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The article argues, roughly, that the sorts of generalizations that machine learning 
models embody are very different from the sort of generalizations the West has 
taken as the truths that matter. ML’s generalizations often are tied to far more 
specific configurations of data and thus are often not understandable by us, 
and often cannot be applied to particular cases except by running the ML model.

This may be leading us to locate the really real not in the eternal (as the West has 
traditional done) but at least as much in the fleeting patterns of dust that result 
from everything affecting everything else all the time and everywhere.