Proof: Did AI really crack a math ‘Holy Grail’?

Mathematicians say OpenAI is just ripping off unpublished work

Buckmaster says the model ‘ripped off’ his work.
Buckmaster says the model ‘ripped off’ his work.
(Image credit: Graham Dickie/The New York Times/Redux)

“Artificial intelligence is suddenly insanely good at math,” said Ben Cohen in The Wall Street Journal, which may be the best proof yet of the technology’s “accelerating and terrifying progress.” A few years ago, AI struggled with even basic math problems. But last month, OpenAI announced that one of its models had solved “the wicked Navier-Stokes problem that had vexed mathematicians for nearly a century.” Navier-Stokes was selected in 2000 as one of math’s seven unsolved “Holy Grail” problems, which Fields Medal– winning mathematician Alain Connes said at the time were “totally inaccessible to computers.” But a swarm of 10,000 OpenAI agents was able to produce an answer in only 88 hours. The company now says it is nearing proof of another one. In doing so, it is demonstrating “how the technology is getting much smarter, much faster than most people anticipated.”

Mathematicians are crying foul, said Zeynep Tufekci in The New York Times. Tristan Buckmaster and Levent Alpöge, who “had been trying for months” to crack the problem, say the new proof “ripped off their unpublished work, which also used an OpenAI model.” OpenAI denies it, but would the company “even be able to tell?” AI agents are “cheating, hacking, attacking, and manipulating whatever they can to arrive” at task solutions. The chilling effect this will have on academia is troubling. Why would mathematicians present at conferences or publish early work if an AI company can swoop in and “steal the thunder?” The issue is not just about plagiarism, said Sophia Chen in Wired. It’s how OpenAI did it. The company “approached the proof with brute force.” Mathematicians, by contrast, “aren’t focused on getting the right answer as fast as possible” but on applying a “thoughtful, deliberate” methodology that other scientists can follow. AI provides a “shortcut” in a way that undermines human understanding.

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