Chinese mathematicians form a 1,000-strong alliance on AI and mathematics
Led by Fields medallist Shing-Tung Yau at Tsinghua, eight institutions plan specialised maths models, open infrastructure for AI-assisted proof, interpretability work and a journal.
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Fields medallist Shing-Tung Yau led the launch of the Yau Laboratory Alliance for Mathematics and Artificial Intelligence at Tsinghua University, bringing together eight Chinese research institutions and a stated 1,000-strong team of mathematicians.
In his speech, reported by Shanghai Daily’s CityNewsService, Yau described AI as carrying both exceptional promise and exceptional risk for academic research: “If artificial intelligence is properly harnessed, the scholarship, breakthroughs and impacts it delivers will be epoch-making. Poorly managed, however, it could bring catastrophic consequences, particularly harming the development of China’s science and technology sector.” He said AI’s transformative power, in his assessment after five decades in the field, surpassed any earlier scientific breakthrough, and that its exponential pace meant progress would require “joint efforts from knowledgeable scholars and innovative young students” rather than the small cohorts that had historically driven advances in basic science.
The alliance’s founding members span Beijing, Shanghai, Nanjing, Shenzhen and Hong Kong, including Tsinghua’s Qiuzhen College, the Yau Mathematical Sciences Center, the Shanghai Institute for Mathematics and Interdisciplinary Sciences, and the Beijing Institute of Mathematical Sciences and Applications. Tsinghua vice president Li Zhihua said it would convert China’s existing mathematical research strength into an advantage for AI innovation. Its stated priorities include building specialised AI models for mathematical research, open infrastructure for AI-assisted mathematics, work on AI interpretability, and a journal for the field.
The launch came five days after OpenAI released 722 AI-written mathematics manuscripts, which set off an international argument about how machine-generated mathematics should be published and checked.