Strong agree. It's the finding of symmetries and folds along non-obvious crease lines, but in semantic space of language <3
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)
> Protein folding and narrative/storytelling have strange homology
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.
Very interesting book. You don't really think about how often metaphors are employed in language, and their influence on how we think. The book makes some rather strong claims regarding science and philosophy being misled by metaphorical thinking, but still a good read.
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.
> According to Lakoff, an individual's experience and attitude towards sociopolitical issues are influenced by being framed in linguistic constructions. In Metaphor and War: The Metaphor System Used to Justify War in the Persian Gulf (1991), he argued that the American involvement in the Persian Gulf War was obscured or "spun" by the metaphors which were used by the first Bush administration to justify it.[3] Between 2003 and 2008, Lakoff was involved with a progressive think tank, the now defunct Rockridge Institute.
I will never not find it amusing how a simple thing like money is obscured or "spun" by a thin veil of pseudointellectual bullshit.
While I've not watched this video I don't think this is an uncommon idea. Or at least many people may see it in practice but never really follow thru on it.
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?
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.
That's a really interesting point. I hadn't drawn that connection before.
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.
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.
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)
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.
A good place to start for anyone that is interested is his book Metaphor's We Live By.
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.
I will never not find it amusing how a simple thing like money is obscured or "spun" by a thin veil of pseudointellectual bullshit.
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?
(The post primarily emphasizes self-referentiality rather than analogy, but I suspect similar things could be said 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.
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.
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.