5 ms·

Its not a statistical next word predictor. The 'predicting the next word' is the learning mechanism of the LLM which leads to a latent space which can encode h

Its not a statistical next word predictor. The 'predicting the next word' is the learning mechanism of the LLM which leads to a latent space which can encode higher level concepts. Basically a LLM 'understands' that much as efficient as it has to be to be able to respond in a reasonable way. A LLM doesn't predict german text or chinese language. It predicts the concept and than has a language layer outputting tokens. And its not just LLMs which are progressing fast, voice synt and voice understanding jumped significantly, motion detection, skeletion movement, virtual world generation (see nvidias way of generating virutal worlds for their car training), protein folding etc.

mort96 6mo ago
I'm sorry but the input to a model is a sequence of tokens and the output is a probability distribution of what's the most likely next token. It's a very very very fancy next token predictor but that is fundamentally what it is. I'm making the argument that this paradigm might not give rise to a general intelligence no matter how much you scale it.
- CamperBob2 6mo ago
  It's a very very very fancy next token predictor Yes, and unless you are prepared to rebut the argument with evidence of the supernatural, that's all there is, period. That's all we are. So tired of the thought-terminating "stochastic parrot" argument.
  - mort96 6mo ago
    I'm not sure why you think you know the human brain works through predicting the next token. It's not supernatural, I believe that an artificial intelligence is possible because I believe human intelligence is just a clever arrangement of matter performing computation, but I would never be presumptuous enough to claim to know exactly how that mechanism works. My opinion is that human intelligence might be what's essentially a fancy next token predictor, or it might work in some completely different way, I don't know. Your claim is that human intelligence is a next token predictor. It seems like the burden on proof is on you.
    - dpark 6mo ago
      > Your claim is that human intelligence is a next token predictor. Literally it is, at least in many of its forms. You accepted CamperBob2’s text as input and then you generated text as output. Unless you are positing that this behavior cannot prove your own general intelligence, it seems plain that “next token generator” is sufficient for AGI. (Whether the current LLM architecture is sufficient is a slightly different question.)
      - mort96 6mo ago
        Before I start typing, I think abstractly about the topic and decide on what I shall write in response. Due to the linear nature of time, typing necessarily happens one word at a time, but I am never producing a probability distribution of words (at least not in a way that my conscious self can determine), I consider an entire idea and then decide what tokens to enter into the computer in order to communicate the idea to you. And while I am typing, and while I am thinking before I type, I experience an array of non-textual sensory input, and my whole experience of self is to a significant extent non-lingual. Sometimes, I experience an inner monologue, sometimes I think thoughts which aren't expressed in language such as the structure of the data flow in a computer program, sometimes I don't think and just experience feelings like a kiss or the sun on my skin or the euphoria of a piece of music which hits just right. These experiences shape who I am and how I think. When I solve difficult programming problems or other difficult problems, I build abstract structures in my mind which represents the relevant information and consider things like how data flows, which parts impact which other parts, what the constraints are, etc. without language coming in to play at all. This process seems completely detached from words. In contrast, for a language model, there is no thinking outside of producing words. It seems self-evident to me that at least parts of the human experience fundamentally can not be reduced to next token prediction. Further, it seems plausible to me that some of these aspects may be necessary for what we consider general intelligence. Therefore, my position is: it is plausible that next token prediction won't give rise to general intelligence, and I do not find your argument convincing.
        AntiUSAbah 6mo ago
  - godshatter 6mo ago
    Do LLMs even learn? The companies that build them build new models based partly on the conversations the older models have had with people, but do they incorporate knowledge into their neural nets as they go along? Can an LLM decide, without prompting or api calls, to text someone or go read about something or do anything at all except for waiting for the next prompt? Do LLMs have any conceptual understanding of anything they output? Do they even have a mechanism for conceptual understanding? LLMs are incredibly useful and I'm having a lot of fun working with them, but they are a long way from some kind of general intelligence, at least as far as I understand it.
    - CamperBob2 6mo ago
      Yes, to all of your questions. You need to use a recent LLM in an agentic harness. Tell it to take notes, and it will. After a bit of further refinement, we'll start to call that process "learning." Eventually the question of who owns the notes, who gets to update them, and how, will become a huge, huge deal.
    - AntiUSAbah 6mo ago
      "Do LLMs even learn?" They learned already a lot more than any of us will. Additinal to this, you have a prompt and you can teach it things in the prompt. Like if you give it examples how it should parse things, with examples in the prompt, it becomes better in doing it. I would say yes they learn. "Can an LLM decide" I would argue that you frame that wrong. If a LLM is the same thing as the pure language part of our brain, than the agent harness and the stuff around it, would be another part of our brain. I find it valid to use the LLM with triggers around it. Nonetheless, we probably can also design an architecture which has a loop build in. "Do LLMs have any conceptual understanding" Thats what a LLM has in their latent space. Basically to be able to predict the next token in such a compressed space, they 'invent' higher meaning in that space. You can ask a LLM about it actually. Yeah for AGI we are not there yet and we do not know how it will look like.
somewhereoutth 6mo ago
LLM proponents believe that these higher level encodings in latent space do in fact match the real world concepts described by our language(s). However, a much simpler explanation for what we see with LLMs is that instead the higher level encodings in latent space match only the patterns of our language(s), and no deeper encoding/understanding is present. It's Plato's Cave - the shadows on the wall are all an LLM ever sees, and somehow it is expected to derive the real reality behind them.
- AntiUSAbah 6mo ago
  Could be, yes for sure but I think it would be very naive in the current state of progress we are in, to down play what progress is happening. At least Mythos model with its 10 Trillion parameter might indicate that the scaling law is valid. Its a little bit unfortunate that we still don't know that much more about that model.
turtlesdown11 6mo ago
> Its not a statistical next word predictor. it absolutely is a next word predictor

mort96 6mo ago

I'm sorry but the input to a model is a sequence of tokens and the output is a probability distribution of what's the most likely next token. It's a very very very fancy next token predictor but that is fundamentally what it is. I'm making the argument that this paradigm might not give rise to a general intelligence no matter how much you scale it.

