Here are 100 books that The Annotated Turing fans have personally recommended if you like
The Annotated Turing.
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My passion for generative AI first ignited in 2016 when I spoke about it at a conference, and ever since then, I can’t stop! I've created an online course, a newsletter and even wrote a book to spread knowledge on this groundbreaking technology. As an instructor, I empower others to explore the boundless potential of generative AI applications. Day in day out, I assist clients in crafting their own generative AI solutions, tailoring them to their unique needs.
I truly believe that this is the book that brought my generation of AI experts into the fold. Despite having studied AI and ML, this book took me by the hand and grounded me in the fundamentals. I love the fact that it covers everything from mathematical basics to industry-level techniques.
Written by the OGs of deep learning, it's an absolute must-read for anyone serious about the field. Highly recommend for students and engineers alike.
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all…
As a child of the microcomputer revolution in the late 1970s, I’ve always been fascinated by the concept of a general-purpose machine that I could control. The deep learning revolution of 2010 or so, followed most recently by the advent of large language models like ChatGPT, has completely altered the landscape. It is now difficult to interpret the behavior of these systems in a way that doesn’t argue for intelligence of some kind. I’m passionate about AI because, decades after the initial heady claims made in the 1950s, AI has reached a point where the lofty promise is genuinely beginning to be kept. And we’re just getting started.
Goodfellow’s Deep Learning is a must in the field because it was the first. Prince’s new book is an essential follow-up to be up-to-date with the latest model types, including diffusion models (think Stable Diffusion or DALL-E), transformers (the heart of large language models), graph networks (reasoning over relationships), and reinforcement learning.
The math level is similar to what you’ll find in Goodfellow’s book.
An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice.
Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced…
As a child of the microcomputer revolution in the late 1970s, I’ve always been fascinated by the concept of a general-purpose machine that I could control. The deep learning revolution of 2010 or so, followed most recently by the advent of large language models like ChatGPT, has completely altered the landscape. It is now difficult to interpret the behavior of these systems in a way that doesn’t argue for intelligence of some kind. I’m passionate about AI because, decades after the initial heady claims made in the 1950s, AI has reached a point where the lofty promise is genuinely beginning to be kept. And we’re just getting started.
Woolridge presents the history of artificial intelligence from the point of view of an insider. This book is one of the few accounts of AI history presenting a measured perspective, one that has weathered more than one boom and bust cycle.
The book is nicely complemented by his recent series of lectures, which can be easily found on YouTube. I read Woolridge as saying, “Yes, something new has happened with the advent of large language models, but much work remains.”
From Oxford's leading AI researcher comes a fun and accessible tour through the history and future of one of the most cutting edge and misunderstood field in science: Artificial Intelligence
The somewhat ill-defined long-term aim of AI is to build machines that are conscious, self-aware, and sentient; machines capable of the kind of intelligent autonomous action that currently only people are capable of. As an AI researcher with 25 years of experience, professor Mike Wooldridge has learned to be obsessively cautious about such claims, while still promoting an intense optimism about the future of the field. There have been genuine…
As a child of the microcomputer revolution in the late 1970s, I’ve always been fascinated by the concept of a general-purpose machine that I could control. The deep learning revolution of 2010 or so, followed most recently by the advent of large language models like ChatGPT, has completely altered the landscape. It is now difficult to interpret the behavior of these systems in a way that doesn’t argue for intelligence of some kind. I’m passionate about AI because, decades after the initial heady claims made in the 1950s, AI has reached a point where the lofty promise is genuinely beginning to be kept. And we’re just getting started.
Artificial intelligence is, of necessity, an academic pursuit, at least initially. McCorduck’s book is her account of the history and development of AI. She was not a historian coming to events after the fact but a living witness. Her circle of friends included all the key figures, the people those of us who fell into AI later didn’t have the opportunity to know.
This book, personal and human, not technical and heavy, reveals the humanness of the process. Yes, artificial intelligence was the goal, but human intelligence (and frailty) were central to its emergence.
