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 results on reinforcement learning and inverse reinforcement learning.
Pieter leads Collibra Research. With 20 years experience translating science in product and business, he is responsible for envisioning and developing technological and scientific research capabilities. Pieter writes, teaches and advises on computing and management aspects of data innovation, accountability and citizenship. He published five books and dozens of journal articles on these topics. Pieter has a B.S, M.S.and Ph.D. in Computer Science from the Vrije Universiteit Brussel. Prior to cofounding the company, Pieter was a professor at VU University of Amsterdam. Today he is a visiting scientist at Harvard, Columbia University and the San Diego Supercomputer Center. He also serves as an expert to the European Commission and several governments. He lives in New York City with his family.
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