deep learning

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)

…

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

…

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

…

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

…

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

…

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

…

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

…

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

…

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

…

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

…

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,

…

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

…

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

…

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

…

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

…

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

…

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

…

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

…

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

…

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

…

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

…

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

…

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

…

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

…

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

…

-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,

…

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,

…

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

…

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,

…

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

…

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

…

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. 

…

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

…

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

…

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

…

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

…

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

…

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

…

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

…

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

…

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

…

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

…

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,

…

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

Money in the Metaverse: Digital Assets, Online Identities, Spatial Computing and Why Virtual Worlds Mean Real Business

by David G. W. Birch and Victoria Richardson  · 28 Apr 2024  · 249pp  · 74,201 words

about this direction in investing is because historical robo-advising was essentially jazzed-up machine learning. The custobots that Gartner is talking about will use deep-learning algorithms to deliver something very different, and they will require very different services from financial institutions. As an obvious example, companies may have to provide

The Means of Prediction: How AI Really Works (And Who Benefits)

by Maximilian Kasy  · 15 Jan 2025  · 209pp  · 63,332 words

Story Misses 3. What This Book Does Part II. How AI Works 4. What Is Artificial Intelligence? 5. Supervised Learning 6. Overfitting and Underfitting 7. Deep Learning 8. The Exploration/Exploitation Trade-Off 9. Key Ideas to Remember Part III. Machine Power 10. Social Welfare 11. The Means of Prediction 12. Agents

…

called cross-validation. Supervised learning relies on picking the model that does best, according to this cross-validation criterion. One method for making predictions uses deep learning, a type of supervised learning that is based on training neural nets. Neural nets allow for modeling very complicated relationships. They have been extremely successful

…

relatively unsuccessful academic niche since the 1960s. This dramatically changed in the early 2000s. Based on scaling of data and computational power, the methods of deep learning in neural networks reached a critical point. The performance of these machine learning methods suddenly beat all other approaches to the problems of AI. This

…

future. Availability of data, however, might be such a limiting factor, in ways that vary widely across domains. On one extreme, where big successes using deep learning were possible, are games such as chess and go, where almost infinite data can be generated by computer self-play. A close second might be

…

of prediction errors on the holdout sample. A variant of this approach (of using sample splitting for tuning), called early stopping, is often used in deep learning, which we discuss in the next chapter. In early stopping, during the training of a neural network, predictions are repeatedly updated a little bit, in

…

attractive in settings with very large datasets and complicated models, where the cost of computation is a concern. This includes the most successful applications of deep learning. Deep learning forms the basis of almost all the recent spectacular successes of AI. It is also one of the most mystified branches of machine learning, evoking

…

created artificial brains. Our technical discussion will serve to dispel some of this mystification. But first we should answer the question, What is deep learning, actually? Stay tuned! 7 Deep Learning Alchemists have long dreamed of creating artificial life. In the early sixteenth century, the Swiss alchemist and medical pioneer Paracelsus provided a recipe

…

least artificial neural nets, since the 1960s. These researchers were adherents of what used to be known as the connectionist paradigm, which is now called deep learning. The connectionist paradigm stood in contrast to the symbolic paradigm. The latter, which was once dominant in the field of AI, sought to build AI

…

based on higher-level abstractions, instead of reducing intelligence to the level of neurons. The connectionist paradigm, deep learning, and artificial neural nets were the subject of a somewhat obscure academic niche field within AI for several decades. Now, since shortly after the turn

…

of the millennium, they dominate most branches of AI. John Hopfield and Geoffrey Hinton, two of the pioneers of deep learning from its time of obscurity, received the 2024 Nobel Prize in Physics. Why did this belated success of neural nets occur? Some recent innovations notwithstanding

…

, many of the ideas behind deep learning have been around for quite a while. Rather than involving major conceptual breakthroughs, the delayed success of neural nets can be attributed to the increased

…

growth has been sustained since the early 1970s, but there might be fundamental physical limits preventing the continuation of this trend in the future. How Deep Learning Works Deep learning is a special case of supervised learning, where artificial neural networks are used to make predictions. We have already discussed numerous examples of prediction

…

are predicted from images of faces), predicting translated text in one language from text in another language, and so forth. The neural networks used in deep learning define a particular class of prediction functions. A function takes a bunch of numbers—pixel values, say, or some numerical encoding of text—and returns

…

. If the network has multiple functions layered on top of each other like this, the network is called deep. This is the technical meaning of deep learning: Complicated prediction functions are built from a chain of simpler functions. (The technical meaning aside, calling this approach deep was also good marketing. The term

…

deep learning clearly resonated with investors and journalists, arguably more than connectionist paradigm could.) Why would we call a prediction function built in this way, as a

…

network depends on a set of numbers called weights. If we change the weights, we get different predictions for the same inputs. The goal of deep learning is to find weights that deliver good predictions. In the neural network interpretation, the weights describe the strength of synaptic connections between different neurons. Learning

…

. But relative to other methods in machine learning, this procedure is quite simple, in terms of the calculations involved. Long before the recent rise of deep learning, researchers in optimization and statistics had devised a wide range of more complicated and sophisticated ways to learn, improve predictions, and avoid overfitting. It is

…

the relative simplicity of the calculations involved in deep learning (compared to its technical antecedents) that has allowed it to scale up so much. Its simplicity makes it possible to deal with massive amounts of

…

time. Availability of this specialized hardware has contributed to the rise of deep learning. Neural Nets Are Not Artificial Brains It is tempting to think of deep learning and neural nets as artificial brains. Don’t. Despite the original biological inspiration, modern deep learning has little in common with biological neural nets, or with the process

…

of learning in human brains or animal brains. Three differences between biological and artificial neural nets stand out. First, the way deep learning algorithms update the parameters of a neural net propagates information backward through the network. This is something that does not happen for biological neurons. Information

…

parallel. These functions are quite different from functions that would model biological neurons more realistically. Instead of thinking of deep learning in terms of artificial brains, it is more useful to think of deep learning as the craft of building complicated functions out of simpler functions—to model complicated relationships, as encountered in domains

…

earlier in the sentence. Transformer models learn to pay attention to this relevant context. The development of such specialized architectures for neural nets, and of deep learning more generally, is based on a large amount of tinkering and trial and error. It does indeed resemble alchemy at times, in the spirit of

…

Paracelsus or of the Muslim alchemists of the Jabirian corpus. There is something rather unsettling about this state of deep learning—at least for people with a taste for theory and an interest in machine learning, like me: Neural nets work better than they should, according

…

figure 3, models that are too complex tend to overfit, and deliver poor predictions out-of-sample. How is it possible that the models of deep learning can have many more parameters (weights) than observations and still deliver good predictions, seemingly avoiding the fate of overfitting? And why do they often extrapolate

