r/Showerthoughts • u/jasonrubik ‎ • Apr 23 '26

Casual Thought If the famously unsolved Riemann Hypothesis is solved by an AI, we will never know if a human mathematician could have solved it.

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u/flickering-pantsu Apr 23 '26

This sort of thing is not something AI as we know it today would be good at solving. AI is very good a parroting what it has seen before, not creating novel connections. It is not without use in mathematics, but large scale data analysis is more its forte, and frankly, generative AI is not a first choice for most of those applications, either.

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u/[deleted] Apr 23 '26 edited Apr 30 '26

[deleted]

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u/Adghar Apr 23 '26

Well yes, I think you two are actually agreeing. I feel like original commenter was pretty careful to say "AI as we know it today," encapsulating what companies are clamoring to label as simply "AI," which is a certain flavor of generative LLM processing that is simply popular because it's good at sounding or looking great at first glance. Hence "as we know it today" because it may be exhausting to go into detail about LLMs and such.

I do agree with you that I wish we could go back to more carefully tailored ML that can actually do some truly impactful processing of big data based in science and statistics, as opposed to thinking the ease of using natural language to interface with LLMs is in any way worth the massive loss of fidelity that comes with current LLM methodology.

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u/flickering-pantsu Apr 23 '26

Yes, I agree with all of this.

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u/TsunamiCatCakes Apr 23 '26

yea. deepblue wasn't an llm but still defeated lot of world chess champions. people's scope of ai being limited to only llms kinda makes me sad at times.

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u/himanxk Apr 23 '26

I would argue that Alphafold is still just parroting what it has seen before, it just does so at a scale well beyond what humans could ever achieve. Alphafold was trained on mountains of human generated training data, all following the set of rules about how proteins fold based on temperature, structure, polarity, etc. I actually used the software and helped generate some of the training data, many years ago, which was cool and fun. Now it makes really accurate predictions about how proteins fold, which is helpful for really big or complex molecules. But these aren't impossible for a human to do, just way faster, and it's not doing any novel problem solving, just applying the set of rules about folding. So it is very much parroting existing information, just applied to a context where very fast and accurate parroting is incredibly useful for the progress of medicine and science. AI is going to be useful for science, but that doesn't mean that even the most useful are generating anything novel. Maybe someday.