Books

See also: Guides, Blog posts, Podcasts

This page is the AI Wiki index of books about artificial intelligence, organised by type. It collects textbooks used in university courses, popular non-fiction written for general readers, earlier classics from the founders of the field, and fiction that has shaped how the public thinks about thinking machines. Every entry has been checked against publisher records, author bibliographies, or library catalogues. Where a book has gone through multiple editions, the most widely used edition is noted in the table.

The list is not exhaustive. AI has been written about since the 1940s, and the number of trade books published since the public release of ChatGPT in November 2022 alone runs into the hundreds. The selection here favours titles that are widely cited in the literature, that have been adopted as course texts, or that have shaped public debate enough to be quoted across newspapers, podcasts, and policy papers.

Overview

Books about AI fall into four rough groups. Textbooks and academic works present the mathematical and algorithmic foundations of the field. They are usually long, dense, and updated in new editions every few years. Popular non-fiction explains the same ideas to general readers, often weaving in interviews, case studies, and predictions. The third category covers earlier classics, books written before the deep learning revolution that still get assigned in graduate seminars or quoted in modern essays. The fourth category is fiction, mostly science fiction, that has either shaped the public imagination about AI or has been written by authors who studied the technical literature carefully.

The tables below use sentence case in column headings. Years refer to the first edition unless noted. Publishers are listed by their imprint at the time of first publication. ISBNs are given where they uniquely identify a book; for older titles with many reprints, ISBN is omitted.

Textbooks and academic books

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Popular non-fiction

The table below groups popular AI books into four threads: alignment and existential risk, history and progress reporting, ethics and social criticism, and industry and policy. The first three groups overlap heavily in practice; the categorisation in the rightmost column is meant as a hint, not a strict label.

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Earlier classics

The books below were written before the deep learning era, but each remains widely read and cited. They cover symbolic AI, cognitive science, the philosophy of mind, and the early debates about what a thinking machine could or should be.

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Fiction

AI fiction has shaped public expectations of the field for almost a century. The books below are the ones most often cited in discussions of AI in film, in coursework on technology and society, and in the published interviews of researchers themselves. Asimov's robot stories, Dick's androids, and Gibson's cyberspace are the recurring reference points.

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Notable autobiographies and memoirs

The books below are first-person accounts by people who built the field, ran the labs, or wrote the policies. They are listed separately because their value as primary sources differs from the analytical non-fiction in the popular section.

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See also

• Guides
• Blog posts
• Podcasts
• Videos
• Movies
• Artificial intelligence
• Machine learning
• Deep learning
• Reinforcement learning
• AI safety
• AI Alignment
• Superintelligence
• Existential risk
• Artificial general intelligence
• Nick Bostrom
• Stuart Russell
• Fei-Fei Li
• Kai-Fu Lee
• Gary Marcus
• Mustafa Suleyman
• Marvin Minsky
• Geoffrey Hinton
• Yoshua Bengio
• Perceptron
• AI winter
• Symbolic AI
• Turing test

