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Man, people in the "it's just maths and probably" camp are in for a world of hurt when they learn that everything is just maths and probability. The observatio

Man, people in the "it's just maths and probably" camp are in for a world of hurt when they learn that everything is just maths and probability. The observation that LLMs are just doing math gets you nowhere, everything is just doing math.

perching_aix 1y ago
I largely agree, and upon reading this article is sadly also in that camp of applying this perspective to be dismissive. However, I find it incredibly valuable generally to know things aren't magic, and that there's a method to the madness. For example, I had a bit of a spat with a colleague who was 100% certain that AI models are not only unreliable because from a human perspective, insignificant changes to their inputs can cause significant changes to their outputs, but because, in their idea, they were actually random, in the nondeterministic sense. That I was speaking in hypotheticals when I took an issue with this, as he recalled my beliefs about superdeterminism, and inferred that "yeah if you know where every atom is in your processor and the state they're in, then sure, maybe they're deterministic, but that's not a useful definition of deterministic". Me "knowing" that they're not only not any more special than any other program, but that it's just a bunch of matrix math, provided me with the confidence and resiliency necessary to reason my colleague out of his position, including busting out a local model to demonstrate the reproducibility of model interactions first hand, that he was then able to replicate on his end on a completely different hardware. Even learned a bit about the "magic" involved myself along the way (that different versions of ollama may give different results, although not necessarily).
- pxc 1y ago
  > [The] article is sadly also in that camp of applying this perspective to be dismissive. TFA literally and unironically includes such phrases as "AI is awesome". It characterizes AI as "useful", "impressive" and capable of "genuine technological marvels". In what sense is the article dismissive? What, exactly, is it dismissive of?
  - perching_aix 1y ago
    > TFA literally and unironically includes such phrases as "AI is awesome". It characterizes AI as "useful", "impressive" and capable of "genuine technological marvels". This does not contradict what I said. > In what sense is the article dismissive? What, exactly, is it dismissive of? Consider the following direct quotes: > It’s like having the world’s most educated parrot: it has heard everything, and now it can mimic a convincing answer. or > they generate responses using the same principle: predicting likely answers from huge amounts of training text. They don’t understand the request like a human would; they just know statistically which words tend to follow which. The result can be very useful and surprisingly coherent, but it’s coming from calculation, not comprehension I believe these examples are self-evidently dismissive, but to further put it into words, the article - ironically - rides on the idea that there's more to understanding then just pattern recognition at a large scale, something mystical and magical, something beyond the frameworks of mathematics and computing, and thus these models are no true scotsmans. I wholeheartedly disagree with this idea; I find the sheer capability of higher level semantic information extraction and manipulation to be already a clear and undeniable evidence of an understanding. This is one thing the article is dismissive of (in my view). They even put it into words: > As impressive as the output is, there’s no mystical intelligence at play – just a lot of number crunching and clever programming. Implying that real intelligence is mystical, not even just in the epistemological but in the ontological sense, too. > But here at Zero Fluff, we don’t do magic – we do reality. Please. It also blatantly contradicts very easily accessible information on how a typical modern LLM works; no, they are not just spouting off a likely series of words (or tokens) in order, as if they were reciting from somewhere. This is also a common lie that this article just propagates further. If that's really how they worked, they'd be even less useful than they presently are. This is another thing the article is dismissive of (in my view).
    - JustinCS 1y ago
      I agree, real intelligence may also potentially be explained as all "math and probability", whether it's neurons or atoms. A key difference between our brains and LLMs is that the underlying math behind LLMs is still substantially more comprehensible to us, for now. It's common to believe that we have a more mystical quality, a consciousness, due to a soul, or just being vastly more complex, but few can draw a line clearly. That said, this article certainly gives a more accurate understanding of LLMs compared to thinking of them as if they had human-like intelligence, but I think it goes too far in insinuating that they'll always be limited due to being "just math". On a side note, this article seems pretty obviously the product of AI generation, even if human edited, and I think it has lots of fluff, contrary to the name.
