Computer learns to pick out salient features to identify images. Then we are shocked that when trained with no supervision the salient features aren’t what we would have chosen.
I see this as a great ah-ha moment. Humans also have visual systems that can be tricked by optical illusions. The patterns presented while seemingly incomprehensible to us make sense to computers for the same reason our optical illusions do to us -- taking short cuts in visual processing that would fire on patterns not often or ever seen in the real world. Which BTW means even as is, this type of visual identification is still useful, since the random images generating false hits aren’t just any random images, but ones that have visual features similar to the targets identified, even if we humans can’t see the similarities or even if they look like white noise.
Now that we know what computers are picking out as salient features, we can modify the algorithms to add additional constraints on what additional salient features must or must not be in an object identified, such that it would correspond more closely to how humans would classify objects. Baseball’s must have curvature for instance not just zig-zag red lines on white.