Altman. Any quoted emails, documents, or Slack messages come from copies or screenshots of those documents
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colleague’s house for a party that had been planned before Altman’s firing. There were guests from other AI companies as well, including Google DeepMind and Anthropic. Right before the event, an alert went out to all attendees. “We are adding a second themed room for tonight: ‘The no-
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billion in OpenAI, it laid off ten thousand workers to cut costs. After Google watched OpenAI outpace it, it centralized its AI labs into Google DeepMind. As Baidu raced to develop its ChatGPT equivalent, employees working to advance AI technologies for drug discovery had to suspend their research and cede their
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had been deeply concerned about AI for some time. In 2012, he’d met Demis Hassabis, the professorial CEO of the London-based AI lab DeepMind Technologies. Shortly thereafter, Hassabis had also paid Musk a visit at his SpaceX factory. As the two men sat in the canteen, surrounded by
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not work in this scenario. Superintelligence, Hassabis said with amusement, would simply follow humans into the galaxy. Musk, decidedly less amused, invested $5 million in DeepMind to keep tabs on the company. Later, at his 2013 birthday party in the lush wine-growing landscapes of Napa Valley, Musk had gotten into
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but it’s not going to kill everyone,” he said. “AI could render humanity extinct.” In late 2013, when Musk learned that Google would acquire DeepMind, he was convinced that such a union would end very badly. Publicly, he warned that if Google gave a hypothetical AGI an objective to maximize
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not be controlled by Larry.” But although Musk didn’t know it, Google had already dispatched a team of AI researchers via private jet to DeepMind’s offices to vet the acquisition. As part of the evaluation, Jeff Dean, one of the earliest and most senior Googlers, had reviewed a
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, Musk would regularly characterize Hassabis as a supervillain who needed to be stopped. Musk would make unequivocally clear that OpenAI was the good to DeepMind’s evil. In the summer of 2016, not long after OpenAI was founded, several employees met Hassabis and reported back to the office
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: DeepMind did intend to take over the world; Musk’s characterization seemed correct. The following year, Musk hosted an off-site meeting for OpenAI employees
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at his SpaceX factory and launched into a rant about Hassabis. Before founding DeepMind, Hassabis had spent seven years running a video game design studio he’d founded. “He literally made a video game where an evil genius tries
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too busy fucking windsurfing to realize that Demis is gathering all the power.” Musk’s paranoia about Hassabis would become a source of entertainment for DeepMind employees. Hassabis was incredibly ambitious and could be intense, certainly, but he was also kind and measured. “The creation of OpenAI felt like this
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semi-hysterical reaction to a fairly mild-mannered man,” recalls a former DeepMind researcher. “It seemed a little absurd.” * * * — On Musk’s list of recommended books was Superintelligence: Paths, Dangers, Strategies, in which Oxford philosopher Nick Bostrom
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back and forth between academic deliberations about different approaches to AI research and, Musk’s particular fixation, whether there was still time to beat out DeepMind and Google, essential, they believed, to correcting the course of AI development. The critical bottleneck, everyone agreed, was talent: Most of the top AI
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reach enough to invest in it presently was viewed largely as pseudoscience and quackery. But Hassabis had embraced that term to describe the ambitions of DeepMind, despite a belief among his own research staff that this was distasteful, shameless marketing. The Rosewood group equally felt that the same goal, AGI,
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spent more than $7 million out of its $11 million in expenses on compensation and benefits. Musk was getting impatient. It didn’t help that DeepMind was suddenly garnering worldwide adulation. In March 2016, its program AlphaGo beat Lee Sedol, one of the world’s best human players in the ancient
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Chinese game of Go. (“Deepmind is causing me extreme mental stress,” Musk wrote to OpenAI leadership shortly before the five-game match. “If they win, it will be really bad
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that year, Musk wrote again to Altman, Brockman, and Sutskever: Subject line: I feel I should reiterate. My probability assessment of OpenAI being relevant to DeepMind/Google without a dramatic change in execution and resources is 0%. Not 1%. I wish it were otherwise. Even raising several hundred million won’t
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five agents to face off against the world’s best team of five human players. Consciously or not, it was a page out of DeepMind’s book. Dota 2 had a worldwide championship that would be live streamed and spotlight OpenAI’s research in clear and dramatic win-or-lose
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terms. DeepMind had moved on to a similar project attempting to beat top human players in the strategy game StarCraft II, which could create an arbitrary yet
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Microsoft into an AI leader—both in software and in hardware—on par with Google. “The thing that’s interesting about what Open AI and Deep Mind and Google Brain are doing is the scale of their ambition,” wrote Scott to Nadella and Gates in mid-June, referring to Google’s AI
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There was also a time when scientists believed that a computer beating humans in chess or Go would be a conclusive measure of success. Now DeepMind’s AlphaGo is seen as a compelling demonstration of what software can be made to do but once again not yet a conclusion to the
