Three of the 722 mathematics papers OpenAI released on 7 October using an AI model have already been retracted. Their value now rests on checking by mathematicians, who have not yet had time to read them all, according to New Scientist.
Reality
- Evidence38
- Adoption
- Not scored
- Hype gap+30
- Incentives
- Not scored
- Confidence45
OpenAI released hundreds of results on open problems in math and theoretical computer science, many already verified in the Lean programming language. Those are all but certain to be correct, so the months of reading ahead are about whether they hold new ideas.
Perspective Coverage
5 publishers
- Builder
- Builder 33%
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- Operator 55%
- Investor
- Investor 12%
Reality
- Evidence62
- Adoption
- Not scored
- Hype gap+35
- Incentives70
- Confidence60
OpenAI published 722 math manuscripts across 372 result families, all produced by an internal model it has not released. About 300 come with Lean formalizations a machine can check, Crypto Briefing reported, so the rest wait on human reviewers whom one mathematicians' group is urging to stop working with OpenAI.
Perspective Coverage
3 publishers
- Builder
- Builder 30%
- Operator
- Operator 47%
- Investor
- Investor 23%
Reality
- Evidence55
- Adoption20
- Hype gap+35
- Incentives65
- Confidence55
buildConfirmed6 publishers OpenAI posted 372 results from an internal model to GitHub, at about three hours of ChatGPT Pro compute each on average. Finding proofs is now cheap enough that the slow step is mathematicians checking and judging them.
Perspective Coverage
6 publishers
- Builder
- Builder 44%
- Operator
- Operator 31%
- Investor
- Investor 25%
Reality
- Evidence58
- Adoption10
- Hype gap+35
- Incentives72
- Confidence60
OpenAI posted 722 model-written math manuscripts in 372 families on GitHub, many with Lean proofs a computer can check. A mathematician citing one has to learn first whether it is a checked proof or a paper still waiting for review.
Perspective Coverage
10 publishers
- Builder
- Builder 40%
- Operator
- Operator 27%
- Investor
- Investor 33%
Reality
- Evidence55
- Adoption
- Not scored
- Hype gap+45
- Incentives70
- Confidence60
Schneier and Rafi put a near-term ceiling on how fast AI will break cryptography
Anthropic published two AI-derived cryptanalysis results in July and OpenAI's model disproved an 80-year-old conjecture in May, but both authors argue the models are still weak exactly where deep new theory is required.
Reality
- Evidence34
- Adoption26
- Hype gap+18
- Incentives55
- Confidence38