周一，OpenAI 宣布在数学，计算机科学，物理学方面取得了令人印象深刻的突破，同时，称公司也是不断演变的传播其发现的方法。 但是，尽管它明显地纠正了路线，但与The Verge交谈的研究人员越来越厌倦了公司的怪癖。
研究人员已经厌倦了OpenAI守门术
专家“shepherds”将决定如何最好地分享计算机科学和物理学的最新突破。
OpenAI和数学家之间已经发生了几个月的动荡，他们仍在为10月初无礼地向他们丢弃的数百种预印进行斗争。 这一次，OpenAI 称正与专家“shepherds”合作，决定如何最好地分享最新突破。
这三个结果都涉及数学、计算机科学和物理学中一些最严重的未解决的问题。 有些人延长了OpenAI以前的工作，另一些则打破了新的局面。 依据OpenAI的"shepherds"的建议，对于数学结果，AI产生的输出OpenAI 发布“让研究人员自由组织自己的努力解释结果。” OpenAI还将召集一组外部研究人员“解释结果”。 与此同时，物理学根本不是发布，OpenAI 称将“推迟”到外部专家组处理如何释放这些材料的问题。
- 数学结果很惊人它旨在解决卡基亚猜想的所有方面。 大致来说，问题在于每个方向旋转一根针或铅笔需要多少空间。 三维版本的证明是纽约大学数学家洪王今年早些时候获得著名菲尔兹奖章的成就之一。 Brown大学数学教授Brendan Hassett告诉The Verge OpenAI的说法 " 看起来支持得很好 "，尽管他强调他还没有检查Lean形式化，这是一种计算核实结果的手段。
- 物理结果探索标准模型，是物理学中最成功和最完整的理论框架。 它涉及量子染色体动力学，它描述了强核力，是自然界的四种基本力之一(与引力，电磁力，弱核力并列)及其与现代粒子物理学的基石杨米尔斯理论的关系。 杨米尔斯理论也是数学领域七个千年奖问题之一的核心，尽管OpenAI的作品并没有解决尚未解决的“质量差距 ”。
- 最终结果声称推翻了指数时间假说，这是理论计算机科学中广泛持有的假设，即某些问题不能大大快于野蛮力量的逼近。 它与P对NP密切相关，后者是另一个千年奖问题，也是外地最大的未解决的问题之一，它询问可以轻易检查的问题是否也容易解决。
鉴于披露的程度不同，评估这类工作的复杂性也不同，研究人员很难充分判断公司的调查结果。 即使有了所有的信息，这样做也需要时间。 但是，与The Verge 称交谈的专家有充分理由认真对待这些结果，他们把结果描述为“困难”、“重大突破”和“重大突破 ”。
“首先只有少数杰出的科学家可以看他们，这没有任何意义。”
OpenAI的行为令人叹为观止，许多人热切希望广大社区能够完成审查人工智能实验室调查结果的艰巨任务，并质疑OpenAI为何通过手选专家来监督这一进程而设置更多障碍。
大多数专家 Verge表示，欢迎OpenAI决定与外部研究人员协商，但认为公开宣布结果也意味着让任何人都可以仔细审查这项工作。 称 Constantin Kogler是高级研究所的研究员，欢迎与先前的数百个发布相比，但他质疑限制接触OpenAI所选择的专家的决定。 称认为
得克萨斯大学奥斯汀分校的物理学教授Andreas Karch告诉The Verge, “大型实验室有足够的力量。 他们不应该挑赢家和输家。” 卡奇最初建议通过“竞争过程”挑选研究人员，尽管他承认这很可能会产生类似的群体，他称之为“第一率 ”。 后来，他质疑为什么OpenAI除了任命其专家组之外，还 " 现在不能公布证据 "。 称表示：「这样可以实现准入民主化
“我的感觉是，对于任何方法，都会有人心烦意乱。”
这正是数学和人工智能咨询小组(AGMAI)的守门方式，一个专家组OpenAI曾承诺就如何分享其调查结果进行协商，并警告不要在宣布前一天就公布。 “我们建议大赦国际公司不要采用这种做法，”他们认为，选择某些人首先接受产出是“不必要的”，借用选定的研究人员的声誉，并有可能出现不平等、有偏见的选择和拖延等问题。 相反，他们建议人工智能实验室公布学术储存库的产出，提供正式的证明和如何选择问题的信息，并为努力了解结果的人提供资金和其他支持。
在公布最新结果后，AGMAI敦促OpenAI“毫不拖延地公开其宣布结果背后的自然语言和精益效应：
“虽然这些都是物理学的中心问题，但它们也是数学中的重要问题。 量子杨-米尔斯理论的存在是千年奖问题的一部分，构建四个维度的相互作用量子场理论几十年来一直是数学物理学的目标。
我们非常尊重OpenAI要求领导这项工作的科学家。 但是，正如我们在最近的发言中所解释的那样，理解应当公开发展，每个人都有同样的机会。 OpenAI有称，他们推迟释放直到精益正规化完成。 根据他们自己的说法，这项工作现已完成，因此没有任何科学理由进一步拖延释放。”
公开提供研究结果也可以导致更好的科学，并鼓励更广泛地采用诸如AI,Hassett 称等新工具。 称先生说，“世界各地许多团体努力取得这些成果，有助于希望使用这些工具的人迅速采用新的工具。
“没有人类的贡献，基本成果正在如此迅速地得到解决。.....这些论文很难阅读和核实。”
寻找与OpenAI领域研究人员沟通的方法，正在日益侵犯着让每个人满意可能是不可能的。 称公司正在试验不同的方法，承认每个方法都有权衡。 称斯坦福数学家Jared Duker Lichtman。 “所以这只是一个寻找最不冒犯的选择的问题。
晓玉他作为佐治亚理工学院的数学教授，认为问题比OpenAI如何选择公布其调查结果更深。 “结果令人印象深刻，”他称说，“但我认为，他们正在展示他们如何负责任和深思熟虑地释放结果，而同时又不能改变主要问题：根本结果是在没有人类贡献的情况下如此迅速地解决的，而文件是难以阅读和核实的。”
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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.
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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