OpenAI’s feud with mathematicians is only escalating

摘要: Twenty-five leading mathematicians

signed an open letter

arguing that AI labs are threatening their intellectual work as they seek to one-up each ot

正文

Twenty-five leading mathematicians

signed an open letter

arguing that AI labs are threatening their intellectual work as they seek to one-up each other with solutions to famous math problems. Each signatory has been awarded the Fields Medal, considered the most prestigious prize in mathematics.

This week, NYU professor Tristan Buckmaster

OpenAI of pressuring him not to credit a collaborator who works for Anthropic for solving an important math problem, and wondered if the company had used their work with Codex to produce its own

groundbreaking proof

over a marathon weekend of inference.

On Thursday, OpenAI

withdrew

its sponsorship of a math event at CalTech after the company was criticized by researchers at the university.

While the ability of AI models to solve the world’s outstanding mathematical challenges could be a boon to humanity, the signatories of the new letter argue that will only be the case if those solutions can be understood and communicated by the math community and, ultimately, the rest of the world.

“Often these solutions are announced in a rush, leaving no time for a proper writeup, the isolation of new methods and ideas, and citing relevant previous work of others,” they wrote — and OpenAI’s proof remains unverified. “As in all creative professions, this raises severe attribution and plagiarism questions. Moreover, without the willing mathematicians who must take care of their development and integration into the mathematical canon, AI-conceived ideas would never become fully alive and the crucial human transmission chain between mathematicians would be lost.”

With other mathematicians growing paranoid and wondering if their Codex use was in turn fed into OpenAI’s new models, there is real fear that the culture of open research will be threatened. Today, if frontier labs see a useful path to a discovery, they can spend tens of millions of dollars using LLMs to beat the original researchers to a proof — a dynamic that will inc

来源: TechCrunch AI