@Andrea__M
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AI is now taking on some of the hardest open problems that scientists have posed. But research is broader than solving problems. Can AI also propose research questions that experts find genuinely interesting?
We built After42 to find out. Experts judged 45 of its 135 final questions genuinely interesting, across mathematics, statistics, theoretical computer science, and physics, with wide variation across subfields.
The key idea: develop questions by trying to solve them.
We also asked whether AI can judge which questions are interesting. We find that model judges' top choices align with expert consensus but do not consistently capture individual experts' preferences.
We release the expert-assessed questions as an open resource for researchers to discuss, refine, and pursue, as a step toward integrating AI into the broader research enterprise.
after42.orgView quoted post on X
Statement of Yang-Mills Working Group, a scientific collaboration to interpret AI result related to the existence of quantum chromodynamics & completions of the Standard Model, including a subproblem proving the existence of the Quantum Yang-Mills theory. buff.ly/BwV89TkView quoted post on X
github.com GitHub - openai/math Contribute to openai/math development by creating an account on GitHub. I can see the end-game in the current quest for the mathematical foundations of deep learning:View quoted post on X
A neural network will find a (very long) proof that deep learning works. No human will be able to understand it, but the proof will be formally verified.
