报告人：刘兵 教授

主持人：赵东岩 研究员

时   间：2023/5/25  15:00 - 17:00

地   址：必赢71886网址登录燕园校区理科二号楼2736；昌平校区115教室

腾讯会议：406-513-280

报告题目：Autonomous AI: Open World Learning and Adaptation

报告摘要:   

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.

报告人简介:   

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.