AI BriefingRich Sutton on AI Creativity and Discovery
Key point
For generative AI to achieve true scientific discovery, a reinforcement-learning-based loop combining evaluation and selection—beyond mere imitation—is essential.
Details
Supervised-learning-based generative AI is a model that imitates existing data, which is useful for summarization or answer generation, but has limitations when it comes to new scientific and mathematical discoveries. This is because new attempts that go beyond the scope of the data risk being regarded as mere hallucination.
For true creativity and discovery, three stages must be combined: Variation, Evaluation, and Selective Retention. This is similar to natural selection or the scientific method, and in terms of machine learning, it connects with the core principles of Reinforcement Learning.
Successful systems such as AlphaGo, AlphaFold, and Claude-Code did not simply imitate data, but found new and valuable results through evaluation and selective retention. Therefore, for AI to become an autonomous scientist, it needs to share clear goals and achieve automation of creativity, in which it generates and evaluates its own outputs and repeats the process of discovery.
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