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
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