Hi schuler,
Very interesting! Some questions:
Most processes are multi-step (flowchart): they require multiple actions in sequence (probably all a neural network as well) to get a result. How do you calculate a score for the learning from that? Store all intermediate values? But how do you pinpoint the exact step that was weakest and should be tweaked most? Or would you need a kind of unit test for each step?
Is the learning always a separate pass to create a file with biases and weights, or can the network keep on learning as it goes? It would need some feedback for that, which is probably generated by a different process and so might have a different format, and might have to be processed itself before it becomes useful. How would you do that? Use another neural network to process the feedback? But that should have learning feedback as well. Etc.
In human vision, first we detect edges and then shapes. Those shapes are extrapolated and normalized (rotate, tilt, pan, resize, etc), and should then be handled by their own neural network for processing. Handed over to the right sub-process / step in the flowchart, so to say. How would you go about doing that?