Notices by Alan Johnson (acjay@hachyderm.io)

@darnell Mine was exposed :(

This is so f'ing unacceptable.

@Gergovie @clive @thomasfuchs The text that LLMs are trained on are an artifact of understanding and reasoning processes. And to the extent that the text outputs can capture the essence of those processes, LLMs mimic the processes themselves.

@Gergovie @clive @thomasfuchs But because LLMs are so internally complex, we're reduced to discussing them by analogy, and I think that chronically leads to over- and underestimating their utility.

@clive @thomasfuchs @Gergovie I think we pretty much agree. It's mimicry of those things. It's extremely unclear that you can even compose LLMs with other subsystems in a rigorous way to address those shortcomings.

@clive @thomasfuchs @Gergovie It reminds me of Prolog a bit. When I first learned it, I was like "holy shit, this is incredible". But then you learn the fundamental limitations, and how the workarounds to those limitations undermine all the good parts. Then you understand why it remains a niche technology.

It's possible we're already pretty close to the local maximum of LLMs as a technology. If so, I still do think it's pretty impressive.

@thomasfuchs @clive @Gergovie A similar argument could be made to debunk the notion that the human brain is capable of actual thinking. After all, it's just a bunch of neurons, preconfigured by genetics, trained on sensory data.

To be clear, I don't think that LLMs "think" in the exact way as humans, but I do believe there's a very fuzzy boundary.

@Gergovie @clive @thomasfuchs I think that's way too reductive. LLMs absolutely do something that *looks* like understanding and reasoning.

The problem is that we don't have great ways to characterize what it is they *do*, so it's really hard to know when their output is good enough to use in place of actual logic and interpretation.