Data Science Institute-Industry-Innovation Seminar: Jeff Dean, SVP for Google AI

Deep Learning to Solve Challenging Problems

For the past eight years, Google Research teams have 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 many Google products. As part of our work in this
space, we have built and 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.

We have also collaborated closely with Google’s platforms team
to design and deploy new computational hardware called Tensor
Processing Units, specialized for accelerating machine learning
computations. In this talk, I’ll highlight some of our recent research
accomplishments, and will relate them to the National Academy of
Engineering’s Grand Engineering Challenges for the 21st Century,
including the use of machine learning for healthcare, robotics,
language understanding and engineering the tools of scientific
discovery. I’ll also cover how machine learning is transforming many
aspects of our computing hardware and software systems. This talk
describes joint work with many people at Google.

Jeff Dean (ai.google/research/people/jeff) joined Google in 1999 and is currently a Google Senior Fellow and SVP for Google AI
and related research efforts. His teams are working on systems for speech recognition, computer vision, language understanding,
and various other machine learning tasks. He has co-designed/implemented many 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, the open-source TensorFlow system for machine learning, and a variety of internal
and external libraries and developer tools.
Jeff received a Ph.D. in Computer Science from the University of Washington in 1996, working with Craig Chambers on
whole-program optimization techniques for object-oriented languages. He received a B.S. in computer science & economics from
the University of Minnesota in 1990. 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), a Fellow of the American Association for the
Advancement of Sciences (AAAS), and a winner of the ACM Prize in Computing.
Hosted with The Fu Foundations School of Engineering and Applied Science