On 11/20/2014 04:25 PM, Jim Bromer wrote:
I was admitting that my plan to teach a computer to interpret simple text by heavily annotating it to assist the program to relate new input to previous text would be equivalent to saying that I would be 'programming' it to respond appropriately to the text.
Jim, in the text above you mention "annotating it." Do you mean heavily annotating the text that you are supposed to understand? If so, then, is the purpose to see how much needs to be added to text to make it "understandable"?
Do you see your development time being spent on annotating text, or on making the mechanism that will relate this heavily annotated text to existing data?
Below you mention that the programming would simulate "understanding." In my thinking, understanding is the result of having pertinent rules and facts in a functioning logic system - like a Clips project. Since I see understanding as this "stuff" that you acquired in your data, I'm not sure how it can be a simulation? Do you mean that the annotated text and it's processing will simulate an intelligent process (AI) that can break down this new text and turn it into rules and facts?
curious as to what you are seeing here... Stan
But the question is: would this method work to give such a program more traction than the typical AGI effort? Because if it did then then even though my programming (using annotated text) would only simulate 'understanding' the fact that it would work would mean that it might be used as a model for developing the program further. My goal then is to create a weak general AI (a weak-AGI) program that was capable of initially doing a tiny bit of thinking for itself but which I could gradually improve over time.
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