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a return of disturbing historical patterns of conquest and extractivism.[*] The following year, a paper called “Decolonial AI” from Shakir Mohamed and William Isaac at DeepMind and Marie-Therese Png at the University of Oxford reinforced a suspicion I had begun to develop: The AI industry, in equal parts fueled by
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as “brain does not seem to be damaged.” Generative AI models also remain vulnerable to cybersecurity hacks. In 2023, researchers at several universities and Google DeepMind replicated Dawn Song’s data extraction attack against ChatGPT. They found that prompting it to repeat a word like poem or book forever caused the
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to appreciate Sutskever’s conviction in rallying people around a single focus: one that would ultimately allow OpenAI—then an underdog—to beat Google and DeepMind at their own game. It wasn’t that Sutskever was particularly persuasive. If Altman was the politician, Sutskever was the opposite. He never minced
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neural networks are slightly conscious,” he tweeted in 2022, even as other researchers warned that such rhetoric could fan popular misunderstandings of the technology. One DeepMind scientist specialized in the study of cognition and consciousness replied in the comments, “…in the same sense that it may be that a large field
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and retaining talent. Employees wondered whether external candidates were securing offers from OpenAI simply to use as leverage for negotiating higher offers with Google or DeepMind. OpenAI needed to find a way to legitimize itself as a research organization. This was frequently discussed at lunches and in company meetings, as
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startling. Not even in Silicon Valley did other companies and investors move until after ChatGPT to funnel unqualified sums into scaling. That included Google and DeepMind, OpenAI’s original rival. It was specifically OpenAI, with its billionaire origins, unique ideological bent, and Altman’s singular drive, network, and fundraising talent,
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to recruit and retain talent, significantly helped along by the capital raised from OpenAI LP, which allowed the company to finally compete with Google and DeepMind on salaries. In October 2020, with OpenAI’s elevating recognition, Altman hired Steve Dowling, a seasoned executive who’d led communications at Apple, to
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with a “code red” threat to the business, leaving Dean grumbling that the tech giant had missed a major opportunity to act earlier. At DeepMind, the GPT-3 API launch roughly coincided with the arrival of Geoffrey Irving, who had been a research lead in OpenAI’s Safety clan before
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moving over. Shortly after joining DeepMind in October 2019, Irving had circulated a memo he had brought with him from OpenAI, arguing for the pure language hypothesis and the benefits of
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the lab to allocate more resources to the direction of research. After ChatGPT, panicked Google executives would merge the efforts at DeepMind and Google Brain under a new centralized Google DeepMind to advance and launch what would become Gemini. GPT-3 also caught the attention of researchers at Meta, then still
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exchange, and debate ideas, encouraged adherents to work at the major AI labs, especially those they felt needed more AI safety watchdogs, like OpenAI and DeepMind, to shape and mold their trajectory. The influx of members in AI safety also popularized the community’s lexicon more broadly in the AI industry
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hosted an OpenAI party near the conference convention center to represent the company and recruit interested candidates among the nearly ten thousand in-person attendees. DeepMind, Meta, and Google were holding competing recruitment parties at the exact same time throughout the city. As the party went on, a recruiter at
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on its way to releasing its chatbot, Claude; Google had sounded a “code red” alarm internally and would soon consolidate its AI divisions into Google DeepMind to throw its full weight behind launching a similar product. Though OpenAI had hit the market first with its 10x better offering, it needed to
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recommendations arrived two months after his hearing in July 2023. Written by a consortium of researchers, including from OpenAI’s Safety clan, Microsoft, and Google DeepMind as well as more than a dozen think tanks, many tied to the Doomer community, it pushed once again for a new licensing regime for
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Beijing office. ResNet not only underpins major computer-vision, speech-recognition, and language systems but also was a core ingredient of the first version of DeepMind’s AlphaFold, an AI system released in 2018 that could predict a protein’s 3D structure from its amino acid sequence, crucial for accelerating drug
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subsequent advancements in AlphaFold, using a different neural network, would earn Demis Hassabis and another senior research scientist at DeepMind a 2024 Nobel Prize in Chemistry.) And yet, in October 2023, the ideas championed by the Closed side would gain their greatest endorsement yet
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February after five years on the board, due to conflicts of interest. The previous year, he had cofounded a startup, Inflection, with the now-departed DeepMind cofounder Mustafa Suleyman, which was fast evolving into a direct OpenAI competitor. A month later, Hoffman was followed by Shivon Zilis, Musk’s trusted deputy
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the General Public,” which listed three links. One was an X thread from Geoffrey Irving, the AI safety researcher who had left in 2019 for DeepMind, saying that Altman had “lied to me on various occasions” and “was deceptive, manipulative, and worse to others.” The other two were journalism articles.
