He was extremely skeptical of the capabilities of AI for years but eventually came around to calling it truly capable after testing advanced LLMs.
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.
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", 1873
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.
> 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.
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.
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’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.
And don’t feel you need to go straight to GEB, either. I think his collection of Scientific American columns, Metamagical Themas, is also great and much more accessible.
I read it when I was about 9 or 10, I didn't understand any of it at all but I used to pick it up and look at the pictures of the Escher drawings, which were amazing.
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.
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.
"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."
Aaronson’s thesis is all about how “explicit” self reference might not be needed:
> 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.
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?
He was extremely skeptical of the capabilities of AI for years but eventually came around to calling it truly capable after testing advanced LLMs.
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.
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.
It's like Uber for thinking.
This is very similar to George Lakoff's work. https://en.wikipedia.org/wiki/George_Lakoff
A good place to start for anyone that is interested is his book Metaphor's We Live By.
Hoftstader's video is pressumably based on his own book "Fluid Concepts and Creative Analogies".
> 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.
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.
Scott Aaronson wrote a good post last week on how Hofstadter's theories of intelligence have held up: https://scottaaronson.blog/?p=10046
(The post primarily emphasizes self-referentiality rather than analogy, but I suspect similar things could be said about analogy.)
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?
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.
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.
And don’t feel you need to go straight to GEB, either. I think his collection of Scientific American columns, Metamagical Themas, is also great and much more accessible.
You are guilt-tripping me for using my copy of Godel, Escher, Bach to raise my monitor before I got around to reading it.
I read it when I was about 9 or 10, I didn't understand any of it at all but I used to pick it up and look at the pictures of the Escher drawings, which were amazing.
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.
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.
By:
"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."
Aaronson’s thesis is all about how “explicit” self reference might not be needed:
> 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.
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?
Please read Hofstadter and you will know more about truly interesting questions.
"Seinfeld isn't funny". Hofstadter was one of the pioneers of the "not uncommon" idea.