The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
Co-Scientific Director, MIT Quest for Intelligence; Professor, Brain and Cognitive Sciences; MacArthur Fellow
Who they work with
Joshua Tenenbaum is a professor of computational cognitive science in MIT’s Department of Brain and Cognitive Sciences and a co-scientific director with the MIT Quest for Intelligence. He is also an investigator at the Center for Brains, Minds and Machines and the Computer Science and Artificial Intelligence Laboratory. Tenenbaum’s research straddles cognitive science and artificial intelligence, where his goals are to reverse engineer human intelligence and to build machines that behave in human-like ways and have greater use to society. Through a combination of mathematical modeling, computer simulation, and behavioral experiments, Tenenbaum tries to uncover the logic behind our everyday inductive leaps: constructing perceptual representations, separating “style” and “content” in perception, learning concepts and words, judging similarity or representativeness, inferring causal connections, noticing coincidences and predicting the future. Tenenbaum is a MacArthur Fellow and has received the National Academy of Sciences’ Troland Research Award. He earned a BA from Yale University, and a PhD in brain and cognitive sciences from MIT.
- Yildirim, I., Belledonne, M., Freiwald, W., Tenenbaum, J. (2020). Efficient inverse graphics in biological face processing. Science Advances 6 (10), eaax5979.
- J Mao, J., Gan, C., Kohli, P., Tenenbaum, J.B., Wu, J. (2019). The neuro-symbolic concept learner: Interpreting scenes, words and sentences from natural supervision. Seventh International Conference on Learning Representations (ICLR).
- Lake, B.M., Ullman, T. D., Tenenbaum, J.B., Gershman, S.J., (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, Vol. 40.
- Tenenbaum, J.B., Kemp, C., Griffiths, T.L., Goodman, N.D. (2011). How to grow a mind: Statistics, structure and abstraction. Science, 331 (6022), 1279-1285.
- March 9, 2020: Wired, If AI’s so smart, why can’t it grasp cause and effect?
- Dec. 2, 2019: MIT News, Helping machines perceive some laws of physics.
- Sept. 25, 2019: MIT News, Josh Tenenbaum receives 2019 MacArthur Fellowship.
June 14, 2019: MIT News, Toward artificial intelligence that learns to write code.
April 2, 2019: MIT News, Teaching machines to reason about what they see.
Publications with the MIT-IBM Watson AI Lab