3 ms·

It’s just overly reductive. LLMs do not work like Bayesian or Markov models that fail to scale. In order to predict what word a human will say next past a cer

by swid 4y ago

It’s just overly reductive. LLMs do not work like Bayesian or Markov models that fail to scale. In order to predict what word a human will say next past a certain accuracy, you actually have to model more than text. You have to model that person’s behavior in a sense, or otherwise you can’t reach that accuracy (the tree example would not require that much - I used it since I had seen it before). I like gwern’s[0] example of a dozen people jockeying for power at a dinner table. Part of the next token prediction includes predicting how these Machiavellian schemes play out and why. Next word prediction may give the model a license and reason to figure all that out. [0]: https://gwern.net/scaling-hypothesis https://gwern.net/scaling-hypothesis

YeGoblynQueenne 4y ago
>> It’s just overly reductive. LLMs do not work like Bayesian or Markov models that fail to scale. Yes, they do. That's how language modelling works. It's the same principle whether it's a small or large language model. Scale only improves predictive accuracy, but it doesn't change what is predicted. In fact, you don't need a large language model to model the use of a/an correctly. An n-gram model will do that just fine. Edit: See Equation 1, page 3, for the pre-training objective in the original GPT; see Equation 4, same page, for the fine-tuning objective: https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf https://cdn.openai.com/research-covers/language-unsupervised... The same objective is used throughout the GPT line. Is that more clear?
- YeGoblynQueenne 4y ago
  @swid, would you like me to walk you through the notation of the training objectives in the linked paper? It's basically what I showed above. Let me know if you need some pointers to understand the paper's notation.
  - swid 4y ago
    Thanks for the offer, but it feels to me like you are missing the point I would like to be making. Maybe we are all just stochastic parrots in a sense? (FWIW, I do basically understand the notation). I think the universe might be computable like Stephen Wolfram says - if this is the case - in theory someone could eventually walk you through the math and series of computations to make and then say, "see - humans are not intelligent, they are just biological machines without free will. With unlimited time and computation resources, we can run a human on a Turing machine in a virtual universe." Even if the universe is not fully computable to the point we can perfectly simulate it on a Turing machine, we already do a pretty good job simulating lots of things, and it's not clear the loss of fidelity would make much difference. As I said, "intelligence" could be an emergent property of certain highly complex networks. I don't think the substrate of the network matters (digital vs biological) - it's just certain states may come with a feeling for the network. Being able explain the output of the system by math is very powerful - but it fails to capture emergent properties of the universe we would generally prefer to believe in like consciousness. Anyway - we see slime molds act as path optimizers; lots of algorithms we find useful have physical analogies. I don't know how the brain learns, but biologically, there must be some kind of gradient to follow and reinforce some neural connections while reducing others. Math is the language of the universe, but we believe in free will. Explaining the math of LLMs will not bring us into an agreement - this is unfortunately philosophical I think.
    - YeGoblynQueenne 4y ago
      Thanks for sharing your perspective but (I don't guess you'll be surprised) I don't agree. Maths, formal languages in general (which is basically maths) is how we understand any process that is beyond our immediate experience, i.e. our senses. We can't see the quantum world, for example, but we can describe it with maths and try to understand its behaviour. So while there may or may not be emergent behaviours in LLMs and so on, we will not really know that until we've put it down in maths, pointed to the maths, and said "there, that's the emergent behaviours". Until then all we have is speculation. And I don't like speculation. I like to know how things work. I think that's that thing done in science, also, and that this is the way that we have made any progress as a species. I think I'm in good company in wanting to know how things work, I mean. I don't agree we are "just stochastic parrots" either, not in any sense. Because we can abstract, and formalise, and prove, and demonstrate. You can't do that just by predicting. That's a power over and beyond modelling data. I don't pretend to understand that "power", and I don't expect I will in my lifetime, but that's OK.
      - swid 4y ago
        > .. we can abstract, and formalise, and prove, and demonstrate. You can't do that just by predicting. That's a power over and beyond modelling data. I think we will find this not to be the case. And of course, many people like me do think that the ability to predict well enough implies the ability to do everything else. Since I wrote my reply, I found two other posts on HN that seem relevant to that if you want to read more: AI vs. AGI vs. Consciousness vs. Super-intelligence vs. Agency https://secondbreakfast.co/ai-vs-agi-vs-consciousness-vs-super-intelligence-vs-agency https://secondbreakfast.co/ai-vs-agi-vs-consciousness-vs-sup... Simulators (Self-supervised learning may create AGI or its foundation) https://generative.ink/posts/simulators/ https://generative.ink/posts/simulators/ Here is a quote from that last one - I am still reading it since it is long, but > ... I call this the prediction orthogonality thesis: A model whose objective is prediction can simulate agents who optimize toward any objectives, with any degree of optimality (bounded above but not below by the model’s power).* > This is a corollary of the classical orthogonality thesis, which states that agents can have any combination of intelligence level and goal, combined with the assumption that agents can in principle be predicted. A single predictive model may also predict multiple agents, either independently (e.g. in different conditions), or interacting in a multi-agent simulation. A more optimal predictor is not restricted to predicting more optimal agents: being smarter does not make you unable to predict stupid systems, nor things that aren’t agentic like the weather.

