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No, it's a completely different semantics. F = GMm/r^2 does not mean anything about {P(F|M), P(F|m), ..) -- this this the semantics of associative statistical

No, it's a completely different semantics. F = GMm/r^2 does not mean anything about {P(F|M), P(F|m), ..) -- this this the semantics of associative statistical models, as used in ML. Rather it means a gravitational force is caused by the interaction of two masses over a distance r. Here `F` refers to a force via a scientific model, etc. The formula is a short-hand conceuqnece of a family of explanatory models about mass, inertia, gravity, forces, etc. and is only valid when used in the context of those models. Eg., you cannot equate F = GMm/r^2 to F = kQq/r^2. Not least since we have no gravitational model which applies to tiny charged particles. The formula used in scientific modelling do not have either mathematical or statistical semantics (ie., they dont refer to numbers, nor to associations). They refer to bits of the world via explanations; and are only valid insofar as these explanations apply. Though science seems obsessed by formula, in terms of the goal of science, it's the least important part of the scientific model. Explanations are the goal, and highly circumstantial consequences of these are given formula partly for illustration, partly for engineering. There's rarely anything in any dataset whose model would even be useful for building an explanation. Explanations are build via counter-factuals, and these are resolved by experimental data -- they are not made by it. Indeed, in many cases, the experimental data would be no where nearly modelled lossessly by the candidate hypothesis.

dr_dshiv 3y ago
Here’s a toy example based on a project I’d like to do. I love faraday waves and Chladni plates — roughly speaking, cymatics. Now, if I record high res video of the waves on water in a dish, vibrating at different frequencies, I could likely create a diffusion model that had some latent “understanding” of the relationship between the proportional frequencies of sound and the waves in a particular sized dish. I could test this by holding out certain frequencies and observing whether the diffusion model could recreate them. So, I can ask the question of whether the AI model learned wave physics. Here, there would be no formula, per se, and no explanation. Merely a computational model that could make predictions about physical phenomena. Now, what makes this less scientific than a formula and set of explanations for the experiments? Is it because I can relate the the explanations linguistically to everything else I know about science? So, in that case, If I jointly developed a language model to do the same thing as the diffusion model, ie make predictions based on data—but linguistically capable of connecting the outcomes to scientific concepts, then would then it be scientific?
- mjburgess 3y ago
  The relationship between terms in scientific models and reality isn't linguistic. You cannot create explanatory models from associative models of pixels -- you can create associative models that may have some limited engineering use. (Because, by highly fragile engineered conditions, the pixels track unknown+unstated properties of the physical system). In the case of waves, the governing dynamical wave eq, ie., f(x, t) = potential(x, t) + kinetic(x, t) needs to have terms related to the physics of the system, eg., the properties of the material, to count as even a basic explanation. Broadly, you'd need to describe the dynamics of material properties and how they give rise to the dynamics of sound properties, which requires a family of explanatory models. You would produce those explanatory models by creating novel materials, novel experimental conditions, reasoning counter-factually, etc. over a long period of time. Eventually you may be able to formalise small, circumstantial, parts of those explanations and refute them by using logically entitled experimental data. The relevant relationships here are: explanation, causation, counter-factual possibility, necessity and logical entialment. No where is "association", nor should it ever be if it's science. An associative model of data isn't an explanation, it's a sort of pseudo-empiricism also present just in superstition. You can associate personality markers with positions of constellations with an associative model and get arbitrary predictive accuracy (since, eg., there are enough stars in the sky to choose ones which correlate with anything). This has nothing to do with science, and as even a claim to science, it's outright pseudoscience.
  - dr_dshiv 3y ago
    Thank you. But I don’t like the aggression of calling this “pseudoscience.” As though you have clear claim? Especially if I can easily claim that my “pseudoscience” works better than your “science.” (Because you admit as much)
    - mjburgess 3y ago
      It's pseudoscience to treat it as science, "works" is an engineering condition. "Explains" is all that interests scientists. I can encode an arbitrary amount of information, losslessly, by recording the position of various stars and listing their positions in some order. This is not an explanation of that information. For some purpose, "it works". Many things "work"; it is trivial to rig situations so that coincidences can be exploited. This isn't science. It's hard to over-estimate how profoundly pseudoscientific associative-modelling-as-science is: it's the basis of the history of human superstition, fraud, magical thinking, and so on. So now it can be automated: how obscene it is that vain engineers go around proclaiming to have automated science. This is ridiculous, and to claim to be able to do physics by correlating pixel patterns is a dangerous religion: no such model is safe, no such model reasons, no such model... These models are extremely fragile houses-of-cards that must be understood as the magic tricks they are. It is charlatanism to host a stage show and call it science -- there are many gurus in the world on that grift

