Dan Belov is CTO of DeepMind, an Alphabet company.
In this video from HiPEAC 2018 in Manchester, Dan Belov from DeepMind describe the company’s machine learning technology and some of the challenges ahead.
DeepMind Inc. is well known for state of the art Deep Reinforcement Learning (DRL) algorithms such as DQN on Atari, A3C on DMLab and AlphaGo Zero. I would like to take you on a tour of challenges we encounter when training DRL agents on large workloads with hundreds of terabytes of data. I’ll talk about why DRL poses unique challenges when designing distributed systems and hardware as opposed to simple supervised learning. Finally I’d like to discuss opportunities for DRL to help systems design and operation.
Dan Belov is Chief Technology Officer of DeepMind Inc. His focus has been on building systems that accelerate machine learning research. Prior to DeepMind, he was a Distinguished Engineer at Google Inc. where he worked for 11 years on distributed systems, search infrastructure and security. Dan has a degree from Carnegie Mellon University.
DeepMind is the world leader in artificial intelligence research and its application for positive impact. We’re on a scientific mission to push the boundaries of AI, developing programs that can learn to solve any complex problem without needing to be taught how.
If we’re successful, we believe this will be one of the most important and widely beneficial scientific advances ever made, increasing our capacity to understand the mysteries of the universe and to tackle some of our most pressing real-world challenges. From climate change to the need for radically improved healthcare, too many problems suffer from painfully slow progress, their complexity overwhelming our ability to find solutions. With AI as a multiplier for human ingenuity, those solutions will come into reach.
DeepMind was founded in London in 2010 and back by some of the most successful technology entrepreneurs in the world. Having been acquired by Google in 2014, we are now part of the Alphabet group. We continue to be based in our hometown of London, with additional research centres in Edmonton and Montreal, Canada, and a DeepMind Applied team in Mountain View, California.
