Annotated Bibliography
Part I: The Mechanical Mind
Chapter 1: Engines of Calculation
Swade, Doron. The Difference Engine: Charles Babbage and the Quest to Build the First Computer. Viking, 2001. Definitive biography of Babbage with detailed technical explanations of his engines.
Boole, George. An Investigation of the Laws of Thought. 1854. Primary source for Boolean algebra. Dense but foundational.
Lovelace, Ada. “Notes on the Analytical Engine.” 1843. The famous Notes, especially Note G containing the first published algorithm.
Chapter 2: The Universal Machine
Turing, Alan. “On Computable Numbers, with an Application to the Entscheidungsproblem.” 1936. The foundational paper. Available online. Surprisingly readable.
Hodges, Andrew. Alan Turing: The Enigma. Princeton, 2014. Comprehensive biography covering both personal life and technical contributions.
Chapter 3: Information as Physics
Shannon, Claude. “A Mathematical Theory of Communication.” Bell System Technical Journal, 1948. Landmark paper establishing information theory. Clear writing despite technical depth.
Gleick, James. The Information: A History, A Theory, A Flood. Pantheon, 2011. Excellent popular history connecting Shannon to broader intellectual currents.
Part II: The Birth of AI
Chapter 4: Can Machines Think?
Turing, Alan. “Computing Machinery and Intelligence.” Mind, 1950. Introduces the Turing Test. Witty and philosophically rich.
Crevier, Daniel. AI: The Tumultuous History of the Search for Artificial Intelligence. Basic Books, 1993. Comprehensive history of early AI with good coverage of personalities and politics.
Chapter 5: The Perceptron and Its Discontents
Rosenblatt, Frank. “The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain.” Psychological Review, 1958. Original perceptron paper.
Minsky, Marvin and Seymour Papert. Perceptrons. MIT Press, 1969. The influential critique. Important to read primary source, not just summaries.
Chapters 6-7: Symbolic AI and Alternatives
Nilsson, Nils. The Quest for Artificial Intelligence. Cambridge, 2010. Comprehensive technical history by a key participant. Available free online.
Part III: The Learning Revolution
Chapter 8: Neural Networks Reborn
Rumelhart, David, Geoffrey Hinton, and Ronald Williams. “Learning Representations by Back-propagating Errors.” Nature, 1986. The backpropagation paper that revived neural networks.
Chapter 9: The Statistical Turn
Vapnik, Vladimir. Statistical Learning Theory. Wiley, 1998. Technical but essential for understanding SVMs and statistical learning.
Chapters 10-11: Deep Learning and Sequences
LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. “Deep Learning.” Nature, 2015. Excellent review article summarizing the deep learning revolution.
Hochreiter, Sepp and Jürgen Schmidhuber. “Long Short-Term Memory.” Neural Computation, 1997. The LSTM paper. Technical but clearly written.
Part IV: The Transformer Era
Chapter 12: Attention
Vaswani, Ashish, et al. “Attention Is All You Need.” NeurIPS, 2017. The transformer paper. Clear exposition of the architecture.
Chapter 13: Language Models
Devlin, Jacob, et al. “BERT: Pre-training of Deep Bidirectional Transformers.” 2018. BERT paper establishing pre-training paradigm.
Brown, Tom, et al. “Language Models are Few-Shot Learners.” NeurIPS, 2020. GPT-3 paper documenting emergent capabilities.
Chapter 14: LLMs and Beyond
Ouyang, Long, et al. “Training Language Models to Follow Instructions with Human Feedback.” 2022. InstructGPT/RLHF paper.
General References
Mitchell, Melanie. Artificial Intelligence: A Guide for Thinking Humans. Farrar, 2019. Accessible critical perspective on modern AI.
Marcus, Gary and Ernest Davis. Rebooting AI. Pantheon, 2019. Important skeptical perspective on deep learning limitations.
Sutton, Rich. “The Bitter Lesson.” 2019. Influential essay on the historical dominance of compute over clever algorithms.