Are you still looking for the right reading material for the next year? What should it be? What one person finds exciting is often a matter of personal taste. It’s easy to make a wrong choice. So, how about something useful? Perhaps something about artificial intelligence (AI)? Understanding what this hype is really about could be an investment in the future. But it shouldn’t be too dry or filled with complex math. If you enter a bookstore or search online with these thoughts, you’ll be overwhelmed by a wave of books.
Here are my five definitive recommendations: a fictional biography, a new kind of atlas, a hands-on book for playing and experimenting, a book on the mathematics of AI that’s understandable for non-mathematicians, and a classic for those who want detailed knowledge.
How It All Began: Maniac
Benjamin Labatut’s “Maniac” starts with context and history. The age of artificial intelligence did not begin with the release of ChatGPT. The connection between AI, mathematical logic, military research, the atomic bomb, the Cold War, and game theory is less known. Labatut’s novel weaves these topics into a fictional biography of John von Neumann, the inventor of modern computer architecture. It’s a journey through recent history, ending with the duel between human and machine, the world’s best Korean Go player Lee Sedol, and AI AlphaGo.
The Environment: Atlas of AI
Kate Crawford’s “Atlas of AI” is essential for those interested in the impact of modern AI, without being dazzled by tech industry promises. Crawford reveals that AI is more than a miraculous, intangible form of machine intelligence. In the Atlas, we learn about the tangible, material side of the AI industry: from the world’s lithium mines, to the click factories of the global south where “knowledge workers” prepare data for minimal wages, to automated workplaces, vast data archives, AI training camps, and the Pentagon’s algorithmic warfare team. According to Crawford, AI is primarily “a technology of extraction,” profiting shamelessly from minerals, cheap labor, and an immense amount of data.
The Mathematics: Why Machines Learn
Anil Ananthaswamy’s “Why Machines Learn” is recommended for those who want to understand how machine learning works. Yes, it involves math, but it’s relatively simple. Ananthaswamy repeats concepts, offering different perspectives, allowing readers to understand why and how machines learn.
Learning Through Play: Understanding Artificial Intelligence
Pit Noack and Sophia Sanner’s “Understanding Artificial Intelligence” is a book that combines technical material with engaging content. It includes playful texts, cartoons, infographics, and numerous online example programs. Readers can experiment with how different methods work, how simple scripts generate text, and what language models do when parameters are adjusted.
The Textbook: AI, A Modern Approach
Peter Norvig and Stuart Russell’s “Artificial Intelligence: A Modern Approach” may seem intimidating at first with its extensive format and over 1,000 pages. The book, now in its fourth edition, covers more than just machine learning and neural networks. It begins with search algorithms, showing that search is also AI. The book addresses the history and ethical questions of AI, making it a highly cited textbook in universities.
