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Recent weeks have made it clear that AI excels at solving research problems. How good is it at proposing *interesting* research problems? Thanks to the ratings of 29 experts across math, stats, CS, physics, we found 1/3 were interesting, and we fully expect this to improve. This raises important questions about the desired relationship between {humans,AI} in {academia,industry}...
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"text": "Recent weeks have made it clear that AI excels at solving research problems. How good is it at proposing *interesting* research problems? Thanks to the ratings of 29 experts across math, stats, CS, physics, we found 1/3 were interesting, and we fully expect this to improve. This raises important questions about the desired relationship between {humans,AI} in {academia,industry}...",
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"text": "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?\n\nWe 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.\n\nThe key idea: develop questions by trying to solve them.\n\nWe 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.\n\nWe 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.\n\nhttps://t.co/iOrs6L7E9Y",
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