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of the deal but also Suleyman’s reputation. He was known to those who worked for him at DeepMind as a toxic and abusive bully. After years of HR complaints against him, DeepMind had stripped him of most of his management responsibilities in late 2019, placed him on leave, and subsequently
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Musk, 241. GO TO NOTE REFERENCE IN TEXT As part of the evaluation: “Decoding Google Gemini with Jeff Dean,” posted September 11, 2024, by Google DeepMind, YouTube, 55 min., 55 sec., youtu.be/lH74gNeryhQ; author correspondence with Google spokesperson, November 2024. GO TO NOTE REFERENCE IN TEXT The meeting convinced Musk
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Musk, CourtListener, ECF No. 32, Exhibit 13. GO TO NOTE REFERENCE IN TEXT “It seemed a little”: A Google DeepMind spokesperson also rejected Musk’s characterization of Hassabis. Author correspondence with Google DeepMind spokesperson, November 2024. GO TO NOTE REFERENCE IN TEXT Given a simple objective: Nick Bostrom, Superintelligence: Paths, Dangers,
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s large neural networks are slightly conscious,” Twitter (now X), February 9, 2022, x.com/ilyasut/status/1491554478243258368. GO TO NOTE REFERENCE IN TEXT One DeepMind scientist specialized: Murray Shanahan (@mpshanahan), “…in the same sense that it may be that a large field of wheat is slightly pasta,” Twitter (now X
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(Office of Strategic Services: 1944), cia.gov/static/5c875f3ec660e092cf893f60b4a288df/SimpleSabotage.pdf. GO TO NOTE REFERENCE IN TEXT Chapter 7: Science in Captivity Shortly after joining DeepMind: Copy of that memo. GO TO NOTE REFERENCE IN TEXT But executives weren’t interested: Karen Hao, Salvador Rodriguez, and Deepa Seetharaman, “Mark Zuckerberg
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January 26, 2021, wsj.com/articles/artificial-intelligence-will-define-googles-future-for-now-its-a-management-challenge-11611676945; Giles Turner and Mark Bergen, “Google DeepMind Co-Founder Placed on Leave From AI Lab,” Bloomberg, August 21, 2019, bloomberg.com/news/articles/2019-08-21/google
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239, 391 deep learning, 98–101 discriminatory impacts of, 57, 108–9 ImageNet, 47, 100–101, 117–18, 259 limitations and risks of, 106–10 DeepMind, 6, 17, 24–26, 48, 66, 158–59, 261–62, 384–85 AlphaFold, 309–10 AlphaGo, 59, 93 OpenAI and ChatGPT, 114, 119–20, 132
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72, 100–101, 106, 178 AI scraping, 136 Amodei at, 55, 57 Android, 100, 239 captchas, 98 data centers, 274–75, 285–91, 295–96 DeepMind. See DeepMind DNNresearch, 47, 50, 98–99, 100 Frontier Model Forum, 305–6, 309 GPT-4 and, 249 Imagen model, 240, 242 LaMDA, 153, 253–54
The Age of AI: And Our Human Future
by Henry A Kissinger, Eric Schmidt and Daniel Huttenlocher · 2 Nov 2021 · 194pp · 57,434 words
it with our values. CHAPTER 1 WHERE WE ARE IN LATE 2017, a quiet revolution occurred. AlphaZero, an artificial intelligence (AI) program developed by Google DeepMind, defeated Stockfish—until then, the most powerful chess program in the world. AlphaZero’s victory was decisive: it won twenty-eight games, drew seventy-two
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own. But in an increasing number of applications, machines are devising solutions that seem beyond the scope of human imagination. In 2016, a subdivision of DeepMind, DeepMind Applied, developed an AI (that ran on many of the same principles as AlphaZero) to optimize the cooling of Google’s temperature-sensitive data centers
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. Although some of the world’s best engineers had already tackled the problem, DeepMind’s AI program further optimized cooling, reducing energy expenditures by an additional 40 percent—a massive improvement over human performance.6 When AI is applied
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AI from beating human chess experts to discovering entirely new chess strategies. And its capacity for discovery is not limited to games. As we mentioned, DeepMind built an AI that successfully reduced the energy expenditures of Google’s data centers by 40 percent more than what its excellent engineers could achieve
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GPT-3 on a wide range of language tasks. OpenAI is also working on its next version of GPT, continuing the race. Models such as DeepMind’s RETRO and OpenAI’s GLIDE have improved both efficiency and capacity, able to do more with the same number of model parameters but often
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utterances originated by humans and utterances generated by machines. Advances in AI for scientific discovery have also continued to accelerate. In the summer of 2021, Deep Mind released AlphaFold2, the successor to AlphaFold, which predicts the 3D structure of proteins from their amino acid sequence (see chapter 6). AlphaFold2 and work done