…

regarding how neural networks solve (prediction) problems does not prevent us from having a broad discussion regarding which (prediction) problems they should be solving. In deep learning, as in AI more broadly, such a discussion needs to be the starting point for democratic governance. Self-Supervised Learning and Generative AI As noted

…

of realistic images has also made great advances, notably in algorithms such as Stable Diffusion. Both text generation and image generation build on self-supervised deep learning. The specific model architectures used in these domains are fairly recent inventions. Only time will tell, but one might conjecture that these architectures will be

…

models Source: Wikipedia, “Large Language Model,” accessed October 1, 2024, https://en.wikipedia.org/wiki/Large_language_model Language modeling is one success story of deep learning; image generation is another one. The most successful algorithms for image generation today use diffusion models. Diffusion models are based on the following idea: Start

…

control the foundation model (e.g., OpenAI/Microsoft) and the user who chooses the prompt. Neural nets and deep learning have had surprising successes in many domains, one of which is generative AI. But deep learning remains within the framework of supervised learning—that is, of prediction. There is something very important that is

…

. This is also a very data-hungry approach. This approach can build on the machinery of supervised learning that we discussed before—in particular, on deep learning. Deep learning is indeed what programs like TD-Gammon and AlphaGo used. They trained neural networks to predict the probability of winning, in the recursive manner described

…

, to the extent that they exist, thus need to rely on approaches other than pure reinforcement learning. There is a more general lesson here. Current deep-learning-based methods in AI need lots of data. There are some settings where data have become readily available, for instance via self-play in simulated

…

environments or by scraping the internet for enormous databases of text or images. In the contexts where such enormous amounts of data have become available, deep learning has exceeded all expectations. At the same time, there might be fundamental limits to how much data can be generated in many domains. We can

…

its central processing unit, or CPU. The CPU is where all the actual calculations take place that let a computer run. Machine learning, and especially deep learning, requires a very large number of calculations during the training of new models. The calculations involved are surprisingly simple, however. For the most part, these

…

calculations reduce to a large number of multiplications and additions. This is especially true for modern deep learning. Specialized processors have been developed in recent years that are able to process a huge number of multiplications and additions in parallel (at the same

…

graphics requires a great deal of multiplications and additions that are like those needed for training neural nets. GPUs and TPUs are essential for modern deep learning. Even though there are a few companies that can produce these chips, the market for GPUs is almost entirely controlled by one company, NVIDIA, which

…

AI. This energy hunger has deeper causes, according to the following argument by Geoffrey Hinton, one of the pioneers of deep learning (and winner of the 2024 Nobel Prize in Physics). Deep learning—and machine learning more generally—uses digital computers. Digital computers consist of many transistors, which are used to store and process

…

. Computer scientists are often worried about the robustness of their systems, and about vulnerabilities to adversarial attacks. In the context of image classification, for example, deep learning algorithms might be very successful at identifying different kinds of animals in photos. In the test and training data, they might always correctly distinguish whether

…

still limited. Some of the best mathematicians and theoretical physicists are currently trying to make progress on these questions. If a theoretical understanding of how deep learning works were necessary for democratic control, our prospects would be bleak. But such a theoretical understanding is not necessary. What is necessary is a broadly

…

Spree in Gaza.” +972 Magazine, April 3, 2024. https://www.972mag.com/lavender-ai-israeli-army-gaza/. Bartlett, P. L., A. Montanari, and A. Rakhlin. “Deep Learning: A Statistical Viewpoint.” Preprint, arXiv, March 16, 2021, https://doi.org/10.48550/arXiv.2103.09177. Berger, J. Statistical Decision Theory and Bayesian Inference. Springer

…

–4 (2018): 219–354. Friedman, J., T. Hastie, and R. Tibshirani. The Elements of Statistical Learning. Springer, 2001. Goodfellow, I., Y. Bengio, and A. Courville. Deep Learning. MIT Press, 2016. Jurafsky, D., and J. H. Martin. Speech and Language Processing. 3rd ed. Accessed September 2024. https://web.stanford.edu/~jurafsky/slp3/. Kasy

…

commons, 86 Creator, The (film), 3 cross-validation, 11 data: as basis of AI, 15, 26; as component of the means of prediction, 84–89; deep learning dependent on large amounts of, 45, 63–65; democratic governance of, 16, 145–47; direct management of, 146; externalities generated by, 88–89, 109, 142

…

economics, 24–25; explainability of, 176, 186–88; ingredients of, 23–24; political and economic aspects of, 25–26 decision trees, 178 Deep Blue, 61 deep learning, 11, 27, 44–56; and artificial vs. biological neural nets, 50; computational capacity required for, 49–50, 89–90; connectionist paradigm as basis of, 44

…

compared to, 26; factors in success of, 26–27; as the generation of externalities, 15–16, 88, 109, 143; limitations of, 87–88. See also deep learning; supervised learning marginal productivity, 150–55, 166–67 Maria Theresa, empress of Austria, 19 market power, 104, 109, 154–55 Marx, Karl, 82 Mastodon, 106

…

, 50, 94; building, 50–51; and complex modeling, 11, 41, 50; in diffusion models, 54; energy consumption of, 93; illustration of, 47; role of, in deep learning, 11, 27, 45–50, 56 news media, as change agent, 106–8 +972 Magazine, 31, 133 Nobel Prize, 45, 93, 132, 150, 155 Nordic Graphic

…

of decision functions and, 178 statistics, 26–27 stochastic gradient descent, 49 stock options, 126 stratified sampling, 198 supervised learning, 10, 29–36. See also deep learning; prediction; self-supervised learning surveillance capitalism, 85 survey responses, 136–38 Sweden, 158–60 Swedish Trade Union Confederation, 159 symbolic paradigm, 44 taste-based discrimination

The Age of Extraction: How Tech Platforms Conquered the Economy and Threaten Our Future Prosperity

by Tim Wu  · 4 Nov 2025  · 246pp  · 65,143 words

problems with Rosenblatt’s Perceptron. After devising a solution to its most famous limit in the 1980s, in 2006 he co-introduced the concept of “deep learning,” which did much to enhance the ability of neural networks to educate themselves. Since its 1958 debut, the trademark of a neural network has been

…

coauthors wrote, a neural network “allows a machine to be fed with raw data and to automatically discover the representations needed for detection or classification.” Deep learning added the idea of approaching such pattern recognition in stages, beginning with lower-level categories (like letters) before higher-level categories (like words). Like much

…

AI capable of categorizing new and unseen images correctly. Previous winners had relied mainly on handcrafted code. But Hinton’s three-person team deployed a deep-learning neural net named AlexNet, with eight layers and some 60 million parameters. “AlexNet didn’t just win; it dominated,” wrote one observer, showing that

