How can machine learning algorithms learn more like humans do? Learn how University of Illinois at Chicago Distinguished Professor Bing Liu is using “lifelong learning” to improve machine learning.
Building Lifelong Learning Machines
Presented by Bing Liu, Distinguished Professor at the Department of Computer Science at the University of Illinois at Chicago
Friday, November 17, 2017
Brickyard (BYENG) 209, Tempe campus [map]
The classic machine learning (ML) paradigm works in isolation: given a dataset, a ML algorithm is executed on the data to produce a model. The algorithm does not consider any other information. Although this paradigm has been very successful, it requires a large amount of training data, and is only suitable for well-defined, static and narrow domains. In contrast, we humans learn quite differently. We always learn with the help of our previously learned knowledge. We learn continuously, accumulate the knowledge learned in the past and use it help future learning and problem solving. When faced with an unfamiliar situation, we adapt our knowledge to deal with the situation and also learn from it. Lifelong learning (LL) aims to achieve this capability. Chatbots, personal assistants, and autonomous robots that work in real-life dynamic and open environments all call for LL and they should all be lifelong learning machines. Without the LL capability, an AI system will probably never be truly intelligent. In this talk, I will introduce this emerging ML research area and discuss some applications.
About the speaker
Bio: Bing Liu is a distinguished professor of Computer Science at the University of Illinois at Chicago. He received his Ph.D. in Artificial Intelligence from the University of Edinburgh. His research interests include lifelong learning, sentiment analysis, data mining, machine learning, and natural language processing. He has published extensively in top conferences and journals. Two of his papers have received 10-year Test-of-Time awards from KDD. He also authored four books: one on lifelong learning, two on sentiment analysis, and one on Web mining. Some of his work has been widely reported in the press, including a front-page article in the New York Times. On professional services, he served as the Chair of ACM SIGKDD (ACM Special Interest Group on Knowledge Discovery and Data Mining) from 2013-2017. He has also served as program chair of many leading data mining conferences, including KDD, ICDM, CIKM, WSDM, SDM, and PAKDD, as associate editor of leading journals such as TKDE, TWEB, and DMKD, and as area chair or senior PC member of numerous natural language processing, AI, Web, and data mining conferences. He is a Fellow of ACM, AAAI and IEEE.