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> An LLM was only every meant to be a linguistics model, not a brain or cognitive architecture. See https://gwern.net/doc/cs/algorithm/information/compression/

> An LLM was only every meant to be a linguistics model, not a brain or cognitive architecture. See https://gwern.net/doc/cs/algorithm/information/compression/1999-mahoney.pdf https://gwern.net/doc/cs/algorithm/information/compression/1... from 1999. Answering questions in the Turing test (What are roses?) seems to require the same type of real-world knowledge that people use in predicting characters in a stream of natural language text (Roses are ___?), or equivalently, estimating L(x) [the probability of x when written by a human] for compression.

HarHarVeryFunny 1y ago
I'm not sure what your point is? Perhaps in 1999 it seemed reasonable to think that passing the Turing Test, or maximally compressing/predicting human text makes for a good AI/AGI test, but I'd say we now know better, and more to the point that does not appear to have been the motivation for designing the Transformer, or the other language models that preceded it. The recent history leading to the Transformer was the development of first RNN then LSTM-based language models, then the addition of attention, with the primary practical application being for machine translation (but more generally for any sequence-to-sequence mapping task). The motivation for the Transformer was to build a more efficient and scalable language model by using parallel processing, not sequential (RNN/LSTM), to take advantage of GPU/TPU acceleration. The conceptual design of what would become the Transformer came from Google employee Jakob Uzkoreit who has been interviewed about this - we don't need to guess the motivation. There were two key ideas, originating from the way linguists use syntax trees to represent the hierarchical/grammatical structure of a sentence. 1) Language is as much parallel as sequential, as can be seen by multiple independent branches of the syntax tree, which only join together at the next level up the tree 2) Language is hierarchical, as indicated by the multiple levels of a branching sytntax tree Put together these two considerations suggests processing the entire sentence in parallel, taking advantage of GPU parallelism (not sequentially like an LSTM), and having multiple layers of such parallel processing to hierarchically process the sentence. This eventually lead to the stack of parallel-processing Transformer layers design, which did retain the successful idea of attention (thus the paper name "Attention is all you need [not RNNs/LSTMs]"). As far as the functional capability of this new architecture, the initial goal was just to be as good as the LSTM + attention language models it aimed to replace (but be more efficient to train & scale). The first realization of the "parallel + hierarchical" ideas by Uzkoreit was actually less capable than its predecesssors, but then another Google employee, Noam Shazeer, got involved and eventually (after a process of experimentation and ablation) arrived at the Transformer design which did perform well on the language modelling task. Even at this stage, nobody was saying "if we scale this up it'll be AGI-like". It took multiple steps of scaling, from early Google's early Muppet-themed BERT (following their LSTM-based ELMo), to OpenAI's GPT-1, GPT-2 and GPT-3 for there to be a growing realization of how good a next-word predictor, with corresponding capabilities, this architecture was when scaled up. You can read the early GPT papers and see the growing level of realization - they were not expecting it to be this capable. Note also that when Shazeer left Google, disappointed that they were not making better use of his Transformer baby, he did not go off and form an AGI company - he went and created Character.ai making fantasy-themed ChatBots (similar to Google having experimented with ChatBot use, then abandoning it, since without OpenAI's innovation of RLHF Transformer-based ChatBots were unpredictable and a corporate liability).
- DavidSJ 1y ago
  > I'm not sure what your point is? I was just responding to this claim: > An LLM was only every meant to be a linguistics model, not a brain or cognitive architecture. Plenty of people did in fact see a language model as a potential path towards intelligence, whatever might be said about the beliefs of Mr. Uszkoreit specifically. There's some ambiguity as to whether you're talking about the transformer specifically, or language models generally. The "recent history" of RNNs and LSTMs you refer to dates back to before the paper I linked. I won't speak to the motivations or views of the specific authors of Vaswani et al, but there's a long history, both distant and recent, of drawing connections between information theory, compression, prediction, and intelligence, including in the context of language modeling.
  - HarHarVeryFunny 1y ago
    I was really talking about the Transformer specifically. Maybe there was an implicit hope of a better/larger language model leading to new intelligent capabilities, but I've never seen the Transformer designers say they were targeting this or expecting any significant new capabilities even (to their credit) after it was already apparent how capable it was. Neither Google's initial fumbling of the tech or Shazeer's entertainment chatbot foray seem to indicate that they had been targeting, and/or realized they had achieved, a more significant advance than the more efficient seq-2-seq model which had been their proximate goal. To me it seems that the Transformer is really one of industry/science's great accidental discoveries. I don't think it's just the ability to scale that made it so powerful, but more the specifics of the architecture, including the emergent ability to learn "induction heads" which seem core to a lot of what they can do. The Transformer precursors I had in mind were recent ones, in particular Sutskever et als "Sequence to Sequence learning with Neural Networks [LSTM]" from 2014, and Bahdanau et als "Jointly learning to align & translate" from 2016, then followed by the "Attention is all you need" Transformer paper in 2017.
    - DavidSJ 1y ago
      Circling back to the original topic: at the end of the day, whether it makes sense to expect more brain-like behavior out of transformers than "mere" token prediction does not depend much on what the transformer's original creators thought, but rather on the strength of the collective arguments and evidence that have been brought to bear on the question, regardless of who from. I think there has been a strong case that the "stochastic parrot" model sells language models short, but to what extent still seems to me an open question.