- captn3m0 1y ago
  I also had to argue with a lawyer on the same point - he held a firm belief that “Modern GenAI systems” are different from older ML systems in that they are non-deterministic and random. And that this inherent randomness is what makes them both unexplainable (you can’t guarantee what it would type) and useful (they can be creative).
  - perching_aix 1y ago
    I honestly find this kinda stuff more terrifying than the models themselves.
- xigoi 1y ago
  Last time I checked, modern LLMs could give you a different answer for the same sequence of prompts each time. Did that change?
  - perching_aix 1y ago
    They can, but won't necessarily. If you use a managed service, they likely will, due to batched inference. Otherwise, it's simply a matter of configuring the seed to a fixed value and the temperature to 0. At least that's what I did, and then as long as the prompts were exactly the same, the responses remained exactly the same too. Tested with a quantized gemma3 using ollama, I'd say that's modern enough (barely a month or so old). Maybe lowering the temp is not even necessary as long as you keep the seed stickied, didn't test that.
    - Workaccount2 1y ago
      Just a note for everyone, people do these fixed prompts with fixed seed and 0-temp tests to track model versions. A cool application of the technique
    - perching_aix 1y ago
      Small update: now that I brought it up, I figured I should just test it. And indeed, as long as the seed is pinned, the replies are bit-for-bit identical run to run (note that the conversation context was cleared before each attempt, of course). So even setting the temp to 0 is not actually needed. This is handy in case somebody makes the claim that the randomness (nonzero temp parameter) makes a model perform better. The devil really is in the "insignificant for humans but significant for the model" details, basically. Not in the computational determinism.
      - xigoi 1y ago
        A random number generator also gives deterministic results when you fix the seed. Does that make it not a random number generator?
smohare 1y ago
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ninetyninenine 1y ago
Yeah your brain is also maths and probability. It’s called mathematical modeling and anything we understand in the universe can be modeled. If we don’t understand something we feel a model should exist we just don’t know it yet. AI we don’t have a model. Like we have a model for atoms and we know the human brain is made of atoms so in that sense the brain can be modeled but we don’t have a high level model that can explain things in a way we understand. It’s the same with AI. We understand it from the perspective of prediction and best fit curve at the lowest level but we don’t fully understand what’s going on at a higher level.
- ncarlson 1y ago
  > AI we don’t have a model. So, some engineers just stumbled upon LLMs and said, "Holy smokes, we've created something impressive, but we really can't explain how this stuff works!" We built these things. Piece by piece. If you don't understand the state-of-the-art architectures, I don't blame you. Neither do I. It's exhausting trying to keep up. But these technologies, by and large, are understood by the engineers that created them.
  - ijidak 1y ago
    Not true. How the higher level thought is occurring continues to be a mystery. This is an emergent behavior that wasn’t predicted prior to the first breakthroughs which were intended for translation, not for this type of higher level reasoning. Put it this way, if we truly understood how LLMs think perfectly we could predict the maximum number of parameters that would achieve peak intelligence and go straight to that number. Just as we now know exactly the boundaries of mass density that yield a black hole, etc. The fact that we don’t know when scaling will cease to yield new levels of reasoning means we don’t have a precise understanding of how the parameters are yielding higher levels of intelligence. We’re just building larger and seeing what happens.
    - ncarlson 1y ago
      > How the higher level thought is occurring continues to be a mystery. This is an emergent behavior that wasn’t predicted prior to the first breakthroughs which were intended for translation, not for this type of higher level reasoning. I'm curious what you mean by higher level thought (or reasoning). Can you elaborate or provide some references?
computerthings 1y ago
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112233 1y ago
Not only that, I have observed people reversing the flow and claiming everything is AI because it uses similar maths. E.g. I saw a guy with "AI degree" argue at lenght that weather forecast models are AI because the numerical solver works similarly to the gradient descent. This may seem inconsequential and pretentious at first, but it feels like a "land grab" by the AI-adjacent people, trying to claim authority over anything that numerically minimizes differetiable function value.