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at the Baker Lab at the University of Washington were named by Science as the “2021 Breakthrough of the Year,”8 generating a new DeepMind database of protein structures containing nearly one million proteins as of spring 2022, with plans to grow it to nearly one hundred million (hundreds of
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, https://dailynous.com/2020/07/30/philosophers-gpt-3/#gpt3replies. 6. Richard Evans and Jim Gao, “DeepMind AI Reduces Google Data Centre Cooling Bill by 40%,” DeepMind blog, July 20, 2016, https://deepmind.com/blog/article/deepmind-ai-reduces-google-data-centre-cooling-bill-40. 7. Will Roper, “AI Just Controlled a Military Plane
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-building-better-jobs-in-an-age-of-intelligent-machines. 2. “AlphaFold: A Solution to a 50-Year-Old Grand Challenge in Biology,” DeepMind blog, November 30, 2020, https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology. 3. See Walter Lippmann, Public Opinion (New York
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Breakthrough Performance,” Google AI Blog, April 4, 2022, https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html. 4. Will Douglas Heaven, “DeepMind Says Its New Language Model Can Beat Others 25 Times Its Size,” MIT Technology Review, December 8, 2021, https://www.technologyreview.com/2021/12/08
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/1041557/deepmind-language-model-beat-others-25-times-size-gpt-3-megatron/. 5. Ilya Sutskever, “Fusion of Language and Vision,” The Batch, December 20, 2020, https://read
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. “Summary of the NATO Artificial Intelligence Strategy,” NATO, October 22, 2021, https://www.nato.int/cps/en/natohq/official_texts_187617.htm. 15. Kyle Wiggers, “DeepMind Claims AI Has Aided New Discoveries and Insights in Mathematics,” VentureBeat, December 1, 2021, https://venturebeat.com/2021/12/01
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/deepmind-claims-ai-has-aided-new-discoveries-and-insights-in-mathematics/. 16. So the late diplomat and professor Charles Hill taught his students. BY HENRY A.
The Future We Choose: Surviving the Climate Crisis
by Christiana Figueres and Tom Rivett-Carnac · 25 Feb 2020 · 197pp · 49,296 words
strategy game of Go, learning entirely by itself, essentially accumulating thousands of years of human knowledge, and improving on it, in just forty days.75 Deep Mind, the company that developed AlphaGo Zero, says the technology is not limited to machines that can outcompete human beings in strategy games but is intended
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. Their servers were among the most efficient in the world, and it seemed that any improvements from then on would be marginal. Then they unleashed DeepMind algorithms on the system. Energy demand for cooling was consistently reduced by 40 percent.85 This illustration is just a tiny example of the power
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://snips.ai/content/intro-to-ai/#ai-metrics. 75. David Silver and Demis Hassabis, “AlphaGo Zero: Starting from Scratch,” DeepMind, October 18, 2017, https://deepmind.com/blog/alphago-zero-learning-scratch/. 76. DeepMind, https://deepmind.com/. 77. Rupert Neate, “Richest 1% Own Half the World’s Wealth, Study Finds,” Guardian (U.S. edition), November
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://www.wired.com/story/canada-france-plan-global-panel-study-ai/. 85. Richard Evans and Jim Gao, “DeepMind AI Reduces Google Data Centre Cooling Bill by 40%,” DeepMind, July 20, 2016, https://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40/. 86. United Nations Division for the Advancement of Women (UNDAW
Machine, Platform, Crowd: Harnessing Our Digital Future
by Andrew McAfee and Erik Brynjolfsson · 26 Jun 2017 · 472pp · 117,093 words
A scientific paper published the very next month—January 2016—unveiled a Go-playing computer that wasn’t being foiled anymore. A team at Google DeepMind, a London-based company specializing in machine learning (a branch of artificial intelligence we’ll discuss more in Chapter 3), published “Mastering the Game of
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beat the 633rd-ranked tennis pro would be impressive, but it still wouldn’t be fair to say that it had ‘mastered’ the game.” The DeepMind team evidently thought this was a fair point, because they challenged Lee Sedol to a five-game match to be played in Seoul, South Korea
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effective at “narrow” artificial intelligence, for particular domains like Go or image recognition, but we are far from achieving what Shane Legg, a cofounder of Deep-Mind, has dubbed artificial general intelligence (AGI), which can apply intelligence to a variety of unanticipated types of problems. Polanyi’s Pervasive Paradox Davis and Marcus