…

“deep learning was more than a pipe dream.”[10] AlexNet’s victory might have seemed a vindication of Rosenblatt’s ideas from the 1950s. Yet some remained

…

dubious. Gary Marcus, a cognitive scientist at NYU, wrote a dismissive article in The New Yorker after the event. “Deep learning,” he announced, “takes us, at best, only a small step toward the creation of truly intelligent machines.”[11] He expressed skepticism that one could “build

…

a machine that could understand stories” using deep learning. “Hinton has built a better ladder, but a better ladder doesn’t necessarily get you to the moon.” But the main tech platforms thought differently

…

-layer Perceptron with a backpropagation algorithm, but that solution was described only in 1986. BACK TO NOTE REFERENCE 9 “AlexNet and ImageNet: The Birth of Deep Learning,” Pinecone, accessed November 22, 2024, https://www.pinecone.io/​learn/​series/​image-search/​imagenet/. BACK TO NOTE REFERENCE 10 Gary Marcus, “Is

…

‘Deep Learning’ a Revolution in Artificial Intelligence?,” New Yorker, November 25, 2012, https://www.newyorker.com/​news/​news-desk/​is-deep-learning-a-revolution-in-artificial-intelligence. BACK TO NOTE REFERENCE 11 Cade Metz, “ ‘The Godfather of A

…

, 95–99 chatbots, 20, 79, 88, 90, 94–95, 98–99, 101 Connectionist, 91–93 data extraction for/by, 4, 84, 88, 93–95, 98 deep learning by, 93–94 distinguishing humans from, 6, 142–43 as economic equalizer, 6–7, 144 emotional attachments to, 100–101 ImageNet data used for, 84

…

, 80–81 of human biometrics, 142–44 predictive, 68, 85–88 The Death and Life of Great American Cities (Jacobs), 19 Deep Blue computer, 92 deep learning, AI, 93–94 DeepMind firm, 98 DeepSeek chatbot, 94–95, 101 de Garton, Ms., 162–63 Denmark, land ownership in, 127–29 dependence, 72, 140

The Everything Blueprint: The Microchip Design That Changed the World

by James Ashton  · 11 May 2023  · 401pp  · 113,586 words

focus on diversifying the product portfolio so that it targeted key markets and increased investment in each of them. When Nvidia and Arm partnered on ‘deep learning inferencing’ in March 2018 – essentially helping to train computers to think like a human brain – Haas was front and centre. At Arm’s 2018 TechCon

Mastering Machine Learning With Scikit-Learn

by Gavin Hackeling  · 31 Oct 2014

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

Throwing Rocks at the Google Bus: How Growth Became the Enemy of Prosperity

by Douglas Rushkoff  · 1 Mar 2016  · 366pp  · 94,209 words

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

The Driver in the Driverless Car: How Our Technology Choices Will Create the Future

by Vivek Wadhwa and Alex Salkever  · 2 Apr 2017  · 181pp  · 52,147 words

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

Superminds: The Surprising Power of People and Computers Thinking Together

by Thomas W. Malone  · 14 May 2018  · 344pp  · 104,077 words

20,000 categories of objects, including human faces, human bodies, and… cat faces.19 This system used a particularly promising approach to machine learning called deep learning, which loosely simulates the way the different layers of neurons in a brain are connected to one another. Neuromorphic Computing Still another intriguing approach to

Empire of Ants: The Hidden Worlds and Extraordinary Lives of Earth's Tiny Conquerors

by Susanne Foitzik and Olaf Fritsche  · 5 Apr 2021  · 335pp  · 86,900 words

new combinations within Ophiocordyceps. I. Myrmecophilous hirsutelloid species. Studies in Mycology, 90, 119–60. Fredericksen, M. A. et al. (2017). Three-dimensional visualization and a deep-learning model reveal complex fungal parasite networks in behaviorally manipulated ants. Proceedings of the National Academy of Sciences USA, 114, 12590–95. Hughes, D. P. et

Learn Algorithmic Trading

by Sebastien Donadio  · 7 Nov 2019

Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage

by Douglas B. Laney  · 4 Sep 2017  · 374pp  · 94,508 words

Road to Nowhere: What Silicon Valley Gets Wrong About the Future of Transportation

by Paris Marx  · 4 Jul 2022  · 295pp  · 81,861 words

Seriously Curious: The Facts and Figures That Turn Our World Upside Down

by Tom Standage  · 27 Nov 2018  · 215pp  · 59,188 words

Terms of Service: Social Media and the Price of Constant Connection

by Jacob Silverman  · 17 Mar 2015  · 527pp  · 147,690 words

Ghost Work: How to Stop Silicon Valley From Building a New Global Underclass

by Mary L. Gray and Siddharth Suri  · 6 May 2019  · 346pp  · 97,330 words

The Government of No One: The Theory and Practice of Anarchism

by Ruth Kinna  · 31 Jul 2019  · 405pp  · 103,723 words

Hands-On Machine Learning With Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

by Aurelien Geron  · 14 Aug 2019

The Art of Statistics: How to Learn From Data

by David Spiegelhalter  · 2 Sep 2019  · 404pp  · 92,713 words

The Runaway Species: How Human Creativity Remakes the World

by David Eagleman and Anthony Brandt  · 30 Sep 2017  · 345pp  · 84,847 words

The Fifth Domain: Defending Our Country, Our Companies, and Ourselves in the Age of Cyber Threats

by Richard A. Clarke and Robert K. Knake  · 15 Jul 2019  · 409pp  · 112,055 words

Augmented: Life in the Smart Lane

by Brett King  · 5 May 2016  · 385pp  · 111,113 words

The Blockchain Alternative: Rethinking Macroeconomic Policy and Economic Theory

by Kariappa Bheemaiah  · 26 Feb 2017  · 492pp  · 118,882 words

Artificial You: AI and the Future of Your Mind

by Susan Schneider  · 1 Oct 2019  · 331pp  · 47,993 words

Model Thinker: What You Need to Know to Make Data Work for You

by Scott E. Page  · 27 Nov 2018  · 543pp  · 153,550 words

The Big Fix: How Companies Capture Markets and Harm Canadians

by Denise Hearn and Vass Bednar  · 14 Oct 2024  · 175pp  · 46,192 words

Don't Trust Your Gut: Using Data to Get What You Really Want in LIfe

by Seth Stephens-Davidowitz  · 9 May 2022  · 287pp  · 69,655 words

How the Railways Will Fix the Future: Rediscovering the Essential Brilliance of the Iron Road

by Gareth Dennis  · 12 Nov 2024  · 261pp  · 76,645 words

Collaborative Society

by Dariusz Jemielniak and Aleksandra Przegalinska  · 18 Feb 2020  · 187pp  · 50,083 words

