OpenAI 宣布数学、物理、计算机三项重大科学发现，引入外部专家引导审议
OpenAI announces three big science discoveries and a new way to work with academics
- 1OpenAI 宣布数学、计算机科学、物理三项重大成果，距上次批量发布数百份手稿不足一个月
- 2此次计算机科学与物理结果由外部专家引导审议，数学结果则直接公开模型输出供研究者查验
- 3计算机科学论文宣称证伪指数时间假设 ETH，含 1999 年原提出者的三人专家组将引导核查
- 4数学结果称解决各维度 Kakeya 等猜想，物理称给出含 QCD 的四维量子场论严格存在性证明
OpenAI 正尝试用外部专家背书让 AI 生成的重大科学结论获得学界认可，但其指定人员先行获取成果的发布模式遭自家顾问小组公开反对。
- 影响：AI 实验室正把模型能力叙事延伸到基础科学成果，企业评估厂商技术实力时需要区分宣称与已验证结论；相关发布与核查规范仍在学界争论中，此类成果短期内难以当作可靠依据。
- 参考：可考虑把厂商发布的 AI 科研成果先标记为待独立验证，等 Impagliazzo 三人组与 David Gross 团队的审议结论后再更新技术判断；值得跟踪学界关于 AI 生成成果发布规范的公开讨论。
- 依据：三项成果的内容与专家安排均为 OpenAI 官方口径，暂无独立验证；物理内容未完整公开，其 AGMAI 顾问小组已建议 AI 公司勿采用指定人员先获成果的做法。
Less than a month after its mass release of hundreds of manuscripts that it said contained solutions to long-standing mathematics questions, OpenAI is back with three more “major” discoveries in math, computer science, and physics. What’s different this time is the choice to have external experts that OpenAI describes as “shepherds” guiding the results in computer science and physics, while for the math problem it says that they advised it to release the AI output for researchers to work through their own explanations.
OpenAI results added to GitHub today:
Local smoothing for the Euclidean wave equation. The proof uses an auxiliary partial differential equation to establish Sogge’s local smoothing for waves in every spatial dimension at least two. It also yields the Kakeya maximal and dimension conjectures and Stein’s spherical restriction estimates throughout the interior of the conjectured range. We are releasing the model output directly, as we did in our October 6 release.
The Exponential Time Hypothesis is False. The paper gives a deterministic algorithm that exactly counts satisfying assignments of fixed-width CNF formulas in subexponential time and polynomial space. The case of 3-SAT refutes the Exponential Time Hypothesis. An independent working group of computer scientists led by Russell Impagliazzo, Ramamohan Paturi, and Ryan Williams will examine and explain the proof. At the group’s request, we are making the materials public immediately so others can examine them as well.
This approach could have its own issues, the company admits, and the AGMAI panel of math advisers OpenAI put together just said yesterday that “we recommend that AI companies do not adopt” the practices of designating certain people to receive the output first, citing unequal access, borrowed reputation, and biased selection as a few of the issues. Instead, they recommended that in the future, AI labs release output on scholarly repositories, with Lean-formalized proofs, info on how the problems were chosen, funding for people working on understanding the results, and other information.
The physics information has not been released in full, but is a “mathematically rigorous existence proof of certain interacting quantum field theories containing gauge fields and fermions in four dimensions, including Quantum Chromodynamics (QCD) and a unified completion of the Standard Model of particle physics.” According to OpenAI, “David Gross will lead a group including Michael Douglas, Arthur Jaffe, Igor Klebanov, and Nikita Nekrasov that will shepherd this work.”
The computer science collaboration is said to prove that the Exponential Time Hypothesis (ETH), which has been widely believed to be true, is actually false. OpenAI says verification of the result will be guided by a group of three experts that includes UC San Diego’s Russell Impagliazzo and Ramamohan Paturi, who were part of the group that originally proposed it in 1999, as well as MIT professor Ryan Williams. The group decided to release the AI output write-up and Lean proof on GitHub today.
They also released an open letter to the community asking for input on how to examine the claim, and about setting norms for dealing with AI-generated breakthroughs:
We would welcome discussion of three broader questions:
What expectations should govern early access to AI-generated results, disclosure of conflicts of interest, and the use of unpublished material for subsequent research?
How should credit recognize both new discoveries and the work of checking, fixing, simplifying, explaining, and extending them?
How can we support students and researchers as methods and research directions change, while preserving the understanding and judgment needed to question results and develop new ideas?
There’s similar documentation for the math breakthrough, where its model “produced a proof of the local smoothing conjecture in every dimension and therefore also resolve the Kakeya conjecture and Stein’s restriction conjecture in all dimensions.” Just a few months ago, NYU professor Hong Wang was awarded a prestigious Fields Medal by the International Mathematical Union for resolving the Kakeya conjecture in three dimensions.
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- Richard Lawler
来源：The Verge · AI · theverge.com