EE380 Computer Systems Colloquium

Machine Learning: Concepts and questions as programming
Wednesday, October 12, 2016 - 4:30pm
Gates B03
Brenden Lake (NYU)
Abstract / Description: 

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.


The Stanford EE Computer Systems Colloquium (EE380) meets on Wednesdays 4:30-5:45 throughout the academic year. Talks are given before a live audience in Room B03 in the basement of the Gates Computer Science Building on the Stanford Campus. The live talks (and the videos hosted at Stanford and on YouTube) are open to the public.

Brenden Lake ( studies computational problems that are easier for people than they are for machines. He is a Moore-Sloan Data Science Fellow at New York University, receiving his Ph.D. from MIT in 2014 and the Robert J. Glushko Prize for Outstanding Doctoral Dissertations in Cognitive Science. Brenden received his M.S. and B.S. in Symbolic Systems from Stanford University in 2009.