Both AI and cognitive science can gain by studying the human solutions to difficult computational problems [1]. My talk will focus on two problems: concept learning and question asking. Compared to the best algorithms, people can learn new concepts from fewer examples, and then use their concepts in richer ways -- for imagination, extrapolation, and explanation, not just classification. Moreover, learning is often an active process; people can ask rich and probing questions in order to reduce uncertainty, while standard active learning algorithms ask simple and stereotyped queries. I will discuss my work on program induction as a cognitive model and potential solution for extracting richer concepts from less data, with applications to learning handwritten characters [2] and learning recursive visual concepts from examples. I will also discuss program synthesis as a model of question asking in simple games [3]. [1] Lake, B. M., Ullman, T. D., Tenenbaum, J. B., and Gershman, S. J. (2016). Building machines that learn and think like people. Preprint available on arXiv:1604.00289. [2] Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B. (2015). Human-level concept learning through probabilistic program induction. Science, 350(6266), 1332-1338. [3] Rothe, A., Lake, B. M., and Gureckis, T. M. (2016). Asking and evaluating natural language questions. In Proceedings of the 38th Annual Conference of the Cognitive Science Society.
