The Deep Learning Revolution (The MIT Press)
by Terrence J. Sejnowski · 27 Sep 2018
. Printed and bound in the United States of America. Library of Congress Cataloging-in-Publication Data Names: Sejnowski, Terrence J. (Terrence Joseph), author. Title: The deep learning revolution / Terrence J. Sejnowski. Description: Cambridge, MA : The MIT Press, 2018. | Includes bibliographical references and index. Identifiers: LCCN 2017044863 | ISBN 9780262038034 (hardcover : alk. paper)
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If you use voice recognition on an Android phone or Google Translate on the Internet, you have communicated with neural networks1 trained by deep learning. In the last few years, deep learning has generated enough profit for Google to cover the costs of all its futuristic projects at Google X, including self-driving cars
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and Functional Architecture in the Cat’s Visual Cortex,” which reported for the first time the response properties of single neurons recorded with a microelectrode. Deep learning networks have an architecture similar to the hierarchy of areas in the visual cortex. 1969—Marvin Minsky and Seymour Papert published Perceptrons, which pointed
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data; information can be used to create knowledge; knowledge leads to understanding; and understanding leads to wisdom. Welcome to the brave new world of deep learning.1 Deep learning is a branch of machine learning that has its roots in mathematics, computer science, and neuroscience. Deep networks learn from data the way that babies
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s tensor processing unit (TPU) is now deployed on servers around the world, delivering an order-of-magnitude improvement in performance for deep learning applications. An example of how quickly deep learning can change the landscape is the impact it has had on language translation—a holy grail for artificial intelligence since it depends
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on the ability to understand a sentence. The recently unveiled new version of Google Translate based on deep learning represents a quantum leap improvement in the quality of translation between natural languages. Almost overnight, language translation went from a fragmented hit-and-miss
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jumble of phrases to seamless sentences (figure 1.3). Previous computer methods searched for combinations of words that could be translated together, but deep learning looks for dependencies across whole sentences. Alerted about the sudden improvement of Google Translate, on November 18, 2016, Jun Rekimoto at the University of
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leopard. No one has ever explained what leopard wanted at that altitude.10 (Hemingway is #1.) The next step will be to train larger deep learning networks on paragraphs to improve continuity across sentences. Words have long cultural histories. Vladimir Nabokov, the Russian writer and English-language novelist who wrote Lolita
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on slides is done by experts who make mistakes, mistakes that have deadly consequences. This is a pattern recognition problem for which deep learning should excel. And indeed, a deep learning network trained on a large dataset of slides for which ground truth was known reached an accuracy of 0.925, good but not
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review, or discovery, will be taken over by artificial intelligence, which can sort through thousands of documents for legal evidence without getting tired. Automated deep learning systems will also help law firms comply with the increasing complexity of governmental regulations. They will make legal advice available for the average person who
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given to the winner at the end of a sequence of moves, which paradoxically can improve decisions made much earlier. When coupled with many powerful deep learning networks, this leads to many domain-dependent bits of intelligence. And, indeed, cases have been made for different domaindependent kinds of intelligence: social, emotional,
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steadily increasing since the advent of computer programs that play at championship levels, and so has the machine augmented intelligence of the human players.40 Deep learning will boost the intelligence not just of scientific investigators but of workers in all professions. Scientific instruments are generating data at prodigious rate. Elementary particle
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than almost anyone else in the world and will not forget anything, becoming, in effect, your virtual doppelganger. By pressing both Internet tracking and deep learning into service, the educational opportunities for the children of today’s children will be better than the best available today to wealthy families. These grandchildren
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beginning to benefit. Alexa, a wildly popular digital assistant operating in tandem with the Amazon Echo smart speaker, responds to natural language requests based on deep learning. Amazon Web Services (AWS) has introduced toolboxes called “Lex,” “Poly” and “Comprehend” that make it easy to develop the same natural language interfaces based
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networks in the 1980s and as the president of the Neural Information Processing Systems (NIPS) Foundation, which has overseen discoveries in machine learning and deep learning over the last thirty years. My colleagues and I in the neural network community were for many years the underdogs, but our persistence and patience
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earlier level. The decision demon weighs the degree of excitement and importance of its informants. This form of evidence evaluation is a metaphor for current deep learning networks, which have many more levels. From Peter H. Lindsay and Donald A. Norman, Human Information Processing: An Introduction to Psychology, 2nd ed. (New
