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How much of this is due to the fact that machines are still binary? As complicated as the layers are that we’ve built on top, the gates we’re playing with at the lowest level only understand 1 or 0, on or off.
Approximately 0%. Deep learning models process floating point data. The fact that these floats are discretized (on 16 or 32 bits) does not account for any meaningful part of the generalization problem in deep learning.
Could there be an inherent language problem? As in current machine architecture doesn’t give us the words to describe the problem accurately. The problems get translated down to a set of instructions. This or that. No fuzziness to allow for something almost right but different.
Or do we have a broad enough language, but need machines to write and not just read to adapt. Adaptation means modification, maybe not just of a better training set, but of additional models. If a model can identify something that surprises it, can it construct a better solution?
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