Over the past decade Deep Learning has revolutionized much of Data Mining and Artificial Intelligence. Several factors have contributed to this virtuous cycle, primarily the ready availability of data in the cloud and a shift in the hardware resources that can be used for computation, mostly away from memory intensive models to compute intensive ones. For instance, large amounts of image and video data are available thanks to cheap and ubiquitous sensors. Processing them is only possible with equally copious amounts of low-precision computation. At the same time, expressive machine learning frameworks have allowed statistical modelers to design complex models with ease and to deploy them at scale, thus increasing the demand for computation even further. In this talk I will illustrate how these interaction cycles are likely to shape machine learning in the future.

This is a joint talk with Alex Smola.

Edo Liberty is a Principal Scientist at AWS and the head of Amazon AI Labs. Edo received his B.Sc. in Physics and Computer Science from Tel Aviv University and his PhD in Computer Science from Yale University, where he was also a postdoctoral fellow in Applied Mathematics. Edo then co-founded and ran a New York based startup. Later, Edo joined Yahoo Research in Israel and taught Data Mining at Tel Aviv university for three years. Before joining Amazon, Edo led Yahoo Research in New York and Yahoo’s Scalable Machine Learning group. His research interests include data mining, optimization, streaming and online algorithms, machine learning, and numerical linear algebra. He is the author of more than thirty academic papers on these topics including award winning works on streaming matrix approximation and fast random projections.