Part 6/13:
In exploring applications like mathematics, the speaker emphasizes that math is provable and decidable, which means that, in theory, if an AI can master mathematical reasoning, it can fundamentally understand and generate proofs just like a human mathematician—but potentially much faster. The analogy is that, much like how a mathematician practices by self-play, AI models can simulate this process internally, leading to mastery in hierarchical reasoning and the discovery of new neural architectures.
This self-bootstrapping process suggests that we are advancing toward AI systems that can essentially “train themselves,” significantly accelerating their own development and sophistication.