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based on the architecture of the visual system that used convolutional filters and a simple form of Hebbian plasticity and was a direct precursor of deep learning networks. And, for a third, Teuvo Kohonen, an electrical engineer at Helsinki University, developed a self-organizing network that could learn to cluster similar
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networks was possible. 1986—David Rumelhart and Geoffrey Hinton publish “Learning Internal Representations by Error-Propagation,” which introduced the “backprop” learning algorithm now used for deep learning. 1988—Richard Sutton publishes “Learning to Predict by the Methods of Temporal Differences” in Machine Learning. Temporal difference learning is now believed to be the
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2012 paper “ImageNet Classification with Deep Convolutional Neural Networks” reduces the error rate for correctly classifying objects in images by 18 percent. 2017—AlphaGo, a deep learning network program, beats Ke Jie, the world champion at Go. 6 The Cocktail Party Problem Chapter The Cocktail Party 6 Problem © Massachusetts Institute of
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open. Recent experiments on neural network learning of language support the gradual acquisition of inflectional morphology, consistent with human learning.12 The success of deep learning with Google Translate and other natural language applications in capturing the nuances of language further supports the possibility that brains do not need to use
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many networks yield the same behavior, the key to understanding them is the learning algorithms used by brains, which should be easier to discover. Understanding Deep Learning Whereas, in convex optimization problems, there are no local minima and convergence is guaranteed to the global minimum, in nonconvex optimization problems, this is
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a network than those we receive from humans? Recall that consciousness does not have access to the inner workings of 124 Chapter 8 brains. Deep learning networks typically provide not one but several leading predictions in rank order, which gives us some information about the confidence of a conclusion. Supervised neural
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zip codes on letters, using the Modified National Institute of Standards and Technology (MNIST) Figure 9.1 Geoffrey Hinton and Yann LeCun have mastered deep learning. This photo was taken at a meeting of the Neural Computation and Adaptive Perception Program of the Canadian Institute for Advanced Research around 2000, a
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what the distributed representations at the top of the hierarchy were meant to accomplish. This illustrates the potential for fruitful symbiotic relationships between biology and deep learning. Deep Learning Meets the Visual Hierarchy A philosopher of the mind, Patricia Churchland specializes in neurophilosophy at UC, San Diego.13 That knowledge ultimately depends on how
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following their intuitions; the theory of thermodynamics that explained how the engines worked came later, along with improvements in their efficiency. The analysis of deep learning networks by physicists and mathematicians is well under way. 134 Chapter 9 Working Memory and Persistence of Activity Neuroscience has come a long way since
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-range dependencies are preserved selectively. This version of working memory in neural networks lay dormant for twenty years until it was awakened and implemented in deep learning networks, where it has been spectacularly successful in many domains that depend on learning sequences of inputs and outputs, such as movies, music, movements,
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at Amherst, on difficult problems in reinforcement learning, a branch of machine learning inspired by associative learning in animal experiments (figure 10.2). Unlike a deep learning network, whose only job is to transform inputs into outputs, a reinforcement network interacts in a closed loop with the environment, receiving sensory input,
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championship Go translate to solving other complex problems? Much of human learning is based on observation and mimicry, and we need far fewer examples than deep learning to learn to recognize a new object. Unlabeled sensory data are abundant, and powerful unsupervised learning algorithms might use these data to advantage before
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any supervision takes place. In chapter 7, an unsupervised version of the Boltzmann learning algorithm was used to initialize deep learning networks, and in chapter 6, independent component analysis (ICA), an unsupervised learning algorithm, extracted sparse population codes from natural images and in chapter 9,
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at Figure 11.1 Logo of the Neural Information Processing Systems conferences. Founded thirty years ago, NIPS conferences are the premier conferences on machine and deep learning. Courtesy of the NIPS Foundation. Neural Information Processing Systems 163 Figure 11.2 Edward “Ed” Posner at Caltech, who founded the NIPS conferences, which
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new Halıcıoğlu Data Science Institute. Master’s in Data Science degrees (MDSs) are becoming as popular as MBAs. Neural Information Processing Systems 165 Deep Learning at the Gaming Table Deep learning came of age at the 2012 NIPS Conference at Lake Tahoe (figure 11.3). Geoffrey Hinton, an early pioneer in neural networks, and
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temporal segmentation to video,19 a performance good Figure 12.6 Marian Stewart-Bartlett demonstrating facial expression analysis. The time lines are the output of deep learning networks that are recognizing facial expression for happiness, sadness, surprise, fear, anger, and disgust. Courtesy of Marian StewartBartlett. Robert Wright/LDV Vision Summit 2015.
