Building Intelligent Systems with Large Scale Deep Learning
For the past five years, the Google Brain team has conducted research on difficult problems in artificial intelligence, on building large-scale computer systems for machine learning research, and, in collaboration with many teams at Google, on applying our research and systems to dozens of Google products. Our group has open-sourced the TensorFlow system (tensorflow.org), a widely popular system designed to easily express machine learning ideas, and to quickly train, evaluate and deploy machine learning systems. In this talk, I'll highlight some of the design decisions we made in building TensorFlow, discuss research results produced within our group in areas such as computer vision, language understanding, translation, healthcare, and robotics, and describe ways in which these ideas have been applied to a variety of problems in Google's products, usually in close collaboration with other teams.
Jeff joined Google in 1999 and is currently a Google Senior Fellow in Google's Research Group, where he co-founded and leads the Google Brain team, Google's deep learning and artificial intelligence research team in Mountain View. He and his collaborators are working on systems for speech recognition, computer vision, language understanding, and various other machine learning tasks. He has co-designed/implemented five generations of Google's crawling, indexing, and query serving systems, and co-designed/implemented major pieces of Google's initial advertising and AdSense for Content systems. He is also a co-designer and co-implementor of Google's distributed computing infrastructure, including the MapReduce, BigTable and Spanner systems, protocol buffers, LevelDB, the recently open-sourced TensorFlow system for machine learning, and a variety of internal and external libraries and developer tools. He received a Ph.D. in Computer Science from the University of Washington in 1996. He is a Fellow of the ACM and the AAAS, a member of the U.S. National Academy of Engineering and the American Academy of Arts and Sciences, and a recipient of the Mark Weiser Award and the ACM Prize in Computing.