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It's been a long time since I read Hofstader's book "Fluid Concepts and Creative Analogies" that this is presumably based on, so I'll be interested to watch the

It's been a long time since I read Hofstader's book "Fluid Concepts and Creative Analogies" that this is presumably based on, so I'll be interested to watch the video as a refresher, later. :) He also wrote a follow-on book, "Surfaces and Essences: Analogy as the Fuel and Fire of Thinking", published after this video was made, that I've never read. From what I recall, to Hofstafer analogy making isn't some higher level cognitive process, certainly not a language based one, but basically is THE cognitive process all the way from perception on up, and is the mechanism by which we form object categories in the first place. As always Hofstader's ideas are interesting, but I can't say I agree with them. It seems that the key evolutionary benefit, and function, of a brain is prediction, which is the superpower that moves us from being stuck in the present to being able to "see" (predict) the future, and therefore from being merely reactive to being able to proactively plan and predict future outcomes (what will the sabre-tooth do, where is the water supply?) based on our experience. Given the never-same-twice nature of sensory perception, before you can predict you need to be able to generalize/categorize, which I think Hofstader would regarded as analogy making (how is this thing I'm seeing similar to what I've previously seen?), although it seems the actual mechanism involved is embeddings or embedding-like representations where similar inputs have similar representations, and what might more simply be considered as associative recall provides the generalization from view/instance to identity/category. So, is it really analogies all the way up, or are our perception and cognitive processes better regarded as generalization and prediction, which seem not only seem to have direct and obvious neural realizations, but also match the evolutionary needs that we would expect to exist?

