Reaching Beyond Human Accuracy With AI Datacenters

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Gregory Diamos of Baidu’s Silicon Valley AI Lab (SVAIL), recently made the trip from Silicon Valley to Atlanta as a part of the Machine Learning Center at Georgia Tech’s Seminar Series.

Diamos presented his talk, “Reaching Beyond Human Accuracy With AI Datacenters” to a room packed with Georgia Tech students of all levels, faculty members and members of the public.

During the talk, Diamos combined theoretical and experiment insights to help explain why deep learning scales predictably with bigger datasets and faster computers. He also showed how some problems are relatively easier than others, and how to tell the difference. He gave real-world examples of important open problems that cannot be solved by small-scale systems but are within reach of the largest machines in the world. Diamos also made the case for specializing datacenters to support AI applications using deep learning efficiently. He outlined a high-level architecture for such a design and left the audience with powerful tools to reach beyond human accuracy to confront some of the hardest open problems in computing.

To see a recording of Diamos’ talk, please click here.

To see a list of ML@GT’s fall seminars, please visit our website.

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