HarHarVeryFunny 1y ago

I'm not sure what your point is? Perhaps in 1999 it seemed reasonable to think that passing the Turing Test, or maximally compressing/predicting human text makes for a good AI/AGI test, but I'd say we now know better, and more to the point that does not appear to have been the motivation for designing the Transformer, or the other language models that preceded it. The recent history leading to the Transformer was the development of first RNN then LSTM-based language models, then the addition of attention, with the primary practical application being for machine translation (but more generally for any sequence-to-sequence mapping task). The motivation for the Transformer was to build a more efficient and scalable language model by using parallel processing, not sequential (RNN/LSTM), to take advantage of GPU/TPU acceleration. The conceptual design of what would become the Transformer came from Google employee Jakob Uzkoreit who has been interviewed about this - we don't need to guess the motivation. There were two key ideas, originating from the way linguists use syntax trees to represent the hierarchical/grammatical structure of a sentence. 1) Language is as much parallel as sequential, as can be seen by multiple independent branches of the syntax tree, which only join together at the next level up the tree 2) Language is hierarchical, as indicated by the multiple levels of a branching sytntax tree Put together these two considerations suggests processing the entire sentence in parallel, taking advantage of GPU parallelism (not sequentially like an LSTM), and having multiple layers of such parallel processing to hierarchically process the sentence. This eventually lead to the stack of parallel-processing Transformer layers design, which did retain the successful idea of attention (thus the paper name "Attention is all you need [not RNNs/LSTMs]"). As far as the functional capability of this new architecture, the initial goal was just to be as good as the LSTM + attention language models it aimed to replace (but be more efficient to train & scale). The first realization of the "parallel + hierarchical" ideas by Uzkoreit was actually less capable than its predecesssors, but then another Google employee, Noam Shazeer, got involved and eventually (after a process of experimentation and ablation) arrived at the Transformer design which did perform well on the language modelling task. Even at this stage, nobody was saying "if we scale this up it'll be AGI-like". It took multiple steps of scaling, from early Google's early Muppet-themed BERT (following their LSTM-based ELMo), to OpenAI's GPT-1, GPT-2 and GPT-3 for there to be a growing realization of how good a next-word predictor, with corresponding capabilities, this architecture was when scaled up. You can read the early GPT papers and see the growing level of realization - they were not expecting it to be this capable. Note also that when Shazeer left Google, disappointed that they were not making better use of his Transformer baby, he did not go off and form an AGI company - he went and created Character.ai making fantasy-themed ChatBots (similar to Google having experimented with ChatBot use, then abandoning it, since without OpenAI's innovation of RLHF Transformer-based ChatBots were unpredictable and a corporate liability).

DavidSJ 1y ago
> I'm not sure what your point is? I was just responding to this claim: > An LLM was only every meant to be a linguistics model, not a brain or cognitive architecture. Plenty of people did in fact see a language model as a potential path towards intelligence, whatever might be said about the beliefs of Mr. Uszkoreit specifically. There's some ambiguity as to whether you're talking about the transformer specifically, or language models generally. The "recent history" of RNNs and LSTMs you refer to dates back to before the paper I linked. I won't speak to the motivations or views of the specific authors of Vaswani et al, but there's a long history, both distant and recent, of drawing connections between information theory, compression, prediction, and intelligence, including in the context of language modeling.
- HarHarVeryFunny 1y ago
  I was really talking about the Transformer specifically. Maybe there was an implicit hope of a better/larger language model leading to new intelligent capabilities, but I've never seen the Transformer designers say they were targeting this or expecting any significant new capabilities even (to their credit) after it was already apparent how capable it was. Neither Google's initial fumbling of the tech or Shazeer's entertainment chatbot foray seem to indicate that they had been targeting, and/or realized they had achieved, a more significant advance than the more efficient seq-2-seq model which had been their proximate goal. To me it seems that the Transformer is really one of industry/science's great accidental discoveries. I don't think it's just the ability to scale that made it so powerful, but more the specifics of the architecture, including the emergent ability to learn "induction heads" which seem core to a lot of what they can do. The Transformer precursors I had in mind were recent ones, in particular Sutskever et als "Sequence to Sequence learning with Neural Networks [LSTM]" from 2014, and Bahdanau et als "Jointly learning to align & translate" from 2016, then followed by the "Attention is all you need" Transformer paper in 2017.
  - DavidSJ 1y ago
    Circling back to the original topic: at the end of the day, whether it makes sense to expect more brain-like behavior out of transformers than "mere" token prediction does not depend much on what the transformer's original creators thought, but rather on the strength of the collective arguments and evidence that have been brought to bear on the question, regardless of who from. I think there has been a strong case that the "stochastic parrot" model sells language models short, but to what extent still seems to me an open question.