内容来源：https://www.theverge.com/ai-artificial-intelligence/1019921/openai-breakthrough-math-computer-science-physics

内容总结：

OpenAI近日宣布在数学、计算机科学和物理学领域取得多项突破，并表示正在探索与外界沟通研究成果的新方式。然而，尽管公司似乎做出了姿态调整，接受《The Verge》采访的研究人员却对该公司的一系列做法愈发感到不满。

过去几个月，OpenAI与数学界的关系颇为动荡。十月初，该公司曾将数百篇预印本论文直接丢给研究人员，令后者至今仍在艰难消化。这一次，OpenAI表示正与外部专家“牧羊人”合作，由其决定如何以最佳方式分享最新突破。

三项成果分别涉及数学、计算机科学和物理学中一些最重大的未解难题。部分成果延续了OpenAI此前的工作，另一些则开辟了全新领域。根据“牧羊人”的建议，每项成果采用了不同的发布策略：数学成果方面，OpenAI公开了AI生成的输出内容，“让研究人员自行组织力量来解释该结果”；计算机科学成果方面，OpenAI将组建一个外部研究小组“来解释结果”；而物理学成果则完全未予发布，OpenAI表示将“听从”外部专家组的意见来决定如何发布相关材料。

数学成果尤为引人注目。该成果声称解决了所有维度下的挂谷猜想（Kakeya conjecture）。粗略来说，该问题探究的是在空间中让一根针或铅笔指向每个方向所需的最小空间。今年早些时候，纽约大学数学家王虹因证明三维情形下的挂谷猜想而荣获菲尔兹奖。布朗大学数学教授Brendan Hassett对《The Verge》表示，OpenAI的主张“看起来有充分依据”，但他强调自己尚未检查Lean形式化验证——一种通过计算验证结果的手段。

物理学成果涉及标准模型——物理学中最成功、最完整的理论框架。该成果关注量子色动力学，即描述强核力（自然界四种基本力之一，与引力、电磁力和弱核力并列）及其与杨-米尔斯理论关系的学科，后者是现代粒子物理学的基石。杨-米尔斯理论也是数学领域七大千禧年大奖难题之一的核心，不过OpenAI的工作并未涉及尚未解决的“质量间隙”问题。

最后一项成果声称证伪了指数时间假说——理论计算机科学中一个被广泛接受的假设，即某些问题无法以显著快于暴力求解的方式解决。该问题与P versus NP密切相关，后者是另一个千禧年大奖难题，也是该领域最大的未解问题之一，追问的是那些解可以被快速验证的问题是否也能被快速求解。

鉴于各项成果的披露程度不同，且评估此类工作的复杂性较高，研究人员难以全面评判该公司的发现。即便拥有全部信息，完成评估也需要时间。但接受《The Verge》采访的专家表示，有充分理由认真对待这些成果，他们分别用“巨大”、“重大突破”和“意义深远”来形容。

然而，研究人员对科学本身的兴奋之情，被对OpenAI行为方式的恼怒所冲淡——这种感觉在经历了数月被他们形容为令人疲惫不堪的AI生成研究成果轰炸之后，已变得似曾相识。许多人迫切希望更广泛的学术界能尽快着手对这些发现进行艰苦的审查工作，并质疑OpenAI为何要通过手挑专家来监督流程，从而设置额外障碍。

接受《The Verge》采访的大多数专家对OpenAI咨询外部研究人员的决定表示欢迎，但认为公开发布成果也意味着应让任何人都有机会进行审查。“我对他们像今天这样发布问题没有意见，”高等研究院研究员Constantin Kogler说，他对此次仅发布三项成果而非此前数百项的规模表示欢迎。但他质疑限制只有OpenAI选定的专家才能查看的做法。“只有少数知名科学家能先看到这些结果，这毫无道理，”他说。

