Systems that sense, learn, and reason from streams of data promise to provide extraordinary value to people and society. Beyond delivering value, harnessing theoretical principles to build systems that operate in the open world can teach us about the sufficiency of existing models--and light the path to new research. I will discuss directions with learning and inference in the open world, highlighting key ideas in the context of projects in transportation, energy, and healthcare. Finally, I will discuss opportunities for building systems with new kinds of open-world competencies by weaving together components that leverage advances from several research subdisciplines.
Eric Horvitz is a Principal Researcher at Microsoft Research. His interests span theoretical and practical challenges with learning, inference, and decision making under uncertainty. He is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and has served as President of the organization. He has also served on the NSF Computer & Information Science & Engineering (CISE) Advisory Board, the DARPA Information Science and Technology Study Group (ISAT), the Naval Research Advisory Committee (NRAC), and the board of the Decision Education Foundation (DEF). He received his PhD and MD degrees at Stanford University. More information can be found at http://research.microsoft.com/~horvitz
Catherine Copetas, copetas [atsymbol] cs.cmu.edu