3 ms·

If you want to understand how statistical AI systems work, then you should understand the basics of formalism. They are curve-fitting algorithms that approximat

If you want to understand how statistical AI systems work, then you should understand the basics of formalism. They are curve-fitting algorithms that approximate the probability distribution of a hisotrical dataset. That's all they are. By analogising to any animal or human mind, you aren't describing anythign that acutally exists. A neutral network algorithm isnt neural and it isnt a network. It's a statistical curve-fitting algorith. One oughtnt study trees to understand a decision tree either. This language is entirely metaphorical. There are no neurones in an NN, there are just summation entires in a matrix. This matrix comprises weights, which define the orientation and scale of the line pieces which form the curve being fit to the data.

FeepingCreature 2y ago
They're, in particular, multi-level curve-fitting algorithms. A formalism which, I'm sure you are aware, can express any bounded computation. In particular, depending on how you set up the tape, neural networks are comfortably Turing complete. We're not talking about 2-layer perceptrons anymore here. In my opinion, you should look less at the formalism and more at the empirically demonstrated performance.
- mjburgess 2y ago
  I'm not aware that it can express any bounded computation. But such is irrelevant anyway: there is no function from the distributional structure of text tokens to reasoning. There is nothing to approximate, and this is a system trained just to approximate some presumed function: our mental capacities arent patterns in glyphs. Whatever property you might imagine a transformer architecture to have (and it is vastly fewer than the set needed for general computation), the problem here is that it's being applied to approximate the structure of historical text data which isnt being generated from such a function. Indeed, there is no function which generates text data, there's a very large number of independent generating processes that give rise to the distribution of text. The phrase "the war in ukraine" acquires a different semantics over 2010-2030 in a radically different way than, "I liked that film" does. The capacities which produce distributions of text tokens are highly varied, complex, invovle a vast array of our mental processes, and so on. There's literally almost nothing in the distribution of text tokens that corresponds to any features of these processes. The structure of language is conventional, and rests on our familiarity with such conventions. Otherwise, let's end all science, everything to be known about the world derives from how "e" occurs alongside "lectron"
  - stevenhuang 2y ago
    Computability is of critical relevance. Our mental capacities can be patterns. https://en.m.wikipedia.org/wiki/Predictive_coding https://en.m.wikipedia.org/wiki/Predictive_coding > There's literally almost nothing in the distribution of text tokens that corresponds to any features of these processes. There's nothing in evolutionary fitness that necessitates intelligence or reasoning ability either. Yet here we are. Sorry but if you don't see the connections then you need to do some reading on theory of mind, cognition, information theory, physics, philosophy. All of the fundamental basis are met to allow reasoning to emerge in LLMs. It is clear now where your confusion lies, and why you are led to believe so strongly that LLMs cannot reason: it is because you are an ML practitioner you overweight your expertise yet you don't know what you don't know, and have foundational gaps in your knowledge. You lack the context in these other fields. If you had them, your position should be closer to agnostic than this strong belief of yours that LLMs in their current form cannot reason.
    - mjburgess 2y ago
      lol, well when I've written my PhD on those areas we can return to whether I'm an expert on them or not. One does not form "connections" between them as in some wide-eye conspiracy theorist.. "predictive coding" has little to do with "prediction" in the ML sense. The latter concerned with making a quantitive estimate of some variable by summarising historical data. What we are doing when we revise a "mental model" is done by counter-factual simulation of possible future states. Statistical AI models conditional probability structures, and computes predictions as an expectation over weighted summarised historical data. This is not a means of performing counter-factual simulation. One trivial, sadly empirical, way to see this is to note that each marginal token generated is of constant time and energy use. Yet trivially, reasoning and a variety of other mental capacities should require abitarily different time to run. Eg., simulating a complex scenario is necessarily more intensive than a simple one, and so on. Yet I am quite annoyed that we need such dumb observations to make this point. It speaks of a profound ignorance of "theory of mind, cognition, information theory, physics, philosophy" and especially neurology and zoology which are your most significant missing terms. It is no real mystery what the structure of various mental capacities involves; nor any mystery what s(Ws(Ws(WX+B)+B...)...)...) computes. Even involving anything beyond trivial applied statistics and trivial results in science shouldnt be required here. This stuff is very obvious.

