sbrothy
Gold Member
- 1,296
- 1,178
A Triumvirate of AI Driven Theoretical Discovery by Yang-Hui-He
Subject:
History and Overview (math.HO); Artificial Intelligence (cs.AI); High Energy Physics - Theory (hep-th); History and Philosophy of Physics (physics.hist-ph)
Synopsis:
"Recent years have seen the dramatic rise of the usage of AI algorithms in pure mathematics and fundamental sciences such as theoretical physics. This is perhaps counter-intuitive since mathematical sciences require the rigorous definitions, derivations, and proofs, in contrast to the experimental sciences which rely on the modelling of data with error-bars. In this Perspective, we categorize the approaches to mathematical discovery as "top-down", "bottom-up" and "meta-mathematics", as inspired by historical examples. We review some of the progress over the last few years, comparing and contrasting both the advances and the short-comings in each approach. We argue that while the theorist is in no way in danger of being replaced by AI in the near future, the hybrid of human expertise and AI algorithms will become an integral part of theoretical discovery."
-------------------------------------------------------------------------------------------------------------------------------------
I find this interesting (although admittedly much of it goes over my head). Specifically, what surprises me with the author's angle is that, as I've understood the usefulness of AI, it is useful mostly within language and graphics. Disciplines which somewhat came as a surprise to those who foresaw that AI (and perhaps machine learning) would show their strengths within mathematics and logic. That it turns out to be so helpful in the realm of mathematics and physics somewhat surprises me. It might be a tall order to ask you to conclude something from a single arXiv preprint but can it really be so?
Subject:
History and Overview (math.HO); Artificial Intelligence (cs.AI); High Energy Physics - Theory (hep-th); History and Philosophy of Physics (physics.hist-ph)
Synopsis:
"Recent years have seen the dramatic rise of the usage of AI algorithms in pure mathematics and fundamental sciences such as theoretical physics. This is perhaps counter-intuitive since mathematical sciences require the rigorous definitions, derivations, and proofs, in contrast to the experimental sciences which rely on the modelling of data with error-bars. In this Perspective, we categorize the approaches to mathematical discovery as "top-down", "bottom-up" and "meta-mathematics", as inspired by historical examples. We review some of the progress over the last few years, comparing and contrasting both the advances and the short-comings in each approach. We argue that while the theorist is in no way in danger of being replaced by AI in the near future, the hybrid of human expertise and AI algorithms will become an integral part of theoretical discovery."
-------------------------------------------------------------------------------------------------------------------------------------
I find this interesting (although admittedly much of it goes over my head). Specifically, what surprises me with the author's angle is that, as I've understood the usefulness of AI, it is useful mostly within language and graphics. Disciplines which somewhat came as a surprise to those who foresaw that AI (and perhaps machine learning) would show their strengths within mathematics and logic. That it turns out to be so helpful in the realm of mathematics and physics somewhat surprises me. It might be a tall order to ask you to conclude something from a single arXiv preprint but can it really be so?