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third quarter of 2015, however, deep learning was being used in approximately 1,200 projects across the company, having surpassed the performance of other methods. DeepMind, which has been particularly effective in combining deep learning with another technique called reinforcement learning,†† has turned its attention and its technologies not only to
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the right temperature. These people monitor thermometers, pressure gauges, and many other sensors and make decisions over time about how best to cool the facility. DeepMind wanted to see whether machine learning could be used instead. They took years of historical data on data centers’ computing load, sensor readings, and environmental
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40%, and the facility’s overhead—the energy not used directly for IT equipment, which includes ancillary loads and electrical losses—improved by about 15%. DeepMind cofounder Mustafa Suleyman told us these were among the largest improvements the Google data center team had ever seen. Suleyman also stressed to us that
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DeepMind’s approach is highly generalizable. The neural networks used by the team do not need to be completely reconfigured for each new data center. They
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successes in the field so far have used supervised learning techniques, and a few have used reinforcement learning (for instance, the data center optimized by DeepMind). However, the main way humans learn is through unsupervised learning. A toddler learns everyday physics by playing with blocks, pouring water out of a glass
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humans endowed with common sense watch over the decisions and actions of the artificial intelligence, and intervene if they see anything amiss. This is what DeepMind did when its neural networks took over optimization of a data center. The human controllers were always present and in the loop, able to take
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/+KentonVarda/posts/TSDhe5CvaFe. †† Reinforcement learning is concerned with building software agents that can take effective actions within an environment in order to maximize a reward. DeepMind’s first public demonstration of its abilities in this area was the “deep Q-network” (DQN) system, which was built to play classic Atari 2600
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presented to it. Volodymyr Mnih et al., “Human-Level Control through Deep Reinforcement Learning,” Nature 518 (February 28, 2015): 529–33, https://storage.googleapis.com/deepmind-data/assets/papers/DeepMindNature14236Paper.pdf. ‡‡ Setting up a properly functioning neural network may sound easy—just pour in the data and let the system make
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”: Sam Byford, “Google vs. Go: Can AI Beat the Ultimate Board Game?” Verge, March 8, 2016, http://www.theverge.com/2016/3/8/11178462/google-deepmind-go-challenge-ai-vs-lee-sedol. 5 “There is a beauty to the game of Go”: Ibid. 5 “Looking at the match in October”: “S
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/slides/WSDM2016-Jeff-Dean.pdf. 78 When control of an actual data center: Richard Evans and Jim Gao, “DeepMind AI Reduces Google Data Centre Cooling Bill by 40%,” DeepMind, July 20, 2016, https://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40. 79 Tech giants including Microsoft: Tom Simonite, “Google and
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–99 DAO and, 302–5 failure modes of, 317–19 as solution to problem of corporate dominance, 308–9 deep learning systems, 76–79, 84 DeepMind, 4, 77–78 deep Q-network (DQN), 77n Deep Thunder, 121 Defense Department, US, 103–4 delivery services, 184–85 demand in two-sided networks
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also Android acquiring innovation by acquiring companies, 265 Android purchased by, 166–67 Android’s share of Google revenue/profits, 204 autonomous car project, 17 DeepMind, 77–78 hiring decisions, 56–58 iPhone-specific search engine, 162 and Linux, 241 origins of, 233–34 and self-driving vehicles, 82 as stack
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, 295 Google AdSense, 139 Google DeepMind, 4, 77–78 Google News, 139–40 Google search data bias in, 51–52 incorporating into predictive models, 39 Graboyes, Robert, 274–75 Granade, Matthew
Growth: A Reckoning
by Daniel Susskind · 16 Apr 2024 · 358pp · 109,930 words
is little doubt that at some point in the twenty-first century we will say the same about other private companies, such as OpenAI and DeepMind, that are developing the key technologies of our time. Perhaps the most important realization regarding which institutions are best placed to conduct R&D, though
