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That's literally what we cannot do in a neural net. It's why they're so problematic: you can determine the propagation functions of individual nodes perfectly,

That's literally what we cannot do in a neural net. It's why they're so problematic: you can determine the propagation functions of individual nodes perfectly, and that knowledge tells you exactly nothing about all of the many things it's values contribute to. There is no "concrete thing" at the node level: a single node fundamentally can't see a wing, or a color, or anything else, that's only emergent behaviour of node constellations, and one node can contribute to many constellations simultaneously. Heck, there often isn't even a "concrete thing" at the many of the constellation levels, concrete things don't start to emerge until you're looking at the full state of all end nodes.

klmadfejno 6y ago
That's the machine learning 101 worldview. Initial level nodes are likely just minor inscrutable transformations. Later layers will be coding for features that, at least to some degree if it's a tractable problem, humans can understand and agree with as useful features. They'll be fuzzy and not as clearly defined as a human would frame the problem, but their purpose can be explored and generally identified. In any case, speaking of them as representing singular features for simplification is appropriate. Maybe it's not one node that codes for legs, but two nodes that codes for legs like this and legs like that, but that's not relevant to the point.