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a company called “Emotient” to commercialize the automatic analysis of facial expressions. Paul Ekman and I served on its Scientific Advisory Board. Emotient developed deep learning networks that had an accuracy of 96 percent in real time and with natural behavior, under a broad range of lighting conditions, and with nonfrontal
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accomplish.19 But who could have predicted how well neural networks would scale in their performance? The Wolfram language that supports Mathematica now also supports deep learning applications, one of which was the first to provide online object recognition in images.20 Stephen introduced me to Beatrice Golomb, who was working
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world. But now there are openly available alternatives to TensorFlow: CNTK from Microsoft, MVNet, backed by Amazon and other major Internet companies, and other viable deep learning programs, such as Caffe, Theano, and PyTorch. Hot Chips In 2011, I organized “Growing High Performance Computing in a Green Environment,” a symposium sponsored
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need to move forward, not look backward. At every step along the way, adding a new feature from brain architecture has boosted the functionality of deep learning networks: the hierarchy of cortical areas; the brain’s coupling of deep with reinforcement learning; working memory in recurrent cortical networks; and long-term
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after Minsky’s death, Alex Graves, Greg Wayne, and colleagues, researchers at DeepMind, achieved the next step toward a general artificial intelligence based on deep learning by adding a dynamic external memory.25 Activity patterns can only be stored temporarily in a deep recurrent neural network, which makes it difficult to
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systems across spatial and temporal scales: gene networks, metabolic networks, immune networks, neural networks, and social networks—it’s networks all the way down. Deep learning depends on optimizing a cost function. What are the cost functions in nature? The inverse of cost in evolution is called fitness, but that is
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1992; and many other foundational books on machine learning, including Richard Sutton and Andrew Barto’s Reinforcement Learning: An Introduction, and the leading textbook Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The Press’s Robert Prior helped guide the present volume around many an unexpected bend in its
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I did not know this at the time. Recommended Reading Recommended Recommended Reading Reading © Massachusetts Institute of TechnologyAll Rights Reserved An Introduction to Neuroscience The Deep Learning Revolution only briefly touches on neuroscience, which is itself a vast field with a rapidly advancing scientific frontier. The part of neuroscience most relevant to
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deep neural networks, 35 depends on optimizing a cost function, 267 meets the visual hierarchy, 132–133 origin and roots of, 3 understanding, 119–122 Deep learning systems, 159. See also specific topics DeepLensing, 21 DeepMind, 17, 20, 154. See also AlphaGo Deepstack, 15, 24 Index Defense Advanced Research Projects Agency
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Goldilocks problem in, 112 Language acquisition, 184. See also Chomsky, Noam Language disorders, 190 Language translation. See Translation 331 Larochelle, Hugo, 302n4 Law firms, automated deep learning systems and, 15 Lawrence, David T., 44f, 291n9 Learning, 258. See also specific topics Chomsky and, 248f, 249f, 250, 251 forms of, 154–159
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201, 267. See also Multilayer learning algorithms; Unsupervised learning algorithms; specific algorithms building a new generation of chips to run, 205 complex systems and, 196 deep learning and, 133, 140–141, 201 explored through simulations of small networks, 258 explosion of, 110 in historical context, 137, 172–173 unifying concepts and,
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Minsky, Marvin Lee Petascale computing, 206, 208 Peterson, Roger Torey, 30f, 290n3 Phonemes, 113, 114f, 115, 116, 158 Piantoni, Giovanni, 227, 228f Picture captioning with deep learning, 135, 136f Pinker, Steven, 300n11 Pitts, Walter H., 106, 200, 298n21, 312n11 Planning Workshop on Facial Expression Understanding, 180–181 Plasticity critical period of, in
Prophecy: Prediction, Power, and the Fight for the Future, from Ancient Oracles to AI
by Carissa Véliz · 21 Apr 2026 · 503pp · 129,255 words
rise of the internet and cloud computing, further increasing data availability and processing power. The 2010s marked a turning point with the practical application of deep learning, fueled by big data and improved hardware like GPUs. Advances in algorithms paved the way for machine learning—prediction machines. With prediction come all the
