SAN FRANCISCO—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.
Deep learning trends at Google. Source: SIGMOD/Jeff Dean
Dean pointed to Google’s speech recognition team, which through the use of neural networks reduced word errors by 30%. The team used the networks to replace the acoustic model of its speech recognition pipeline — which uses raw audio waveforms to determine sounds and words — and achieved “the biggest single improvement in two decades.”
Jeff Dean. Source: SIGMOD
The fundamental problems being solved by ML and neural networks can be found in other fields such as medical and satellite imaging. In those cases, a house may want to be identified on a map and fitted for a solar panel consultation or a diabetic patient must be screened for ocular degeneration. The same models that are used for speech recognition could be easily tweaked to serve other issues.
“There is a lot of parallelism in these models,” Dean added, pointing to the Google Translate app that can now translate signs into a different language in real time using pixel identification.
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