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On the Front Lines of AI Automation: Life Sciences and Chip Design

Given the right task, AI-driven machines can be empowered with supercharged IQs that make the smartest humans look dumb, or at least inefficient.  As we watch advances made in task automation, we see it burgeoning into fields previously (last week) thought unimaginable. Two recent reports underline the trend. In one, Carnegie Mellon University is teaming […]

Video: Deep Reinforcement Learning and Systems Infrastructure at DeepMind

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.”