(5/5) Thanks to the many contributors to the book! @markkho.bsky.social @norijacoby.bsky.social @eringrant.bsky.social @fredcallaway.bsky.social @tomerullman.bsky.social @jhamrick.bsky.social @tobigerstenberg.bsky.social @spiantado.bsky.social
@noahdgoodman.bsky.social @ebonawitz.bsky.social

(4/5) Here's the table of contents. An Open Access version of the book is available through the MIT Press website.

(3/5) That same perspective is valuable for understanding modern AI systems. In particular, Bayesian models highlight the inductive biases that make it possible for humans to learn from small amounts of data, and give us tools for building machines with the same capacity.

(2/5) Bayesian models start by considering the abstract computational problems intelligent systems have to solve and then identifying their optimal solutions. Those solutions can help us understand why people do the things we do.