GMoromisato 1mo ago
Ironically, I think he was right! In fact some of his initial experiments, like copycat, were about predicting patterns. You can see next-token-prediction from there. But I think he always held out for an algorithmic/logical method rather than a purely statistical one. If he had accepted the "Bitter Lesson", I think he would have been at the forefront of LLMs.
- mentalgear 1mo ago
  Maybe you haven't noticed that the "Bitter Lesson" had itself a "Bitter Lesson" - that scaling pure data and compute did not lead to AGI: diminishing training returns, GPT-5 disappointment, even openAI stating it was the last 'pure scale' model. The path forward all big llm providers ("ai" labs) have gone is neuro-symbolic (even though they publicly would never labeled it as such to not admit critics like Gary Marcus were right - even though all their actions actually point in that direction).
  - hbogert 1mo ago
    Neuro symbolic, rly? Can you please elaborate what it is that made you conclude that?
    - jephs 1mo ago
      I think me just means neural network models RLed to Chain of Thought reasoning? The thinking tokens are the symbolic bit. Smolensky's latest paper posted here the other day has some thoughts on how modern neural networks might beconsidered neurosymbolic, or rather "gradient symbolic processing," from another perspective entirely. I wouldn't say the bitter lesson has given out! If you haven't noticed, these things keep getting bigger and bigger.
      - augment_me 1mo ago
        Quite a leap to call a random embedding a neurosymbolic representation
hammock 1mo ago
> to Hofstafer analogy making isn't some higher level cognitive process, certainly not a language based one, but basically is THE cognitive process This is so funny to me, because as many people know, sharing an analogy with another person is the fastest way to LOSE an argument with someone, or otherwise spiral it into an unproductive place. I think it’s Scott Adams who used to say analogies work well for explaining. They work terribly for persuasion.
- skinfaxi 1mo ago
  > They work terribly for persuasion. I just experienced this in a conversation. An analogy offers an opportunity to engage with the straw man and miss the forest for the trees.
  - partyficial 1mo ago
    Every analogy, by design, has both similarities and differences. The intention is to focus on the similarities, and ignore the differences. If I say, your face is like the moon, I mean it is bright, not that it is a million miles away.
- deleted 1mo ago
  [deleted]
- Terr_ 1mo ago
  I think that's just because every analogy gives an entire "second front" of ideas for a hostile recipient to find a "flaw", when they ignore the intended boundary between the stuff that does/doesn't matter to the analogy. Ex: Explainer: "Getting a spleen means cutting open the patient and taking it out. It's just like how I'm going to unzip this section of the patient-shaped doll, and remove this little purple bean. In both cases a hole is necessary in a similar location." Hostile listener: "Nonsense! I can just buy beans at the store! So just buy a spleen! No hole!"
- onthrowaway457 1mo ago
  Constructing explicit analogies for persuasion seems to be a bit of a different thing. I’m not sure if Hofstadter puts it this way, but to me even the core aspects of your sentences in this post have roots in analogies. What does it mean to lose an argument or to spiral it to a different place? There is no place, there is no lost item, but we talk about these abstract ideas in ways that largely depend upon understanding things like physical objects and space and movement.
AndrewRoxby 1mo ago
Yes, in the text he explicitly argues for categorization and analogizing being two sides of the same coin and functionally equivalent. For instance - what is an ‘embedding’? In itself its an analogy. Lakoff explored similar ground.
abecedarius 1mo ago
> processes better regarded as generalization and prediction, which seem not only seem to have direct and obvious neural realizations If the workings of those circuits are obvious to you, I'd really like to learn. Do you mean the level of analysis at https://transformer-circuits.pub/ https://transformer-circuits.pub/ ? (That looks like good work but not a deep understanding.) Hofstadter referenced this back in the day as a promising beginning: https://en.wikipedia.org/wiki/Sparse_distributed_memory https://en.wikipedia.org/wiki/Sparse_distributed_memory which sounds kind of similar in style to the embeddings you bring up.
- HarHarVeryFunny 1mo ago
  The predictive nature of our cortex seems even more direct that that, although perhaps obvious is overstating it. To read about the 6-layer circuit of our cortex, google for "cortical microcircuit". Basically the repeating structure of our cortex (across all regions) is this vertical connectivity pattern ("circuit") between the 6 layers, and also the connections coming into and out of the cortex at the top and bottom of those layers. There are also horizontal connections within layers of the cortex, but these are short-distance/localized likely for coordinating activity with a local region (cortical column). Our cortex is connected in a loop with our thalamus (cf recent talk of looped transformers!), which can be separated into thalamic core and matrix. Basically our senses feed into the thalamic core, and cortical outputs (predictions) feed into the thalamic matrix. The thalamic connections/loop seem to act a bit like a patch panel connecting different regions of the cortex. The cortical microcircuit is a real physical circuit - a pattern of synaptic connections between layers and inputs/outputs. Those Transformer circuits are more functional than physical. We know how a Transformer is physically connected, with attention heads etc, and this "circuit" analysis then describes how it functions, e.g. with attention heads in adjacent transformer layers effectively combining to form "induction heads" that copy data across embeddings.
killerstorm 1mo ago
When my daughter was 6 months old, she once pointed at a towel and said "P". Apparently she recognize Winnie the Pooh from a book she saw earlier. If you think about it, this requires recognizing something common in things which are very different: e.g. book and towel have different texture and color, and you somehow need to separate what's depicted from the background. So that's pretty much innate, core brain function. I mean, any animal with vision can recognize an object from the background - otherwise vision is useless. But for humans (and some animals) this translates to depiction of object on a flat surface very easily. So, yeah, analogies-all-the-way-down seems plausible. Even object-vs-background and depiction-vs-paper is itself an analogy.
- HarHarVeryFunny 1mo ago
  Well, recognizing (recalling) Pooh on a towel as being the same as a picture of Pooh in book can also just be regarded as generalization, same as recognizing that someone looking left is the same person as them looking right, etc. Our cortex (all of it - visual, auditory, association. motor..) is remarkably simple and regular - it's six layers of neurons with a specific circuit design, that appears to make it a predictor, with top down predictions meeting bottom up sensory feedback. As noted, it makes a ton of sense that evolution would have equipped us with a predictor (and the more powerful the better). Prediction directly supports reasoning and planning (multi-step what-if prediction). So, my question for Hofstader, or you, would be given that the brain seems to be literally built for prediction, then what is to be gained by regarding this as analogy making instead? Is there any evidence for this? Even as a philosophical viewpoint, doesn't that make for very convoluted explanations of simple things like reasoning and language? (I explained prediction-based reasoning above in one sentence - the analogy-based explanation takes a whole dense book!). One could certainly use the language of analogies to describe perception (Pooh's ears on the towel are analogous to his ears in the book, etc), but what is the benefit of doing so, rather than the simpler view of this just being generalization (which is specifically what the animal needs), and where is the evidence in the brain?
  - killerstorm 1mo ago
    "Generalization" is rather unspecific. Analogy-making points to more specific mechanism: ability to identify features within representations, and modulating associative lookup using those features. I.e. it's not as simple as D(x, y) i.e. distance between embeddings, but something like D(f(x), f(y)) where f projects representation to a specific feature space. The evidence can be found e.g. in LLM interpretability - people were able to identify features as something concrete. Also in our cognition - we can tell _how_ two things are similar, or find Y similar to Y in context of F. "Prediction" by itself seems like a black box: if we are trying to predict sensory input, that gives no explanation to our ability to focus on specific details, or explains why some prediction failures are more likely, etc. OTOH if we say that cortex might be 'disassembling' sensory signal into high-level features and is trying to predict those (features themselves might be identified as "something useful for prediction", i.e. something which explains a lot of variance), it's much easier to connect low-level "prediction" to our high-level cognition.