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desired outcome is; then they feed it input in the form of data, but they don’t tell it how to get to the outcome. Deep learning, a subfield of machine learning that is focused on neural networks with many layers, starts with a model—a mathematical representation of the problem—and
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, 26, 63, 64–86, 91, 186, 210, 211, 246, 277, 284–86 chatbots. See chatbots conservative nature of, 234 and correlation versus causation, 103–8 deep learning, 66, 68 difference between humans and, 61 ethics of, 289–91 existential risk from, 110–11, 181–83, 198, 208 general, 190 generative, as fortune
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, 269, 272, 278 by algorithms, 59 expected utility theory and, 203 managerial, 165 mathematization of, 23, 24–63 political, 13–15, 56 Dee, John, 74 deep learning, 66, 68 Defense Department, 10, 80 defiance, 122, 145, 149, 225, 238, 268, 284–85 defying the odds, 229–57 communities and, 248–51 creativity
Scarcity Brain: Fix Your Craving Mindset and Rewire Your Habits to Thrive With Enough
by Michael Easter · 25 Sep 2023 · 318pp · 95,383 words
choice. Instead, it’s a summation of repeated choices that make a different choice harder to make for environmental, biological, and historical reasons. It’s deep learning. Much like gaining weight. Few people set out to become obese. But over time, the weight accumulates, and we find ourselves obese. It happens through
We Are as Gods: A Survival Guide for the Age of Abundance
by Peter H. Diamandis and Steven Kotler · 13 Apr 2026 · 225pp · 76,418 words
for the twenty-first century, when that drunken sailor’s loopy squiggle would start to resemble a hockey stick—the hallmark of exponential explosion. The Deep Learning Breakthrough: 2006–2012 It didn’t look like the start of the next AI revolution. It looked like a four-page paper in Science with
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the error rate from the previous world record of 26.2 percent to 15.3—a leap so dramatic it stunned the AI community. Overnight, deep learning went from academic curiosity to industry obsession. “This was the big moment,” Nvidia CEO Jensen Huang (whose company makes the GPU powering today’s AI
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revolution) told Business Insider. “The big bang of deep learning. A pivotal moment that marked the beginning of the AI revolution.” The victory was particularly sweet for Hinton, who had spent decades being told that
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months, Google hired Hinton and his entire lab, and every major tech company began racing to build their own deep learning teams. The paradigm had shifted, and the world was paying attention. The deep learning uprising was no longer theoretical. What began as an obscure development had become a mainstream explosion. The six years
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Neural Networks.” “This was the big moment”: Lloyd Lee, “Nvidia CEO Jensen Huang Shouts Out OpenAI Cofounder Ilya Sutskever for Sparking ‘the Big Bang of Deep Learning,’ ” Business Insider, June 22, 2024, https://www.businessinsider.com/nvidia-ceo-jensen-huang-openai-ilya-sutskever-ai-revolution-2024-6. The six years from 2006
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to 2012: Giuliano Giacaglia, “A Brief Overview of Deep Learning,” Holloway, November 2, 2022, https://www.holloway.com/g/making-things-think/sections/a-brief-overview-of-deep-learning. In recognition of this work: “Fathers of Deep Learning Revolution Receive ACM A. M. Turing Award,” Association for Computing Machinery, 2018 ACM
The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence
by Sebastian Mallaby; · 30 Mar 2026 · 607pp · 161,998 words
Alex Krizhevsky showed up at a conference in Italy and announced something astonishing. Working from his bedroom at his parents’ home, Krizhevsky had trained a deep-learning system that smashed all previous records in computer vision: In a competition called ImageNet, devised by the pioneering Stanford computer scientist Fei-Fei Li, his
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components would have to be integrated. The progress in image recognition was therefore just one piece of the puzzle. The larger challenge was to combine deep learning, which would solve challenges such as computer vision, with reinforcement learning, which would deliver other facets of intelligence, including the ability to hatch plans
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and think strategically. To deliver on this premise, the business plan promised a “Deep Learning Agent” that would master games without being told what the rules were. This sort of model would experiment with millions of possible actions, observe their
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she wants, she learns the value of good manners. Reinforcement learning equips machines to do the same: to act, and to learn by acting. Unlike deep learning, which involved layered neural networks, reinforcement learning was a conceptual framework rather than a computational architecture. RL researchers described their systems in general terms. Like
