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Rich Sutton on AI creativity and discovery
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A new and possibly controversial perspective: In this video, I explain the sense in which generative AI trained by supervised learning is incapable of making novel discoveries. https://t.co/LhAU6AyDkh The text of the speech: AI Creativity and Discovery Good day ladies and
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- Praise: Rich Sutton emphasizes the importance of evaluation in AI creativity, suggesting that generative AI needs a structured approach to retain valuable outputs, which could enhance its utility in science and engineering.
- Praise: The discussion around AlphaGo and Claude Code illustrates the potential of AI systems to autonomously evaluate and iterate, highlighting their capabilities in discovery and problem-solving.
- Praise: The idea that AI can generate novel outputs while being grounded in training data opens avenues for innovative applications, merging randomness with learned knowledge.
- Concern: There is skepticism regarding the claim that AI can only produce either random or good outputs, as many believe that AI can achieve both through advanced training techniques.
- Concern: Concerns are raised about the limitations of current AI models in discerning valuable outputs, suggesting that without proper evaluation mechanisms, creative potential may be wasted.