69 points tosh 4 days ago 26 comments
jshen 1 hour ago | parent
A good place to start for anyone that is interested is his book Metaphor's We Live By.
FLeXMurphy 1 hour ago | parent
I will never not find it amusing how a simple thing like money is obscured or "spun" by a thin veil of pseudointellectual bullshit.
HarHarVeryFunny 1 hour ago | parent
goatlover 1 hour ago | parent
And it's relevant today given how people like to anthropomorphize LLMs, and compare biology to digital machines we make. Of course there are similarities. But the point about metaphorical thinking is we are mislead by treating metaphors as literally true.
Anyway if the stronger claims in the book are at all true, it might impact how an alien species thinks differently than us (given a lot of our metaphors are biologically based). And could present significant difficulties for decoding an alien signal.
pixl97 1 hour ago | parent
People with more knowledge, especially practical working knowledge over many fields, tend to have much more freedom in finding solutions.
Now, an interesting question is how good at LLMs are at analogy, especially deeper transferable concepts?
ameliaquining 1 hour ago | parent
(The post primarily emphasizes self-referentiality rather than analogy, but I suspect similar things could be said about analogy.)
jameshart 1 hour ago | parent
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.
CPLX 56 minutes ago | parent
And if anyone's reading this and hasn't read Hofstadter, you're making a mistake, it's utterly perspective-changing stuff. Well, it was for me, at least.
bogzz 53 minutes ago | parent
CPLX 40 minutes ago | parent
Then, after I graduated from college, a friend of mine who was finishing his physics degree mentioned that he had just finished it and that it was life-changing. I read it again and deeply regretted having put it down for those 12 intervening years.
iainmerrick 33 minutes ago | parent
gowld 1 hour ago | parent
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.
MarkusQ 41 minutes ago | parent
"an LLM AI just solved Navier Stokes"
I assume you mean:
"an LLM AI [company] just [claimed that a team of mathematicians they hired, using their AI] [may have] solved [part of] Navier Stokes[, definitely prompted by (and possibly by looking at) the work of human mathematicians."
reasonableklout 40 minutes ago | parent
> But the idea that you’d need explicit self-referentiality before you could get convincing and world-changing conversational intelligence? Let it be buried in a Westminster Abbey or Arlington National Cemetery for the most important wrong ideas in human history
I am not as confident as you that an LLM cannot explain the lean proof of Navier-Stokes. Rather, I would expect human mathematicians to try and understand the proof without assistance, so as to obtain community understanding in a lossless way.
Hofstadter generally seems depressed about the possibility that human cognition is not so special or complicated, and that AI/LLMs may have replicated or even surpassed it. Here’s another piece from 2023 of his: https://www.lesswrong.com/posts/kAmgdEjq2eYQkB5PP/douglas-ho...
Q: How have LLMs, large language models, impacted your view of how human thought and creativity works? D H: Of course, it reinforces the idea that human creativity and so forth come from the brain's hardware. There is nothing else than the brain's hardware, which is neural nets. But one thing that has completely surprised me is that these LLMs and other systems like them are all feed-forward. It's like the firing of the neurons is going only in one direction. And I would never have thought that deep thinking could come out of a network that only goes in one direction, out of firing neurons in only one direction. And that doesn't make sense to me, but that just shows that I'm naive.
It also makes me feel that maybe the human mind is not so mysterious and complex and impenetrably complex as I imagined it was when I was writing Gödel, Escher, Bach and writing I Am a Strange Loop. I felt at those times, quite a number of years ago, that as I say, we were very far away from reaching anything computational that could possibly rival us. It was getting more fluid, but I didn't think it was going to happen, you know, within a very short time.
And so it makes me feel diminished. It makes me feel, in some sense, like a very imperfect, flawed structure compared with these computational systems that have, you know, a million times or a billion times more knowledge than I have and are a billion times faster. It makes me feel extremely inferior. And I don't want to say deserving of being eclipsed, but it almost feels that way, as if we, all we humans, unbeknownst to us, are soon going to be eclipsed, and rightly so, because we're so imperfect and so fallible. We forget things all the time, we confuse things all the time, we contradict ourselves all the time. You know, it may very well be that that just shows how limited we are.
adverbly 36 minutes ago | parent
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?
sigbottle 22 minutes ago | parent
> 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, but trying to make grand 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.
patcon 1 hour ago | parent
It's akin to the amino acid interactions in proteins that hold biological matter together, and determine it's shape and active form. Protein folding and narrative/storytelling have strange homology :)
(I work in this area via collective intelligence, and these ideas are very dear to me during the past decade. It's neat to see the intuitions seemingly becoming validated in language models)
gilleain 1 hour ago | parent
Hmmmmmm. While I get what you mean, and I don't disagree, please be cautious when making analogies between biological systems and more distant fields.
Yes, folding is driven by hydrophobic collapse due to interactions between residue sidechains. Really, though, we are just describing two 'complex systems', where large numbers of diverse interactions between elements leads to diverse and emergent structures.
tony_cannistra 25 minutes ago | parent
delichon 51 minutes ago | parent
The drive toward the formation of metaphors is the fundamental human drive, which one cannot for a single instant dispense with in thought, for one would thereby dispense with man himself.
Friedrich Nietzsche, “On Truth and Lies in a Nonmoral Sense", 1873ilaksh 34 minutes ago | parent
He is still an AI skeptic in as many ways that he can reasonably be, but doesn't deny the raw ability. Actually he thinks it will eclipse humans and he is a doomer.
He sees it as being an extremely empty type of intelligence though.
But I hope that people who have an intuitive understanding of contemporary machine learning (not me) will sometimes watch videos like this and think about things at a higher level. LLMs have a LOT of assumptions built in.
jgalt212 34 minutes ago | parent