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. In order to tame infinity, in other words, an infinity machine has to develop algorithms that narrow the search for the best action. Relative to deep learning, with its mind-boggling nonlinearities and impressive practical results, reinforcement learning seemed theoretical and primitive. But to Mnih and other believers in RL, the promise
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of agents that could learn from experience remained thrilling. Whereas deep learning depended on the availability of training data—human-labeled cat photos, for example—reinforcement learning held out the hope that an AI could collect its
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Wierstra had done their PhDs, combining the two approaches was actually encouraged. Besides, neuroscience strongly suggested that reinforcement learning would be a necessary complement to deep learning. After all, the reward signals in reinforcement learning resembled the dopamine signals in the human brain. If the brain was the template for artificial general
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’s commitment to intellectual diversity.[11] Two recruits worked on statistical methods for quantifying uncertainty and incorporating probabilities into models.[12] Two had worked on deep learning at New York University under Yann LeCun.[13] Others, including Wierstra, were focused on the intersection between artificial intelligence and human intelligence. A computational psychologist
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Mnih arrived in London, DeepMind’s eclecticism seemed somewhat contrarian. The excitement about neural networks had intensified further: Without drawing from other branches of AI, deep learning seemed poised to deliver progress on tasks ranging from medical diagnostics to translation.[14] But DeepMind stuck to its interdisciplinary vision. Whatever the progress in
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in New York who would later become DeepMind’s research director. Later, David Silver joined, adding advice on reinforcement learning. Mnih set about training a deep-learning system to interpret the raw pixels on the Atari video screen, providing the agent with a perceptual input. Then he bolted on an established reinforcement
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fill out the Q-table was dramatically shortened. Mnih also confronted a challenge that hearkened back to Hinton’s reservations about combining reinforcement learning and deep learning. A basic RL agent will often gather similar experiences as it explores one part of its environment: Think of a game-playing agent experiencing one
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of the hippocampus. To Mnih and Kavukcuoglu, the goal was to turn correlated game-playing experiences into the sort of randomized teaching materials required for deep learning.[23] Memory replay soon boosted the performance of Mnih’s systems, and whichever way you looked at it, the success marked an inflection point.
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them. Then, once the team had assembled, Hassabis had shown exquisite judgment. In choosing the Atari challenge, he had understood that the moment to fuse deep learning and reinforcement learning had arrived. The result was another ImageNet moment—not just for vision, but for agents. Chapter 7 Thiel Trouble On October 8
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million.[8] His fundraising negotiations with Nosek and Founders Fund had already started. Nosek understood the case for an audacious fundraising target. The excitement about deep learning was pushing up AI salaries. The harnessing of powerful GPU chips was pushing up the cost of hardware. The mission of building AGI was of
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ever. Besides, DeepMind was more than just a team. As the Atari success demonstrated, the company embodied a way of surpassing the deep-learning paradigm for which Hinton was famous. Deep-learning systems matched this onto that—images onto words, and so forth. The Atari agent had taught itself multiple games. It was capable
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day!”[10] In spring 2014, Silver suggested to Hassabis that Go’s day was approaching. The success of the Atari project, combining reinforcement learning with deep learning, provided just the sort of springboard that Silver had been waiting for.[11] It was this suggestion, the culmination of conversations begun at Cambridge, that
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of victory.[28] “Impossible,” Huang muttered. Guez set to work, repeating the by now familiar process of organizing data into a form that would support deep learning. He isolated the inputs (board positions taken from the database of games played by human experts) and labeled them with outputs (whether those board positions
