While the current era of machine learning is focused on recognizing patterns, the future will be all about making decisions, Michael I. Jordan, a Distinguished Professor at UC Berkeley, explains in this keynote. However, it's not easy to get there. Real-world decisions have consequences, are powered by context, are often interlaced with others’ decisions, have multiple levels of related decisions, and must be explainable. He talks about how today’s recommendation engines, for instance, can wind up sending hundreds of people to the same restaurant or movie, or along the same traffic route. Professor Jordan covers how the future of machine learning relies on economic principles. He discusses how decision-making is different from recommendations, how market forces factor into decision making, and will explain a framework for building a decision-making system.
Professor Jordan’s research interests include machine learning, optimization, control theory, and computational biology. He is a member of the National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Sciences. He has received the Ulf Grenander Prize from the American Mathematical Society, the IEEE John von Neumann Medal, the IJCAI Research Excellence Award, and the ACM/AAAI Allen Newell Award.