On th other hand, you can imagine data-driven epistemology, where you don't really formulate any hypotheses. You just have a lot of pattern-matching power, completely agnostic of the knowledge domain
When you say "pattern-matching," what do you mean? Because when I imagine pattern-matching, I imagine that one has a library of patterns, which are matched against sensory data- and those library of patterns are the 'hypotheses.'
But where does this library come from? It seems to be something along the lines of "if you see it once, store it as a pattern, and increase the relevance as you see it more times / decrease or delete if you don't see it enough" which looks like an approximation to "consider all hypotheses, updating their probability upward when you see them and try to keep total probability roughly balanced."
That is, I think we agree; but I think when we use phrases like "pattern-matching" it helps to be explicit about what we're talking about. Distinguishing between patterns and hypotheses is dangerous!
Probably a better term would be "unsupervised learning". For example, deep learning and various clustering algorithms allow us to figure out whether the data had any sorts of non-temporal regularities. Or we may try to see if the data predicts itself - if we see X, in Y seconds we'll see Z. That doesn't seem to be equivalent to considering infinitely many hypotheses. In Solomonoff induction, hypothesis is the algorithm capable of generating data, and based on the new incoming information, we can decide whether the algorithm fits the data or not. In unsupervised learning, on the other hand, we don't necessarily have an underlying model, or the model may not be generative.
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