FeepingCreature 2y ago

They're, in particular, multi-level curve-fitting algorithms. A formalism which, I'm sure you are aware, can express any bounded computation. In particular, depending on how you set up the tape, neural networks are comfortably Turing complete. We're not talking about 2-layer perceptrons anymore here. In my opinion, you should look less at the formalism and more at the empirically demonstrated performance.

mjburgess 2y ago
I'm not aware that it can express any bounded computation. But such is irrelevant anyway: there is no function from the distributional structure of text tokens to reasoning. There is nothing to approximate, and this is a system trained just to approximate some presumed function: our mental capacities arent patterns in glyphs. Whatever property you might imagine a transformer architecture to have (and it is vastly fewer than the set needed for general computation), the problem here is that it's being applied to approximate the structure of historical text data which isnt being generated from such a function. Indeed, there is no function which generates text data, there's a very large number of independent generating processes that give rise to the distribution of text. The phrase "the war in ukraine" acquires a different semantics over 2010-2030 in a radically different way than, "I liked that film" does. The capacities which produce distributions of text tokens are highly varied, complex, invovle a vast array of our mental processes, and so on. There's literally almost nothing in the distribution of text tokens that corresponds to any features of these processes. The structure of language is conventional, and rests on our familiarity with such conventions. Otherwise, let's end all science, everything to be known about the world derives from how "e" occurs alongside "lectron"
- stevenhuang 2y ago
  Computability is of critical relevance. Our mental capacities can be patterns. https://en.m.wikipedia.org/wiki/Predictive_coding https://en.m.wikipedia.org/wiki/Predictive_coding > There's literally almost nothing in the distribution of text tokens that corresponds to any features of these processes. There's nothing in evolutionary fitness that necessitates intelligence or reasoning ability either. Yet here we are. Sorry but if you don't see the connections then you need to do some reading on theory of mind, cognition, information theory, physics, philosophy. All of the fundamental basis are met to allow reasoning to emerge in LLMs. It is clear now where your confusion lies, and why you are led to believe so strongly that LLMs cannot reason: it is because you are an ML practitioner you overweight your expertise yet you don't know what you don't know, and have foundational gaps in your knowledge. You lack the context in these other fields. If you had them, your position should be closer to agnostic than this strong belief of yours that LLMs in their current form cannot reason.
  - mjburgess 2y ago
    lol, well when I've written my PhD on those areas we can return to whether I'm an expert on them or not. One does not form "connections" between them as in some wide-eye conspiracy theorist.. "predictive coding" has little to do with "prediction" in the ML sense. The latter concerned with making a quantitive estimate of some variable by summarising historical data. What we are doing when we revise a "mental model" is done by counter-factual simulation of possible future states. Statistical AI models conditional probability structures, and computes predictions as an expectation over weighted summarised historical data. This is not a means of performing counter-factual simulation. One trivial, sadly empirical, way to see this is to note that each marginal token generated is of constant time and energy use. Yet trivially, reasoning and a variety of other mental capacities should require abitarily different time to run. Eg., simulating a complex scenario is necessarily more intensive than a simple one, and so on. Yet I am quite annoyed that we need such dumb observations to make this point. It speaks of a profound ignorance of "theory of mind, cognition, information theory, physics, philosophy" and especially neurology and zoology which are your most significant missing terms. It is no real mystery what the structure of various mental capacities involves; nor any mystery what s(Ws(Ws(WX+B)+B...)...)...) computes. Even involving anything beyond trivial applied statistics and trivial results in science shouldnt be required here. This stuff is very obvious.