CamperBob2 6mo ago
It's a very very very fancy next token predictor Yes, and unless you are prepared to rebut the argument with evidence of the supernatural, that's all there is, period. That's all we are. So tired of the thought-terminating "stochastic parrot" argument.
- mort96 6mo ago
  I'm not sure why you think you know the human brain works through predicting the next token. It's not supernatural, I believe that an artificial intelligence is possible because I believe human intelligence is just a clever arrangement of matter performing computation, but I would never be presumptuous enough to claim to know exactly how that mechanism works. My opinion is that human intelligence might be what's essentially a fancy next token predictor, or it might work in some completely different way, I don't know. Your claim is that human intelligence is a next token predictor. It seems like the burden on proof is on you.
  - dpark 6mo ago
    > Your claim is that human intelligence is a next token predictor. Literally it is, at least in many of its forms. You accepted CamperBob2’s text as input and then you generated text as output. Unless you are positing that this behavior cannot prove your own general intelligence, it seems plain that “next token generator” is sufficient for AGI. (Whether the current LLM architecture is sufficient is a slightly different question.)
    - mort96 6mo ago
      Before I start typing, I think abstractly about the topic and decide on what I shall write in response. Due to the linear nature of time, typing necessarily happens one word at a time, but I am never producing a probability distribution of words (at least not in a way that my conscious self can determine), I consider an entire idea and then decide what tokens to enter into the computer in order to communicate the idea to you. And while I am typing, and while I am thinking before I type, I experience an array of non-textual sensory input, and my whole experience of self is to a significant extent non-lingual. Sometimes, I experience an inner monologue, sometimes I think thoughts which aren't expressed in language such as the structure of the data flow in a computer program, sometimes I don't think and just experience feelings like a kiss or the sun on my skin or the euphoria of a piece of music which hits just right. These experiences shape who I am and how I think. When I solve difficult programming problems or other difficult problems, I build abstract structures in my mind which represents the relevant information and consider things like how data flows, which parts impact which other parts, what the constraints are, etc. without language coming in to play at all. This process seems completely detached from words. In contrast, for a language model, there is no thinking outside of producing words. It seems self-evident to me that at least parts of the human experience fundamentally can not be reduced to next token prediction. Further, it seems plausible to me that some of these aspects may be necessary for what we consider general intelligence. Therefore, my position is: it is plausible that next token prediction won't give rise to general intelligence, and I do not find your argument convincing.
      - AntiUSAbah 6mo ago
- godshatter 6mo ago
  Do LLMs even learn? The companies that build them build new models based partly on the conversations the older models have had with people, but do they incorporate knowledge into their neural nets as they go along? Can an LLM decide, without prompting or api calls, to text someone or go read about something or do anything at all except for waiting for the next prompt? Do LLMs have any conceptual understanding of anything they output? Do they even have a mechanism for conceptual understanding? LLMs are incredibly useful and I'm having a lot of fun working with them, but they are a long way from some kind of general intelligence, at least as far as I understand it.
  - CamperBob2 6mo ago
    Yes, to all of your questions. You need to use a recent LLM in an agentic harness. Tell it to take notes, and it will. After a bit of further refinement, we'll start to call that process "learning." Eventually the question of who owns the notes, who gets to update them, and how, will become a huge, huge deal.
  - AntiUSAbah 6mo ago
    "Do LLMs even learn?" They learned already a lot more than any of us will. Additinal to this, you have a prompt and you can teach it things in the prompt. Like if you give it examples how it should parse things, with examples in the prompt, it becomes better in doing it. I would say yes they learn. "Can an LLM decide" I would argue that you frame that wrong. If a LLM is the same thing as the pure language part of our brain, than the agent harness and the stuff around it, would be another part of our brain. I find it valid to use the LLM with triggers around it. Nonetheless, we probably can also design an architecture which has a loop build in. "Do LLMs have any conceptual understanding" Thats what a LLM has in their latent space. Basically to be able to predict the next token in such a compressed space, they 'invent' higher meaning in that space. You can ask a LLM about it actually. Yeah for AGI we are not there yet and we do not know how it will look like.

somewhereoutth 6mo ago

LLM proponents believe that these higher level encodings in latent space do in fact match the real world concepts described by our language(s). However, a much simpler explanation for what we see with LLMs is that instead the higher level encodings in latent space match only the patterns of our language(s), and no deeper encoding/understanding is present. It's Plato's Cave - the shadows on the wall are all an LLM ever sees, and somehow it is expected to derive the real reality behind them.

AntiUSAbah 6mo ago
Could be, yes for sure but I think it would be very naive in the current state of progress we are in, to down play what progress is happening. At least Mythos model with its 10 Trillion parameter might indicate that the scaling law is valid. Its a little bit unfortunate that we still don't know that much more about that model.

turtlesdown11 6mo ago

> Its not a statistical next word predictor. it absolutely is a next word predictor