Isn't there a basic misreading of Hofstadter in this post? I always understood strange loop and GEB to be about consciousness not cognition.

Weirdly, Aaronson doesn't even seem to be conflating the two:

> Consciousness and subjective experience of course remain extremely mysterious.

I think he's just misreading Hofstadter as stating that cognition depends on self-reference?

I got a bit lost in the comment thread for that article, but I don't understand why LLMs are not considered self referential... They are auto regressive as one of the commenters pointed out, and Scott just sort of waved his hand and said that if auto regression is sufficient then things like Conway's game of Life would also qualify as self-referential.

I don't see why Conway's game of life should not be considered self-referential though... I mean it's isn't it Turing complete? I don't see how any definition of self-referentiality should require throwing out systems which are minimally turing complete... If Turing complete is not enough, doesn't that imply that computable artificial intelligence is impossible in the first place?

I find the discourse under this blog fascinating.

> The big, old ideas about intelligence that ended up basically vindicated were the ideas about how intelligence is about prediction, and prediction is about compression, and compression is about finding better and better upper bounds on Kolmogorov complexity.

Well, sure - prediction works as a baseline, if you abstract out everything else about what counts as intelligence and subsume it under this framework. Any protocol for intelligence can be entirely reduced down to this without trying to understand anything about the structure of intelligence. Solomonoff induction is "vacuous" in this sense too - it doesn't try to understand any intension about the turing machines it finds simple, it just brute forces over all of them. So you're making a claim about intension, whether you want to or not.

It's really no different than say, Darwinians, saying, "what matters is victory at the end". I think the statement has value in the context of some discussions; if the discussion has veered too far in one direction, push as a reminder. That's good. But trying to make grand universal statements like this makes it vacuous.

It's one of these classic unfalsifiable too general statements. The way the end of the article is framed is also icky - the way I'm reading it, it needs to prove all the old curmudgeon evil theories wrong. It reminds me of internet debates around what "the scientific method" is or whatever. It has this kind of zeal that needs to pit itself against the "enemy" and assert itself as the sole right viewpoint, even when say, naive "let's just be empirical" is wrong (e.g. recently had a discussion here about Mach and Boltzmann about this and had a similar interaction).

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Basically, "shut up and calculate" type theories are never correct, and furthermore, you yourself don't shut up and calculate, or think that way at all, and a higher level intelligence won't conceive itself as operating on that anyways. So what are we doing here? It seems like a way to get mad and feel like an intellectual victim.

If self reference is universal then building something on top of ingredients from the universe will include that property. Sort of like root node properties can be found in the leaf nodes.

Embedding spaces are all about analogy.

I’m confused why there seems to be a dismissal of the most basic ‘strange loop’ of the LLM - the fact that it’s evaluating a context to choose the next word, then reevaluating in a context where that word has been appended.

That always seemed to me like the essence of a Hofstadterish strange loop, so the emergence of Hofstadterish phenomena (self rep, etc) doesn’t seem surprising.

I don't quite know what Aaronson is trying to say.

First, LLM AI systems have incredibly huge blind spots despite their incredible performance on many tasks, so self-reference might be the key to what's missing (or not). For example, an LLM AI just solved Navier Stokes, but could not explain the LEAN proof, while a human could.

Second, Hofstadter had more than one idea about intelligence and the mind (see the OP topic of this HN discussion!), and LLMs are quite on-point regarding analogy-forming.

So it may be well be that self-reference and analogy are both part of intelligence, and self-reference is missing and that is leading to major weaknesses.

Third, Aaronson links to a (paywalled) Hofstadter essay form 2023, which was eons ago in AI, and from the intro it seems to be about the sadness of AI replacing humans, not a disparagement of AI ability.