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That's like saying that it's not astronomy if I'm using a radio telescope, only optical telescopes are real astronomy. It makes zero sense. The tools are irrele

That's like saying that it's not astronomy if I'm using a radio telescope, only optical telescopes are real astronomy. It makes zero sense. The tools are irrelevant. We don't have physics with and without ml journals. It's like saying AlphaFold isn't biology because it uses ml. It's absurd One goal of linguistics is to produce an account of how language works. If you understand how something works you can reproduce it. This was always a core part of linguistics. Just look at Chomsky's work. Or at what counts as evidence in a linguistics paper. It is a model that "explains" some feature of language. NLP does exactly the same thing as linguistics. It produces explanations and makes predictions about language. But it does so with ML. I think you're confused about optics and rasterization. There are countless ways to relate the two. Rasterization is not unphysical, no more so than say, finite element methods are unphysical. Heck physics is in the name of PBR. It's an approximation. And a fine one that people actually use in both science and games.

mjburgess 2y ago
My master's project was applying non-gradient-based ML methods to parameter optimisation in quantum metrology -- I'm aware that the reskin on curve-fitting algorithms called "Machine Learning" may be used as part of science. We've had taylor series approximations to functions for 300 years. But insofar as we're talking about LLMs, and most products you can use, we're talking about an engineering use. Engineers build systems that "perform" as you say explanatory accuracy is not a kind of engineering performance. It would equally well be said that, for a video game studio, "the more physicists I fired, the more the games perform better" -- for, of course, physicists have never studied 'making a video-game image convincing', and their techniques are best run on supercomputers, since they'd build explanatory simulations with explanatory accuracy. Video game developers do not do this. I have developed video games also, so I know. Convincingly placing pixels on a screen as-if governed by the laws of physics does not require actually simulating them. It requires generating frames from a "point of view" that never expose the absense of real physics, or approximations thereof. The world isnt made of triangles, and you cannot clip thru solid objects. The goal of a person making an LLM is similar to the goal of a video game developer, these are engineering goals: to give a user an experience which they will pay for. Now, a 2d 80s video-game has more actual physics in it than an LLM has models of language use, but still, this is irrelevant to the goals of these creators. A linguist could analyse an LLM as the conditional probability structure of the english language, as captured largely recent electronic texts -- but this structure is partly what precisely linguists and others are trying to explain. This is why all curve-fitting to historical data isn't in itself science: it is a restatement of the very target of explanation, the data. The job of scientific fields is to explain that curve. Its explotation by engineers is explanation-free. It isnt a science, and replaces no science.
- light_hue_1 2y ago
  And my PhD and dayjob is doing research on this. If a student told me they had this view of what ML is, I would tell them that we've failed to educate them. The thought that physics doesn't care about performance or approximation is silly. Just look at AlphaFold. Heck, I talk to climatologists and material scientists that want the equivalent all the time. Prediction is the heart of all science. Whether we're talking neroscience, linguistics, physics, etc. You think people who run things on supercomputers want to do so for some idealistic notion of what science is? No. They have to do so because they don't have good approximations. Just like with protein folding. Places like DESRES used to build supercomputers for that. This is over now. ML models learn representations of data which can then be reused for many tasks. There's a whole field where we try to understand those representations. Those are explanations of what's going on given that they're such good predictions. The embeddings you get from an LLM are better models of language than anything linguistics ever accomplished. Their goal should be to explain them and probe their limits instead of complaining. If linguists has come up with gpt they would have been celebrating, the method by which you do science is irrelevant as long as it works. I'll close by quoting Dawkins. "Science. It works, bitches". That's the value of science. Can we predict which molecule will cure cancer? Everything else is ideology and silly thoughts from before the paradigm shift.
  - mjburgess 2y ago
    Prediction is not the heart of science, this is early 20th C. mumbojumbo and humean nonesense that gets repeated by curve-fitters because it's all they do. Explanation is the heart of science, not prediction. All predictions newton would have made of the orbits of the planets would have been wrong (and so on). And this goes for the vast majority of textbooks physics when its applied to very many ordinary situations: no predictive power at all. ML models learn "representations" of the data, yes. They are models of measures. The model is just an f=sample({(all possible measures,)}). Science provides representations of the data generating process, ie., reality. It says why those are the measures, why the temperature of gas has that value, not a report on what those values were. Nor even a compressed conditional probability model of those values -- there is no atomic theory in a zip of temperatures. The purpose of a scientific model is to explain these data-representations, not merely to predict them based on some naive regularity assumption about the measurement device: that it will always measure that way in the future. The reality of the ML is that it offers only intra-distribution generalisation, nothing of the kind of generalisation science offers where all possible distributions induced by intervention on the (explanatory) variables of scientific models are captured. And this is often a scam that only academics can get away with, ex hyp., just assuming that the test distribution "will turn out as expected" in premise. The reality is that this sort of repetition of historical data, requires extreme control over the data generating process which gives rise to the test distribution. How is that control delivered in practice? If its a medical lab, through untold amounts of toil delivering, say, histological slides "just right" so this dumb process almost works. If its a face tracking, well you'd be hope its not being used by the police -- because they aint orienting the camera at 3.001m at ISO 151.5 from the masses. This is the problem with models that obtain predictive power without explanatory content: they rely on prediction time being rigged with, in-practice, extreme control mechanisms that are wholly unstated and unknown by the "modellers". Because these are no models of reality at all, but mere repetitions of historical data with unknown, unmodelled and hence unexplained similarity. Science concerns itself with explanation, that is its goal. Prediction is instrumental. Engineering's goal is the utility of the product, and so any strategy whatsoever, even a dumb, "if its happened before, itll happen again" is permitted. There is no textbook of physics which models reality by saying, "Well, we suppose in the future, the positions and velocities will just follow the same distribution, but we've no idea why, and how dare you ask, and get out, and doesnt my Ideal-Gas-TransformerModel look pretty? It gets the pressure right for Argon at 20.0001 C in glass jars at about 2.002L"

