Forty-Two Mathematicians Call for a Warning on the Dangers of AI
Forty-two mathematicians, including Fields Medal winners, are calling on the Royal Society to alert public authorities and the media about the dangers of advanced AI. Their letter highlights estimates from laboratories placing the existential risk above ten percent and warns about sensitive areas well beyond mathematics.
Existential Risk Above 10% and Threats Beyond Mathematics
The signatories cite estimates from AI laboratories that place the existential risk above ten percent and assert that these assessments should not be dismissed as mere "hype." They also point out that it may be too late to intervene once the danger becomes evident to the general public. Their warning pertains to other fields beyond mathematics: they deem it necessary to anticipate equivalent progress in cybersecurity, autonomous weapons control, the creation of biological and chemical agents, as well as in targeted disinformation. They indicate that their action is not motivated by the consequences for the mathematical discipline itself.
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Call to the Royal Society to Engage Government and Media
Forty-two mathematicians have sent an open letter to the president of the Royal Society, urging the British Academy of Sciences to alert the government and the media about the risks associated with advanced AI. None of the signatories are affiliated with an AI company. Among them are Fields Medal winners, including Martin Hairer, Peter Scholze, and Wendelin Werner.
Recent Capabilities Cited: Open Problems Solved by Models
The mathematicians mention recent advancements: within three months, advanced models developed by OpenAI and Anthropic have provided solutions to previously unanswered research questions, including one of the seven Millennium Prize Problems. According to the letter, these tools now demonstrate results comparable to those of the greatest human mathematicians in many areas, and there is a notable probability that they will acquire superior abilities to humans within comparable timeframes.
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