perching_aix 1y ago

I largely agree, and upon reading this article is sadly also in that camp of applying this perspective to be dismissive. However, I find it incredibly valuable generally to know things aren't magic, and that there's a method to the madness. For example, I had a bit of a spat with a colleague who was 100% certain that AI models are not only unreliable because from a human perspective, insignificant changes to their inputs can cause significant changes to their outputs, but because, in their idea, they were actually random, in the nondeterministic sense. That I was speaking in hypotheticals when I took an issue with this, as he recalled my beliefs about superdeterminism, and inferred that "yeah if you know where every atom is in your processor and the state they're in, then sure, maybe they're deterministic, but that's not a useful definition of deterministic". Me "knowing" that they're not only not any more special than any other program, but that it's just a bunch of matrix math, provided me with the confidence and resiliency necessary to reason my colleague out of his position, including busting out a local model to demonstrate the reproducibility of model interactions first hand, that he was then able to replicate on his end on a completely different hardware. Even learned a bit about the "magic" involved myself along the way (that different versions of ollama may give different results, although not necessarily).

pxc 1y ago
> [The] article is sadly also in that camp of applying this perspective to be dismissive. TFA literally and unironically includes such phrases as "AI is awesome". It characterizes AI as "useful", "impressive" and capable of "genuine technological marvels". In what sense is the article dismissive? What, exactly, is it dismissive of?
- perching_aix 1y ago
  > TFA literally and unironically includes such phrases as "AI is awesome". It characterizes AI as "useful", "impressive" and capable of "genuine technological marvels". This does not contradict what I said. > In what sense is the article dismissive? What, exactly, is it dismissive of? Consider the following direct quotes: > It’s like having the world’s most educated parrot: it has heard everything, and now it can mimic a convincing answer. or > they generate responses using the same principle: predicting likely answers from huge amounts of training text. They don’t understand the request like a human would; they just know statistically which words tend to follow which. The result can be very useful and surprisingly coherent, but it’s coming from calculation, not comprehension I believe these examples are self-evidently dismissive, but to further put it into words, the article - ironically - rides on the idea that there's more to understanding then just pattern recognition at a large scale, something mystical and magical, something beyond the frameworks of mathematics and computing, and thus these models are no true scotsmans. I wholeheartedly disagree with this idea; I find the sheer capability of higher level semantic information extraction and manipulation to be already a clear and undeniable evidence of an understanding. This is one thing the article is dismissive of (in my view). They even put it into words: > As impressive as the output is, there’s no mystical intelligence at play – just a lot of number crunching and clever programming. Implying that real intelligence is mystical, not even just in the epistemological but in the ontological sense, too. > But here at Zero Fluff, we don’t do magic – we do reality. Please. It also blatantly contradicts very easily accessible information on how a typical modern LLM works; no, they are not just spouting off a likely series of words (or tokens) in order, as if they were reciting from somewhere. This is also a common lie that this article just propagates further. If that's really how they worked, they'd be even less useful than they presently are. This is another thing the article is dismissive of (in my view).
  - JustinCS 1y ago
    I agree, real intelligence may also potentially be explained as all "math and probability", whether it's neurons or atoms. A key difference between our brains and LLMs is that the underlying math behind LLMs is still substantially more comprehensible to us, for now. It's common to believe that we have a more mystical quality, a consciousness, due to a soul, or just being vastly more complex, but few can draw a line clearly. That said, this article certainly gives a more accurate understanding of LLMs compared to thinking of them as if they had human-like intelligence, but I think it goes too far in insinuating that they'll always be limited due to being "just math". On a side note, this article seems pretty obviously the product of AI generation, even if human edited, and I think it has lots of fluff, contrary to the name.
captn3m0 1y ago
I also had to argue with a lawyer on the same point - he held a firm belief that “Modern GenAI systems” are different from older ML systems in that they are non-deterministic and random. And that this inherent randomness is what makes them both unexplainable (you can’t guarantee what it would type) and useful (they can be creative).
- perching_aix 1y ago
  I honestly find this kinda stuff more terrifying than the models themselves.
xigoi 1y ago
Last time I checked, modern LLMs could give you a different answer for the same sequence of prompts each time. Did that change?
- perching_aix 1y ago
  They can, but won't necessarily. If you use a managed service, they likely will, due to batched inference. Otherwise, it's simply a matter of configuring the seed to a fixed value and the temperature to 0. At least that's what I did, and then as long as the prompts were exactly the same, the responses remained exactly the same too. Tested with a quantized gemma3 using ollama, I'd say that's modern enough (barely a month or so old). Maybe lowering the temp is not even necessary as long as you keep the seed stickied, didn't test that.
  - Workaccount2 1y ago
    Just a note for everyone, people do these fixed prompts with fixed seed and 0-temp tests to track model versions. A cool application of the technique
  - perching_aix 1y ago
    Small update: now that I brought it up, I figured I should just test it. And indeed, as long as the seed is pinned, the replies are bit-for-bit identical run to run (note that the conversation context was cleared before each attempt, of course). So even setting the temp to 0 is not actually needed. This is handy in case somebody makes the claim that the randomness (nonzero temp parameter) makes a model perform better. The devil really is in the "insignificant for humans but significant for the model" details, basically. Not in the computational determinism.
    - xigoi 1y ago
      A random number generator also gives deterministic results when you fix the seed. Does that make it not a random number generator?