References

1. MIT Press catalogue, entries for Deep Learning (Goodfellow, Bengio, Courville, 2016), Reinforcement Learning: An Introduction (Sutton and Barto, 2nd ed. 2018), Probabilistic Machine Learning (Murphy, 2022 and 2023), Foundations of Machine Learning (Mohri, Rostamizadeh, Talwalkar, 2nd ed. 2018), and Taming Silicon Valley (Marcus, 2024). ↩
2. Springer catalogue, entries for Pattern Recognition and Machine Learning (Bishop, 2006), The Elements of Statistical Learning (Hastie, Tibshirani, Friedman, 2nd ed. 2009), and An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2nd ed. 2021). ↩
3. Cambridge University Press catalogue, entries for Mathematics for Machine Learning (Deisenroth, Faisal, Ong, 2020), Information Theory, Inference, and Learning Algorithms (MacKay, 2003), Bayesian Reasoning and Machine Learning (Barber, 2012), Understanding Machine Learning (Shalev-Shwartz and Ben-David, 2014), Computer Vision: Algorithms and Applications (Szeliski, 2nd ed. 2022), Dive into Deep Learning (Zhang et al.), and The Quest for Artificial Intelligence (Nilsson, 2010). ↩
4. Pearson product page, Artificial Intelligence: A Modern Approach, 4th edition (Russell and Norvig, 2020). ↩
5. O'Reilly catalogue, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (Geron, 3rd ed. 2022), Designing Machine Learning Systems (Huyen, 2022), and AI Engineering (Huyen, 2024). ↩
6. Manning catalogue, Deep Learning with Python (Chollet, 2nd ed. 2021), Grokking Deep Learning (Trask, 2019), and Build a Large Language Model (From Scratch) (Raschka, 2024). ↩
7. Oxford University Press catalogue, Superintelligence (Bostrom, 2014), The Emperor's New Mind (Penrose, 1989), and Shadows of the Mind (Penrose, 1994). ↩
8. Penguin Random House catalogue, including Crown imprint (The Coming Wave, Weapons of Math Destruction), Viking imprint (The Singularity Is Near, The Singularity Is Nearer, Human Compatible), Knopf (Life 3.0), Pantheon (Rebooting AI), Currency (AI 2041), Penguin Press (Empire of AI, Possible Minds), and Dutton (Genius Makers). ↩
9. W. W. Norton catalogue, The Alignment Problem (Christian, 2020), Hello World (Fry, 2018), and The Second Machine Age (Brynjolfsson and McAfee, 2014). ↩
10. Hachette Book Group catalogue, including Little, Brown (The Age of AI, Genesis, Consciousness Explained), Mariner Books (A Brief History of Intelligence), and Basic Books (Goedel, Escher, Bach; Mindstorms; The Mind's I; The Book of Why; A Thousand Brains; Rule of the Robots; The Master Algorithm; Rise of the Robots). ↩
11. Yale University Press catalogue, Atlas of AI (Crawford, 2021) and The Computer and the Brain (von Neumann, 1958, reprinted by Yale). ↩
12. Polity Books catalogue, Race After Technology (Benjamin, 2019). ↩
13. NYU Press catalogue, Algorithms of Oppression (Noble, 2018). ↩
14. Henry Holt and Company catalogue, Code Dependent (Murgia, 2024) and Plans and the Structure of Behavior (Miller, Galanter, Pribram, 1960). ↩
15. Flatiron Books catalogue, The Worlds I See (Li, 2023). ↩
16. Houghton Mifflin Harcourt catalogue, AI Superpowers (Lee, 2018), Final Jeopardy (Baker, 2011), and The Human Use of Human Beings (Wiener, 1950). ↩
17. PublicAffairs catalogue, Power and Progress (Acemoglu and Johnson, 2023), The Age of Surveillance Capitalism (Zuboff, 2019), and The Big Nine (Webb, 2019). ↩
18. Harvard Business Review Press catalogue, I, Human (Chamorro-Premuzic, 2023). ↩
19. Thomas Dunne Books / St. Martin's Press catalogue, Our Final Invention (Barrat, 2013) and Automating Inequality (Eubanks, 2018). ↩
20. Stanford Law Books / Stanford University Press catalogue, Genie in the Machine (Plotkin, 2009). ↩
21. Internet Speculative Fiction Database (ISFDB) records for Asimov, Dick, Gibson, Stephenson, Heinlein, Clarke, Chiang, Egan, Vinge, Simmons, Powers, Suarez, Hertling, McEwan, Ishiguro, Hall, and VanderMeer titles in the fiction table. ↩
22. Nobel Prize biographies, Kazuo Ishiguro (Literature, 2017), Daron Acemoglu and Simon Johnson (Economic Sciences, 2024). ↩
23. ACM Turing Award announcement, Sutton and Barto (2024). ↩
24. Author and publisher pages for the free online editions of Deep Learning (deeplearningbook.org), Pattern Recognition and Machine Learning (Microsoft Research release, 2024), The Elements of Statistical Learning and An Introduction to Statistical Learning (Stanford), Mathematics for Machine Learning (mml-book.com), Dive into Deep Learning (d2l.ai), Neural Networks and Deep Learning (neuralnetworksanddeeplearning.com), Speech and Language Processing 3rd edition draft (Stanford), Information Theory, Inference, and Learning Algorithms (inference.org.uk), and Bayesian Reasoning and Machine Learning (UCL). ↩