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Thinking Fast and Slow in AI

August 02, 17:30

Francesca Rossi

Panel moderator: Georg Weissenbacher

Francesca Rossi

IBM Research, USA

Thinking Fast and Slow in AI, August 02

IBM Fellow and AI Ethics Global Leader

Future of Autonomous Driving

August 07, 17:30

Keynote: Shai Shalev-Shwartz

Panel Participants: Gila Kamhi (Intel, Haifa), Shai Shalev-Shwartz (Mobileye and The Hebrew University of Jerusalem), Sanjit Seshia (UC Berkeley)

Panel moderator: Georg Weissenbacher

Shai Shalev-Shwartz

Mobileye and The Hebrew University of Jerusalem

Future of Autonomous Driving, August 07

CTO of Mobileye and Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem

Gila Kamhi

Wireless & Connectivity Solutions, Intel Corporation

Future of Autonomous Driving, August 07

Chief AI Officer

Sanjit Seshia

University of California, Berkeley

Future of Autonomous Driving, August 07

Professor at the Department of Electrical Engineering and Computer Sciences

Francesca Rossi

IBM Research, USA

Thinking Fast and Slow in AI, August 02

Abstract:

Current AI systems lack several important human capabilities, such as adaptability, generalizability, self-control, consistency, common sense, and causal reasoning. We believe that existing cognitive theories of human decision making, such as the thinking fast and slow theory, can provide insights on how to advance AI systems towards some of these capabilities. In this talk, I will present the work done by IBM and collaborators in this space, including the definition of a general architecture that is based on fast/slow solvers and a metacognitive component. I will then present experimental results on the behavior of an instance of this architecture, for AI systems that make decisions about navigating in a constrained environment. The results will show how combining the fast and slow decision modalities allows the system to evolve over time and gradually pass from slow to fast thinking with enough experience, and that this greatly helps in decision quality, resource consumption, and efficiency.

Bio:

Francesca Rossi is an IBM Fellow and the IBM AI Ethics Global Leader. She is a computer scientist with over 30 years of experience in AI research. Before joining IBM, she has been a professor of computer science at the University of Padova, Italy, for 20 years. Her research interests focus on artificial intelligence, specifically they include constraint reasoning, preferences, multi-agent systems, computational social choice, and collective decision making. She is also interested in ethical issues in the development and behavior of AI systems, in particular for decision support systems for group decision making. She is a fellow of both AAAI and of EurAI and she has been president of IJCAI and the Editor in Chief of the Journal of AI Research. She will be the next president of AAAI. She co-leads the IBM AI ethics board and she actively participate in many global multi-stakeholder initiatives on AI ethics. She is a member of the board of directors of the Partnership on AI and the industry representative in the steering committee of the Global Partnership on AI. She is a fellow of both the worldwide association of AI (AAAI) and of the European one (EurAI), and she will be the next president of AAAI from July 2022.

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