dr_dshiv 3y ago

Here’s a toy example based on a project I’d like to do. I love faraday waves and Chladni plates — roughly speaking, cymatics. Now, if I record high res video of the waves on water in a dish, vibrating at different frequencies, I could likely create a diffusion model that had some latent “understanding” of the relationship between the proportional frequencies of sound and the waves in a particular sized dish. I could test this by holding out certain frequencies and observing whether the diffusion model could recreate them. So, I can ask the question of whether the AI model learned wave physics. Here, there would be no formula, per se, and no explanation. Merely a computational model that could make predictions about physical phenomena. Now, what makes this less scientific than a formula and set of explanations for the experiments? Is it because I can relate the the explanations linguistically to everything else I know about science? So, in that case, If I jointly developed a language model to do the same thing as the diffusion model, ie make predictions based on data—but linguistically capable of connecting the outcomes to scientific concepts, then would then it be scientific?

mjburgess 3y ago
The relationship between terms in scientific models and reality isn't linguistic. You cannot create explanatory models from associative models of pixels -- you can create associative models that may have some limited engineering use. (Because, by highly fragile engineered conditions, the pixels track unknown+unstated properties of the physical system). In the case of waves, the governing dynamical wave eq, ie., f(x, t) = potential(x, t) + kinetic(x, t) needs to have terms related to the physics of the system, eg., the properties of the material, to count as even a basic explanation. Broadly, you'd need to describe the dynamics of material properties and how they give rise to the dynamics of sound properties, which requires a family of explanatory models. You would produce those explanatory models by creating novel materials, novel experimental conditions, reasoning counter-factually, etc. over a long period of time. Eventually you may be able to formalise small, circumstantial, parts of those explanations and refute them by using logically entitled experimental data. The relevant relationships here are: explanation, causation, counter-factual possibility, necessity and logical entialment. No where is "association", nor should it ever be if it's science. An associative model of data isn't an explanation, it's a sort of pseudo-empiricism also present just in superstition. You can associate personality markers with positions of constellations with an associative model and get arbitrary predictive accuracy (since, eg., there are enough stars in the sky to choose ones which correlate with anything). This has nothing to do with science, and as even a claim to science, it's outright pseudoscience.
- dr_dshiv 3y ago
  Thank you. But I don’t like the aggression of calling this “pseudoscience.” As though you have clear claim? Especially if I can easily claim that my “pseudoscience” works better than your “science.” (Because you admit as much)
  - mjburgess 3y ago
    It's pseudoscience to treat it as science, "works" is an engineering condition. "Explains" is all that interests scientists. I can encode an arbitrary amount of information, losslessly, by recording the position of various stars and listing their positions in some order. This is not an explanation of that information. For some purpose, "it works". Many things "work"; it is trivial to rig situations so that coincidences can be exploited. This isn't science. It's hard to over-estimate how profoundly pseudoscientific associative-modelling-as-science is: it's the basis of the history of human superstition, fraud, magical thinking, and so on. So now it can be automated: how obscene it is that vain engineers go around proclaiming to have automated science. This is ridiculous, and to claim to be able to do physics by correlating pixel patterns is a dangerous religion: no such model is safe, no such model reasons, no such model... These models are extremely fragile houses-of-cards that must be understood as the magic tricks they are. It is charlatanism to host a stage show and call it science -- there are many gurus in the world on that grift