OpenAI didn't solve it, they stole it and resorted to brute force
openai
| Source: Mastodon | Original article
OpenAI did not solve a Millennium math problem; it is accused of appropriating a niche research path from human mathematicians and relying on brute‑force techniques.
OpenAI’s claim of solving a Clay Institute Millennium Prize problem has been met with a sharp rebuttal from the researchers whose work it allegedly appropriated. Tristan Buckmaster and a colleague, who were using OpenAI’s models to explore a niche approach to the problem, say the company’s reported solution follows the exact research path they were pursuing and relies on unpublished results they had not shared publicly.
OpenAI’s own post, published on September 1, acknowledges that the effort was sparked by rumors of a breakthrough on the prize problem, but it does not address the allegation that the AI system was fed the team’s private data. The mathematicians’ accusations have been echoed in a series of recent reports, which describe the episode as “unethical practice” and a “dirty fight” over credit for a potential $1 million discovery.
The dispute matters because it touches on the broader question of how AI systems can be trained on proprietary or unpublished scientific material. If an AI can reproduce a breakthrough by mining confidential research, the line between assistance and theft becomes blurred, raising concerns for academic institutions, funding bodies and the companies that develop large‑scale models. The controversy also revives earlier tensions between OpenAI and the mathematics community, highlighted in our September 12 coverage of OpenAI’s fallout with mathematicians over sponsorship of Caltech’s “Mathathon.”
Going forward, observers will watch for an official response from OpenAI, possible investigations by the Clay Institute, and any legal or policy actions that could set precedents for AI‑driven research. The episode may also prompt universities and research groups to reassess data‑sharing practices with AI providers, and could influence how future AI contributions to fundamental science are credited and regulated.
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