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18min chapter
#170 Richard Sutton on Pursuing AGI Through Reinforcement Learning
Eye On A.I.
Developing Embodied Systems and Understanding Environments
The chapter explores the importance of creating embodied systems with sensory inputs for long-term interaction, weighing the benefits of simulations versus robotics for learning. It discusses the complexity of simulated versus real worlds, architecture and algorithms in understanding environments, and the necessity of suitable world models for tasks like autonomous driving. The conversation touches on self-driving cars, Tesla's models using neural networks, reinforcement learning techniques for planning, and the ambition of refining algorithms to create a mind that can augment human capabilities.
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