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I think the proper test is how stable the learning system is in the face of adversarial examples. I have no indications either way that we could, using current
by danharaj 9y ago
I think the proper test is how stable the learning system is in the face of adversarial examples. I have no indications either way that we could, using current ideas, create a system that learns and is autonomous like a human brain while regulating itself from going too far off the rails. An optical illusion won't lead to you thinking you're the king of France, nor do there seem to be basilisks that will actually crash a brain.
It remains to be seen if the same holds for artificial learning systems. Of course! The gap between current learning systems and even the brain of a rodent is vast and we have yet no idea how to get there.
- yters 9y agoThe incompleteness/inconsistency theorem I mention seems to indicate no such ML system is possible. It will either encounter examples it cannot deal with, or it will be deceived. ML can only be guaranteed to work within very limited domains.
- munificent 9y ago> An optical illusion won't lead to you thinking you're the king of France I'm not sure what you're getting at here. By definition, optical illusions are perceptual mistakes limited to the optical system. It is certainly possible for an optical illusion to trick someone into doing something they don't intend to do because they are fooled into believing the illusion. Imagine painting a set of stairs with a disorienting pattern of shading that makes them appear off, leading you to trip and fall down them. I don't think it's necessarily for a perceptual illusion to hijack your entire belief system in order to be considered an effective adversarial attack.
- yters 9y agoThe whole point of these examples is the DNN gives erroneous classifications with near complete certainty. This looks to me like hijacking a belief system.