Really enjoyed this. Don't know if this will contribute or not, but the problem I've always had with the Black Swan metaphor is that it's an academic exercise quite unrelated to the real world. If swans were the only thing ever seen, and they had always been white, it would be somewhat understandable, though foolish, for someone to lay down a law that "all swans are white" (and therefore - on Talib's thesis - a black swan represents something that "shocks the system.") But swans aren't "all there are." I've seen black dogs and white dogs, black horses and white horses, black sheep and white sheep, etc ... so upon seeing a lot of swans, I would never assume that they must all be white, no matter how many I'd seen and no matter how often they came up white because there is nothing like a mathematical proof that compels swans to be white. All we would know (as I think you point out) is they have all come up white - voice of Homer Simpson here - *so far ...*
(This, btw, is part of CS Lewis's critique of Hume's argument against the miraculous based on 'uniform human experience.' Even if human experience on this matter was uniform, it would be no proof against the miraculous and, btw, the experience is *hardly* uniform.)
So the appearance of even one black swan that immediately and irrevocably wrecks that 'law' is only "shocking" to the degree one sillily put one's confidence in a causal statement that wasn't logically compulsory. This would I think constitute another example of "us[ing] background knowledge to piece together a constantly moving and updating picture of the world."
The questions that become salient now, of course, are these: What is it for an intelligence to "have a world-model"? And how does a world-model-having intelligence come to have that world-model?
Roughly, those who answer the first question along one dimension will answer the second by insisting we can program world-models, and those who answer the first question along a different dimension will answer the second by insisting world-models cannot be programmed. At those extremes, and in between them, much thinking has taken place and still needs to take place.
Thank you Erik for the insights! Loved the Sherlock Holmes example for abduction, most understandable for me so far.
I'm looking forward to your future LLM articles, in case you'd be interested in some suggestions, here's a few:
1) are the differences between the LLMs (GPT, Gemini, Claude) “just” because of differences in the fine-tuning process?
2) related question: the moments when the model appears smart/intelligent are basically “just” because of a smart fine-tuning, or because the model kind of “learned” something really?
3) I read an interesting thought somewhere that since machine learning is about finding patterns, wondering about “emergent” capabilities is like teaching the calculator that 1+1=2 and then wondering that it can calculate 2+2=4, when we didn’t exactly teach it that. Is this parallel a good one? Or can anything indeed emerge in a LLM?
4) the idea of a multimodal LLM getting better and better with scaling thanks to learning, seems to me like expecting my computer to learn to think as soon as I store enough photos from around the world on my hard drive (because I would give the machine enough examples). Of course my parallel is exaggerated, but is it equatable in principle, or am I missing some crucial point about why the machine learning approaches are indeed much different from the hard drive example?
(Fwiw, one of my duties is teaching symbolic logic, and I would be remiss if I didn't register a complication. "All humans are mortal; Socrates is human; therefore, Socrates is mortal," as it stands, is not an instance of modus ponens. It's a valid Aristotelian syllogism in the Barbara mood. Its validity can't be shown in propositional logic, but it can be shown in first-order quantificational logic, if you translate it into quantificational form. ("For all x, if x is human, then x is mortal," etc.) Therein, it can be shown valid by using modus ponens, but you have to apply universal instantiation first.)
I found this very enlightening - where I must say I have only superficial knowledge about how LLM's work - but clear ideas on how human intelligence works, viz. via models of the world and actors significant to our lives - what you call "abduction" (hadn't heard of it). In the context of information flow (especially also, of course disinformation) and interpretation (the higher form of perception) this now has to include models of how that information was created, i.e. with what intent based on models of the sources. Hard to envision AI developing that ability anytime soon - and becoming AGI - which again I was unaware of - but found the definition I found on Wikipedia quite laughable in terms of the tests envisioned there to differentiate it from "lower AI".
Mistakes, blind spots … or just points of failure (mathematical singularities) that are presented as plausible but upon verification are just undefined outputs.