Demystifying Smart Cities

by Anders Lisdorf

Snowden's Box: Trust in the Age of Surveillance

by Jessica Bruder and Dale Maharidge  · 29 Mar 2020  · 159pp  · 42,401 words

The Meaning of Everything: The Story of the Oxford English Dictionary

by Simon Winchester  · 1 Jan 2003  · 200pp  · 71,482 words

The Art of Statistics: Learning From Data

by David Spiegelhalter  · 14 Oct 2019  · 442pp  · 94,734 words

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

by Martin Kleppmann  · 16 Mar 2017  · 1,237pp  · 227,370 words

Underwater: How Our American Dream of Homeownership Became a Nightmare

by Ryan Dezember  · 13 Jul 2020  · 279pp  · 87,875 words

How to Run a Government: So That Citizens Benefit and Taxpayers Don't Go Crazy

by Michael Barber  · 12 Mar 2015  · 350pp  · 109,379 words

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

by Martin Kleppmann  · 17 Apr 2017

The People vs Tech: How the Internet Is Killing Democracy (And How We Save It)

by Jamie Bartlett  · 4 Apr 2018  · 170pp  · 49,193 words

Live Work Work Work Die: A Journey Into the Savage Heart of Silicon Valley

by Corey Pein  · 23 Apr 2018  · 282pp  · 81,873 words

You've Been Played: How Corporations, Governments, and Schools Use Games to Control Us All

by Adrian Hon  · 14 Sep 2022  · 371pp  · 107,141 words

Life as a Passenger: How Driverless Cars Will Change the World

by David Kerrigan  · 18 Jun 2017  · 472pp  · 80,835 words

Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech

by Sara Wachter-Boettcher  · 9 Oct 2017  · 223pp  · 60,909 words

The Biology of Desire: Why Addiction Is Not a Disease

by Marc Lewis Phd  · 13 Jul 2015  · 288pp  · 73,297 words

Gray Day: My Undercover Mission to Expose America's First Cyber Spy

by Eric O'Neill  · 1 Mar 2019  · 299pp  · 88,375 words

The Age of Em: Work, Love and Life When Robots Rule the Earth

by Robin Hanson  · 31 Mar 2016  · 589pp  · 147,053 words

Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions That Shape Social Media

by Tarleton Gillespie  · 25 Jun 2018  · 390pp  · 109,519 words

Whistleblower: My Journey to Silicon Valley and Fight for Justice at Uber

by Susan Fowler  · 18 Feb 2020  · 205pp  · 71,872 words

The Raging 2020s: Companies, Countries, People - and the Fight for Our Future

by Alec Ross  · 13 Sep 2021  · 363pp  · 109,077 words

Pattern Breakers: Why Some Start-Ups Change the Future

by Mike Maples and Peter Ziebelman  · 8 Jul 2024  · 207pp  · 65,156 words

Virtual Competition

by Ariel Ezrachi and Maurice E. Stucke  · 30 Nov 2016

The AI-First Company

by Ash Fontana  · 4 May 2021  · 296pp  · 66,815 words

Explaining Humans: What Science Can Teach Us About Life, Love and Relationships

by Camilla Pang  · 12 Mar 2020  · 256pp  · 67,563 words

Inventing the Future: Postcapitalism and a World Without Work

by Nick Srnicek and Alex Williams  · 1 Oct 2015  · 357pp  · 95,986 words

Radical Markets: Uprooting Capitalism and Democracy for a Just Society

by Eric Posner and E. Weyl  · 14 May 2018  · 463pp  · 105,197 words

Rebel Ideas: The Power of Diverse Thinking

by Matthew Syed  · 9 Sep 2019  · 280pp  · 76,638 words

The Ethical Algorithm: The Science of Socially Aware Algorithm Design

by Michael Kearns and Aaron Roth  · 3 Oct 2019

Autonomous Driving: How the Driverless Revolution Will Change the World

by Andreas Herrmann, Walter Brenner and Rupert Stadler  · 25 Mar 2018

Practical Doomsday: A User's Guide to the End of the World

by Michal Zalewski  · 11 Jan 2022  · 337pp  · 96,666 words

What We Owe the Future: A Million-Year View

by William MacAskill  · 31 Aug 2022  · 451pp  · 125,201 words

Text Analytics With Python: A Practical Real-World Approach to Gaining Actionable Insights From Your Data

by Dipanjan Sarkar  · 1 Dec 2016

Work in the Future The Automation Revolution-Palgrave MacMillan (2019)

by Robert Skidelsky Nan Craig  · 15 Mar 2020

Framers: Human Advantage in an Age of Technology and Turmoil

by Kenneth Cukier, Viktor Mayer-Schönberger and Francis de Véricourt  · 10 May 2021  · 291pp  · 80,068 words

Bulletproof Problem Solving

by Charles Conn and Robert McLean  · 6 Mar 2019

The Hype Machine: How Social Media Disrupts Our Elections, Our Economy, and Our Health--And How We Must Adapt

by Sinan Aral  · 14 Sep 2020  · 475pp  · 134,707 words

Futureproof: 9 Rules for Humans in the Age of Automation

by Kevin Roose  · 9 Mar 2021  · 208pp  · 57,602 words

Industry 4.0: The Industrial Internet of Things

by Alasdair Gilchrist  · 27 Jun 2016

The 100-Year Life: Living and Working in an Age of Longevity

by Lynda Gratton and Andrew Scott  · 1 Jun 2016  · 344pp  · 94,332 words

Talk on the Wild Side

by Lane Greene  · 15 Dec 2018  · 284pp  · 84,169 words

The Intelligence Trap: Revolutionise Your Thinking and Make Wiser Decisions

by David Robson  · 7 Mar 2019  · 417pp  · 103,458 words

Ageless: The New Science of Getting Older Without Getting Old

by Andrew Steele  · 24 Dec 2020  · 399pp  · 118,576 words

Neurodiversity at Work: Drive Innovation, Performance and Productivity With a Neurodiverse Workforce

by Amanda Kirby and Theo Smith  · 2 Aug 2021  · 424pp  · 114,820 words

Whiplash: How to Survive Our Faster Future

by Joi Ito and Jeff Howe  · 6 Dec 2016  · 254pp  · 76,064 words

World Without Mind: The Existential Threat of Big Tech

by Franklin Foer  · 31 Aug 2017  · 281pp  · 71,242 words

A Brief History of Motion: From the Wheel, to the Car, to What Comes Next

by Tom Standage  · 16 Aug 2021  · 290pp  · 85,847 words

The Autonomous Revolution: Reclaiming the Future We’ve Sold to Machines

by William Davidow and Michael Malone  · 18 Feb 2020  · 304pp  · 80,143 words

The Age of AI: And Our Human Future

by Henry A Kissinger, Eric Schmidt and Daniel Huttenlocher  · 2 Nov 2021  · 194pp  · 57,434 words