“他们手挑专家组这件事简直是一场灾难，”德克萨斯大学奥斯汀分校物理学教授Andreas Karch对《The Verge》表示。“大型实验室已经拥有足够的权力，他们不应该来挑选赢家和输家。”Karch最初建议通过“竞争性流程”选拔研究人员，不过他承认这很可能产生类似的专家组，他形容该群体为“一流的”。随后，他质疑OpenAI为什么“不能现在就发布证明”，同时再指定其专家团队。“这样既能实现访问的民主化，又能表明他们确实在意让结果变得有意义、可理解，”他说。

这正是数学与人工智能咨询小组（AGMAI）在公告前一天所警告的那种守门行为。该小组由OpenAI此前承诺就其成果分享方式征询意见的专家组成。“我们建议AI公司不要采用这种做法，”他们表示，认为选定某些人优先获取输出内容是“不必要的”，借用了被选研究人员的声誉，并存在访问不平等、选择偏见和延误等风险。相反，他们建议AI实验室在学术存储库上发布输出内容，附带形式化证明和问题选取方式的信息，并为从事理解这些成果工作的人员提供资金和其他支持。

在最新成果发布后，AGMAI敦促OpenAI“立即公开其宣布的”物理学成果背后的自然语言和Lean输出内容：“虽然这些是物理学的核心问题，但它们同样是数学的重要问题。量子杨-米尔斯理论的存在性是千禧年大奖难题的一部分，而在四维空间中构造相互作用量子场论一直是数学物理学数十年来的目标。我们非常尊重OpenAI邀请来领导这项工作的科学家。但正如我们在近期声明中所解释的，理解应当在公开环境中发展，让每个人都拥有同等的访问权。OpenAI曾表示推迟发布是因为Lean形式化尚未完成。据他们自己所说，现在形式化已经完成，因此没有任何科学理由继续推迟发布。”

Hassett表示，公开提供这些发现还可能促进更好的科学研究，并鼓励更广泛地采用AI等新工具。“让世界各地的许多团队共同攻克这些结果，有助于希望使用新工具的人快速采用它们，”他说。

找到一种能让OpenAI日益涉足领域的研究人员都满意的沟通方式，或许是不可能的。该公司表示正在尝试不同方法，并承认每种方法都有取舍。“我觉得无论采用哪种方式，总会有人不满意，”斯坦福大学数学家Jared Duker Lichtman说。“所以问题只是找到冒犯性最小的选项。”

佐治亚理工学院数学教授何晓宇认为，问题比OpenAI选择如何公布成果更为深层。“这些结果非常令人印象深刻，”他说，“但我认为他们是在做表面文章，试图表现出对发布结果方式的负责和深思熟虑，却无法改变核心问题：基础性成果正以极快的速度在没有人类贡献的情况下被解决，而且论文难以阅读和验证。”

中文翻译：

周一，OpenAI宣布在数学、计算机科学和物理学领域取得了令人瞩目的突破，并声称公司正在探索一种新的方式来对外沟通其研究成果。然而，尽管公司显然做出了方向性调整，接受《The Verge》采访的研究人员却对该公司的一系列做法愈发感到厌倦。

研究人员对OpenAI封锁其突破成果的做法越来越不满

“专家牧羊人”将决定如何最好地分享计算机科学和物理学的最新突破。

“专家牧羊人”将决定如何最好地分享计算机科学和物理学的最新突破。

过去几个月，OpenAI与数学家之间的关系颇为动荡，后者至今仍在艰难消化十月初被毫不客气地甩到他们面前的数百篇预印本论文。这一次，OpenAI表示正在与专家“牧羊人”合作，以决定如何最好地分享其最新突破。

三项成果均涉及数学、计算机科学和物理学中一些最重大的未解难题。部分成果延续了OpenAI此前的工作，另一些则开辟了全新领域。根据OpenAI“牧羊人”的建议，每项成果采用了不同的发布策略。对于数学成果，OpenAI发布了AI生成的输出内容，“让研究人员自由组织自己的工作来解释这一结果”。对于计算机科学发现，OpenAI也将组建一个外部研究小组“来解释该结果”。而物理学方面则完全未予发布，OpenAI表示将“听从”外部专家小组关于如何发布材料的意见。

