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The company focuses on reinforcement learning—where AI models learn through trial and error—rather than the large-language-model approach most AI companies use. Silver argues that LLMs learn from human text and thus human intelligence, whereas AI systems trained through reinforcement learning could discover new science, technology, government, or economics independently.
Silver proposes placing AI agents inside simulations to learn and collaborate, scaling from confined worlds like the game of Go to real-world complexity. He believes this approach may also offer a better path to building AI aligned with human values because it does not rely on learning from human behavior.
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