Power, for All: How It Really Works and Why It's Everyone's Business

by Julie Battilana and Tiziana Casciaro  · 30 Aug 2021  · 345pp  · 92,063 words

Internet for the People: The Fight for Our Digital Future

by Ben Tarnoff  · 13 Jun 2022  · 234pp  · 67,589 words

The Nature of Software Development: Keep It Simple, Make It Valuable, Build It Piece by Piece

by Ron Jeffries  · 14 Aug 2015  · 444pp  · 118,393 words

Novacene: The Coming Age of Hyperintelligence

by James Lovelock  · 27 Aug 2019  · 94pp  · 33,179 words

The Wires of War: Technology and the Global Struggle for Power

by Jacob Helberg  · 11 Oct 2021  · 521pp  · 118,183 words

Nexus: A Brief History of Information Networks From the Stone Age to AI

by Yuval Noah Harari  · 9 Sep 2024  · 566pp  · 169,013 words

MegaThreats: Ten Dangerous Trends That Imperil Our Future, and How to Survive Them

by Nouriel Roubini  · 17 Oct 2022  · 328pp  · 96,678 words

Amazon: How the World’s Most Relentless Retailer Will Continue to Revolutionize Commerce

by Natalie Berg and Miya Knights  · 28 Jan 2019  · 404pp  · 95,163 words

Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity

by Daron Acemoglu and Simon Johnson  · 15 May 2023  · 619pp  · 177,548 words

The Premonition: A Pandemic Story

by Michael Lewis  · 3 May 2021  · 285pp  · 98,832 words

The Price of Tomorrow: Why Deflation Is the Key to an Abundant Future

by Jeff Booth  · 14 Jan 2020  · 180pp  · 55,805 words

Shape: The Hidden Geometry of Information, Biology, Strategy, Democracy, and Everything Else

by Jordan Ellenberg  · 14 May 2021  · 665pp  · 159,350 words

Leadership by Algorithm: Who Leads and Who Follows in the AI Era?

by David de Cremer  · 25 May 2020  · 241pp  · 70,307 words

Amazon Unbound: Jeff Bezos and the Invention of a Global Empire

by Brad Stone  · 10 May 2021  · 569pp  · 156,139 words

The Seventh Sense: Power, Fortune, and Survival in the Age of Networks

by Joshua Cooper Ramo  · 16 May 2016  · 326pp  · 103,170 words

The Future of the Brain: Essays by the World's Leading Neuroscientists

by Gary Marcus and Jeremy Freeman  · 1 Nov 2014  · 336pp  · 93,672 words

The Business of Platforms: Strategy in the Age of Digital Competition, Innovation, and Power

by Michael A. Cusumano, Annabelle Gawer and David B. Yoffie  · 6 May 2019  · 328pp  · 84,682 words

Architecting Modern Data Platforms: A Guide to Enterprise Hadoop at Scale

by Jan Kunigk, Ian Buss, Paul Wilkinson and Lars George  · 8 Jan 2019  · 1,409pp  · 205,237 words

Lost in Math: How Beauty Leads Physics Astray

by Sabine Hossenfelder  · 11 Jun 2018  · 340pp  · 91,416 words

A History of Judaism

by Martin Goodman  · 25 Oct 2017  · 768pp  · 252,874 words

The Loop: How Technology Is Creating a World Without Choices and How to Fight Back

by Jacob Ward  · 25 Jan 2022  · 292pp  · 94,660 words

12 Bytes: How We Got Here. Where We Might Go Next

by Jeanette Winterson  · 15 Mar 2021  · 256pp  · 73,068 words

The Mutant Project: Inside the Global Race to Genetically Modify Humans

by Eben Kirksey  · 10 Nov 2020  · 599pp  · 98,564 words

The Future Is Analog: How to Create a More Human World

by David Sax  · 15 Jan 2022  · 282pp  · 93,783 words

The Globotics Upheaval: Globalisation, Robotics and the Future of Work

by Richard Baldwin  · 10 Jan 2019  · 301pp  · 89,076 words

The AI Economy: Work, Wealth and Welfare in the Robot Age

by Roger Bootle  · 4 Sep 2019  · 374pp  · 111,284 words

The Long History of the Future: Why Tomorrow's Technology Still Isn't Here

by Nicole Kobie  · 3 Jul 2024  · 348pp  · 119,358 words

Arriving Today: From Factory to Front Door -- Why Everything Has Changed About How and What We Buy

by Christopher Mims  · 13 Sep 2021  · 385pp  · 112,842 words

A Thousand Brains: A New Theory of Intelligence

by Jeff Hawkins  · 15 Nov 2021  · 253pp  · 84,238 words

Attention Factory: The Story of TikTok and China's ByteDance

by Matthew Brennan  · 9 Oct 2020  · 282pp  · 63,385 words

Exponential: How Accelerating Technology Is Leaving Us Behind and What to Do About It

by Azeem Azhar  · 6 Sep 2021  · 447pp  · 111,991 words

Gods and Robots: Myths, Machines, and Ancient Dreams of Technology

by Adrienne Mayor  · 27 Nov 2018

Tools and Weapons: The Promise and the Peril of the Digital Age

by Brad Smith and Carol Ann Browne  · 9 Sep 2019  · 482pp  · 121,173 words

Bezonomics: How Amazon Is Changing Our Lives and What the World's Best Companies Are Learning From It

by Brian Dumaine  · 11 May 2020  · 411pp  · 98,128 words

Dawn of the New Everything: Encounters With Reality and Virtual Reality

by Jaron Lanier  · 21 Nov 2017  · 480pp  · 123,979 words

Data Science from Scratch: First Principles with Python

by Joel Grus  · 13 Apr 2015  · 579pp  · 76,657 words

Human + Machine: Reimagining Work in the Age of AI

by Paul R. Daugherty and H. James Wilson  · 15 Jan 2018  · 523pp  · 61,179 words

Rocket Billionaires: Elon Musk, Jeff Bezos, and the New Space Race

by Tim Fernholz  · 20 Mar 2018  · 328pp  · 96,141 words

The One Device: The Secret History of the iPhone

by Brian Merchant  · 19 Jun 2017  · 416pp  · 129,308 words

Prediction Machines: The Simple Economics of Artificial Intelligence

by Ajay Agrawal, Joshua Gans and Avi Goldfarb  · 16 Apr 2018  · 345pp  · 75,660 words