GMoromisato 1mo ago

Ironically, I think he was right! In fact some of his initial experiments, like copycat, were about predicting patterns. You can see next-token-prediction from there. But I think he always held out for an algorithmic/logical method rather than a purely statistical one. If he had accepted the "Bitter Lesson", I think he would have been at the forefront of LLMs.

mentalgear 1mo ago
Maybe you haven't noticed that the "Bitter Lesson" had itself a "Bitter Lesson" - that scaling pure data and compute did not lead to AGI: diminishing training returns, GPT-5 disappointment, even openAI stating it was the last 'pure scale' model. The path forward all big llm providers ("ai" labs) have gone is neuro-symbolic (even though they publicly would never labeled it as such to not admit critics like Gary Marcus were right - even though all their actions actually point in that direction).
- hbogert 1mo ago
  Neuro symbolic, rly? Can you please elaborate what it is that made you conclude that?
  - jephs 1mo ago
    I think me just means neural network models RLed to Chain of Thought reasoning? The thinking tokens are the symbolic bit. Smolensky's latest paper posted here the other day has some thoughts on how modern neural networks might beconsidered neurosymbolic, or rather "gradient symbolic processing," from another perspective entirely. I wouldn't say the bitter lesson has given out! If you haven't noticed, these things keep getting bigger and bigger.
    - augment_me 1mo ago
      Quite a leap to call a random embedding a neurosymbolic representation

hammock 1mo ago

> to Hofstafer analogy making isn't some higher level cognitive process, certainly not a language based one, but basically is THE cognitive process This is so funny to me, because as many people know, sharing an analogy with another person is the fastest way to LOSE an argument with someone, or otherwise spiral it into an unproductive place. I think it’s Scott Adams who used to say analogies work well for explaining. They work terribly for persuasion.

skinfaxi 1mo ago
> They work terribly for persuasion. I just experienced this in a conversation. An analogy offers an opportunity to engage with the straw man and miss the forest for the trees.
- partyficial 1mo ago
  Every analogy, by design, has both similarities and differences. The intention is to focus on the similarities, and ignore the differences. If I say, your face is like the moon, I mean it is bright, not that it is a million miles away.
deleted 1mo ago
[deleted]
Terr_ 1mo ago
I think that's just because every analogy gives an entire "second front" of ideas for a hostile recipient to find a "flaw", when they ignore the intended boundary between the stuff that does/doesn't matter to the analogy. Ex: Explainer: "Getting a spleen means cutting open the patient and taking it out. It's just like how I'm going to unzip this section of the patient-shaped doll, and remove this little purple bean. In both cases a hole is necessary in a similar location." Hostile listener: "Nonsense! I can just buy beans at the store! So just buy a spleen! No hole!"
onthrowaway457 1mo ago
Constructing explicit analogies for persuasion seems to be a bit of a different thing. I’m not sure if Hofstadter puts it this way, but to me even the core aspects of your sentences in this post have roots in analogies. What does it mean to lose an argument or to spiral it to a different place? There is no place, there is no lost item, but we talk about these abstract ideas in ways that largely depend upon understanding things like physical objects and space and movement.

