Title: ML will change the world – what’s taking it so long?

Speaker: Rajat Monga (Google)

Date and location: Friday, October 4, 11 – 12 pm, Wozniak Lounge

Abstract: Over the last few years we have seen papers claiming image recognition, speech recognition and more recently natural language understanding that is better than humans. With such amazing results, everything around us should be way smarter than it really is. Why on earth is it taking so long to make things better? Do we need better algorithms – are deep learning, reinforcement learning, GANs not good enough yet? More data – maybe Imagenet was not big enough? More compute – bigger Pods with TPUs and GPUs – to exaflops and beyond? Notwithstanding our superhuman results, we still have a lot of improvements to make in even these basic areas of vision, speech and text understanding, and it will likely take a combination of algorithms, data and computation to get there. But these are not enough. The businesses that deliver value to all the people around us, are barely leveraging all the existing advances in machine learning. Often they don’t know where to start, and even when they do the tools are too hard and the problem isn’t just in building the models. This talk will go over some of the issues faced by the industry today, and how ML can change the world.

Bio: Rajat Monga co-created and led TensorFlow at Google, powering machine learning research and products worldwide. As a founding member of the team he has been involved in co-designing and co-implementing DistBelief and more recently TensorFlow, an open source machine learning system. Prior to this role, he led teams in AdWords, built out the engineering teams and co-designed web scale crawling and content-matching infrastructure at Attributor, co-implemented and scaled eBay’s search engine and designed and implemented complex server systems across a number of startups. Rajat received a B.Tech. in Electrical Engineering from Indian Institute of Technology, Delhi.