AI数学突破与创造力局限

Consistently insightful, with no agenda beyond understanding how technology shapes our world. — Vincent N., a paying member

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The backlash is worsening

American college graduates are angry. In my Saturday column, I write:

The narrative around AI has been about promises for tomorrow, but sacrifices today. […] Engaging with this resistance won’t be effective if AI leaders throw distant fantasies at people dealing with muddy water coming from their kitchen tap today.

AI’s creative expression

An OpenAI reasoning model solved an 80-year-old open problem in discrete geometry. The interesting point here is that the reasoning model found an unexpected bridge between two different fields of math. Discrete geometry and algebraic number theory are meaningfully separate cultures. An expert in one field might know something about the other, but not at the depth required for breakthrough research.

The human mathematicians who validated the proof reckon that the connection the AI found was both unexpected and non-obvious. In a sense, it’s reminiscent of AlphaGo’s Move 37.

Because AI systems can span different domains of knowledge, my hunch is that one of the biggest dividends we will see from them in these fields will be connecting domains that live in isolation because of the structure and specialization of modern science.

But of course, another way AI will impact research is by speeding up the scientific method: the full hypothesis-experiment-analysis loop.

Robin, a multi-agent system, completed the full cycle of hypothesis generation, experimental selection, data analysis and refinement. Human scientists ran specified experiments in a wet lab. The outcome was the identification of an existing drug that could be repurposed to treat macular degeneration.

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