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I really do want to ask the author that question, given that he focuses so much on the "weird" idea that everything turns out to be just numbers moving around over time. "What do you suppose the input to the human brain looks like ?" Since I have kids I have come to realize that the same thing you see in neural nets you see in human beings. Understanding exists, but it is mostly not how human beings respond to the world around them. Mostly we are a minimally generalized dictionary, we know a long long very damn long list of "tricks". If A happens, B will follow. There's very little along the lines of "objects fall along a parabolic trajectory". This leads to generalization errors, and the surprising thing is you see those in humans ! Kids having learned to open one type of door do not know how to deal with an (even very slightly) jammed doorknob, they don't recognize differently shaped doorknobs as doorknobs, etc. First few days they don't even realize that if pushing won't work, pulling might. So the understanding of opening doors really does start out on the level of "move the free end of the small cylindrical object in the middle of the door that's parallel to the floor down, and then push", and if any of those conditions fails, well, door's going to stay shut. And this is exactly the very hard problem you encounter with neural nets : finding the right balance between specificity and generalization. But one saving grace is that if you specialize in enough special cases, you can get around without having a general understanding, and that's exactly what's happening with kids. |