——数学成果看起来令人震惊。它声称解决了所有维度下的挂谷猜想。粗略来说，该问题问的是：在空间中旋转一根针或一支铅笔使其指向各个方向，所需的最小空间是多少。三维版本的证明是纽约大学数学家王虹今年早些时候获得享有盛誉的菲尔兹奖的成果之一。布朗大学数学教授布伦丹·哈塞特对《The Verge》表示，OpenAI的主张“看起来有充分支持”，但他强调自己尚未检查Lean形式化验证——一种通过计算验证结果的手段。

——物理学成果探索了标准模型，这是物理学中最成功、最完整的理论框架。它涉及量子色动力学，该理论研究强核力——自然界四种基本力之一（与引力、电磁力和弱核力并列）——及其与杨-米尔斯理论的关系，后者是现代粒子物理学的基石。杨-米尔斯理论也是数学界七大千禧年大奖难题之一的核心，不过OpenAI的工作并未涉及尚未解决的“质量间隙”问题。

——最后一项成果声称否证了指数时间假说，这是理论计算机科学中一个被广泛接受的假设，即某些问题无法以显著快于暴力求解的方式解决。它与P versus NP密切相关——这是另一个千禧年大奖难题，也是该领域最大的未解问题之一，其核心问题是：那些解可以被轻易验证的问题，是否也同样容易求解。

鉴于披露程度各不相同，且评估此类工作的复杂性，研究人员很难全面评判该公司的发现。即便拥有全部信息，评判也需要时间。但接受《The Verge》采访的专家表示，有充分理由认真对待这些成果，他们分别用“巨大”、“重大突破”和“意义深远”来形容。

“一开始只有少数杰出科学家能看到这些成果，这毫无道理。”

但研究人员对科学本身的兴奋之情，被对OpenAI行为的恼怒所冲淡——在经历了数月他们所描述的令人疲惫的AI生成成果轰炸之后，这种感觉并不陌生。许多人渴望更广泛的学术界能够尽快开展审视该AI实验室成果的艰巨工作，并质疑OpenAI为何要通过精心挑选专家来监督流程，从而设置额外障碍。

《The Verge》采访的大多数专家对OpenAI咨询外部研究人员的决定表示欢迎，但认为公开发布结果也意味着应让任何人都能审视这些工作。“我对他们像今天这样发布问题是满意的，”高等研究院研究员康斯坦丁·科格勒说，他对此次发布三项成果——相比此前发布的数百项——更为可控表示欢迎。但他质疑限制只有OpenAI挑选的专家才能查看的决定。“一开始只有少数杰出科学家能看到这些成果，这毫无道理，”他说。

“他们亲手挑选专家组这件事简直是一场灾难，”德克萨斯大学奥斯汀分校物理学教授安德烈亚斯·卡奇对《The Verge》表示。“大型实验室已经拥有足够大的权力了。他们不应该来挑选赢家和输家。”卡奇起初建议通过“竞争性流程”来选择研究人员，不过他承认这可能会产生类似的小组——他称之为“一流的”。随后，他质疑OpenAI为何“不能现在就发布证明”，在任命专家组的同时也公开材料。“这将使获取渠道民主化，同时仍然表明他们关心让结果有意义且可理解，”他说。

“我的感觉是，无论采用哪种方式，总会有人不满意。”

这正是数学与人工智能咨询小组（AGMAI）在公告前一天所警告的那种封锁行为。该小组是OpenAI此前承诺就其成果分享方式征询意见的专家组。“我们建议AI公司不要采用这种做法，”他们表示，认为挑选特定人群优先获取输出结果是“不必要的”，借用了被选研究人员的声誉，并存在获取不平等、选择有偏见和延迟等风险。相反，他们建议AI实验室在学术存储库上发布输出结果，附带形式化证明和问题选择信息，并为致力于理解这些结果的人员提供资金和其他支持。

在最新成果发布后，AGMAI敦促OpenAI“毫不拖延地公开其宣布的”物理学成果背后的自然语言和Lean输出：

“虽然这些是物理学中的核心问题，但它们同样是数学中的重要问题。量子杨-米尔斯理论的存在性是千禧年大奖难题的一部分，而在四维空间中构造相互作用量子场论数十年来一直是数学物理学的目标。

