Further Reading — Chapter 2: A Brief History of AI

Tier 1: Accessible and Essential

Turing, Alan. "Computing Machinery and Intelligence." Mind, 59(236), 1950, pp. 433–460. The founding document of AI as a field of inquiry. Turing's writing is witty, clear, and remarkably prescient. The first several pages are accessible to any reader, and the objections he anticipates (and his responses) remain directly relevant to today's debates. Freely available online.

Mitchell, Melanie. Artificial Intelligence: A Guide for Thinking Humans. Farrar, Straus and Giroux, 2019. A computer scientist's honest, accessible tour of AI's history and current state. Mitchell is particularly good at explaining what AI systems can and cannot do, and at deflating hype without dismissing genuine progress. An excellent companion to this chapter.

Christian, Brian. The Alignment Problem: Machine Learning and Human Values. W.W. Norton, 2020. While focused on the alignment of AI systems with human values, the opening chapters provide an excellent accessible history of machine learning's development. Christian is a gifted storyteller who makes technical history vivid.

Nils J. Nilsson. The Quest for Artificial Intelligence: A History of Ideas and Achievements. Cambridge University Press, 2009. A comprehensive history written by one of the field's pioneers. More detailed than most popular accounts, but written for a general audience. Available as a free PDF from the author's website. Particularly strong on the 1950s–1980s era.

Tier 2: Deeper Exploration

Wooldridge, Michael. A Brief History of Artificial Intelligence: What It Is, Where We Are, and Where We Are Going. Flatiron Books, 2021. A compact, readable history by an Oxford computer scientist. Wooldridge covers the same eras as this chapter but with additional technical depth and a European perspective that complements the typically US-centric AI narrative.

Crevier, Daniel. AI: The Tumultuous History of the Search for Artificial Intelligence. Basic Books, 1993. Published at the tail end of the second AI winter, this book captures the drama of AI's early decades with the immediacy of recent history. Its coverage of the expert systems boom and bust is particularly detailed and draws on extensive interviews with key figures.

Crawford, Kate. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press, 2021. A critical examination of AI's material infrastructure and social consequences. Crawford's historical perspective focuses not on the technology itself but on the labor, resources, and power structures behind it. A valuable counterpoint to technology-focused histories.

Vaswani, Ashish, et al. "Attention Is All You Need." Advances in Neural Information Processing Systems, 2017. The original transformer paper. While technically dense, the abstract and introduction are accessible and give you a sense of how the authors framed their contribution. Understanding that this single paper reshaped the entire AI landscape provides valuable perspective.

Marcus, Gary, and Ernest Davis. Rebooting AI: Building Artificial Intelligence We Can Trust. Pantheon, 2019. A skeptical but informed assessment of modern AI's limitations, written by two prominent AI researchers. Marcus and Davis argue that current deep learning approaches, despite their impressive results, are missing something fundamental. Their critique provides a useful counterbalance to optimistic narratives.

Lighthill, James. "Artificial Intelligence: A General Survey." In Artificial Intelligence: A Paper Symposium. Science Research Council, 1973. The report that helped trigger the first AI winter. Reading it today reveals both genuine insights about AI's limitations and the risks of overcorrecting based on short-term disappointments. An important primary source for understanding how evaluation of AI research has shaped the field's trajectory.

Bostrom, Nick, and Eliezer Yudkowsky. "The Ethics of Artificial Intelligence." In The Cambridge Handbook of Artificial Intelligence, 2014. While focused on ethical questions rather than history per se, this essay provides useful context for understanding how concerns about AI's trajectory have evolved. The authors represent the "AI safety" perspective that has become increasingly prominent in recent years.