The world’s largest gathering of mathematicians convened in Seattle from Jan. 8 to Jan. 11 — 5,444 mathematicians, 3,272 talks. This year the program diverged somewhat from the its traditional kaleidoscopic panorama. An official theme, “Mathematics in the Age of A.I.,” was set by Bryna Kra, the president of the American Mathematical Society, which hosts the event in collaboration with 16 partner organizations. In one configuration or another, the meeting, called the Joint Mathematics Meetings, or the J.M.M., has been held more or less annually for over a century.

Dr. Kra intended the A.I. theme as a “wake-up call.” “A.I. is something that is in our lives, and it’s time to start thinking about how it impacts your teaching, your students, your research,” she said in an interview with The New York Times. “What does it mean to have A.I. as a co-author? These are the kinds of questions that we have to grapple with.”

On the second evening, Yann LeCun, the chief A.I. scientist at Meta, gave a keynote lecture titled “Mathematical Obstacles on the Way to Human-Level A.I.” Dr. LeCun got a bit into the technical weeds, but there were digestible tidbits.

“The current state of machine learning is that it sucks,” he said during the lecture, to much chortling. “Never mind humans, never mind trying to reproduce mathematicians or scientists; we can’t even reproduce what a cat can do.”

Instead of the generative large language models powering chatbots, he argued, a “large-scale world model” would be the better bet for advancing and improving the technology. Such a system, he said in an interview after the lecture, “can reason and plan because it has a mental model of the world that predicts consequences of its action.” But there are obstacles, he admitted — some mathematically intractable problems, their solutions nowhere in sight.