From Bacteria to Bach and Back: The Evolution of Minds

by Daniel C. Dennett  · 7 Feb 2017  · 573pp  · 157,767 words

Infinite Powers: How Calculus Reveals the Secrets of the Universe

by Steven Strogatz  · 31 Mar 2019  · 407pp  · 116,726 words

Facebook: The Inside Story

by Steven Levy  · 25 Feb 2020  · 706pp  · 202,591 words

Bold: How to Go Big, Create Wealth and Impact the World

by Peter H. Diamandis and Steven Kotler  · 3 Feb 2015  · 368pp  · 96,825 words

Know Thyself

by Stephen M Fleming  · 27 Apr 2021

The Patient Will See You Now: The Future of Medicine Is in Your Hands

by Eric Topol  · 6 Jan 2015  · 588pp  · 131,025 words

The Internet Trap: How the Digital Economy Builds Monopolies and Undermines Democracy

by Matthew Hindman  · 24 Sep 2018

The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine’s Computer Age

by Robert Wachter  · 7 Apr 2015  · 309pp  · 114,984 words

Open for Business Harnessing the Power of Platform Ecosystems

by Lauren Turner Claire, Laure Claire Reillier and Benoit Reillier  · 14 Oct 2017  · 240pp  · 78,436 words

Tech Titans of China: How China's Tech Sector Is Challenging the World by Innovating Faster, Working Harder, and Going Global

by Rebecca Fannin  · 2 Sep 2019  · 269pp  · 70,543 words

The Creativity Code: How AI Is Learning to Write, Paint and Think

by Marcus Du Sautoy  · 7 Mar 2019  · 337pp  · 103,522 words

Future Politics: Living Together in a World Transformed by Tech

by Jamie Susskind  · 3 Sep 2018  · 533pp

Finding Alphas: A Quantitative Approach to Building Trading Strategies

by Igor Tulchinsky  · 30 Sep 2019  · 321pp

Robot Rules: Regulating Artificial Intelligence

by Jacob Turner  · 29 Oct 2018  · 688pp  · 147,571 words

Your Face Belongs to Us: A Secretive Startup's Quest to End Privacy as We Know It

by Kashmir Hill  · 19 Sep 2023  · 487pp  · 124,008 words

The Crux

by Richard Rumelt  · 27 Apr 2022  · 363pp  · 109,834 words

Growth: A Reckoning

by Daniel Susskind  · 16 Apr 2024  · 358pp  · 109,930 words

The Technology Trap: Capital, Labor, and Power in the Age of Automation

by Carl Benedikt Frey  · 17 Jun 2019  · 626pp  · 167,836 words

The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future

by Orly Lobel  · 17 Oct 2022  · 370pp  · 112,809 words

Searches: Selfhood in the Digital Age

by Vauhini Vara  · 8 Apr 2025  · 301pp  · 105,209 words

The Chaos Machine: The Inside Story of How Social Media Rewired Our Minds and Our World

by Max Fisher  · 5 Sep 2022  · 439pp  · 131,081 words

Elon Musk

by Walter Isaacson  · 11 Sep 2023  · 562pp  · 201,502 words

On the Edge: The Art of Risking Everything

by Nate Silver  · 12 Aug 2024  · 848pp  · 227,015 words

Artificial Unintelligence: How Computers Misunderstand the World

by Meredith Broussard  · 19 Apr 2018  · 245pp  · 83,272 words

The Future Is Faster Than You Think: How Converging Technologies Are Transforming Business, Industries, and Our Lives

by Peter H. Diamandis and Steven Kotler  · 28 Jan 2020  · 501pp  · 114,888 words

Scary Smart: The Future of Artificial Intelligence and How You Can Save Our World

by Mo Gawdat  · 29 Sep 2021  · 259pp  · 84,261 words

Risk: A User's Guide

by Stanley McChrystal and Anna Butrico  · 4 Oct 2021  · 489pp  · 106,008 words

Life After Google: The Fall of Big Data and the Rise of the Blockchain Economy

by George Gilder  · 16 Jul 2018  · 332pp  · 93,672 words

The Joys of Compounding: The Passionate Pursuit of Lifelong Learning, Revised and Updated

by Gautam Baid  · 1 Jun 2020  · 1,239pp  · 163,625 words

Ten Lessons for a Post-Pandemic World

by Fareed Zakaria  · 5 Oct 2020  · 289pp  · 86,165 words

Evil Geniuses: The Unmaking of America: A Recent History

by Kurt Andersen  · 14 Sep 2020  · 486pp  · 150,849 words

The Age of Surveillance Capitalism

by Shoshana Zuboff  · 15 Jan 2019  · 918pp  · 257,605 words

Machine, Platform, Crowd: Harnessing Our Digital Future

by Andrew McAfee and Erik Brynjolfsson  · 26 Jun 2017  · 472pp  · 117,093 words

Radical Technologies: The Design of Everyday Life

by Adam Greenfield  · 29 May 2017  · 410pp  · 119,823 words

The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future

by Kevin Kelly  · 6 Jun 2016  · 371pp  · 108,317 words

Red Moon

by Kim Stanley Robinson  · 22 Oct 2018  · 492pp  · 141,544 words

Surviving AI: The Promise and Peril of Artificial Intelligence

by Calum Chace  · 28 Jul 2015  · 144pp  · 43,356 words

Thank You for Being Late: An Optimist's Guide to Thriving in the Age of Accelerations

by Thomas L. Friedman  · 22 Nov 2016  · 602pp  · 177,874 words

Exponential Organizations: Why New Organizations Are Ten Times Better, Faster, and Cheaper Than Yours (And What to Do About It)

by Salim Ismail and Yuri van Geest  · 17 Oct 2014  · 292pp  · 85,151 words

Rise of the Robots: Technology and the Threat of a Jobless Future

by Martin Ford  · 4 May 2015  · 484pp  · 104,873 words

Alpha Trader

by Brent Donnelly  · 11 May 2021

Entangled Life: How Fungi Make Our Worlds, Change Our Minds & Shape Our Futures

by Merlin Sheldrake  · 11 May 2020

The Costs of Connection: How Data Is Colonizing Human Life and Appropriating It for Capitalism

by Nick Couldry and Ulises A. Mejias  · 19 Aug 2019  · 458pp  · 116,832 words

Calling Bullshit: The Art of Scepticism in a Data-Driven World

by Jevin D. West and Carl T. Bergstrom  · 3 Aug 2020

Applied Artificial Intelligence: A Handbook for Business Leaders

by Mariya Yao, Adelyn Zhou and Marlene Jia  · 1 Jun 2018  · 161pp  · 39,526 words