我们对OpenAI邀请来领导这项工作的科学家深表敬意。但正如我们在近期声明中所解释的，理解应该在公开环境中发展，每个人都应获得同等的机会。OpenAI曾表示他们推迟发布是因为Lean形式化尚未完成。据他们自己所说，现在这已经完成了，因此没有任何科学理由继续推迟发布。”

哈塞特表示，公开提供这些发现还可能带来更好的科学，并鼓励更广泛地采用AI等新工具。“让世界各地的许多团队来钻研这些结果，有助于那些希望使用新工具的人快速采用它们，”他说。

“基础性成果正以如此之快的速度被解决，却几乎没有人类的贡献……这些论文难以阅读和验证。”

找到一种能让OpenAI日益涉足领域的研究人员都满意的沟通方式，或许是不可能的。该公司表示正在尝试不同方法，并承认每种方法都有取舍。“我的感觉是，无论采用哪种方式，总会有人不满意，”斯坦福大学数学家贾里德·杜克·利希特曼说。“所以问题只是找到最不令人反感的选项。”

佐治亚理工学院数学教授何晓宇认为，问题比OpenAI选择如何公布成果更为深层。“这些结果非常令人印象深刻，”他说，“但我认为他们是在做表面文章，试图表现得负责任、深思熟虑地发布结果，却无法改变核心问题：基础性成果正以如此之快的速度被解决，却几乎没有人类的贡献，而且这些论文难以阅读和验证。”

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英文来源：

On Monday, OpenAI announced impressive breakthroughs in mathematics, computer science, and physics, along with what the company says is an evolving approach to communicating its findings. But despite its apparent course correction, researchers who spoke to The Verge are growing increasingly tired of the company’s antics.
Researchers are getting tired of OpenAI gatekeeping its breakthroughs
Expert ‘shepherds’ will decide how best to share the latest breakthroughs in computer science and physics.
Expert ‘shepherds’ will decide how best to share the latest breakthroughs in computer science and physics.
It has been a turbulent few months between OpenAI and mathematicians, who are still grappling with the hundreds of preprints unceremoniously dumped on them at the start of October. This time, OpenAI says it is working with expert “shepherds” to decide how best to share its latest breakthroughs.
All three results concern some of the most consequential unsolved problems in mathematics, computer science, and physics. Some extend OpenAI’s previous work and others break new ground. Following advice from OpenAI’s “shepherds,” each has a different release strategy. For the mathematics result, OpenAI released the AI-generated output, “leaving researchers free to organize their own efforts to explain the result.” For its computer science finding, OpenAI will also assemble an external group of researchers “to explain the result.” Physics, meanwhile, has not been released at all, and OpenAI said it will “defer” to an external group of experts about how to release the materials.

