Martin is a former University Professor and renowned Research Scientist at Google DeepMind. His work focuses on advancing the field of artificial intelligence through deep reinforcement learning. We discuss what reinforcement learning does differently in executing complex tasks, overcoming feedback loops in reinforcement learning, the pitfalls of typical agent-based learning methods, and how being a robotic soccer champion exposed the value of deep learning. We unpack the advantages of deep learning over modeling agent approaches, how finding a solution can inspire a solution in an unrelated field, and why he is currently focusing on data efficiency.
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Martin shares what reinforcement learning does differently in executing complex tasks, overcoming feedback loops in reinforcement learning, the pitfalls of typical agent-based learning methods, and how being a robotic soccer champion exposed the value of deep learning. We unpack the advantages of deep learning over modeling agent approaches, how finding a solution can inspire a solution in an unrelated field, and why he is currently focusing on data efficiency. Gain insights into the trade-offs between exploration and exploitation, how Google DeepMind is leveraging large language models for data efficiency, the potential risk of using large language models, and much more.
“You really want to go all the way down to learn the direct connections to actions only via learning [for training AI].” — Martin Riedmiller [0:07:55]
“I think engineers often work with analogies or things that they have learned from different [projects].” — Martin Riedmiller [0:11:16]
“[With reinforcement learning], you are spending the precious real robots time only on things that you don’t know and not on the things you probably already know.” — Martin Riedmiller [0:17:04]
“We have not achieved AGI (Artificial General Intelligence) until we have removed the human completely out of the loop.” — Martin Riedmiller [0:21:42]
Links Mentioned in Today’s Episode:
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