The Machine Learning Center at Georgia Tech presents a seminar by Gregory Diamos of Baidu’s Silicon Valley AI Lab (SVAIL). Daimos will present his talk titled, "Reaching Beyond Human Accuracy With AI Datacenters". The event will be held in the Marcus Nanotechnology Building, Rooms 1116-1118, from 12:15-1:15 p.m. and is open to the public.
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.
Greg Diamos leads computer systems research at Baidu’s Silicon Valley AI Lab (SVAIL), where he helped develop the Deep Speech and Deep Voice systems. Before Baidu, Greg contributed to the design of compiler and microarchitecture technologies used in the Volta GPU at NVIDIA. Greg holds a Ph.D. from the Georgia Institute of Technology, where he led the development of the GPU-Ocelot dynamic compiler, which targeted CPUs and GPUs from the same program representation.