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Yesterday I encountered a "wrong-on-the-internet" rando professing his excitement for "using machine learning" in to throttle speeds in the right places to avoid quality loss.

While completely not worth engaging with, I feel like this is a useful example to understand why this idiocy is so infuriating...

This is a problem domain where the constraints and effects are pretty much entirely comprehensible in terms of known physical models. Any suboptimal behavior is entirely a matter of nobody having spent the time to apply known models. But sure, let's instead spend the time hooking up ML, CV to evaluate results, and waste tons (literally) of plastic training a model to learn a poor approximation of what we already know.

But this is a general pattern that's terrifying...

The proponents of this kind of shit want to throw away the whole concept of having and using scientific knowledge obtained by experiment, with documentation of how it was obtained, evidence supporting the resulting models, falsifiability, etc., and replace it with a worse version of the way humans tens of thousands of years ago came to believe things about the world: simplistic pattern recognition.

@dalias they want badly to believe that minds can be reduced to simple stochastic models. This wasn't an entirely unreasonable hypothesis 20 years ago, but at this point it doesn't look like it's correct.

Cassandrich

@ravenonthill The concept is still vaguely plausible, but their idea for how to achieve it is utter bullshit.

If you compare how human minds are "trained", there are multiple feedback layers in the form of consequences, and most importantly, we select very carefully what training inputs are used rather than throwing giant mostly wrong and mostly evil corpuses at children, and most of the training is experiential not ingesting word soup.