@Alyer Babtu

Is there any general work on the topology/qualitative mapping behavior of these ML and AI systems ? ... What is the actual possible range of outputs of a given system ?

There's always a lot of ongoing work, but there are so many individual differences in implementations that generalizations are hard to come by. Perhaps the most approachable examinations are things like DeepDream, because of the way it maps the output back to the input image itself, thereby allowing a human to better see how certain features are more or less likely to trigger a false positive.

But perhaps we are using inappropriate notions of “close”, i.e. have the wrong topologies, and so hinder understanding of the system dynamics.

This is one of the major ongoing problems in these sorts of machine learning systems. You can't really interrogate them at a high level, a la expert systems, to find out what they really have "learned". But, as these incidents of false positives show, it is very often the case that what is given significant weight is all too often a terribly small sub-set of the data points that were in the training set. @Phaete

Then this should be easy for you.

Very: you have engaged in the common pseudo-science tactic of citing irrelevant research as an attempt to create an air of legitimacy, despite the fact that any rational observer can see that neither of the papers you reference either supports your claim or refutes mine in any way. Such intellectual dishonesty is indeed easy to detect, so I suggest you refrain from doing it if you sincerely want this conversation to continue.

Things don't get clearer at lower resolution, you only scan low res because there is a lack somewhere else.

I made no claims that things would get "clearer". I very directly did assert that you might be able to get more accurate recognition of objects if a major source for false positives is overtraining on less significant details. There are all kinds of reasons it makes great sense to train on degraded images, because I don't know of anyone who wants their self-driving car to dangerously malfunction just because a little rain gets on the lens, or a little snow gets on a sign. 4245aae303f420130ba8470b3d9559f798b8674b4bb88b99c22dc837e6ffd1bb