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by Klockan
3629 days ago
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The difference is that all human written systems was once understood by a human. ANN on the other hand creates unlabeled spaghetti which gets impossible to reason about if you scale that up to megabytes. You could try to fix a bug by changing one of the values, but that would ruin the ANN since all the values were chosen to be optimal. Thus the only way to make it better would be to change lots of unlabeled values at the same time and that is something no human can do. The only plausible way to fix a bug in an ANN is to incorporate said bug in the training set. |
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My point was not something about manipulating weights directly vs. augmenting the training set. The analogy is roughly, find a misclassification : add particular input to training set :: find a bug : add a patch/test case. I'm playing devil's advocate, of course, and there are obvious, important differences.
> The difference is that all human written systems was once understood by a human
But that's not one of them! In practice, we create software systems by getting some rough intuition about an interface, wiring together some components, then checking the results empirically. Basic components that everything else is built on, C compilers for instance, are based on ambiguous, even contradictory specifications.