Carnegie Mellon University
The human mind remains an unparalleled engine of innovation, with its unique ability to make sense of complex information and find deep analogical connections driving progress in science and technology over the past millennia. The recent explosion of online information available in virtually every domain should present an opportunity for accelerating this engine; instead, it threatens to slow it as the information processing limits of individual minds are reached.
In this talk I discuss our efforts towards building a universal knowledge accelerator: a system in which the sensemaking people engage in online is captured and made useful for others, leading to virtuous cycles of constantly improving information sources that in turn help people more effectively synthesize and innovate. Approximately 70 billion hours per year in the U.S. alone are spent on complex online sensemaking in domains ranging from scientific literature to health; capturing even a fraction of this could provide significant benefits. We discuss three integrated levels of research that are needed to realize this vision: at the individual level in understanding and capturing higher order cognition; at the computational level in developing new interaction systems and AI partners for human cognition; and at the social level in developing complex and creative crowdsourcing and social computing systems.
Niki Kittur is an Associate Professor and holds the Cooper-Siegel Chair in the Human-Computer Interaction Institute at Carnegie Mellon University. His research on crowd-augmented cognition looks at how we can augment the human intellect using crowds and computation. He has authored and co-authored more than 70 peer-reviewed papers, 13 of which have received best paper awards or honorable mentions. Dr. Kittur is a Kavli fellow, has received an NSF CAREER award, the Allen Newell Award for Research Excellence, major research grants from NSF, NIH, Google, and Microsoft, and his work has been reported in venues including Nature News, The Economist, The Wall Street Journal, NPR, Slashdot, and the Chronicle of Higher Education. He received a BA in Psychology and Computer Science at Princeton, and a PhD in Cognitive Psychology from UCLA.
While much of AI has focused on automating human intelligence and behavior, there exists an even more exciting parallel thread---how artificial intelligence can amplify human intelligence. We focus on advancing reinforcement learning for human-in-the-loop scenarios, where an automated agent may be helping to teach a student, work with a clinician, or benefit from other human experts. I will discuss several of our recent results and our ongoing research in this direction.
Emma Brunskill is an assistant professor of computer science at Stanford University. She is a Rhodes Scholar, a Microsoft Faculty Fellow, a NSF CAREER awardee and a ONR Young Investigator Program recipient and her group's has been recognized by multiple best paper nominations. She seeks to create interactive machine learning agents that help people realize their goals. To do this her group works on both the algorithmic and theoretical reinforcement learning challenges that arise in this pursuit, and in testing these ideas in real systems, with a particular focus on creating learning software that learns.