• The mathematics result appears striking. It purports to resolve the Kakeya conjecture in all dimensions. Roughly speaking, the problem asks how little space is needed to rotate a needle or pencil in every direction. A proof of the three-dimensional version was among the achievements for which NYU mathematician Hong Wang was awarded a prestigious Fields Medal earlier this year. Brown University mathematics professor Brendan Hassett told The Verge OpenAI’s claim “looks well supported,” though he stressed he had not yet checked the Lean formalization, a means of computationally verifying results.
• The physics result explores the Standard Model, the most successful and complete theoretical framework in physics. It concerns quantum chromodynamics, which describes the strong nuclear force, one of nature’s four fundamental forces (alongside gravitational force, electromagnetic force, and weak nuclear force) and its relationship to Yang-Mills theory, a cornerstone of modern particle physics. Yang-Mills theory is also at the heart of one of the seven Millennium Prize problems in mathematics, though OpenAI’s work does not address the unresolved “mass gap.”
• The final result claims to disprove the exponential time hypothesis, a widely held assumption in theoretical computer science that certain problems cannot be solved substantially faster than brute-force approaches. It is closely related to P versus NP, another Millennium Prize problem and one of the field’s biggest unsolved problems, which asks whether problems whose solutions can be easily checked are also easy to solve.
  Given the varying levels of disclosure and the complexity of evaluating such work, it is difficult for researchers to fully judge the company’s findings. Even with all of the information, doing so would take time. But experts who spoke to The Verge said there are good reasons to take the results seriously, variously describing them as “huge,” “major breakthroughs,” and “significant.”
  “It doesn’t make any sense that first only a few distinguished scientists can look at them.”
  But researchers’ excitement about the science was tempered by exasperation with OpenAI’s conduct, a familiar feeling after months of what they describe as an exhausting barrage of AI-generated findings. Many were eager for the wider community to get on with the arduous task of scrutinizing the AI lab’s findings and questioned why OpenAI was putting up additional barriers by handpicking experts to oversee the process.
  Most experts The Verge spoke to welcomed OpenAI’s decision to consult outside researchers, but argued that publicly announcing results should also mean making that work available for anyone to scrutinize. “I am fine with them releasing the problems as they did today,” said Constantin Kogler, a researcher at the Institute for Advanced Study, welcoming the more manageable batch of three results compared with the hundreds released previously. But he questioned the decision to restrict access to experts of OpenAI’s choosing. “It doesn’t make any sense that first only a few distinguished scientists can look at them,” he said.
  “The fact that they handpick the group is a disaster,” Andreas Karch, a physics professor at the University of Texas at Austin, told The Verge. “The large labs have enough power as it is. They should not be picking winners and losers.” Karch initially suggested selecting researchers through a “competitive process,” though he acknowledged it would likely produce a similar group, which he described as “first rate.” Later, he questioned why OpenAI “can’t release the proof now,” in addition to appointing its team of experts. “This would democratize access while still showing that they care about making the results meaningful and understandable,” he said.
  “My sense is that for any approach, there is going to be someone who is upset.”
  It is exactly the kind of gatekeeping the Advisory Group on Mathematics and Artificial Intelligence (AGMAI), a group of experts OpenAI had previously pledged to consult on how to share its findings, warned against just a day before the announcement. “We recommend that AI companies do not adopt this practice,” they said, arguing that selecting certain people to receive the output first is “unnecessary,” borrows the reputation of selected researchers, and risks unequal access, biased selection, and delays, among other issues. Instead, they recommended that AI labs release output on scholarly repositories, with formalized proofs and information on how the problems were chosen, and provide funding and other support for people working on understanding the results.
  Following the publication of the latest results, AGMAI urged OpenAI to “make public without delay the natural-language and Lean outputs behind its announced” results in physics:
  ”While these are central questions in physics, they are also important questions in mathematics. The existence of quantum Yang–Mills theory is part of a Millennium Prize problem and the construction of interacting quantum field theories in four dimensions has been a goal of mathematical physics for decades.
  We have great respect for the scientists asked by OpenAI to lead this work. But as we explained in our recent statement, understanding should develop in the open, with everyone given the same access. OpenAI have said they delayed release until the Lean formalization was complete. By their own account this is now complete, so there is no scientific reason for delaying the release any further.”
  Making the findings openly available could also lead to better science and encourage wider adoption of new tools like AI, Hassett said. “Having many groups around the world wrestle with these results supports rapid adoption of new tools by those that would like to use them,” he said.
  ”Fundamental results are being solved so quickly without human contribution ... the papers are difficult to read and verify.”
  Finding a way to communicate with researchers in the fields OpenAI is increasingly encroaching on that satisfies everyone may be impossible. The company says it is experimenting with different approaches and acknowledges that each has tradeoffs. “My sense is that for any approach, there is going to be someone who is upset,” said Stanford mathematician Jared Duker Lichtman. “So it’s just a question of finding the least offensive option.”
  Xiaoyu He, a mathematics professor at the Georgia Institute of Technology, thinks the problem is deeper than how OpenAI chooses to announce its findings. ”The results are very impressive,” he said, “but I think they are making a show of trying to be responsible and thoughtful about how they release results while not being able to change the main issues: that fundamental results are being solved so quickly without human contribution, and that the papers are difficult to read and verify.”
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