3 ms·

the more I look at these images the more convinced I become that world models are the major missing piece and that these really are ultimately just stochastic s

by culi 7mo ago

the more I look at these images the more convinced I become that world models are the major missing piece and that these really are ultimately just stochastic sentence machines. Maybe Chomsky was right

bmitc 7mo ago
> that these really are ultimately just stochastic sentence machines I thought that's exactly what they are?
- mastermage 7mo ago
  I am so perplexed what exactly where people thinking they were. Its nothing else than highly sofisticated statistics.
  - tmountain 7mo ago
    From that perspective, which is totally correct, it makes you wonder what other domains of knowledge look like when pushed to the boundaries of our capabilities as a species.
    - mastermage 7mo ago
      That is a genuinely thought provoking idea.
  - culi 7mo ago
    Do you know of any other statistical model that can "hallucinate". They clearly have emergent capabilities that come from scale that are absent in any other statistical model we've ever dreamt up. We know that LLMs build complex internal representations of language, logic, and concepts rather than just shallow word-counting. If you deny that then you probably have an elementary understanding of how they work. Not even Chomsky denies that. The real argument imo is whether those internal representations constitute an actual "understanding" of the world or just flatten out to something much less interesting.
    - mastermage 7mo ago
      > Do you know of any other statistical model that can "hallucinate". Actualy most statistical models can "hallucinate", specifically those that are capable of interpolation. I have witnessed this for example in Gaussian Processes. In my own scientific work.
- culi 7mo ago
  No, they have "attention". There is unique logic going on in the deep layers of the neural network. Even the standard introductory exercise artificial neural networks, handwritten digit recognition, already shows deeper understanding. These simple networks take in raw pixels and somewhere in the many layers recognize "curves" and "edges" and then "circles" and "boxes" and whatnot and eventually "digits". I think there's a genuine debate about whether or not this is a form of intelligence. I think the oversimplified argument of them just being stochastic sentence machines mostly comes from people who don't understand how they work. But I also think there's a much more nuanced version of this argument offered by people like Chomsky that should be taken seriously
  - bmitc 7mo ago
    > No, they have "attention". There is unique logic going on in the deep layers of the neural network. Any specifics? That doesn't say anything about them not being sentence generators. And it's pretty well known that the LLMs constantly spew out fantastically grammatically correct sentences that have no logic to them whatsoever. > These simple networks take in raw pixels and somewhere in the many layers recognize "curves" and "edges" and then "circles" and "boxes" and whatnot and eventually "digits". That sounds like a version of anthropomorphizing. It is my understanding that it is a completely open problem as to what neural networks are actually doing in their internal, deep layers. > I think the oversimplified argument of them just being stochastic sentence machines mostly comes from people who don't understand how they work. I mean, that's effectively a logical fallacy, so it's not a strong argument.
060880 7mo ago
The Chomsky argument feels like it's moving in a different direction than what's actually useful to know. Whether or not these models have "real" understanding, they're clearly capable of solving problems that were previously considered to require understanding. The more interesting question is whether world models, if they existed, would actually improve the failure modes people care about — like hallucination and planning — or whether we'd just get better stochastic sentence machines with an extra layer of abstraction on top.

by culi 7mo ago

bmitc 7mo ago

> that these really are ultimately just stochastic sentence machines I thought that's exactly what they are?

mastermage 7mo ago
I am so perplexed what exactly where people thinking they were. Its nothing else than highly sofisticated statistics.
- tmountain 7mo ago
  From that perspective, which is totally correct, it makes you wonder what other domains of knowledge look like when pushed to the boundaries of our capabilities as a species.
  - mastermage 7mo ago
    That is a genuinely thought provoking idea.
- culi 7mo ago
  Do you know of any other statistical model that can "hallucinate". They clearly have emergent capabilities that come from scale that are absent in any other statistical model we've ever dreamt up. We know that LLMs build complex internal representations of language, logic, and concepts rather than just shallow word-counting. If you deny that then you probably have an elementary understanding of how they work. Not even Chomsky denies that. The real argument imo is whether those internal representations constitute an actual "understanding" of the world or just flatten out to something much less interesting.
  - mastermage 7mo ago
    > Do you know of any other statistical model that can "hallucinate". Actualy most statistical models can "hallucinate", specifically those that are capable of interpolation. I have witnessed this for example in Gaussian Processes. In my own scientific work.
culi 7mo ago
No, they have "attention". There is unique logic going on in the deep layers of the neural network. Even the standard introductory exercise artificial neural networks, handwritten digit recognition, already shows deeper understanding. These simple networks take in raw pixels and somewhere in the many layers recognize "curves" and "edges" and then "circles" and "boxes" and whatnot and eventually "digits". I think there's a genuine debate about whether or not this is a form of intelligence. I think the oversimplified argument of them just being stochastic sentence machines mostly comes from people who don't understand how they work. But I also think there's a much more nuanced version of this argument offered by people like Chomsky that should be taken seriously
- bmitc 7mo ago
  > No, they have "attention". There is unique logic going on in the deep layers of the neural network. Any specifics? That doesn't say anything about them not being sentence generators. And it's pretty well known that the LLMs constantly spew out fantastically grammatically correct sentences that have no logic to them whatsoever. > These simple networks take in raw pixels and somewhere in the many layers recognize "curves" and "edges" and then "circles" and "boxes" and whatnot and eventually "digits". That sounds like a version of anthropomorphizing. It is my understanding that it is a completely open problem as to what neural networks are actually doing in their internal, deep layers. > I think the oversimplified argument of them just being stochastic sentence machines mostly comes from people who don't understand how they work. I mean, that's effectively a logical fallacy, so it's not a strong argument.

060880 7mo ago

The Chomsky argument feels like it's moving in a different direction than what's actually useful to know. Whether or not these models have "real" understanding, they're clearly capable of solving problems that were previously considered to require understanding. The more interesting question is whether world models, if they existed, would actually improve the failure modes people care about — like hallucination and planning — or whether we'd just get better stochastic sentence machines with an extra layer of abstraction on top.