Original Reddit post

I’ve been thinking about how much current AI discourse is shaped by an ML/LLM-centric view of the field. The current state of the art is genuinely impressive, but some of what looks radically new can also be understood as older ideas becoming newly practical. Mathematics seems like a good example: LLMs may make automated theorem proving and formal methods applicable to a much broader range of mathematical work. But there is a much older intellectual lineage here—from Principia Mathematica to the Logic Theorist and the broader symbolic-AI tradition. A lot of the foundational questions were posed surprisingly early, then partly displaced by approaches that proved more practical or scalable. I find revisiting those older ideas useful because they can illuminate what is actually interesting about present advances. In some cases, I even think the earlier conceptual formulations are more provocative than the modern tools themselves. The general idea of an automated proof assistant, for example, raises questions that aren’t exhausted by the particulars of Lean, Agda, or whatever system an LLM happens to use successfully. So even if LLMs become extremely good at operating these tools, that doesn’t automatically surface the most distinctive or interesting questions about what they’re doing. Those questions may require interpretation—and some of the best interpretive frameworks may already exist in older traditions of logic, automated reasoning, symbolic AI, and philosophy of mathematics. I’m curious whether others see the current moment this way: less as LLMs creating an entirely new intellectual program, and more as making previously marginal or impractical programs newly viable. submitted by /u/Amichayg

Originally posted by u/Amichayg on r/ArtificialInteligence