AndrewRoxby 1mo ago

Yes, in the text he explicitly argues for categorization and analogizing being two sides of the same coin and functionally equivalent. For instance - what is an ‘embedding’? In itself its an analogy. Lakoff explored similar ground.

abecedarius 1mo ago

> processes better regarded as generalization and prediction, which seem not only seem to have direct and obvious neural realizations If the workings of those circuits are obvious to you, I'd really like to learn. Do you mean the level of analysis at https://transformer-circuits.pub/ https://transformer-circuits.pub/ ? (That looks like good work but not a deep understanding.) Hofstadter referenced this back in the day as a promising beginning: https://en.wikipedia.org/wiki/Sparse_distributed_memory https://en.wikipedia.org/wiki/Sparse_distributed_memory which sounds kind of similar in style to the embeddings you bring up.

HarHarVeryFunny 1mo ago
The predictive nature of our cortex seems even more direct that that, although perhaps obvious is overstating it. To read about the 6-layer circuit of our cortex, google for "cortical microcircuit". Basically the repeating structure of our cortex (across all regions) is this vertical connectivity pattern ("circuit") between the 6 layers, and also the connections coming into and out of the cortex at the top and bottom of those layers. There are also horizontal connections within layers of the cortex, but these are short-distance/localized likely for coordinating activity with a local region (cortical column). Our cortex is connected in a loop with our thalamus (cf recent talk of looped transformers!), which can be separated into thalamic core and matrix. Basically our senses feed into the thalamic core, and cortical outputs (predictions) feed into the thalamic matrix. The thalamic connections/loop seem to act a bit like a patch panel connecting different regions of the cortex. The cortical microcircuit is a real physical circuit - a pattern of synaptic connections between layers and inputs/outputs. Those Transformer circuits are more functional than physical. We know how a Transformer is physically connected, with attention heads etc, and this "circuit" analysis then describes how it functions, e.g. with attention heads in adjacent transformer layers effectively combining to form "induction heads" that copy data across embeddings.

killerstorm 1mo ago

When my daughter was 6 months old, she once pointed at a towel and said "P". Apparently she recognize Winnie the Pooh from a book she saw earlier. If you think about it, this requires recognizing something common in things which are very different: e.g. book and towel have different texture and color, and you somehow need to separate what's depicted from the background. So that's pretty much innate, core brain function. I mean, any animal with vision can recognize an object from the background - otherwise vision is useless. But for humans (and some animals) this translates to depiction of object on a flat surface very easily. So, yeah, analogies-all-the-way-down seems plausible. Even object-vs-background and depiction-vs-paper is itself an analogy.

HarHarVeryFunny 1mo ago
Well, recognizing (recalling) Pooh on a towel as being the same as a picture of Pooh in book can also just be regarded as generalization, same as recognizing that someone looking left is the same person as them looking right, etc. Our cortex (all of it - visual, auditory, association. motor..) is remarkably simple and regular - it's six layers of neurons with a specific circuit design, that appears to make it a predictor, with top down predictions meeting bottom up sensory feedback. As noted, it makes a ton of sense that evolution would have equipped us with a predictor (and the more powerful the better). Prediction directly supports reasoning and planning (multi-step what-if prediction). So, my question for Hofstader, or you, would be given that the brain seems to be literally built for prediction, then what is to be gained by regarding this as analogy making instead? Is there any evidence for this? Even as a philosophical viewpoint, doesn't that make for very convoluted explanations of simple things like reasoning and language? (I explained prediction-based reasoning above in one sentence - the analogy-based explanation takes a whole dense book!). One could certainly use the language of analogies to describe perception (Pooh's ears on the towel are analogous to his ears in the book, etc), but what is the benefit of doing so, rather than the simpler view of this just being generalization (which is specifically what the animal needs), and where is the evidence in the brain?
- killerstorm 1mo ago
  "Generalization" is rather unspecific. Analogy-making points to more specific mechanism: ability to identify features within representations, and modulating associative lookup using those features. I.e. it's not as simple as D(x, y) i.e. distance between embeddings, but something like D(f(x), f(y)) where f projects representation to a specific feature space. The evidence can be found e.g. in LLM interpretability - people were able to identify features as something concrete. Also in our cognition - we can tell _how_ two things are similar, or find Y similar to Y in context of F. "Prediction" by itself seems like a black box: if we are trying to predict sensory input, that gives no explanation to our ability to focus on specific details, or explains why some prediction failures are more likely, etc. OTOH if we say that cortex might be 'disassembling' sensory signal into high-level features and is trying to predict those (features themselves might be identified as "something useful for prediction", i.e. something which explains a lot of variance), it's much easier to connect low-level "prediction" to our high-level cognition.