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?
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.
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)
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
But if “working” is a necessary condition for an explanation (some explanations work better than others), then won’t scientific explanations eventually become subject to the optimization drive of engineering?
Well, Kepler’s model does a good job predicting? Not perfectly, but astonishingly well.
I’m still not sure that the distinction between predictive model and explanatory model is so clear. Kepler wanted to explain the universe through the harmony of the spheres. Through that objective, he used the data to discover a beautiful and robust predictive model. Was he doing science?
Insofar as modelling is a 'predictive' activity in the engineering sense of useful estimates of observables -- it tends to end in pseudoscience.
Originally the idea of spheres was a good one (and not obtainable via any compression of measurements) -- it was obtained through reasoning by analogy. but when epicycles were added over-and-over, you effectively were using a universal functional approximator to match observable data.
Since the solar system doesn't change much, the epicycle approach works (by coincidence) -- but it's pseudoscience.
A model of gravity which can account for any possible solar system is an explanation, even if it's so hard to use we cannot actually do predictions with it (the status of much science).
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?