[[{"fid":"467","view_mode":"landscape_left","fields":{"format":"landscape_left","field_file_image_alt_text[und][0][value]":"Greg Corrado","field_file_image_title_text[und][0][value]":"","field_file_caption_credit[und][0][value]":"%3Cp%3EGreg%20Corrado%3C%2Fp%3E%0A","field_file_caption_credit[und][0][format]":"full_html"},"type":"media","attributes":{"alt":"Greg Corrado","height":267,"width":400,"class":"media-element file-landscape-left"},"link_text":null}]]Industrial scale applications of machine learning are surprisingly important in the products and services we enjoy today. Over the last few years classical artificial neural networks have reemerged as one of the most powerful, practical machine learning tools available. More than it was driven by algorithmic advances, this “deep learning” renaissance has been fueled by the availability of ever larger data stores and clever use of vast computational resources. Greg will describe Google's large scale distributed neural network framework and the applications of neural networks to the domains of image recognition, speech recognition, and text understanding.
Greg Corrado is a senior research scientist at Google working in artificial intelligence, computational neuroscience, and scalable machine learning. He has worked for some time on brain inspired computing, and most recently has served as one of the founding members and a technical lead on Google's large scale deep learning project. Before coming to Google, he worked at IBM Research on the SyNAPSE neuromorphic silicon chip. He did his graduate work in Neuroscience and in Computer Science at Stanford University, and his undergraduate in work Physics at Princeton University.