In the autumn of 1960, twenty-year-old humanities student Pamela McCorduck encountered both the fringe science of early artificial intelligence, and C. P. Snow's Two Cultures lecture on the chasm between the sciences and the humanities. Each encounter shaped her life. Decades later her lifelong intuition was realized: AI and the humanities are profoundly connected. During that time, she wrote the first modern history of artificial intelligence, Machines Who Think, and spent much time pulling on the sleeves of public intellectuals, trying in futility to suggest that artificial intelligence could be important. Memoir, social history, group biography of the founding fathers…
I’m a theologian who started out as a computer scientist. Teaching classes on AI got me wondering, not just whether we’d ever be able to create a human-like AI, but why we wanted to do so in the first place. It seemed to me that computers were the most helpful when they did the things we are not very good at—crunching big calculations, or exploring Mars—stuff we can’t do. That got me thinking that there might be something spiritual going on, that in a world where we increasingly no longer believed in God or angels, we were lonely. That we didn’t want a tool but a companion.
There’s a lot of fear-mongering going around regarding the possibility of a superintelligent AI that could take over or even wipe out humanity.
Larson gives a clear rationale for why this is not going to happen and further, why it is a big mistake to expect computer reasoning to be like human reasoning. He explains the ways computers think and how they differ from the ways we do.
In short, we don’t have a clue how to give computers either consciousness or common sense. Without these, worrying about superhuman intelligence is worrying about the wrong thing. There is no way a computer using current AI methods could evolve into a general intelligence.
"If you want to know about AI, read this book...It shows how a supposedly futuristic reverence for Artificial Intelligence retards progress when it denigrates our most irreplaceable resource for any future progress: our own human intelligence."-Peter Thiel
A cutting-edge AI researcher and tech entrepreneur debunks the fantasy that superintelligence is just a few clicks away-and argues that this myth is not just wrong, it's actively blocking innovation and distorting our ability to make the crucial next leap.
Futurists insist that AI will soon eclipse the capacities of the most gifted human mind. What hope do we have against superintelligent machines?…
I am a mathematician, based at Oxford University, following up the ideas of the Nobel prizewinner Roger Penrose on fundamental physics. But I am best known for writing a biography of Alan Turing, the founder of computer science. I did this at a time when he was almost unknown to the public, long before computers invaded popular culture. And it meant giving a serious account of two kinds of secret history: the codebreaking of the Second World War and the life of an unapologetic gay man. Since then I have also created a supporting website. When I was drawn to find out about Alan Turing, it was not only because he was a mathematician. I seized the chance to bring together many themes from science, history, and human life. This broad approach is reflected in my recommendations. I am choosing books that hint at the great scope of themes related to Turing’s life and work.
My second choice relates more subtly to Turing’s sudden end in 1954. In 1955, Turing’s colleague Max Newman gave a talk on logic in his honour. This greatly impressed a student, Roger Penrose, who was also studying the quantum mechanics and relativity that had first fascinated the young Turing. Years later, Penrose announced an astonishing thesis relating logic and physics. This book explains the theory he developed. It claims that the brain must exploit quantum-mechanical physics that no computer can emulate. Turing famously promoted the prospects for computer-based Artificial Intelligence, but he would have taken this anti-AI thesis more seriously than any other argument: it takes up his own interests and develops his own kind of thinking.
Penrose’s books are not about science, they are actually doing scientific thinking. His humour and wonderful pictures enhance the direct personal engagement. The theory is highly controversial but has set a remarkable twenty-first-century…
The motivation for this book arose, in part, from a need for detailed replies to a number of queries and criticisms from readers of the author's previous book, The Emperor's New Mind , many of whom have gone to extraordinary lengths to avoid the conclusion that there must be something non-computational involved in thinking. Penrose searches for a means, within the constraints of the hard facts of science, whereby a scientifically describable brain might be able to perform the needed non-computational actions. He develops the argument of how quantum effects might have a fundamental relevance to consciousness and to non-computable…
My passion for generative AI first ignited in 2016 when I spoke about it at a conference, and ever since then, I can’t stop! I've created an online course, a newsletter and even wrote a book to spread knowledge on this groundbreaking technology. As an instructor, I empower others to explore the boundless potential of generative AI applications. Day in day out, I assist clients in crafting their own generative AI solutions, tailoring them to their unique needs.
Bishop’s book laid the mathematical groundwork for me, making it a solid foundation for anyone venturing into Generative AI.
