Artificial intelligence#
This part covers AI beyond machine learning: the classic algorithms for search, logic, planning and decisions, the models behind today’s language systems, and the questions from psychology, cognitive science and philosophy that AI keeps running into.
Start with search and logic, then decisions and reinforcement learning; both follow Russell & Norvig and Berkeley’s CS 188. The LLM chapter covers the transformer and the models built on it. The remaining chapters are about minds and society: how people explain and reason, how cognitive science thinks about representation, what counts as a scientific explanation, fairness and ethics, and where AI may be headed.
Uninformed and A* search, local search, constraint satisfaction, propositional and first-order logic, planning, and rule learning.
Game trees, utility theory, the value of information, MDPs, reinforcement learning, deep RL, and RL for LLMs.
The shapes inside a decoder-only transformer, KV caching, self-attention worked by hand, and a reading list of influential transformer models.
How people explain things (Miller, Lombrozo), plus book notes on evolutionary and moral psychology, the evolution of intelligence, and therapy.
How minds might form representations (nativism, empiricism, Bayesian models, deep learning), language, theory of mind, symbolic AI, and cognitive biases.
What makes something a scientific explanation (DN model, statistical relevance, causal mechanisms, unification, pragmatics), plus Deutsch, Kuhn and Durant.
Fairness definitions and fixes, harms of computer vision, a legal test for proxy variables, ethics and moral trade, STS, and effective altruism.
Short thoughts on AGI, then notes on Stuart Russell’s Human Compatible and the essays in Possible Minds.