Deep learning has enabled rapid progress in diverse problems in vision, speech, healthcare, and beyond.  This progress has been driven by breakthroughs in algorithms that can harness massive datasets and powerful compute accelerators like GPUs.

In this talk, I will combine theoretical and experiment insights to help explain why deep learning scales predictably with bigger datasets and faster computers.  I will also show how some problems are relatively easier than others, and how to tell the difference.

I will show examples of important open problems that cannot be solved by small-scale systems but are within reach of the largest machines in the world.

I will make the case for specializing datacenters to support AI applications using deep learning efficiently.  I will outline a high-level architecture for such a design, and leave you with powerful tools to reach beyond human accuracy to confront some of the hardest open problems in computing.