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The deployment of artificial intelligence (AI), particularly of systems that learn from data and experience, is rapidly expanding in our society. Verified artificial intelligence (AI) is the goal of designing AI-based
systems that have strong, verified assurances of correctness with respect to mathematically-specified requirements. In this talk, I will consider Verified AI from a formal methods perspective. I will describe five challenges for achieving Verified AI, and five corresponding principles for addressing these challenges. I will illustrate these challenges and principles with examples and sample results from the domain of intelligent cyber-physical systems, with a particular focus on autonomous vehicles.
Sanjit A. Seshia is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He received an M.S. and Ph.D. in Computer Science from Carnegie Mellon University, and a B.Tech. in Computer Science and Engineering from the Indian Institute of Technology, Bombay. His research interests are in formal methods for dependable and secure computing, with a current focus on the areas of cyber-physical systems, computer security, and robotics. He has made pioneering contributions to the areas of satisfiability modulo theories (SMT), SMT-based verification, and inductive program synthesis. He is co-author of a widely-used textbook on embedded, cyber-physical systems and has led the development of technologies for cyber-physical systems education based on formal methods. His awards and honors include a Presidential Early Career Award for Scientists and Engineers (PECASE), an Alfred P. Sloan Research Fellowship, and the Frederick Emmons Terman Award for contributions to electrical engineering and computer science education. He is a Fellow of the IEEE.
