An anonymous reader quotes a report from The New York Times: OpenAIsaid on Tuesday that its newest artificial intelligence technology had solved one of the "Millennium Problems," a collection of important unanswered math questions meant to push the world's leading mathematicians to new heights. The announcement is an another clear sign that A.I. is fundamentally changing the upper reaches of mathematics, which have long been viewed as a pinnacle of human achievement. The change has excited some mathematicians, while stirring concern among others. Over the past year, A.I. systems successfully solved a wide range of problems that have bedeviled mathematicians for decades. But these problems were not as complex, nor as closely watched, as the one that OpenAI's technology has solved over the past several days. The Millennium Problems are among the most heavily researched in the field. "This is a spectacular culmination of the arc we have seen over the past twelve months," OpenAI researcher Sebastien Bubeck said of the company's new solution. The company announced that one of its latest models, which has not yet been released to the public, needed just 88 hours to solve what mathematicians call "the Navier-Stokes existence and smoothness problem." This problem involves a series of equations that are often used to predict the weather. The equations describe the movement of water and other fluids. The Navier-Stokes problem, which has no clear practical value, asks whether these equations completely break down in certain situations. OpenAI's proof claims to have defined just such a situation. This would imply, at least theoretically, that the laws of physics themselves would break down under certain conditions: that, for example, water could be made to spontaneously explode. But mathematicians and physicists do not believe that this mathematical breakdown could really lead to such an outcome in the physical world. The Navier-Stokes problem was one of seven "Millennium Problems" selected by the Clay Mathematics Institute in the year 2000 as a way of tracking the progress of mathematics in the new millennium. The institute, founded by an American businessman named Landon T. Clay, offered a million dollars for the first correct solution to each problem. Before OpenAI's announcement, only one of the problems had been solved. OpenAI says it managed to solve the Navier-Stokes problem by coordinating as many as 10,000 AI agents, an extremely expensive process that may have cost millions of dollars in computing power. For those curious about the findings, the company has released a paper (PDF) and formal Lean proof.

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Sam Altman claims that producing a single almond in California uses more water than 38,000 ChatGPT queries. Is this comparison actually accurate? How would you calculate the water usage of both, considering an average ChatGPT task/query (text only, no image generation)? submitted by /u/Focus_Infinity to r/theydidthemath [link] [comments]

submitted by /u/Confident-Insect-200 to r/theydidthemath [link] [comments]

Scientific American reports that the 2026 Fields Medals went to four mathematicians for work ranging from the theory of knots to the motion of fluid: The Fields Medals went to Hong Wang of New York University and France's Institute of Advanced Scientific Studies (IHES), Yu Deng of the University of Chicago, John Pardon of Stony Brook University and Jacob Tsimerman of the University of Toronto. In the awards' 90-year history, Wang is only the third woman to win one, after mathematicians Maryam Mirzakhani and Maryna Viazovska in 2014 and 2022, respectively. Wang and Deng represent the prizes' only Chinese-born recipients besides mathematician Shing-Tung Yau, who won a Fields Medal in 1982. Hong Wang co-proved the three-dimensional Kakeya conjecture, establishing a fundamental limit on how little space is needed to rotate a line through every possible direction. Mathematician Nets Katz called it the field's "holy grail" problem and said the achievement made her "a central figure" in the area. Yu Deng and his collaborators reconciled the microscopic and macroscopic mathematics of fluid motion, proving that equations describing chaotic molecular interactions and large-scale fluid behavior are fundamentally connected. N.Y.U. mathematician Scott Armstrong called it "a truly spectacular, singular result." John Pardon made an early breakthrough in knot theory by proving that certain sequences of knots can have arbitrarily large "distortion," a measure of how difficult they are to traverse. Princeton mathematician David Gabai said the problem had "attracted much interest among mathematicians during the previous 25 years." Jacob Tsimerman and two collaborators proved the Andre-Oort conjecture, giving mathematicians a stronger way to understand special points on complex geometric objects known as Shimura varieties. Collaborator Jonathan Pila described him as "a brilliant mathematician" known for his "brilliance and resourcefulness." Tsimerman has also advanced Hodge theory and hopes pure mathematics can help researchers better understand AI.

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