A million dollar math problem may just be the beginning for OpenAI

OpenAI claims to have solved a million-dollar math problem. Despite some controversy, this may be just the beginning. (Bhawika Chhabra/Reuters)Social SharingTwo mathematicians, 10,000 AI agents and a million-dollar prize are all part of yet more AI drama this week, with heated back-and-forth over allegations of stolen work and academic malpractice.

But despite the controversy, some experts say it's indicative of the unmistakable force of AI in helping break through sticky problems. To others, it misses the point of academic research and careful human collaboration. 

So what happened in the world of pure mathematics this week and why is it controversial? 

On Tuesday, OpenAI said it had solved a legendary problem in mathematics involving the Navier-Stokes equations. 

Easier said than explained, the equations describe how fluids — including gases and liquids — move and change over time. As equations do, they give us a way to try to predict what will happen if you plug in the variables. The issue — and why this is one of the famed Millennium Prize Problems that come with a million-dollar award if a solution is verified — is not knowing exactly whether the math can blow up and produce physically impossible results. 

The Navier-Stokes equations deal with predicting the behaviour of fluids. (Ben Margot/AP)"It's a very deep mystery," said Ravi Vakil, mathematics professor at Stanford University. "It seems this huge thing that's out of reach, like this mountain you see in the distance, and the question is: can we get there?"

Vakil says it's a way to understand the universe and whether it works the way we think it does. The problem with Navier-Stokes is kind of like saying you can know everything about your foot, your shoe and a ball — but there's no ruling out you can kick that ball into space. 

So "resolving" the problem is about figuring out whether this breakdown can happen. OpenAI says yes: in a certain circumstance, you can get a bonkers, physics-violating result — at least, on paper. 

Moreover, the task "involved on the order of 10,000 concurrent agents" — AI that can perform tasks independently and doesn't need to be prompted constantly — that "had access to tools such as the ability to read from a cached version of the internet and the ability to run code." 

OpenAI says this 3D model of a vortex visually describes how the equations can lead to a 'breakdown.' (OpenAI)Within 88 hours, the company said, its agents had found the solution to this problem, which has roots going back more than 200 years. It's worth mentioning that while this problem is a curiosity, the equations are used for practical applications including the design of airplanes, artificial heart valves and climate modelling.

Vakil isn't surprised the company was able to achieve it, but is still impressed, though he and other experts point out that the claim now needs to be rigorously verified.  

"No one is yet ready to fully vouch for all the details because that requires human time," Vakil, who is originally from Toronto, told CBC News from Stanford, California. 

"It passes machine checks, but we know better than to believe those." 

The controversy began before OpenAI announced its findings and involved two other mathematicians, Tristan Buckmaster of New York University and Levent Alpöge, who works for OpenAI competitor Anthropic. 

Among other things, Buckmaster — who used AI tools such as Anthropic's Claude and OpenAI's Codex — suggested OpenAI learned the pair were close to a solution and pursued it themselves. He alleged OpenAI tried to offer him credit so long as Alpöge's name wasn't on it because he works for a competing AI company. 

At the time, he said he didn't get a response when he asked if OpenAI's agents had been able to see his own use of its Codex tool before coming up with their solution. 

OpenAI acknowledges pursuing the solution after hearing rumours of a potential solution. But it denies its internal system of agents learned from their work, saying after an investigation, it "confirmed that Buckmaster's Codex prompts … could not have influenced the system in any way, including through training." 

To Davide Gaiotto, who specializes in theoretical and mathematical physics at the Perimeter Institute in Waterloo, Ont., it's the modern version of an age-old problem. He uses an example of a professional seminar where you hear about upcoming research. 

"Normally, this would require you to be so fast and capable that you can see what they told you at the seminar and use it to do the final step," Gaiotto said. But now, it may soon be possible to use AI tools to leap faster and farther off those giants' shoulders, so to speak. 

"Because if you have enough money, you can compress months of work into a few hours," Gaiotto told CBC News. 

It's worth noting that this Navier-Stokes work does not exist in a vacuum, and that Buckmaster credits Spanish researchers Diego Córdoba from the Institute for Mathematical Sciences and CUNEF University's Luis Martínez-Zoroa for work on this approach.

The drama, Vakil says, is unfortunate and overshadows the real human accomplishment. 

"These are things that people have been chasing for a hundred years that we didn't think would be within reach," Vakil said, "And it's this sequence of people assisted by technology that made something new happen." 

Buckmaster, in his Sept. 7 statement, said his original intent was to show "the significance that a mathematician and an LLM model can now do all this work in a month." 

Vakil agrees that these tools are changing the game. 

"I do believe that it's clear we're going through a massive transition in how we do science. And we are among the first mathematicians."

Not everyone is happy with that transition. At least 25 winners of the Fields Medal, one of mathematics' most prestigious awards, signed an open letter saying that AI is "misaligned" with the goals of the mathematical community and a "general threat to intellectual work." 

Terence Tao, seen here in November 2014, is among the notable academics calling for better solutions around AI and mathematics. (Steve Jennings/Getty Images)But the furor over the tools to help answer deep questions perhaps misses a point made by Fields Medal-winning UCLA mathematician Terence Tao: Pure math is driven by curiosity. 

In recent social posts, Tao talked about the greater value of not just solving the problem, but seeking what could be learned by studying it. He likens it to kids trying to one-up each other by playing 'who can name the largest number?' The process of exploration is more valuable than the answer. 

"In this modern era of heavy AI use, it is important to remember what it is like to be a child," Tao wrote.  

Anand Ram is a reporter and producer with CBC's science and climate unit. He's worked as a reporter covering technology, business and the environment and as a producer with The National.