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. Both could be described as System One networks, mimicking the fast-thinking parts of human intelligence. But what was really powerful was how the intuitive deep-learning models worked with the introspective reinforcement learning, the tree search—the deeper, slower, System Two side of intelligence. Thanks to Maddison’s policy net,
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all available records of expert human games. There were no more left. Progress stalled again. To get around this bottleneck, Silver drew from reinforcement learning. Deep-learning systems depended on human-created data. But reinforcement-learning agents created their own data through trial and error. By playing millions of games against itself
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Agent Zero would stand as a triumph for Silver’s scientific specialty, reinforcement learning; it would also mark a leap toward machine autonomy. Yesterday’s deep-learning systems had ingested data that represented human knowledge, curated by human programmers. Tomorrow’s reinforcement-learning agents would rely on data that they generated themselves
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things, it was new types of neural networks, not RL, that were going to change everything. To understand this continuing tension between reinforcement learning and deep learning, start with a peek under the hood of AlphaZero. In one sense, DeepMind’s triumph was exactly as the company described: a fantastic demonstration of
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however, AlphaZero demonstrated the progress that LeCun and Sutskever stressed—the progress in the design of neural networks. Ever since the 2000s, the pioneers of deep learning had grappled with a conundrum. In theory, the more layers of neurons they added to a network, the more sophisticated it would become: A larger
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agentic trial and error could deliver impressive versatility. At the same time, however, both systems achieved their advances in reinforcement learning thanks to progress in deep learning. Back in 2013, the Atari system had leveraged a particular kind of network, known as a convolutional neural net, which was built to excel at
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for this moment. If Microsoft’s residual architecture had made it possible to build larger networks, the transformer architecture addressed another long-standing conundrum in deep learning. Many kinds of information—speech, text, videos, stock-price charts—cannot be understood by examining each unit of information in isolation. Rather, each item must
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2015, a flash of algorithmic genius duly appeared, courtesy of three researchers at the University of Montreal, led by Yoshua Bengio, a celebrated pioneer of deep learning. Their paper’s key proposal came to be known as “attention.”[16] The idea addressed a weakness in the Seq2Seq framework. When humans understand linguistic
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this research that was done back then, that really matters!”[9] To realize this lofty goal, Silver teamed up with Oriol Vinyals, a highly cited deep-learning expert. (Before joining DeepMind in 2016, Vinyals had coauthored the Seq2Seq paper with Sutskever.[10]) Together they assembled a large team of researchers, numbering more
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previously worked on sequential data, so sequences of amino acids looked like a familiar problem; naturally, his instinct was to experiment with the type of deep-learning architecture that understood sequences best, a recurrent neural network. But when DeepMind tested an early version of its system on the amino acid sequences used
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protein might fold, rendering prediction of the final shape at least half tractable. But DeepMind chose a better trick. Blessed with a superior feel for deep learning, and equipped with more powerful hardware, it trained its convolutional network to predict the distances between each amino acid.[12] Instead of a binary question
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the leaders of the AI industry, there were three possible responses to the scientists’ apocalyptic warnings. The first was embodied by Yann LeCun, the combative deep-learning pioneer who headed Meta’s AI effort. LeCun flatly denied that AGI was getting close. Therefore, he dismissed the talk of existential threats as noxious
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. The old computer science, based purely on deduction, had been limited, for sure. But the AI revolution had equipped machines to think inductively. Thanks to deep learning and reinforcement learning, classical computers could confront uncertainty and intuit what to do next. Penrose’s quantum speculations about human and machine intelligence had been
