Would a preferred computer vision system experience the Checker shadow illusion? http://en.wikipedia.org/wiki/Checker_shadow_illusion
If yes, computer vision will be as fallible as ours. If no, then there will always be examples, like the ones presented, where computers will see something different than humans.
When we look at the Checker Shadow Illusion, our brains are automatically "parsing" that image into a 3D scene and compensating for the lighting and shadowing. The reason you see square B as being lighter than A is because, if you could reach into the image and remove the cylinder so it's not casting a shadow anymore, square B would be lighter than A. Our brain doesn't think about color in terms of absolute hex values. Instead it tries to compensate for the lighting and positioning of elements, assuming that they're similar to what we would see in real life—a challenge which, by the way, computer vision systems have always struggled with.
That sounds pretty "correct" to me.
People recognize things by building a 3D model in their head, then comparing that to billions of experiential models, finding a match and then using cognition to test that match. "Is that a bird? No, its just a pattern of dog dropping smeared on a bench. Ha ha!"
I meant to talk about what some hypothetical future system could do (which I think was a reasonable context given the comment I replied to), not to characterize current systems.
To get there, computers will clearly have to change utterly their approach. A cascaded approach of quick-math followed by a more 'cognitive' approach on possible matches, could definitely improve on the current state of affairs.
I can't help but believe some of the image recognition mentioned in your article, especially of icons, is built through previous experience with similar iconic images. Symbols for things become associated with the real things. Its a modern adaptation of a much older processing mechanism.
So you're saying people are generative reasoners with very general hypothesis classes rather than discriminative learners with tiny hypothesis classes.
To which the obvious response is, yes, we know that. The question is how to make some computerization of general, generative learning work fast and well.
That being said, here is a much better resource than I am: http://en.wikipedia.org/wiki/G%C3%B6del%27s_incompleteness_t...
I don't know the answer. I do find it fascinating that something as simple as perspective (which we take for granted in a graphic like the checkers illusion) is a fairly recent technique, invented by Renaissance artists.