I wonder if this paper has been peer-reviewed at a decent conference/journal.
The Emergent Symbolic Structure of Artificial Neural Networks
91–100 of 117 posts
Re: The Emergent Symbolic Structure of Artificial Neural Networks
#92For anyone like me who finds theory a little dense, you can prepend "quick" to an arxiv link to get a blogpost-style summary e.g. quickarxiv.org/abs/2608.29530
N.B. quickarxiv.org just redirects to alphaxiv.org, so the following (i.e. change "ar" to "alpha") works just as well:
Re: The Emergent Symbolic Structure of Artificial Neural Networks
#93Earlier quoted context omitted.
But neurotransmitters and action potentials don't help in modeling symbolic structure. That is, yes, ANNs are not brains. There are countless differences. But are there differences at the computational level? ANNs are meant to model brain computation, not brain biology. (There is still a lot to debate there, I'm not saying "ANNs are perfect computational models for the brain")
>But neurotransmitters and action potentials don't help in modeling symbolic structure. This is exactly why Fodor argues that psychology should be explained on its on level with symbols rather than appealing to neurology. But if you're interested in modeling symbols, there's much better options than ANNs (see nearly any programming language ever). >ANNs are meant to model brain computation But we don't really know ho…
We don't know all the details about how the brain computes, you are right.
But we do have a hypothesis: that it is done by a large number of simple units with very high connectivity and in deep layers. This is what neural networks model.
Personally I was skeptical of this model of the brain, but they have achieved remarkable success in practice, as well as Nobel prizes. The neural networks people may have been onto something all along (I say that grudgingly).
Re: The Emergent Symbolic Structure of Artificial Neural Networks
#94Earlier quoted context omitted.
This is nonsense. The human mind cannot visualize more than 3 dimensions. It can perfectly comprehend any number of dimensions as long as they are represented in a vector space. In fact, that's what linear algebra does.
I think you accidentally a word, there. GP is talking about comprehending the capacity of massively multi-dimensional space.
Correct. I implied nothing about visualizing. I was talking about (information) capacity.
Re: The Emergent Symbolic Structure of Artificial Neural Networks
#95Earlier quoted context omitted.
> diminishing these things as 'Next token predictors' seems absurdly reductive. This shows a deep misunderstanding of the paper's claims, which in no way challenge the established view that these bots are next-token predictors. Regardless, if all you want is a next-token selector , save your money and roll a die.
> This shows a deep misunderstanding of the paper's claims, which in no way challenge the established view that these bots are next-token predictors. No, this shows an appreciation of the symbolic richness behind that token 'prediction' which the paper leads on. > Regardless, if all you want is a next-token selector, save your money and roll a die. Tell me, where is the emergent symbology guiding that dice?
> No, this shows an appreciation of the symbolic richness behind that token 'prediction' which the paper leads on.
The paper claims no symbolic richness beyond that evident from the undisputed next-token prediction.
> Tell me, where is the emergent symbology guiding that dice?
There's none. That's my point.
Re: The Emergent Symbolic Structure of Artificial Neural Networks
#96Earlier quoted context omitted.
>But neurotransmitters and action potentials don't help in modeling symbolic structure. This is exactly why Fodor argues that psychology should be explained on its on level with symbols rather than appealing to neurology. But if you're interested in modeling symbols, there's much better options than ANNs (see nearly any programming language ever). >ANNs are meant to model brain computation But we don't really know ho…
We don't know all the details about how the brain computes, you are right. But we do have a hypothesis: that it is done by a large number of simple units with very high connectivity and in deep layers. This is what neural networks model. Personally I was skeptical of this model of the brain, but they have achieved remarkable success in practice, as well as Nobel prizes. The neural networks people may have been onto s…
But the models learn in a very different way than humans, they're certainly vastly more sample inefficient. I'm not saying it isn't impressive or that it isn't necessarily a kind of intelligence, I'm just sceptical as to how much it really tells us about the brain.
Re: The Emergent Symbolic Structure of Artificial Neural Networks
#97Earlier quoted context omitted.
I have issue with the comment because he says he didn't read it, then unfavorably compares it to a previous method, and finally uses that negative review to plug his own article instead. His criticism might be valid, I'm not in a position to judge, but the self-promotion leaves a sour tastes in my mouth and makes me question how much of the criticism is just drummed up to make his own contribution appear more relevan…
to his credit he did say he skimmed the paper, and it's honestly standard practice to do a first pass of skimming a paper before you'd go deeper into reading it anyways
The posted paper does use supervision to find alignments. I need to do a little more math to figure out how well the supervision critique applies, but I think it does. And although the current paper mentions DAS, it fails to discuss recent critiques of supervised abstraction methods (the various citations, including my own, that I provided).
Re: The Emergent Symbolic Structure of Artificial Neural Networks
#98As I am going through the article, I was wondering why is this more interesting than having the ability to recover java programs from byte code. So I asked copilot the same question. It told me that - "Honestly this is where the difference between an engineer and researcher shows up!" .
it is not. in this case these researchers have lost their way, with how symbolics entered the conversation. to remind, it is via wanting to prove that ai vs traditional program is understanding deeper. well guess what, ai is not understanding your input better, its your mind playing tricks with language. For analogy, digital world is not real in a physical sense. if output is food, and input is ingredients, then symb…
> while ai is micro level spice & heat that doesn't especially conscious to the ingredients, just that chemistry appears magical.
Huh? Do you mean “isn’t especially conscious of the ingredients”?
Though, that interpretation seems confusing, because it seems to be meant to be contrasting with “macro slicing dicing stacking them” which doesn’t sound more attentive to what the ingredients are than “micro level spice and heat”.
So, I’m having trouble understanding. (This might be a me problem.)
Re: The Emergent Symbolic Structure of Artificial Neural Networks
#99Earlier quoted context omitted.
We don't know all the details about how the brain computes, you are right. But we do have a hypothesis: that it is done by a large number of simple units with very high connectivity and in deep layers. This is what neural networks model. Personally I was skeptical of this model of the brain, but they have achieved remarkable success in practice, as well as Nobel prizes. The neural networks people may have been onto s…
But the models learn in a very different way than humans, they're certainly vastly more sample inefficient. I'm not saying it isn't impressive or that it isn't necessarily a kind of intelligence, I'm just sceptical as to how much it really tells us about the brain.
Those are fair points.
Re: The Emergent Symbolic Structure of Artificial Neural Networks
#100Earlier quoted context omitted.
I hate that whole intro - the first four sentences - so much. It’s nothing but unsupported assumptions. Basically, a strawman that they can do battle with in the paper. Not an auspicious start.
These aren't really strawmen, they're more or less than mainstream opinion in the cognitive sciences from the 80s to maybe 2015-2020 or so.
I’m aware, but it was a bad mainstream opinion which was contradicted by every bit of evidence ever since early LISP efforts in the late 1960s (still waiting for the release of SHRDLU 2.0!)
65 years is a long time to be beating your head against an obvious dead end.
The article intro presents the claims as self-evident, when they’re not at all.
But, someone rise pointed out this may have been a dig at those attitudes, which makes more sense.