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/1777581/video/33633966. BACK TO NOTE REFERENCE 6 Shane Legg recalls that groups like Jürgen Schmidhuber’s in Switzerland bridged RL and deep learning. Wierstra adds that he had combined deep learning and RL for his PhD. Mnih, author interview; Shane Legg, author interview, November 22, 2023; Daan Wierstra, author interview, December 5, 2023
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British pounds, saying he was worth £100 million. Geoffrey Hinton, author interview, September 6, 2023. BACK TO NOTE REFERENCE 21 The potential upside from fusing deep learning and reinforcement learning influenced Google’s attitude to the pricing. Harrison recalled that DeepMind’s formula “was fairly remarkable and an evolution on what Geoff
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, 2021), 170–71. BACK TO NOTE REFERENCE 12 Huang, author interview. BACK TO NOTE REFERENCE 13 In 2008 Sutskever and a coauthor had built a deep-learning network that selected the correct move 36.9 percent of the time. Ilya Sutskever and Vinod Nair, “Mimicking Go Experts with Convolutional Neural Networks,” Department
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572, no. 7767 (2019): 116–19, nature.com/articles/s41586-019-1390-1. BACK TO NOTE REFERENCE 33 Jeffrey De Fauw et al., “Clinically Applicable Deep Learning for Diagnosis and Referral in Retinal Disease,” Nature Medicine 24, no. 9 (2018): 1342–50, pubmed.ncbi.nlm.nih.gov/30104768. BACK TO NOTE REFERENCE
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. BACK TO NOTE REFERENCE 17 On the crucial contribution of the distogram, see Andrew W. Senior et al., “Improved Protein Structure Prediction Using Potentials from Deep Learning,” Nature 577, no. 7792 (2020): 706–10, doi.org/10.1038/s41586-019-1923-7. BACK TO NOTE REFERENCE 18 Mohammed AlQuraishi, “AlphaFold @ CASP13: ‘What
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–14 transformers for, 274–75 AlphaGeometry, 278 AlphaGo AlphaZero compared with, 198 challenges of, 141–42 Crazy Stone tests of, 147–48 creativity of, 159 deep-learning strategy for, 145–46 Facebook competition for, 154, 156 Fan Hui defeated by, 153–55, 158 Graepel defeated by, 151–52 Guez’s network added
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, Peter, 51 Dean, Jeff, 135–36, 138, 157, 231, 247, 312–13, 334 deduction, 26, 28–29 Deep Blue, 10, 63, 73, 143, 156, 194 deep learning, 51 AlphaGo strategy with, 145–46 in Atari challenge, 101 Krizhevsky’s system and, 91–94 Q-learning and, 101–2, 106 RL combined with
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in CASP contest, 262, 269–73, 276–77 Chau and Li Ka-shing investing in, 124, 126 Chinchilla and, 296–97 culture of, 89–90 deep-learning plan of, 92 DQN of, 108–9 eclectic approach of, 99 Elixir experience aiding, 109 envisioning, 62–63 equity shares in, 83, 85–86, 414nn20
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–53, 361, 363 Large Hadron Collider, CERN, 387–89 Leavitt, Karoline, 370 LeCun, Yann, 99, 132 on AGI, 86, 88, 320–21, 324, 410n32 on deep learning compared with RL, 196–97 Kavukcuoglu recruited by, 135 one-sentence letter signed by, 326–27 Zuckerberg recruiting, 133–34 Lee Sedol, 156, 158–61
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AlphaGo trained with, 152 AlphaProof and, 378 AlphaStar and, 226–27 AlphaZero and, 195–200 in Atari challenge, 101 concept of, 44, 95 deep learning combined with, 102–3 deep learning compared with, 93, 95–96, 196–97 future of, 377–78 Gaia and, 223–24 Gemini experiments with, 357–60 language models with
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, 99 supervised learning, 205 Sutskever, Ilya, 91, 98, 145, 186, 416n15 on AlphaGo scaling up, 149 on AlphaZero, 357 on consciousness and AI, 439n19 on deep learning and RL, 196–97 DeepMind recruiting, 86 Go experiment of, 146, 147, 417n22, 417n26 GPT and, 210–11 on GPT-3, 285 GPT-Zero and
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use either hand-engineered feature extraction methods that are applicable to many different problems, or automatically learn features without supervision problem using techniques such as deep learning. We will focus on the former in the next section. [ 64 ] www.it-ebooks.info Chapter 3 Extracting points of interest as features The feature
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the Bay Area, is the sort of business for which the flex corp structure works well. Vicarious operates in the field of artificial intelligence and deep learning; its most celebrated project to date is an attempt to crack CAPTCHAs (those annoying tests of whether a user is human) using AI. Vicarious claims
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kill switch on its A.I. systems.12 Other researchers are developing tools to visualize the otherwise impenetrable code in machine-generated algorithms built using Deep Learning systems. So the question that we must always be able to answer in the affirmative is whether we can stop it. With both A.I
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