Google Fellow Talks Neural Nets, Deep Learning

By Jessica Lipsky,EETimes, SAN FRANCISCO, Jun. 28, 2016 – 

We are already living with deep learning and large-scale neural networks, as evidenced by the growing number of applications that rely on computer vision, language understanding, and robotics. What we now want most from machine learning, said Google Senior Fellow Jeff Dean to the audience at SIGMOD 2016 keynote today (Tuesday, June 28), is "understanding."

"We now have sufficient computation resources, large enough interesting data sets," Dean told SIGMOD attendees. "We can store tons of interesting data but what we really want is understanding about that data."

In a keynote talk, Dean outlined the history of machine learning (ML) and neural networks and various ways to program models to take advantage of raw data coming through in the form of images or audio. He also detailed how ML has taken shape at Google, which recently announced that it will open a machine learning center in Europe. The company developed its own accelerator chips for artificial intelligence it calls tensor processing units (TPUs) after the open source TensorFlow algorithms it released last year.

"Over time saw more and more successes to applying these techniques to different kinds of problems. This has led to really incredible growth in use of the technology across hundreds of teams at Google," Dean said.

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