by swid 4y ago

YeGoblynQueenne 4y ago

>> It’s just overly reductive. LLMs do not work like Bayesian or Markov models that fail to scale. Yes, they do. That's how language modelling works. It's the same principle whether it's a small or large language model. Scale only improves predictive accuracy, but it doesn't change what is predicted. In fact, you don't need a large language model to model the use of a/an correctly. An n-gram model will do that just fine. Edit: See Equation 1, page 3, for the pre-training objective in the original GPT; see Equation 4, same page, for the fine-tuning objective: https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf https://cdn.openai.com/research-covers/language-unsupervised... The same objective is used throughout the GPT line. Is that more clear?

YeGoblynQueenne 4y ago
@swid, would you like me to walk you through the notation of the training objectives in the linked paper? It's basically what I showed above. Let me know if you need some pointers to understand the paper's notation.
- swid 4y ago
  Thanks for the offer, but it feels to me like you are missing the point I would like to be making. Maybe we are all just stochastic parrots in a sense? (FWIW, I do basically understand the notation). I think the universe might be computable like Stephen Wolfram says - if this is the case - in theory someone could eventually walk you through the math and series of computations to make and then say, "see - humans are not intelligent, they are just biological machines without free will. With unlimited time and computation resources, we can run a human on a Turing machine in a virtual universe." Even if the universe is not fully computable to the point we can perfectly simulate it on a Turing machine, we already do a pretty good job simulating lots of things, and it's not clear the loss of fidelity would make much difference. As I said, "intelligence" could be an emergent property of certain highly complex networks. I don't think the substrate of the network matters (digital vs biological) - it's just certain states may come with a feeling for the network. Being able explain the output of the system by math is very powerful - but it fails to capture emergent properties of the universe we would generally prefer to believe in like consciousness. Anyway - we see slime molds act as path optimizers; lots of algorithms we find useful have physical analogies. I don't know how the brain learns, but biologically, there must be some kind of gradient to follow and reinforce some neural connections while reducing others. Math is the language of the universe, but we believe in free will. Explaining the math of LLMs will not bring us into an agreement - this is unfortunately philosophical I think.
  - YeGoblynQueenne 4y ago
    Thanks for sharing your perspective but (I don't guess you'll be surprised) I don't agree. Maths, formal languages in general (which is basically maths) is how we understand any process that is beyond our immediate experience, i.e. our senses. We can't see the quantum world, for example, but we can describe it with maths and try to understand its behaviour. So while there may or may not be emergent behaviours in LLMs and so on, we will not really know that until we've put it down in maths, pointed to the maths, and said "there, that's the emergent behaviours". Until then all we have is speculation. And I don't like speculation. I like to know how things work. I think that's that thing done in science, also, and that this is the way that we have made any progress as a species. I think I'm in good company in wanting to know how things work, I mean. I don't agree we are "just stochastic parrots" either, not in any sense. Because we can abstract, and formalise, and prove, and demonstrate. You can't do that just by predicting. That's a power over and beyond modelling data. I don't pretend to understand that "power", and I don't expect I will in my lifetime, but that's OK.
    - swid 4y ago
      > .. we can abstract, and formalise, and prove, and demonstrate. You can't do that just by predicting. That's a power over and beyond modelling data. I think we will find this not to be the case. And of course, many people like me do think that the ability to predict well enough implies the ability to do everything else. Since I wrote my reply, I found two other posts on HN that seem relevant to that if you want to read more: AI vs. AGI vs. Consciousness vs. Super-intelligence vs. Agency https://secondbreakfast.co/ai-vs-agi-vs-consciousness-vs-super-intelligence-vs-agency https://secondbreakfast.co/ai-vs-agi-vs-consciousness-vs-sup... Simulators (Self-supervised learning may create AGI or its foundation) https://generative.ink/posts/simulators/ https://generative.ink/posts/simulators/ Here is a quote from that last one - I am still reading it since it is long, but > ... I call this the prediction orthogonality thesis: A model whose objective is prediction can simulate agents who optimize toward any objectives, with any degree of optimality (bounded above but not below by the model’s power).* > This is a corollary of the classical orthogonality thesis, which states that agents can have any combination of intelligence level and goal, combined with the assumption that agents can in principle be predicted. A single predictive model may also predict multiple agents, either independently (e.g. in different conditions), or interacting in a multi-agent simulation. A more optimal predictor is not restricted to predicting more optimal agents: being smarter does not make you unable to predict stupid systems, nor things that aren’t agentic like the weather.