Title: Engineering Human-Level Machine Theory of Mind

Abstract: Despite our tremendous progress in AI, current AI systems, including large language models (LLMs), still cannot adequately understand humans and flexibly interact with humans in real-world settings. One of the key missing ingredients is Theory of Mind, which is the ability to understand humans’ mental states from their behaviors. In this talk, I will discuss how we can engineer human-level machine Theory of Mind. I will first show how we can leverage insights from cognitive science studies to develop model-based approaches for physically grounded, multimodal Theory of Mind. I will then discuss how we can improve multimodal embodied AI assistance based on Theory of Mind reasoning. Finally, I will briefly talk about exciting future works toward building open-ended Theory of Mind models for real-world AI assistants.

Bio: Dr. Tianmin Shu is an Assistant Professor in the Department of Computer Science at Johns Hopkins University. He also holds a secondary appointment with the Department of Cognitive Science at JHU. His research goal is to advance human-centered AI by engineering human-level machine social intelligence to build socially intelligent systems that can understand, reason about, and interact with humans in real-world settings. His work received multiple awards, including the Outstanding Paper Award at ACL 2024 and the 2017 Cognitive Science Society Computational Modeling Prize in Perception/Action. His research has also been covered by multiple media outlets, such as New Scientist, Science News, and VentureBeat. He received his PhD degree from the University of California, Los Angeles, in 2019. Before joining JHU, he was a research scientist at the Massachusetts Institute of Technology.

Zoom: https://jhuapl.zoomgov.com/j/1601961533?pwd=AkW4tbH42OWDnYtnfZXFBHNZqWAG5A.1
Meeting ID: 160 196 1533
Passcode: 773398