The Star Builders: Nuclear Fusion and the Race to Power the Planet

by Arthur Turrell  · 2 Aug 2021  · 297pp  · 84,447 words

Diverse Bodies, Diverse Practices: Toward an Inclusive Somatics

by Don Hanlon Johnson  · 10 Sep 2018  · 358pp  · 106,951 words

Talk to Me: How Voice Computing Will Transform the Way We Live, Work, and Think

by James Vlahos  · 1 Mar 2019  · 392pp  · 108,745 words

Decoding the World: A Roadmap for the Questioner

by Po Bronson  · 14 Jul 2020  · 320pp  · 95,629 words

AIQ: How People and Machines Are Smarter Together

by Nick Polson and James Scott  · 14 May 2018  · 301pp  · 85,126 words

Mindf*ck: Cambridge Analytica and the Plot to Break America

by Christopher Wylie  · 8 Oct 2019

The Book of Why: The New Science of Cause and Effect

by Judea Pearl and Dana Mackenzie  · 1 Mar 2018

Blueprint: The Evolutionary Origins of a Good Society

by Nicholas A. Christakis  · 26 Mar 2019

Code Dependent: Living in the Shadow of AI

by Madhumita Murgia  · 20 Mar 2024  · 336pp  · 91,806 words

If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All

by Eliezer Yudkowsky and Nate Soares  · 15 Sep 2025  · 215pp  · 64,699 words

Doppelganger: A Trip Into the Mirror World

by Naomi Klein  · 11 Sep 2023

Rationality: What It Is, Why It Seems Scarce, Why It Matters

by Steven Pinker  · 14 Oct 2021  · 533pp  · 125,495 words

Nervous States: Democracy and the Decline of Reason

by William Davies  · 26 Feb 2019  · 349pp  · 98,868 words

Why Machines Learn: The Elegant Math Behind Modern AI

by Anil Ananthaswamy  · 15 Jul 2024  · 416pp  · 118,522 words

The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma

by Mustafa Suleyman  · 4 Sep 2023  · 444pp  · 117,770 words

System Error: Where Big Tech Went Wrong and How We Can Reboot

by Rob Reich, Mehran Sahami and Jeremy M. Weinstein  · 6 Sep 2021

Rage Inside the Machine: The Prejudice of Algorithms, and How to Stop the Internet Making Bigots of Us All

by Robert Elliott Smith  · 26 Jun 2019  · 370pp  · 107,983 words

In Our Own Image: Savior or Destroyer? The History and Future of Artificial Intelligence

by George Zarkadakis  · 7 Mar 2016  · 405pp  · 117,219 words

Ghost Road: Beyond the Driverless Car

by Anthony M. Townsend  · 15 Jun 2020  · 362pp  · 97,288 words

The Precipice: Existential Risk and the Future of Humanity

by Toby Ord  · 24 Mar 2020  · 513pp  · 152,381 words

The New Map: Energy, Climate, and the Clash of Nations

by Daniel Yergin  · 14 Sep 2020

Succeeding With AI: How to Make AI Work for Your Business

by Veljko Krunic  · 29 Mar 2020

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

by Pedro Domingos  · 21 Sep 2015  · 396pp  · 117,149 words

The Road to Conscious Machines

by Michael Wooldridge  · 2 Nov 2018  · 346pp  · 97,890 words

The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do

by Erik J. Larson  · 5 Apr 2021

The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity

by Amy Webb  · 5 Mar 2019  · 340pp  · 97,723 words

Only Humans Need Apply: Winners and Losers in the Age of Smart Machines

by Thomas H. Davenport and Julia Kirby  · 23 May 2016  · 347pp  · 97,721 words

Coders: The Making of a New Tribe and the Remaking of the World

by Clive Thompson  · 26 Mar 2019  · 499pp  · 144,278 words

The Organized Mind: Thinking Straight in the Age of Information Overload

by Daniel J. Levitin  · 18 Aug 2014  · 685pp  · 203,949 words

The Third Industrial Revolution: How Lateral Power Is Transforming Energy, the Economy, and the World

by Jeremy Rifkin  · 27 Sep 2011  · 443pp  · 112,800 words

The Glass Cage: Automation and Us

by Nicholas Carr  · 28 Sep 2014  · 308pp  · 84,713 words

The Great Fragmentation: And Why the Future of All Business Is Small

by Steve Sammartino  · 25 Jun 2014  · 247pp  · 81,135 words

Hello World: Being Human in the Age of Algorithms

by Hannah Fry  · 17 Sep 2018  · 296pp  · 78,631 words

The Making of Modern Britain

by Andrew Marr  · 16 May 2007  · 618pp  · 180,430 words

Army of None: Autonomous Weapons and the Future of War

by Paul Scharre  · 23 Apr 2018  · 590pp  · 152,595 words

Python Data Analytics: With Pandas, NumPy, and Matplotlib

by Fabio Nelli  · 27 Sep 2018  · 688pp  · 107,867 words

Machine Translation

by Thierry Poibeau  · 14 Sep 2017  · 174pp  · 56,405 words

Heart of the Machine: Our Future in a World of Artificial Emotional Intelligence

by Richard Yonck  · 7 Mar 2017  · 360pp  · 100,991 words

Future Crimes: Everything Is Connected, Everyone Is Vulnerable and What We Can Do About It

by Marc Goodman  · 24 Feb 2015  · 677pp  · 206,548 words

A World Without Work: Technology, Automation, and How We Should Respond

by Daniel Susskind  · 14 Jan 2020  · 419pp  · 109,241 words

WTF?: What's the Future and Why It's Up to Us

by Tim O'Reilly  · 9 Oct 2017  · 561pp  · 157,589 words

The Perfect Police State: An Undercover Odyssey Into China's Terrifying Surveillance Dystopia of the Future

by Geoffrey Cain  · 28 Jun 2021  · 340pp  · 90,674 words

Leading From the Emerging Future: From Ego-System to Eco-System Economies

by Otto Scharmer and Katrin Kaufer  · 14 Apr 2013  · 351pp  · 93,982 words

Human Compatible: Artificial Intelligence and the Problem of Control

by Stuart Russell  · 7 Oct 2019  · 416pp  · 112,268 words

Reset

by Ronald J. Deibert  · 14 Aug 2020

Possible Minds: Twenty-Five Ways of Looking at AI

by John Brockman  · 19 Feb 2019  · 339pp  · 94,769 words

Ask Your Developer: How to Harness the Power of Software Developers and Win in the 21st Century

by Jeff Lawson  · 12 Jan 2021  · 282pp  · 85,658 words

The Smartphone Society

by Nicole Aschoff

Likewar: The Weaponization of Social Media

by Peter Warren Singer and Emerson T. Brooking  · 15 Mar 2018

Flowers of Fire: The Inside Story of South Korea's Feminist Movement and What It Means for Women's Rights Worldwide