mjburgess 2y ago

My master's project was applying non-gradient-based ML methods to parameter optimisation in quantum metrology -- I'm aware that the reskin on curve-fitting algorithms called "Machine Learning" may be used as part of science. We've had taylor series approximations to functions for 300 years. But insofar as we're talking about LLMs, and most products you can use, we're talking about an engineering use. Engineers build systems that "perform" as you say explanatory accuracy is not a kind of engineering performance. It would equally well be said that, for a video game studio, "the more physicists I fired, the more the games perform better" -- for, of course, physicists have never studied 'making a video-game image convincing', and their techniques are best run on supercomputers, since they'd build explanatory simulations with explanatory accuracy. Video game developers do not do this. I have developed video games also, so I know. Convincingly placing pixels on a screen as-if governed by the laws of physics does not require actually simulating them. It requires generating frames from a "point of view" that never expose the absense of real physics, or approximations thereof. The world isnt made of triangles, and you cannot clip thru solid objects. The goal of a person making an LLM is similar to the goal of a video game developer, these are engineering goals: to give a user an experience which they will pay for. Now, a 2d 80s video-game has more actual physics in it than an LLM has models of language use, but still, this is irrelevant to the goals of these creators. A linguist could analyse an LLM as the conditional probability structure of the english language, as captured largely recent electronic texts -- but this structure is partly what precisely linguists and others are trying to explain. This is why all curve-fitting to historical data isn't in itself science: it is a restatement of the very target of explanation, the data. The job of scientific fields is to explain that curve. Its explotation by engineers is explanation-free. It isnt a science, and replaces no science.

light_hue_1 2y ago
And my PhD and dayjob is doing research on this. If a student told me they had this view of what ML is, I would tell them that we've failed to educate them. The thought that physics doesn't care about performance or approximation is silly. Just look at AlphaFold. Heck, I talk to climatologists and material scientists that want the equivalent all the time. Prediction is the heart of all science. Whether we're talking neroscience, linguistics, physics, etc. You think people who run things on supercomputers want to do so for some idealistic notion of what science is? No. They have to do so because they don't have good approximations. Just like with protein folding. Places like DESRES used to build supercomputers for that. This is over now. ML models learn representations of data which can then be reused for many tasks. There's a whole field where we try to understand those representations. Those are explanations of what's going on given that they're such good predictions. The embeddings you get from an LLM are better models of language than anything linguistics ever accomplished. Their goal should be to explain them and probe their limits instead of complaining. If linguists has come up with gpt they would have been celebrating, the method by which you do science is irrelevant as long as it works. I'll close by quoting Dawkins. "Science. It works, bitches". That's the value of science. Can we predict which molecule will cure cancer? Everything else is ideology and silly thoughts from before the paradigm shift.
- mjburgess 2y ago
  Prediction is not the heart of science, this is early 20th C. mumbojumbo and humean nonesense that gets repeated by curve-fitters because it's all they do. Explanation is the heart of science, not prediction. All predictions newton would have made of the orbits of the planets would have been wrong (and so on). And this goes for the vast majority of textbooks physics when its applied to very many ordinary situations: no predictive power at all. ML models learn "representations" of the data, yes. They are models of measures. The model is just an f=sample({(all possible measures,)}). Science provides representations of the data generating process, ie., reality. It says why those are the measures, why the temperature of gas has that value, not a report on what those values were. Nor even a compressed conditional probability model of those values -- there is no atomic theory in a zip of temperatures. The purpose of a scientific model is to explain these data-representations, not merely to predict them based on some naive regularity assumption about the measurement device: that it will always measure that way in the future. The reality of the ML is that it offers only intra-distribution generalisation, nothing of the kind of generalisation science offers where all possible distributions induced by intervention on the (explanatory) variables of scientific models are captured. And this is often a scam that only academics can get away with, ex hyp., just assuming that the test distribution "will turn out as expected" in premise. The reality is that this sort of repetition of historical data, requires extreme control over the data generating process which gives rise to the test distribution. How is that control delivered in practice? If its a medical lab, through untold amounts of toil delivering, say, histological slides "just right" so this dumb process almost works. If its a face tracking, well you'd be hope its not being used by the police -- because they aint orienting the camera at 3.001m at ISO 151.5 from the masses. This is the problem with models that obtain predictive power without explanatory content: they rely on prediction time being rigged with, in-practice, extreme control mechanisms that are wholly unstated and unknown by the "modellers". Because these are no models of reality at all, but mere repetitions of historical data with unknown, unmodelled and hence unexplained similarity. Science concerns itself with explanation, that is its goal. Prediction is instrumental. Engineering's goal is the utility of the product, and so any strategy whatsoever, even a dumb, "if its happened before, itll happen again" is permitted. There is no textbook of physics which models reality by saying, "Well, we suppose in the future, the positions and velocities will just follow the same distribution, but we've no idea why, and how dare you ask, and get out, and doesnt my Ideal-Gas-TransformerModel look pretty? It gets the pressure right for Argon at 20.0001 C in glass jars at about 2.002L"