1 h 10 min
Responsibly Improving AI with Privacy-Sensitive Data: Principles, Theory, and Practice | Richard M. Karp Distinguished Lecture
Artificial Intelligence, Information Security, Machine Learning and seminar by Brendan McMahan, Google
Hosted by Simons Institute for the Theory of Computing, UC Berkeley
Tuesday 15:30 Los Angeles (GMT-8)
Recording available
Berkeley, United States
Recording
Abstract
Brendan McMahan examines how AI systems can learn from high-quality information that is distributed and privacy-sensitive. He introduces principles for precise discussions of privacy and AI, then connects them to the theory and practical methods needed in real applications, including work on decentralised and privacy-preserving learning.
Topics
privacy-preserving learninghigh-quality information
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