No problem. To me it's purely information that builds a neural architecture that facilitates intelligence creation. Based on experience etc.
It looks like a developmental biology program! Also I should say that I do understand why people study simple end to end loss functions: to isolate other aspects of the machine learning problem and to keep things simple. So I don’t really mean that as a criticism of ML.
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And yes I know
@GaryMarcus said it was a developmental biology program in one of his books, which I think is even more under emphasized than variable binding now. -
To take Hopi's lab's work as ex: len(burrow) could be outcome of a rewarding process or a "while loop." But in either case, need to specify why burrowing only kicks in at certain places, times, types of dirt, etc.; this seems more naturally a program than a set of loss fns.
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