In this episode, we explore how AI is moving beyond code and games to crack hard math problems. We break down reinforcement learning, PPO, horizon problems, and adaptive AI, and we examine how language modeling helps reveal the underlying grammar of mathematical presentations. We also discuss the Andrew Curtis Conjecture and recent breakthroughs, and consider what these advances mean for the future of mathematical discovery.

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