by Hawon Jung  · 21 Mar 2023  · 401pp  · 112,589 words

Irresistible: How Cuteness Wired our Brains and Conquered the World

by Joshua Paul Dale  · 15 Dec 2023  · 209pp  · 81,560 words

The Singularity Is Nearer: When We Merge with AI

by Ray Kurzweil  · 25 Jun 2024

I, Warbot: The Dawn of Artificially Intelligent Conflict

by Kenneth Payne  · 16 Jun 2021  · 339pp  · 92,785 words

Human Frontiers: The Future of Big Ideas in an Age of Small Thinking

by Michael Bhaskar  · 2 Nov 2021

AI 2041: Ten Visions for Our Future

by Kai-Fu Lee and Qiufan Chen  · 13 Sep 2021

Rule of the Robots: How Artificial Intelligence Will Transform Everything

by Martin Ford  · 13 Sep 2021  · 288pp  · 86,995 words

The Alignment Problem: Machine Learning and Human Values

by Brian Christian  · 5 Oct 2020  · 625pp  · 167,349 words

Architects of Intelligence

by Martin Ford  · 16 Nov 2018  · 586pp  · 186,548 words

Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots

by John Markoff  · 24 Aug 2015  · 413pp  · 119,587 words

Artificial Intelligence: A Modern Approach

by Stuart Russell and Peter Norvig  · 14 Jul 2019  · 2,466pp  · 668,761 words

Driverless: Intelligent Cars and the Road Ahead

by Hod Lipson and Melba Kurman  · 22 Sep 2016

AI Superpowers: China, Silicon Valley, and the New World Order

by Kai-Fu Lee  · 14 Sep 2018  · 307pp  · 88,180 words

Warnings

by Richard A. Clarke  · 10 Apr 2017  · 428pp  · 121,717 words

Range: Why Generalists Triumph in a Specialized World

by David Epstein  · 1 Mar 2019  · 406pp  · 109,794 words

Thinking Machines: The Inside Story of Artificial Intelligence and Our Race to Build the Future

by Luke Dormehl  · 10 Aug 2016  · 252pp  · 74,167 words

Quit Like a Woman: The Radical Choice to Not Drink in a Culture Obsessed With Alcohol

by Holly Glenn Whitaker  · 9 Jan 2020  · 334pp  · 109,882 words

The Science and Technology of Growing Young: An Insider's Guide to the Breakthroughs That Will Dramatically Extend Our Lifespan . . . And What You Can Do Right Now

by Sergey Young  · 23 Aug 2021  · 326pp  · 88,968 words

Blockchain Chicken Farm: And Other Stories of Tech in China's Countryside

by Xiaowei Wang  · 12 Oct 2020  · 196pp  · 61,981 words

Artificial Whiteness

by Yarden Katz

Spies, Lies, and Algorithms: The History and Future of American Intelligence

by Amy B. Zegart  · 6 Nov 2021

The Thinking Machine: Jensen Huang, Nvidia, and the World's Most Coveted Microchip

by Stephen Witt  · 8 Apr 2025  · 260pp  · 82,629 words

Digital Empires: The Global Battle to Regulate Technology

by Anu Bradford  · 25 Sep 2023  · 898pp  · 236,779 words

The Optimist: Sam Altman, OpenAI, and the Race to Invent the Future

by Keach Hagey  · 19 May 2025  · 439pp  · 125,379 words

Seeking SRE: Conversations About Running Production Systems at Scale

by David N. Blank-Edelman  · 16 Sep 2018

Four Battlegrounds

by Paul Scharre  · 18 Jan 2023

The Science of Hate: How Prejudice Becomes Hate and What We Can Do to Stop It

by Matthew Williams  · 23 Mar 2021  · 592pp  · 125,186 words

Supremacy: AI, ChatGPT, and the Race That Will Change the World

by Parmy Olson  · 284pp  · 96,087 words

What to Think About Machines That Think: Today's Leading Thinkers on the Age of Machine Intelligence

by John Brockman  · 5 Oct 2015  · 481pp  · 125,946 words

The Economic Singularity: Artificial Intelligence and the Death of Capitalism

by Calum Chace  · 17 Jul 2016  · 477pp  · 75,408 words

Taming the Sun: Innovations to Harness Solar Energy and Power the Planet

by Varun Sivaram  · 2 Mar 2018  · 469pp  · 132,438 words

The Mind Is Flat: The Illusion of Mental Depth and the Improvised Mind

by Nick Chater  · 28 Mar 2018  · 263pp  · 81,527 words

Kissinger: A Biography

by Walter Isaacson  · 26 Sep 2005  · 1,330pp  · 372,940 words

Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps

by Valliappa Lakshmanan, Sara Robinson and Michael Munn  · 31 Oct 2020

New Laws of Robotics: Defending Human Expertise in the Age of AI

by Frank Pasquale  · 14 May 2020  · 1,172pp  · 114,305 words

Artificial Intelligence: A Guide for Thinking Humans

by Melanie Mitchell  · 14 Oct 2019  · 350pp  · 98,077 words

These Strange New Minds: How AI Learned to Talk and What It Means

by Christopher Summerfield  · 11 Mar 2025  · 412pp  · 122,298 words

The Dark Cloud: How the Digital World Is Costing the Earth

by Guillaume Pitron  · 14 Jun 2023  · 271pp  · 79,355 words

AI in Museums: Reflections, Perspectives and Applications

by Sonja Thiel and Johannes C. Bernhardt  · 31 Dec 2023  · 321pp  · 113,564 words

Hands-On Machine Learning With Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

by Aurélien Géron  · 13 Mar 2017  · 1,331pp  · 163,200 words

Fully Automated Luxury Communism

by Aaron Bastani  · 10 Jun 2019  · 280pp  · 74,559 words

Python for Algorithmic Trading: From Idea to Cloud Deployment

by Yves Hilpisch  · 8 Dec 2020  · 1,082pp  · 87,792 words

Being You: A New Science of Consciousness

by Anil Seth  · 29 Aug 2021  · 418pp  · 102,597 words

Empire of AI: Dreams and Nightmares in Sam Altman's OpenAI

by Karen Hao  · 19 May 2025  · 660pp  · 179,531 words

How to Spend a Trillion Dollars

by Rowan Hooper  · 15 Jan 2020  · 285pp  · 86,858 words

Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again

by Eric Topol  · 1 Jan 2019  · 424pp  · 114,905 words

Falter: Has the Human Game Begun to Play Itself Out?

by Bill McKibben  · 15 Apr 2019

The Dawn of Eurasia: On the Trail of the New World Order

by Bruno Macaes  · 25 Jan 2018  · 287pp  · 95,152 words

Genius Makers: The Mavericks Who Brought A. I. To Google, Facebook, and the World

by Cade Metz  · 15 Mar 2021  · 414pp  · 109,622 words

The Quantum Thief

by Hannu Rajaniemi  · 1 Jan 2010  · 324pp  · 91,653 words