smohare 1y ago

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ninetyninenine 1y ago

Yeah your brain is also maths and probability. It’s called mathematical modeling and anything we understand in the universe can be modeled. If we don’t understand something we feel a model should exist we just don’t know it yet. AI we don’t have a model. Like we have a model for atoms and we know the human brain is made of atoms so in that sense the brain can be modeled but we don’t have a high level model that can explain things in a way we understand. It’s the same with AI. We understand it from the perspective of prediction and best fit curve at the lowest level but we don’t fully understand what’s going on at a higher level.

ncarlson 1y ago
> AI we don’t have a model. So, some engineers just stumbled upon LLMs and said, "Holy smokes, we've created something impressive, but we really can't explain how this stuff works!" We built these things. Piece by piece. If you don't understand the state-of-the-art architectures, I don't blame you. Neither do I. It's exhausting trying to keep up. But these technologies, by and large, are understood by the engineers that created them.
- ijidak 1y ago
  Not true. How the higher level thought is occurring continues to be a mystery. This is an emergent behavior that wasn’t predicted prior to the first breakthroughs which were intended for translation, not for this type of higher level reasoning. Put it this way, if we truly understood how LLMs think perfectly we could predict the maximum number of parameters that would achieve peak intelligence and go straight to that number. Just as we now know exactly the boundaries of mass density that yield a black hole, etc. The fact that we don’t know when scaling will cease to yield new levels of reasoning means we don’t have a precise understanding of how the parameters are yielding higher levels of intelligence. We’re just building larger and seeing what happens.
  - ncarlson 1y ago
    > How the higher level thought is occurring continues to be a mystery. This is an emergent behavior that wasn’t predicted prior to the first breakthroughs which were intended for translation, not for this type of higher level reasoning. I'm curious what you mean by higher level thought (or reasoning). Can you elaborate or provide some references?

computerthings 1y ago

[dead]

112233 1y ago

Not only that, I have observed people reversing the flow and claiming everything is AI because it uses similar maths. E.g. I saw a guy with "AI degree" argue at lenght that weather forecast models are AI because the numerical solver works similarly to the gradient descent. This may seem inconsequential and pretentious at first, but it feels like a "land grab" by the AI-adjacent people, trying to claim authority over anything that numerically minimizes differetiable function value.