this is quite interesting, and I share the intuition that superhuman math is likely. I think go and other board games are indeed a good analogy, and we could see takeoff from self-play
even if super it relies on formalization and doesn’t directly generalize to other fields, I still wonder if superhuman math wouldn’t have bigger real consequences than Ryan thinks. Here is some speculation:
AI research
we still don’t have a complete mathematical theory for why deep learning works. A superhuman AI in math could help human experts develop a “theory of deep learning,” which could allow us to move from brute-force experimentation to principled design of deep networks
Our networks are still absurdly inefficient compared to the brain, prob because spike-timing information is more efficient than info coding in weights only. spiking neural networks are too hard to design and train, but better theoretical results would maybe improve the situation
More efficient optimizers to train current networks via computer science theoretical results
Fusion energy
Designing stable plasma confinement in a fusion reactor requires solving intractable differential equations. Maybe maths results could make them more tractable
Current LLMs can actually help us speculate better on what would be possible, I’d be curious to see more people posting here on ways superhuman maths could be impactful.
this is quite interesting, and I share the intuition that superhuman math is likely. I think go and other board games are indeed a good analogy, and we could see takeoff from self-play
even if super it relies on formalization and doesn’t directly generalize to other fields, I still wonder if superhuman math wouldn’t have bigger real consequences than Ryan thinks. Here is some speculation:
AI research
we still don’t have a complete mathematical theory for why deep learning works. A superhuman AI in math could help human experts develop a “theory of deep learning,” which could allow us to move from brute-force experimentation to principled design of deep networks
Our networks are still absurdly inefficient compared to the brain, prob because spike-timing information is more efficient than info coding in weights only. spiking neural networks are too hard to design and train, but better theoretical results would maybe improve the situation
More efficient optimizers to train current networks via computer science theoretical results
Fusion energy
Designing stable plasma confinement in a fusion reactor requires solving intractable differential equations. Maybe maths results could make them more tractable
Current LLMs can actually help us speculate better on what would be possible, I’d be curious to see more people posting here on ways superhuman maths could be impactful.