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jacob_cannell comments on The Brain as a Universal Learning Machine - Less Wrong
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No, even if you classify these false positives as "no image", this will not prevent someone from constructing new false positives.
Basically the amount of training data is always extremely small compared to the theoretically possible number of distinct images, so it is always possible to construct such adversarial positives. These are not random images which were accidentally misidentified in this way. They have been very carefully designed based on the current data set.
Something similar is probably theoretically possible with human vision recognition as well. The only difference would be that we would be inclined to say "but it really does look like a baseball!"
This technique exploits the fact that the CNN is completely deterministic - see my reply above. It may be very difficult for stochastic networks.
CNNs are comparable to the first 150ms or so of human vision, before feedback , multiple saccades, and higher order mental programs kicks in. So the difficulty in generating these fooling images also depends on the complexity of the inference - a more complex AGI with human-like vision given larger amounts of time to solve the task would probably also be harder to fool, independent of the stochasticity issue.