Reward Complexity
Oriol discusses the intricate nature of defining rewards in artificial intelligence, particularly in constrained domains like math. He acknowledges the challenge of creating general models that can effectively translate learned rewards into real-world applications. The conversation highlights the tension between task specialization and the pursuit of broader reasoning capabilities, emphasizing the complexities involved in validating mathematical proofs and the potential pitfalls of formalization.In this clip
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No Priors
No Priors Ep. 74 | With Google DeepMind VP of Research Oriol Vinyals
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