Steps Towards More Human-Like Learning in Machines - Josh Tenenbaum

Explore the frontiers of artificial intelligence and human-like learning in machines through this lecture by Josh Tenenbaum at the Institute for Advanced Study. Delve into the myths of machine learning, core knowledge in human intelligence, and the concept of commonsense core knowledge. Examine probabilistic programming, game engines, and simulation techniques used to model intuitive psychology. Discover how babylike learning and learning in game engines contribute to AI development. Investigate examples in perception, plan interactions, and low-level learning using physics engines and amortized inference. Analyze the hard problem of learning, children's learning processes, and one-shot learning in the Omniglot domain. Explore inverse motor programs, Bayesian inference, and probabilistic programs in classification tasks. Examine generative models, drawing styles, and the wake-sleep algorithm in neural components learning.