I love how it covers Bayesian inference, graphical models, and machine learning fundamentals in a clear, approachable way. I also think, in my personal opinion, that reading my book after this one would be a natural progression to understand where AI is heading, building on the core concepts that Bishop established.
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation pro- gation. Similarly, new models…
I used to think that most nonhuman animals do not have minds in any rich sense of that word. After publishing a book about some influential philosophers who articulate and defend that view, I was pushed by a very good friend to get curious about what nonhuman creatures do. That led to years of reading, reflecting, teaching college courses, and eventually admitting that I had been profoundly wrong. My change of mind culminated in the publication of a book that explores the idea that plants have minds. The books on this list helped me tremendously along the way, and my students have also learned much from them.
I think computers don’t think, and this book taught me how to think about that. I admire it in part because it showed me, a professor of philosophy, how to do scientifically informed philosophy. Unlike so many books on the history of thinking about thinking, just the first chapter of this book is clear, accurate, insightful, and exciting. Equally so is Haugeland’s explanation of what a computer is, making an intellectual adventure of theoretical computer science.
Haugeland uses this to make a compelling case for thinking that computers could genuinely reason. And then he does something that we philosophers tend to love; he launches a provocative critique of that claim, contending that computers can’t think because they don’t “give a damn.” Although it’s now three decades old, this is the book to read if you’re curious about artificial intelligence.
First Edition. Some markings on first end page. Some shelf and edge wear, small tears, to dust jacket. Pages are clean and binding is tight. Solid Book.
I’ve always been fascinated by the power of language to propel everything we think—from our values and beliefs, to political views, to what we take for absolute truth. Once I learned there’s a whole field devoted to studying language called “rhetoric”—the field in which I’m now an expert—there was no turning back. Rhetoric has been around for more than 2,000 years, and since its inception, it has taught people to step back from language and appraise it with a more critical eye to identify how it works, why it’s persuasive, and what makes people prone to believe it. By studying rhetoric, we become less easily swayed and more comfortable with disagreement.
I love Ted Chiang’s short stories. Chiang’s background is in computer science, and he’s drawn to questions concerning the relationship between language, technology, cognition, and the physical universe.
His stories are fascinating thought experiments: They depict how a change in the medium or format of language transforms meaning and opens new possibilities for what language can be and do, what humans can think and know, and what it means to be a thinking, speaking human against the backdrop of a vast, infinitely complex universe. His stories are often backed by years of detailed research.
When I read Chiang, I find myself entangled in a strong emotional bond with his characters even as I ruminate on larger questions about what it means to be a language-using human.
'Lean, relentless, and incandescent.' Colson Whitehead, Pulitzer Prize winning author of The Underground Railroad and The Nickel Boys
This much-anticipated second collection of stories is signature Ted Chiang, full of revelatory ideas and deeply sympathetic characters. In 'The Merchant and the Alchemist's Gate,' a portal through time forces a fabric seller in ancient Baghdad to grapple with past mistakes and the temptation of second chances. In the epistolary 'Exhalation,' an alien scientist makes a shocking discovery with ramifications not just for his own people, but for all of reality. And in 'The Lifecycle of Software Objects,' a woman cares for…
I have been dreaming about Artificial Intelligence (AI) since a young age. I am currently Professor of AI at UNSW, Sydney. I was named by the Australian newspaper as one of the ”rock stars” of Australia’s digital revolution. Although this is highly improbable, I have spoken at the UN, and to heads of state, parliamentary bodies, company boards, and many others about AI and how it is impacting our lives. I've written three books about AI for a general audience that have been translated into a dozen different languages.
This is an entertaining and lighter read than my other recommendations about AI. It is specifically about chatbots trying to pass the Turing Test, and ultimately is a witty story of what it means to be human. For anyone who has ever mistaken an answerphone for a person, or a person for an answerphone!
A playful, profound book that is not only a testament to one man's efforts to be deemed more human than a computer, but also a rollicking exploration of what it means to be human in the first place.
“Terrific. ... Art and science meet an engaged mind and the friction produces real fire.” —The New Yorker
Each year, the AI community convenes to administer the famous (and famously controversial) Turing test, pitting sophisticated software programs against humans to determine if a computer can “think.” The machine that most often fools the judges wins the Most Human Computer Award. But there…
