Introduction: Song Guo received his Ph.D. in computer science from University of Ottawa. He is currently a full professor at Department of Computing, The Hong Kong Polytechnic University. Prior to joining PolyU, he was a full professor with the University of Aizu, Japan. His research interests are mainly in the areas of cloud and green computing, big data, wireless networks, and cyber-physical systems. He has published over 300 conference and journal papers in these areas and received multiple best paper awards from IEEE/ACM conferences. His research has been sponsored by JSPS, JST, MIC, NSF, NSFC, and industrial companies. Dr. Guo has served as an editor of several journals, including IEEE Transactions on Parallel and Distributed Systems (2011-2015), IEEE Transactions on Emerging Topics in Computing (2013-), IEEE Transactions on Green Communications and Networking (2016-), IEEE Communications Magazine (2015-), and Wireless Networks (2013-). He has been actively participating in international conferences as general chair and TPC chair. He is a senior member of IEEE, a senior member of ACM, and an IEEE Communications Society Distinguished Lecturer. Speech Title: AI in Geo-Distributed Systems: From Cloud to Edge Abstract: When accessing cloud-hosted modern applications, users often suffer a significant latency due to the long geo-distance to the central cloud. Edge computing thus emerges as an alternative paradigm that can reduce this latency by deploying services close to users. In this talk, we will analyze the methodology and limitations of popular approaches for supporting AI services on geo-distributed systems along the evolution from cloud computing to edge computing. In particular, we shall discuss how to deal with different sets of challenges in training and inference, the two phases of machine learning based applications, over heterogenous geo-distributed systems. We shall also present our recent studies on data driven resource management among networked collaborative edges.