No other species possesses a social intelligence quite like that of humans. Our ability to understand one another’s minds and actions, and to interact with one another in rich and complex ways, is the basis for much of our success, from governments to symphonies to the scientific enterprise. In this talk, I will explore how to engineer and reverse engineer social intelligence and its developmental roadmap. First, I will discuss my work on building cognitively inspired modes and benchmarks for physically grounded Theory of Mind reasoning, which forms the foundation of commonsense physical-social scene understanding for both humans and machines. Second, I will show how we can use sophisticated social inferences to guide successful and trustworthy human-AI cooperation. Last, I will talk about how we can study the developmental roadmap of machine social intelligence by taking inspiration from the cognitive development of infants.
Host: Muhao Chen, POC: Maura Covaci
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Dr. Tianmin Shu is a postdoctoral associate in the Department of Brain and Cognitive Sciences at Massachusetts Institute of Technology, working with Josh Tenenbaum and Antonio Torralba. He studies social AI and computational social cognition, with the goal of engineering and reverse engineering social intelligence. His work has received the 2017 Cognitive Science Society Computational Modeling Prize in Perception/Action, a Best Paper Award at the Cooperative AI workshop at NeurIPS 2020, and a Best Paper Award at the Shared Visual Representations in Human and Machine Intelligence workshop at NeurIPS 2020. His work has also been covered by multiple media outlets such as New Scientist, Science News, and VentureBeat. He received his Ph.D. degree from the University of California, Los Angeles in 2019.