Abstract: In this talk I’ll highlight several exciting trends in the field of AI and machine learning. Through a combination of improved algorithms and major efficiency improvements in ML-specialized hardware, we are now able to build much more capable, general purpose machine learning systems than ever before. As one example of this, I’ll give an overview of the Gemini family of multimodal models and their capabilities. These new models and approaches have dramatic implications for applying ML to many problems in the world, and I’ll highlight some of these applications in science, engineering, and health. This talk will present work done by many people at Google.

Bio: Jeff Dean joined Google in 1999 where he now serves as Google’s Chief Scientist, focusing on AI advances for Google DeepMind and Google Research. His areas of focus include machine learning and AI, and applications of AI to problems that help billions of people in societally beneficial ways. His work has been integral to many generations of Google’s search engine, its initial ad serving system, distributed computing infrastructure such as BigTable and MapReduce, the Tensorflow open-source machine learning system, as well as many libraries and developer tools.

Jeff received a Ph.D. in Computer Science from the University of Washington and a B.S. in Computer Science & Economics from the University of Minnesota. He is a member of the National Academy of Engineering and of the American Academy of Arts and Sciences, a Fellow of the Association for Computing Machinery (ACM) and of the American Association for the Advancement of Sciences (AAAS), and a winner of the 2012 ACM Prize in Computing and the 2021 IEEE John von Neumann medal.

Sponsored by: The Ken Kennedy Institute