Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we meet AI pioneer Ken Goldberg, use AI techniques to track animal populations, investigate how to improve recommender systems, and find out how external knowledge is used in AI systems. In the latest instalment in our AI pioneers series, we spoke to award-winning roboticist, filmmaker, and artist, Ken Goldberg . We discussed the culture clash within robotics, what art and science have to learn from each other, and how AI will shape the future of art. Deep learning is a powerful tool for understanding animal behaviour and tracking populations.

Powerful AI models have created an existential risk to the field, but researchers can't stop relying on them because they're too useful. Mathematician Tristan Buckmaster believes OpenAI used his work to rush ahead and beat him to solving a legendary math problem with a $1 million bounty. But that's not been enough for him to stop using the company's models--and he's not the only mathematician that feels that way. "Even if you don't agree with any of this, you're kind of stuck. With AI being so useful, it's hard to completely prevent oneself from using it," Buckmaster tells WIRED.

AI just solved one of the world's hardest math problems. In late July, one of the world's most famous mathematicians issued a warning to his field. Math is entering a "turbulent period," the UCLA professor Terence Tao said in front of a packed lecture hall at the International Congress of Mathematicians. AI is getting so good at math, he said, that it's causing a "crisis in our mathematical values and practices." Earlier in the same week that Tao gave his talk at math's most prestigious conference, a researcher at Anthropic had used Claude to disprove an 87-year-old math conjecture.

Steven Strogatz coauthored a book about how math is moving beyond human understanding. He spoke with WIRED about the seismic impact artificial intelligence has had on his life's work. Mathematician and author Steven Strogatz starts to cry when he talks about the artificial-intelligence -driven breakthroughs in his field over the past week. "The science is thrilling," the Cornell University professor says, "but there's a lot of human unpleasantness going with it." On Tuesday, OpenAI said it had used tens of thousands of agents to solve a 90-year-old math problem, which had a $1 million prize attached.

For many mathematicians, it is the weeks, months and years spent circling a problem that appeals. For many mathematicians, it is the weeks, months and years spent circling a problem that appeals. I t was a week that left mathematicians reeling. Hot on the heels of a flurry of cases of artificial intelligence furthering the field, OpenAI declared a major scalp: its latest AI model had cracked a Millennium Prize Problem, a puzzle with a $1m reward that had defied human brains for decades. The achievement bore little resemblance to how mathematical problems normally fall.

Plus: The US has accused six Chinese AI firms of "industrial-scale" theft. What OpenAI's latest controversy tells us about the future of math OpenAI says its agents have solved one of the most important open problems in mathematics. Under normal circumstances, that would be a huge milestone. But the announcement has been overshadowed by accusations that OpenAI failed to credit researchers whose AI-assisted work influenced its solution. Whether those accusations are true or not, the episode may mark a turning point in the history of mathematics. AI models now seem essential for making progress on the field's most important problems, but solving them may demand resources available only to a couple of frontier AI companies.

OpenAI's latest mathematical milestone has quickly become mired in controversy. Today, the company announced that its agents have solved one of the Millennium Prize Problems, some of the most important open problems in mathematics. Under normal circumstances, that solution would be a huge feather in OpenAI's cap. But the announcement has been overshadowed by accusations that OpenAI used NYU mathematician Tristan Buckmaster's and Anthropic employee Levent Alpöge's AI-assisted work on the problem as a jumping-off point and failed to credit them. OpenAI has denied the accusations. It remains uncertain if OpenAI's models made use of the work completed by Buckmaster and Alpöge, though Sébastien Bubeck, a member of the technical staff at OpenAI, said in a press briefing that the team was inspired to pursue the problem after hearing a rumor about Buckmaster and Alpöge's efforts.

Trending Now Say More Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Switch Off Creator Playbook Mashable Voices Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List In My Bag All Series OpenAI provided an update about the release of Astra, and what precautions it's taking. Timothy Beck Werth is the Tech Editor at Mashable, where he leads coverage and assignments for the Tech and Shopping verticals. Tim has over 15 years of experience as a journalist and editor, and he has particular experience covering and testing consumer technology, smart home gadgets, and men's grooming and style products. Previously, he was the Managing Editor and then Site Director of SPY.com, a men's product review and lifestyle website. As a writer for GQ, he covered everything from bull-riding competitions to the best Legos for adults, and he's also contributed to publications such as The Daily Beast, Gear Patrol, and The Awl. Do you understand quantum parallel repetition?

What happens when AI runs out of pictures? A hospital may only ever collect a few dozen scans of a rare condition - for example, an unusual tumour. The radiology department wants software to flag this on a scan - not to replace the specialist, but so a hospital without one still gets their scan checked the same way. The clinicians know what they're looking for. Over a decade, the hospital might gather 40 confirmed cases.

AIhub monthly digest: August 2026 - IJCAI-ECAI in Bremen, the mathematics of simplicity, and does AI change the way we think? Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we report on events at IJCAI-ECAI 2026, learn about the mathematics of simplicity, investigate the accountability vacuum, and find out how AI changes the way we think. On Rashomon sets, the mathematics of simplicity, and why we don't need black boxes: an interview with Cynthia Rudin In the latest in our series of interviews with AI pioneers, we hear from Cynthia Rudin about interpretability, noise, and the case against complexity for complexity's sake. The 35th International Joint Conference on Artificial Intelligence and the 29th European Conference on Artificial Intelligence (IJACI-ECAI 2026) was held from 15-21 August, in Bremen, Germany.