Really enjoyed this. Don't know if this will contribute or not, but the problem I've always had with the Black Swan metaphor is that it's an academic exercise quite unrelated to the real world. If swans were the only thing ever seen, and they had always been white, it would be somewhat understandable, though foolish, for someone to lay down a law that "all swans are white" (and therefore - on Talib's thesis - a black swan represents something that "shocks the system.") But swans aren't "all there are." I've seen black dogs and white dogs, black horses and white horses, black sheep and white sheep, etc ... so upon seeing a lot of swans, I would never assume that they must all be white, no matter how many I'd seen and no matter how often they came up white because there is nothing like a mathematical proof that compels swans to be white. All we would know (as I think you point out) is they have all come up white - voice of Homer Simpson here - *so far ...*
(This, btw, is part of CS Lewis's critique of Hume's argument against the miraculous based on 'uniform human experience.' Even if human experience on this matter was uniform, it would be no proof against the miraculous and, btw, the experience is *hardly* uniform.)
So the appearance of even one black swan that immediately and irrevocably wrecks that 'law' is only "shocking" to the degree one sillily put one's confidence in a causal statement that wasn't logically compulsory. This would I think constitute another example of "us[ing] background knowledge to piece together a constantly moving and updating picture of the world."
Important stuff!
The questions that become salient now, of course, are these: What is it for an intelligence to "have a world-model"? And how does a world-model-having intelligence come to have that world-model?
Roughly, those who answer the first question along one dimension will answer the second by insisting we can program world-models, and those who answer the first question along a different dimension will answer the second by insisting world-models cannot be programmed. At those extremes, and in between them, much thinking has taken place and still needs to take place.
Looking forward to the upcoming posts!
Thank you Erik for the insights! Loved the Sherlock Holmes example for abduction, most understandable for me so far.
I'm looking forward to your future LLM articles, in case you'd be interested in some suggestions, here's a few:
1) are the differences between the LLMs (GPT, Gemini, Claude) “just” because of differences in the fine-tuning process?
2) related question: the moments when the model appears smart/intelligent are basically “just” because of a smart fine-tuning, or because the model kind of “learned” something really?
3) I read an interesting thought somewhere that since machine learning is about finding patterns, wondering about “emergent” capabilities is like teaching the calculator that 1+1=2 and then wondering that it can calculate 2+2=4, when we didn’t exactly teach it that. Is this parallel a good one? Or can anything indeed emerge in a LLM?
4) the idea of a multimodal LLM getting better and better with scaling thanks to learning, seems to me like expecting my computer to learn to think as soon as I store enough photos from around the world on my hard drive (because I would give the machine enough examples). Of course my parallel is exaggerated, but is it equatable in principle, or am I missing some crucial point about why the machine learning approaches are indeed much different from the hard drive example?
Thanks!
(Fwiw, one of my duties is teaching symbolic logic, and I would be remiss if I didn't register a complication. "All humans are mortal; Socrates is human; therefore, Socrates is mortal," as it stands, is not an instance of modus ponens. It's a valid Aristotelian syllogism in the Barbara mood. Its validity can't be shown in propositional logic, but it can be shown in first-order quantificational logic, if you translate it into quantificational form. ("For all x, if x is human, then x is mortal," etc.) Therein, it can be shown valid by using modus ponens, but you have to apply universal instantiation first.)
'I' is abducted me (consciousness)
Apart from abduction, there is also Plato’s concept of learning as remembering which is worth exploring 😊
Thanks for the insightful post! Perhaps you’d find something of interest in my two posts about AI, imagination, and intuition: https://open.substack.com/pub/unexaminedtechnology/p/the-two-is-we-need-to-include-in?r=2xhhg0&utm_medium=ios
Found this ... interesting assertion today on "X" (Twitter):
"Steve Patterson
I don't know who needs to hear this, but LLMs have killed nominalism, and maybe moderate realism too.
Platonism is the conclusion.
Abstract patterns are so real that mindless machines can identify them.
Concepts have been mapped in high-dimensional space. They're real as the stars."
I found this very enlightening - where I must say I have only superficial knowledge about how LLM's work - but clear ideas on how human intelligence works, viz. via models of the world and actors significant to our lives - what you call "abduction" (hadn't heard of it). In the context of information flow (especially also, of course disinformation) and interpretation (the higher form of perception) this now has to include models of how that information was created, i.e. with what intent based on models of the sources. Hard to envision AI developing that ability anytime soon - and becoming AGI - which again I was unaware of - but found the definition I found on Wikipedia quite laughable in terms of the tests envisioned there to differentiate it from "lower AI".
Mistakes, blind spots … or just points of failure (mathematical singularities) that are presented as plausible but upon verification are just undefined outputs.