Thread

1. ghusto · 1791130396 · focus · HN ↗

   The desperately needed TL;DR is that perhaps the actual mathematics itself (i.e. proofs, calculations, etc.) can be done by AI, but why we do it and deciding which problems to solve can only be done by AI. Therefore mathematician do maths.

   I'm not a mathematician, but this seems like a weak and slightly bizarre argument.

   1. smitty1e · 1791131935 · focus · HN ↗

      If training data are purely historical, then how does the AI look forward?

      And if human mathematicians are drummed out of producing future training data, then can AI end up proving itself so much "eating the seed corn", only at scale?

      1. dfdydx · 1791133124 · focus · HN ↗

         If training data are purely historical, then how does the human look forward?

         Seems to me not impossible that given current knowledge, AI generate one nugget more of knowledge (eg a proof of Navier Stokes), and given current knowledge + the nugget, generate yet some more new knowledge.

         Not a given, but not obviously impossible either.

         1. smitty1e · 1791139132 · focus · HN ↗

            > how does the human look forward

            Well, through the metaphysical lens that has both powered innovation and stumped the Really Smart Types since antiquity.

      2. sedan_baklazhan · 1791135733 · focus · HN ↗

         This applies to all professions, not just mathematicians.

         The most common answer I’ve heard so far is “well, AI will train on its own output… maybe”.

         I don’t think that’s even possible.

         1. demibabs · 1791138756 · focus · HN ↗

            AI training on its own output is (probably) fine if it’s validated, like a lean-verified proof to Navier-Stokes.

            Because it being validated as correct resolves the main issue with incestuous training, which is compounding error.

            1. smitty1e · 1791139195 · focus · HN ↗

               But from whence come the fresh insights?

               1. s1artibartfast · 1791141023 · focus · HN ↗

                  The AI provides fresh insights as the output.

                  AI creates novel discovery> incorporates this information > makes new discovery

                  This is how it works for humans too.

                  1. smitty1e · 1791150280 · focus · HN ↗

                     One wonders at the fresh/derived breakdown.

                     "AI slop", for example, appears a regression toward some "mean".

                     1. s1artibartfast · 1791154674 · focus · HN ↗

                        This is also how it works for humans too. What portion of human art produced in 2025 or 1925 do you think was fresh and novel, compared to derived and repetitive?

                        The same is true for science and Engineering.

                        What matters is if you have a method of sorting through the repetitive trash

                        1. smitty1e · 1791156494 · focus · HN ↗

                           It's not my contention that AI & people do not both conform to Sturgeon's Law[0].

                           Rather, that AI is unlikely to produce a fresh Marcin Patrzalek[1].

                           [0] <a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Sturgeon%27s_law" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Sturgeon%27s_law

                           [1] <a href="https:&#x2F;&#x2F;youtu.be&#x2F;zbfKFa-reBE?is=pHxTCFcfyU0evbvi" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;zbfKFa-reBE?is=pHxTCFcfyU0evbvi

   2. FeteCommuniste · 1791134831 · focus · HN ↗

      I&#x27;m wondering how we are going to maintain a critical mass of people who understand frontier mathematics if in another five or ten years the only &quot;mathematicians&quot; truly working at the frontier anymore are AIs. Or maybe &quot;understanding&quot; at depth will become a thing of the past, superseded by broad-strokes grasp of results plus machine verification.

      1. cma · 1791175036 · focus · HN ↗

         For one thing, every topic can be explained with motion graphics offering another 1 or 2 dimensions (time and for the 3d stuff that is hard to see structure of in static 2d, plus things like vr will allow more direct depth perception and different views) than a typical blackboard lecture.

         On Proof and Progress in Mathematics talks about how much gets lost of the geometric understanding when translated to a paper. The kinds of things many mathematicians visualize in their head will be much easier to transmit.

         I don&#x27;t know if that is enough to offset the other affects, but learning and transmitting the understanding should be able to get much easier for a lot of people in principle.

   3. patcon · 1791135320 · focus · HN ↗

      &gt; but why we do it and deciding which problems to solve can only be done by AI

      Sorry, was there a typo here? Both sides of the comparison are AI, and in the affirmative?

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