The University of Texas at Dallas

Erik Jonsson School of Engineering and Computer Science


Challenges of Human-Aware Artifical Intelligence Systems

Subbarao (Rao) Kambhampati

Challenges of Human-Aware Artifical Intelligence Systems
Friday, February 22, 10:45 a.m.
T.I. Auditorium (ECSS 2.102)
Live web stream available here
Email [email protected] to submit questions during the live stream.

Dr. Subbarao (Rao) Kambhampati
Arizona State University

Biography : Dr. Subbarao (Rao) Kambhampati is a professor of computer science at Arizona State University. Kambhampati studies fundamental problems in planning and decision making, motivated in particular by the challenges of human-aware AI systems.

Kambhampati is a fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and the American Association for the Advancement of Science (AAAS). He previously served as the president of AAAI and as a trustee of the International Joint Conference on Artificial Intelligence. He also recently served on the board of directors of Partnership on AI, an international consortium establishing ethical standards and best practices for use of AI.

Kambhampati received his bachelor's degree in electrical engineering (electronics) from Indian Institute of Technology, Madras, as well as his MS and PhD degrees in computer science from the University of Maryland, College Park. Follow him on Twitter @rao2z.

Abstract : Artificial intelligence (AI) research suffers from a longstanding ambivalence to humans — vacillating between replacing them in the workforce and augmenting their efforts. Now, as AI technologies enter our everyday lives at an ever-increasing rate, AI systems must adapt to work synergistically with humans. To do this effectively, AI systems should include aspects of intelligence that help humans work with each other, including emotional and social intelligence.

This talk will explore current research challenges in designing such human-aware AI systems, including modeling the mental states of humans in the loop, recognizing their desires and intentions, providing proactive support, exhibiting explicable behavior, providing cogent explanations on demand and engendering trust. The speaker will survey the progress made so far on these challenges and highlight some promising directions. In addition, the speaker will touch on the additional ethical quandaries that such systems pose.

Overall, the lecture will argue how the quest for human-aware AI systems broadens the scope of AI enterprise, necessitates and facilitates true interdisciplinary collaborations and can greatly improve public acceptance of AI technologies.