报告人：Guy Van den Broeck
时 间：2022/09/6 上午9:30
Modern artificial intelligence, and deep learning in particular, is extremely capable at learning predictive models from vast amounts of data. Many expect that AI will go from powering customer service chatbots to providing mental health services. That it will go from personalized advertisement to deciding who is given bail. That it will go from speech recognition to writing laws. The expectation is that AI will solve society’s problems by simply being more intelligent than we are. Implicit in this bullish perspective is the assumption that AI technology will naturally learn to reason from data: that it can form trains of thought that “make sense”, similar to how a mental health professional, a judge, or a lawyer might reason about a case, or more formally, how a mathematician might prove a theorem. This talk will investigate the question whether this behavior can be learned from data, and how we can design the next generation of artificial intelligence techniques that can achieve such capabilities, focusing on neuro-symbolic learning and tractable deep generative models.
Guy Van den Broeck is an Associate Professor and Samueli Fellow at UCLA, in the Computer Science Department, where he directs the Statistical and Relational Artificial Intelligence (StarAI) lab. His research interests are in Machine Learning, Knowledge Representation and Reasoning, and Artificial Intelligence in general. His papers have been recognized with awards from key conferences such as AAAI, UAI, KR, OOPSLA, and ILP. Guy is the recipient of an NSF CAREER award, a Sloan Fellowship, and the IJCAI-19 Computers and Thought Award.