報告人
Bing Liu
Distinguished Professor
University of Illinois Chicago
報告信息
報告時間：
2023年5月25日 15:00 - 17:00
報告地址：
燕園校區：理科二号樓2736
昌平校區：115教室
主持人
趙東岩 研究員
beat365王選計算機研究所、人工智能研究院
Abstract
As more and more AI agents are used in practice, it is time to think about how to make these agents fully autonomous so that they can (1) learn by themselves in a self-motivated and self-initiated manner rather than being retrained offline periodically on the initiation of human engineers and (2) accommodate or adapt to unexpected or novel circumstances. As the real-world is an open environment full of unknowns or novelties, the capabilities of detecting novelties, characterizing them, adapting to them, gathering ground-truth training data and incrementally learning the unknowns/novelties become critical in making AI agents more and more knowledgeable, powerful, and self-sustainable over time. The key challenge here is how to automate the process so that it is carried out continually on the agent's own initiative and through its own interactions with humans, other agents, and the environment just like human on-the-job learning. In this talk, I will first discuss a prescriptive theory for this learning paradigm and then present an implemented system to demonstrate its feasibility.
Biography
Bing Liu is a distinguished professor at the University of Illinois Chicago. He received his Ph.D. in Artificial Intelligence (AI) from the University of Edinburgh. His current research interests include continual/lifelong learning, lifelong learning dialogue systems, sentiment analysis, machine learning and natural language processing. He has published extensively in prestigious conferences and journals and authored four books: one about lifelong machine learning, two about sentiment analysis, and one about Web mining. Three of his papers have received the Test-of-Time awards, and another one received Test-of-Time honorable mention. Some of his works have also been widely reported in popular and technology press internationally.
He served as the Chair of ACM SIGKDD from 2013-2017 and as program chair of many leading data mining conferences. He is the winner of 2018 ACM SIGKDD Innovation Award, and is a Fellow of ACM, AAAI, and IEEE.
Additional information about him can be found at https://www.cs.uic.edu/~liub/.
