Recommended Additional Reading

AI is a constantly advancing field, here are some highly recommended books to support your learning journey.

1. Artificial Intelligence: A Modern Approach by Stuart Russell & Peter Norvig

   
   

   This is often considered the “bible” textbook in AI; it covers fundamentals across search, planning, reasoning, learning, robotics, and more.

   
   

2. Deep Learning by Ian Goodfellow, Yoshua Bengio & Aaron Courville

   
   

   A comprehensive, somewhat mathematically rigorous guide to deep neural networks and modern architectures.

   
   

3. Life 3.0: Being Human in the Age of Artificial Intelligence by Max Tegmark

   
   

   A more accessible, philosophical / futurist view of how AI could evolve, and how society might handle it.

   
   

4. Superintelligence: Paths, Dangers, Strategies by Nick Bostrom

   
   

   Focuses on the risks of highly capable AI (superintelligence) and strategic issues around alignment and control.

   
   

5. The Alignment Problem: Machine Learning and Human Values by Brian Christian

   
   

   Explores the problem of making AI systems that align with human values; a mix of technical insights and narrative.

   
   

6. Supremacy: AI, ChatGPT and the Race That Will Change the World by Parmy Olson

   
   

   This is more current and covers the competitive dynamics in the AI industry (especially around large models, generative AI).

   
   

7. Hello World: How to Be Human in the Age of the Machine by Hannah Fry

   
   

   A readable, less technical treatment of how algorithms and AI affect everyday life, including ethical issues.

   
   

8. Nexus: A Brief History of Information Networks from the Stone Age to AI by Yuval Noah Harari

   
   

   While broader than just AI, it places AI in the context of the evolution of information networks, giving historical depth.

   
   

9. If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All by Eliezer Yudkowsky & Nate Soares

   
   

   A recent (2025) book that argues strongly about existential risks posed by superhuman AI.

   
   

10. Machine Learning Yearning by Andrew Ng

    
    

    While lighter in theory, this one is more of a practitioner’s guide to structuring ML projects, diagnosing errors, etc. (often included in “practical AI” book lists)