The search for ever-more accurate and detailed simulations of physical phenomenon has driven decades of improvements in both supercomputer architecture and computational methods. It seems increasingly likely that the next several orders of magnitude improvements are likely to come, at least in part, from the use of machine learning and artificial intelligence methods to learn approximations to complex functions and to assist in navigating complex search spaces. Without any aspiration for completeness, I will review some relevant activities in this space and suggest some implications for future research.
Dr. Foster is Senior Scientist and Distinguished Fellow, and also director of the Data Science and Learning Division, at Argonne National Laboratory, and the Arthur Holly Compton Distinguished Service Professor of Computer Science at the University of Chicago. He was previously Director of the Computation Institute at Argonne and the University of Chicago. Dr. Foster’s research deals with distributed, parallel, and data-intensive computing technologies, and applications of those technologies to problems in such domains as materials science, climate change, and biomedicine. Dr. Foster is a fellow of the AAAS, ACM, BCS, and IEEE, and an US Department of Energy Office of Science Distinguished Scientists Fellow. He has received the BCS Lovelace Medal and the IEEE Babbage, Goode, and Kanai awards. Dr. Foster obtained a BSc (Hons I) degree from the University of Canterbury, New Zealand, and a PhD from Imperial College, United Kingdom, both in computer science.