RSS 2020, Keynote + Q&A: Josh Tenenbaum
- CBMM Research
Link to workshop page - https://robohub.org/rss-2020-all-the-papers-and-videos/
Speaker: Josh Tenenbaum
Moderator: Marc Toussaint
**Title:** It's all in your head: Intuitive physics, planning, and problem-solving in brains, minds and machines
**Abstract:** I will overview what we know about the human mind's internal models of the physical world, including how these models arise over evolution and developmental learning, how they are implemented in neural circuitry, and how they are used to support planning and rapid trial-and-error problem-solving in tool use and other physical reasoning tasks. I will also discuss prospects for building more human-like physical common sense in robots and other AI systems.
**Biography:** Joshua Tenenbaum is Professor of Computational Cognitive Science at MIT in the Department of Brain and Cognitive Sciences, the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Center for Brains, Minds and Machines (CBMM). His long-term goal is to reverse-engineer intelligence in the human mind and brain, and to use these insights to engineer more human-like machine intelligence. In cognitive science, he is best known for developing theories of cognition as probabilistic inference in structured generative models, and applications to concept learning, causal reasoning, language acquisition, visual perception, intuitive physics, and theory of mind. In AI, he and his group have developed widely used models for nonlinear dimensionality reduction, probabilistic programming, and Bayesian unsupervised learning and structure discovery. His current research focuses on common-sense scene understanding and action planning, the development of common sense in infants and young children, and learning through probabilistic program induction and neuro-symbolic program synthesis. His work has been published in many leading journals and recognized with awards at conferences in Cognitive Science, Computer Vision, Neural Information Processing Systems, Reinforcement Learning and Decision Making, and Robotics. He is the recipient of the Distinguished Scientific Award for Early Career Contributions in Psychology from the American Psychological Association (2008), the Troland Research Award from the National Academy of Sciences (2011), the Howard Crosby Warren Medal from the Society of Experimental Psychologists (2016), the R&D Magazine Innovator of the Year award (2018), and a MacArthur Fellowship (2019). He is a fellow of the Cognitive Science Society, the Society for Experimental Psychologists, and a member of the American Academy of Arts and Sciences.