New AI books appear every week. These have lasted because they explain the fundamentals that every newer idea builds on.

1. Artificial Intelligence: A Modern Approach — Stuart Russell and Peter Norvig

The standard university textbook for decades. It covers the whole field — search, logic, planning, probability, machine learning, robotics — and is the best single book to see how today’s deep learning fits into the broader history of AI.

2. Deep Learning — Ian Goodfellow, Yoshua Bengio and Aaron Courville

The reference text on neural networks, published by MIT Press in 2016 and freely readable online. It explains the math and methods behind modern deep learning. Some chapters predate transformers, but the foundations remain essential.

3. Reinforcement Learning: An Introduction — Richard Sutton and Andrew Barto

The classic textbook on learning from rewards, with a second edition published in 2018 and available free from the authors. Sutton and Barto received the 2024 ACM Turing Award for their foundational work on reinforcement learning.

4. “Computing Machinery and Intelligence” — Alan Turing (1950)

Not a book but a short paper, and still worth reading. Turing proposes the “imitation game”, now known as the Turing test, and answers objections to the idea of thinking machines with remarkable foresight.

5. Something on the social side

Pair the technical books with at least one book on AI’s effects on society, labor or policy — the field’s hardest questions are no longer only technical.

How to read them

Don’t read textbooks cover to cover. Pick the chapters that match what you’re learning now and use the rest as a reference.

More recent books worth reading

Beyond the classics, several accessible books explain modern AI and its implications:

  • Stuart Russell, Human Compatible (2019) — on the problem of controlling AI systems more capable than us, from a co-author of the standard AI textbook.
  • Melanie Mitchell, Artificial Intelligence: A Guide for Thinking Humans (2019) — a clear, balanced explanation of what AI can and cannot do.
  • Brian Christian, The Alignment Problem (2020) — stories and research on making machine learning systems do what we intend.
  • Kate Crawford, Atlas of AI (2021) — the material, labor and political dimensions of AI.

Pair one technical book with one book on social impact for a balanced view.

Free and online options

Several classics are available freely online from their authors, including the Deep Learning textbook’s website and Sutton and Barto’s Reinforcement Learning (second edition). Turing’s 1950 paper is widely available. Libraries also offer many of these books as e-books.

Building a reading habit

Big textbooks can be intimidating. Read one chapter at a time, alongside a course or project that uses the concepts. Join or start a reading group: discussing a chapter each week keeps momentum and deepens understanding. Keep notes of key ideas and how they connect to the AI tools you use today — for example, how the search algorithms in Russell and Norvig relate to planning in modern agents, or how Sutton and Barto’s ideas underpin reinforcement learning from human feedback.

Matching books to goals

  • Want the big picture? Start with Mitchell, then Russell and Norvig’s introductory chapters.
  • Want to build models? Goodfellow, Bengio and Courville, alongside a hands-on course.
  • Interested in agents and decision-making? Sutton and Barto.
  • Interested in society and policy? Crawford, Christian and Russell’s Human Compatible, read side by side for contrasting perspectives.

What to watch next

  • New editions of standard textbooks updated for large language models.
  • Books on AI policy, labor and economics.
  • Open textbooks and interactive online books.
  • Accessible books on reasoning models and agents.

Key takeaways

  • Russell and Norvig’s textbook is the standard broad introduction to AI.
  • Goodfellow, Bengio and Courville’s Deep Learning covers neural network foundations.
  • Sutton and Barto is the classic reinforcement learning text.
  • Turing’s 1950 paper remains essential reading.
  • Combine technical books with books on AI’s social impact.

Frequently asked questions

Which AI book should a beginner read?

Melanie Mitchell’s Artificial Intelligence: A Guide for Thinking Humans is accessible; Russell and Norvig is better for structured study.

Are these books still relevant with ChatGPT?

Yes. They explain foundations that modern systems build on, though newer developments require recent sources too.

Can I read them for free?

Some, such as the Deep Learning book and Sutton and Barto’s textbook, are freely available online from their authors.