Friday, October 29 | 11:00 AM - 12:00 PM
Stephen F. & Camilla T. Brauer Hall, Brauer 12
6548 Forest Park Pkwy, St. Louis, MO 63112, USA
Dr. Sriraam Natarajan
Professor of Computer Science, The University of Texas at Dallas
Historically, Artificial Intelligence has taken a symbolic route for representing and reasoning about objects at a higher-level or a statistical route for learning complex models from large data. To achieve true AI, it is necessary to make these different paths meet and enable seamless human interaction. First, I briefly will introduce learning from rich, structured, complex and noisy data. Next, I will present the recent progress that allows for more reasonable human interaction where the human input is taken as “advice” and the learning algorithm combines this advice with data. The advice can be in the form of qualitative influences, preferences over labels/actions, privileged information obtained during training or simple precision-recall trade-off. Finally, I will outline our recent work on "closing-the-loop" where information is solicited from humans as needed that allows for seamless interactions with the human expert. While I will discuss these methods primarily in the context of probabilistic and relational learning, I will also present our recent results on reinforcement learning and demonstrate how human input can be effectively used to create appropriate abstractions to guide RL.
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