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develop their Covid-19 vaccine during the pandemic.81 But perhaps the most exciting development in this space has been AlphaFold, a system developed by DeepMind to tackle the ‘protein folding problem’, a benign name for one of the most profound and difficult questions in biology
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. DeepMind already had a spread of achievements behind it before turning to biology, from game-playing triumphs (culminating in AlphaZero, a system that taught itself in
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, no more than a measly 17 per cent of the three-dimensional (3-D) shapes of proteins were known.86 Demis Hassabis, the creator of DeepMind, became intrigued by the work of a biologist called Christian Anfinsen. Back in 1963, Anfinsen had given the field a tantalizing glimpse of a different
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biological origami: the shape of a protein is key for understanding what it does, how it causes diseases, how it interacts with drugs. In 2022, DeepMind made publicly available the 3-D structures it had discovered of hundreds of millions of proteins, nearly all the proteins known to humankind. In the
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success is ‘human parity’.31 That is how technology companies talk about their goals: Facebook’s lead AI researcher aims to build ‘human-level AI’; Deep-Mind’s founder aspires to ‘solve intelligence’; OpenAI’s mission statement is to ‘build safe and beneficial AGI’, or artificial general intelligence, which they explicitly define
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. 84 Ewen Callaway, ‘Revolutionary Cryo-EM is Taking Over Structural Biology’, Nature, 10 February 2020. 85 Lex Fridman discussion with Demis Hassabis. 86 Matthew Sparkes, ‘DeepMind’s AI Uncovers Structure of 98.5 Per Cent of Human Proteins’, New Scientist, 22 July 2021. 87 Christian Anfinsen, ‘Studies on the Principles that
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–6, 175, 187 Coyle, Diane, 293n3 Creative Commons (organization), 185 Daley, William, 293n6 Daly, Herman, Toward a Steady-State Economy (1973), 154 Darwin, Charles, 280n30 DeepMind (company), 189, 200, 202, 244 Deepwater Horizon oil spill (2010), 136 degrowth movement, 149–51 DeLong, Brad, Slouching Towards Utopia, 176 Dene, William de la
How to Spend a Trillion Dollars
by Rowan Hooper · 15 Jan 2020 · 285pp · 86,858 words
the thing. There are accomplished AI systems already in operation, but their skills are non-transferrable. One of the world’s leading AI firms is DeepMind, which is owned by Google. It created a computer program called AlphaZero, which became the greatest chess player of all time when it was given
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of the sort of tension between how AI may help or hamper us a society. In other areas, AI is more unambiguously helpful. In 2016, DeepMind designed an AI to analyse the efficiency of the cooling system used in Google data-centres. These gigantic facilities handle all the Google searches made
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submitted your DNA for ancestry determination, or for sequencing, it may be that your genetic information has been passed on to secondary companies for analysis. DeepMind was reprimanded when it was revealed that it had used sensitive medical information about 1.6 million people registered with the UK National Health Service
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learns on its own. There has been spectacular success with a turbo form of machine learning called deep learning; it’s behind the ability of DeepMind’s AlphaGo and AlphaZero, and it’s the basis of a system developed by OpenAI called Generative Pre-trained Transformer, or GPT. A publicly available
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2001: A Space Odyssey. AI with a working memory, able to apply something learned in one context to use in another, has been demonstrated at Deep-Mind. Complex reasoning is one of the things that humans can do as a matter of course. It means we can respond correctly when we are
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London Underground map and tell someone how to get from Old Street to Putney. It’s something that computers have had trouble with, but which DeepMind is starting to tackle with a neural network-style computer that has access to a short-term memory.12 It’s a small step towards
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human-like thinking, and it’s this sort of success that encourages DeepMind that the neural network approach is the route to get there. AI with theory of mind isn’t far away. We’ve looked at a
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at detecting sexual orientation from facial images’. Journal of Personality and Social Psychology 114(2), 246–257. DOI: 10.17605/OSF.IO/ZN79K 6 See DeepMind: https://deepmind.com/blog/safety-first-ai-autonomous-data-centre-cooling-and-industrial-control/ 7 Hal Hodson (2016) ‘Revealed: Google AI has access to huge haul
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