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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. T

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"
- naasking 2y ago
  Prediction and explanation are basically equivalent IMO. A predictive model entails an explanation, and an explanation entails a predictive model. Predictive models that are more accurate are more accurate explanations, and predictive models that are more precise are more precise explanations, and vice versa. They are not that distinct.
  - mjburgess 2y ago
    They are highly distinct. Compare reporting the temperature tomorrow as a mode of all temperatures in November at your location, with a climate & weather simulation involving: cloud layers, the ocean, etc. The former is likely to be vastly more predictively accurate than the latter, but explains nothing. Explanatory models are often less predictively accurate than these (weakly inductive) predictive models. Their purpose is to tell us how reality works, and that provides some insight as to when we can adopt merely predictive approaches. This is because merely predictive models capture accidental features of measurement which hold up for awhile in some environments, that we wouldn't wish to explain. Without explanatory insight we find merely predictive models catastrophically collapse, and are otherwise, highly fragile. Eg., consider the performance of "predict the mode" in a snow storm. If you want a midly formal analysis of the difference: explanatory models quantify over causal properties of reality, describe their relationship, and provide necessary inferential methods for deducing conclusions from models. They permit arbitrary simulation across all relevant measuring systems. Merely predictive models quantify over historical measurements, assume similarity conditions across them, and assume future similarity to past cases. They provide only extremely weak inferential grounds for any inference. They can offer only repetition of one kind of measure, and cannot simulate the state of other relevant measuring devices or in different environments of measurement. Explanatory models describe reality. Predictive models describe the measurement device you happened to use, in the environment you specifically used it in.
    - nialse 2y ago
      I enjoyed your conversation and just want to chip in that there are as many definitions of science and knowledge as there are philosophers. One don’t have to have only one definition, but usually one have to adhere to the ones within the realm of ones scientific paradigm to be accepted and to develop the science. Normal science as Kuhn called it.

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"

naasking 2y ago
Prediction and explanation are basically equivalent IMO. A predictive model entails an explanation, and an explanation entails a predictive model. Predictive models that are more accurate are more accurate explanations, and predictive models that are more precise are more precise explanations, and vice versa. They are not that distinct.
- mjburgess 2y ago
  They are highly distinct. Compare reporting the temperature tomorrow as a mode of all temperatures in November at your location, with a climate & weather simulation involving: cloud layers, the ocean, etc. The former is likely to be vastly more predictively accurate than the latter, but explains nothing. Explanatory models are often less predictively accurate than these (weakly inductive) predictive models. Their purpose is to tell us how reality works, and that provides some insight as to when we can adopt merely predictive approaches. This is because merely predictive models capture accidental features of measurement which hold up for awhile in some environments, that we wouldn't wish to explain. Without explanatory insight we find merely predictive models catastrophically collapse, and are otherwise, highly fragile. Eg., consider the performance of "predict the mode" in a snow storm. If you want a midly formal analysis of the difference: explanatory models quantify over causal properties of reality, describe their relationship, and provide necessary inferential methods for deducing conclusions from models. They permit arbitrary simulation across all relevant measuring systems. Merely predictive models quantify over historical measurements, assume similarity conditions across them, and assume future similarity to past cases. They provide only extremely weak inferential grounds for any inference. They can offer only repetition of one kind of measure, and cannot simulate the state of other relevant measuring devices or in different environments of measurement. Explanatory models describe reality. Predictive models describe the measurement device you happened to use, in the environment you specifically used it in.
  - nialse 2y ago
    I enjoyed your conversation and just want to chip in that there are as many definitions of science and knowledge as there are philosophers. One don’t have to have only one definition, but usually one have to adhere to the ones within the realm of ones scientific paradigm to be accepted and to develop the science. Normal science as Kuhn called it.