Open AI:s vd Sam Altman. (Shutterstock)

Forskarbråk om Open AI:s mattegenombrott: ”Vet inte om vår data användes”

Ett bråk har blossat upp kring hur Open AI kom fram till den lösning som bolaget presenterat på ett känt matematikproblem, rapporterar The Guardian.

Matematikprofessorn Tristan Buckmaster vid New York University säger att Open AI skalat upp arbetet med att lösa det så kallade Navier-Stokes-problemet efter att ha fått höra att han och en forskare på konkurrenten Anthropic var nära ett eget genombrott.

Buckmaster pekar också på att deras pågående arbete var lagrat i Open AI:s kodverktyg Codex och därför kan ha varit tillgängligt för bolaget.

”Jag vet inte vad deras modell gjorde, eller hur. Jag vet inte om våra data användes. Jag anklagar ingen för någonting”, skriver Buckmaster i ett dokument.

Open AI förnekar att forskarnas arbete har använts, men säger att det inte kan uteslutas att deras data bidragit till att förbättra Open AI:s tjänster.

Navier-Stokes – ett av de så kallade millennieproblemen

Millennieproblemen är sju särskilt svårlösta matematiska problem som valdes ut av Clay Mathematics Institute år 2000. En miljon dollar utlovades för lösningen på vart och ett.

Fram tills nu har bara ett av problemen fått en allmänt erkänd lösning: Poincarés förmodan, som löstes av den ryske matematikern Grigorij Perelman.

bakgrund
 
Navier–Stokes-ekvationen
Wikipedia (en)
The Navier–Stokes equations ( nav-YAY STOHKS) describe the motion of viscous fluids. This system of partial differential equations was named after Claude-Louis Navier and George Gabriel Stokes, who developed them over a few decades of progressive work, from 1822 (Navier) to 1842–1850 (Stokes). Siméon Denis Poisson independently achieved the same results. The Navier–Stokes equations mathematically express momentum balance for Newtonian fluids and make use of the conservation of mass. They are sometimes accompanied by an equation of state relating pressure, temperature and density. They arise from applying Newton's second law to fluid motion, together with the assumption that the stress in the fluid is the sum of a diffusing viscous term (proportional to the gradient of velocity) and a pressure term—hence describing viscous flow. The Navier–Stokes equations generalize the Euler equations in that the latter model only considers inviscid flow. The Navier–Stokes equations are of great scientific and engineering interest because they may be used to model a wide variety of scenarios. In their full or simplified forms, they can assist in the design of aircraft and cars, the study of blood flow, the design of power stations, the analysis of pollution, and many other problems. Coupled with Maxwell's equations, they comprise the fundamentals of magnetohydrodynamics. The Navier–Stokes equations are also of great interest to pure mathematics. The Navier–Stokes existence and smoothness problem concerns whether they have smooth (meaning infinitely differentiable) or bounded solutions in three dimensions, as opposed to a breakdown of solutions. This is one of seven Millennium Prize Problems, notable open mathematics problems for which the Clay Mathematics Institute offered $1 million prizes in 2000 for correct solutions. On 8 September 2026, artificial intelligence company OpenAI announced a proof of a breakdown of Navier–Stokes solutions in three-dimensional Euclidean space, developed by its researchers using as many as 10,000 coordinated agents running an internal frontier model, along with a formalization in the Lean proof assistant. The claim has not been verified by external mathematicians or the Clay Mathematics Institute, while OpenAI stated it would not claim the Millennium Prize. The announcement was accompanied by a priority dispute with Levent Alpöge (employed at rival AI company Anthropic) and Tristan Buckmaster, who had derived a set of closely related results on the Euler equations. The method used to generate the claimed solution built upon a method developed by Diego Cordoba and Luis Martinez Zoroa in 2023 to prove blowup phenomena in related fluid equations.
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