Statistical challenges and misreadings of literature create unreplicable science [pdf]

The conclusion I’m coming to is that this, too, is an exponent of an inherent weakness: we like to be told stories. As children we like to be told stories and this remains. We now have a complete spectrum of story-telling: science fiction, fiction, non-fiction, …, and news. News reporting itself is a continuum, with tabloid gossip on one side and political coverage on the other. We have so much stories to choose from.

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.

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"

Just "bad-sponsored"