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> As of right now, a well trained convolutional neural network is no more than a mechanical pattern matching algorithm on steroids. And computers are at best mechanical pattern matching machines. This isn't something that's subject to empirical evidence, and appeals to ignorance or the limits of our current understanding will not do. Computers cannot be any more than that by definition (and arguably the word "matching" is being used in an analogous fashion; rather, a computer configured with an algorithm is such that given an initial state, it will lead to a final state that, when interpreted by a human being, can be interpreted consistently in the desired manner, i.e., a final state of 0 means that the initial configuration encoded two state that match, and a final state of 1 means that they did not). Abstraction is, by definition, NOT reducible to a mechanical process like this. The human interpreter is the one possessing abstract concepts and who interprets by means of meanings conventionally assigned to symbols or machine states. A machine may contain a state that we call an image, but no process can in-principle abstract anything from this aggregation of states. To claim otherwise is to completely misunderstand what symbol manipulation is. (Even a human being couldn't abstract anything from an image in a mode analogous to the way in which a computer operates. By analogy, given a matrix of RGB values, could you tell me what's in the image? Or could you at best compute, say, a value that, when looked up in an already given table of values, gives you a label such as "sheep"?) However, that does not mean that AI cannot perform well, at least within narrow constraints. It may very well be possible to improve AI techniques to such a degree that it can assign the label "sheep" correctly with high accuracy. There simply is an in-principle difference between AI and actual intelligence. |