
In a chaotic few months, OpenAI has demonstrated it can do two things with remarkable consistency: make impressive breakthroughs in mathematics, then colossally screw up announcing them. OpenAI is now trying to do better. Somehow, it has botched that too. OpenAI's latest attempt to repair fractured relations with a mathematical community it has repeatedly alienated is to consult a new independent advisory group of elite practitioners. But mathematicians speaking to The Verge,
An AI company has created an independent advisory group of elite mathematicians to help coordinate the announcement of numerous mathematical breakthroughs its unreleased model has reportedly produced. This matters because the company has previously bungled the announcement of such breakthroughs, creating confusion over credit and treatment of mathematicians, so the advisory group is meant to help handle future releases more responsibly. The group's formation itself has been confusing, with many mathematicians initially uncertain whether it was truly independent or company-appointed, and one advisory member acknowledged that the company's promotional announcement did little to establish the group's credibility. The underlying tension is that the company has promised over 100 solved mathematical problems waiting to be released, which researchers worry could disrupt their field if not handled with proper academic rigor and engagement with existing literature.

Deep Blue took down Garry Kasparov at chess in 1997, AlphaGo beat Lee Sedol at Go in 2016, and poker bots have been beating professionals for years. But one classic game called Stratego held out. Even DeepMind, with its exceptional budget, couldn't build a machine that reliably beat the best human players. Now, a team of researchers from Carnegie Mellon, MIT, New York University, and Stanford University has done it. Their AI, called Ataraxos, beat Pim Niemeijer, arguably the best Stratego player

Opus 5.5’s biggest tell is the word “dependable,” which pops up 23 times more often than in human samples.
Comparing speech synthesis systems across languages and voices has lacked standardized metrics, making it harder to track progress in this rapidly advancing field.
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