Thread

1. jgord · 1789807859 · focus · HN ↗

   Its a reasonable view to take that "human math" [ math residing in human minds ] is the only math that counts.

   Math that only resides in the weights of models, or arcane forms such as a long lean proof or even an unread textbook .. is not the math that we should be striving for.

   Likewise all other technology [ and culture ].

   LLMs and AI / AGI / ASI could lead to a new renaissance of math discussion and expansion of human math and science. Or the opposite, where we outsource all our thinking to the AI, and no new generation of artisans is trained by doing hard problems, and in a generation we have killed off human math.

   Likewise all of the fields of human intellect. We need to make sure we protect future generations of doctors, biologists, software developers, architects, engineers, librarians, musicians, artists ...

   A moratorium on AI development might be the only way to achieve this preservation of human culture.

   1. svara · 1789808306 · focus · HN ↗

      I want to agree with this, but I have a hard time seeing how it can be done.

      Tao is speaking of a very particular kind of mathematics, that done out of pure curiosity.

      But maths, even at the highest levels, often finds applications sooner or later.

      It will be economically impossible to justify boycotting correct mathematics that no humans understand on grounds only of purity.

      This may happen very soon: one of the obvious applications of novel mathematical results is in building stronger AI models.

      1. layer8 · 1789811554 · focus · HN ↗

         > Tao is speaking

         Tao isn’t the article author, it’s a guest post.

      2. traes · 1789813410 · focus · HN ↗

         > one of the obvious applications of novel mathematical results is in building stronger AI models.

         This gets repeated a lot and seems to be one of the primary stated goals of making AI solve math problems, but I still have no idea by what mechanism this is even supposed to happen. I guess they could make some minor improvements to matrix multiplication algorithms or whatever but I don't see what groundbreaking theorem could possibly significantly improve LLMs.

         1. svara · 1789817693 · focus · HN ↗

            It's the kind of thing where it's sort of expected that you wouldn't know, right?

            I think we don't really understand why deep learning works as well as it does, the thinking around that is, as far as I can tell, mostly a collection of empirical observations.

            A fundamental theory of learning that can be used to predict optimal network architectures might enable smaller models that consume less energy.

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