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.
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.”