I think I could write something that is a little like an active programming language that would be able to integrate highly constrained forms of English sentences into a model of the references of the sentences so long as the 'effects' (or local universe) of those references are specified by various sentences (either implicitly or explicitly). The constrained forms of language would hold the ambiguity of the parts of the sentences to a workable level. This would be an old world AI project and some people would claim that it wasn't working (even if it did) because not all grammatical English sentences would be allowed and the most common meanings of some sentences would not be the same as the meaning that would be inferred by the stylized grammar of the program. The program would be able to do more than just repeat the relations input into the program. It could implicitly construe certain relations based on similar cases. The working theory here is if the potential for massive ambiguity that can be found in human language could be eliminated then it should be possible to write an AGI calculator. This would not be a numerical programming language but it would logically infer (after a lot of detail and some trial and error learning) how the references of constrained English sentences that were input were related (as might be done with a simple but detailed story).
Of course the program would not know things that human beings know unless the idea and the background that would be a prerequisite to fitting the idea were input. So my question is whether this would constitute a breakthrough in AGI? Would this represent advancement in AI? If such a programming language was possible wouldn't that suppose that continued advances might be made by using it and extending it creatively? Is the fact that it would be narrow AI mean that it is incapable of being an AGI program? I think it would be an advancement because it may be impossible. My opinion, which I think is shared with a lot of other people, is that the main problem with similar ideas from old AI is that the complexity that follows the ambiguity of a natural language would make the program infeasible. But as the program is exposed to more examples of a kind-of-event, further references to that kind of event could then become more ambiguous even if the language was heavily constrained. If the program is to infer certain possibilities based on the consideration of similar cases, then with a greater diversity of cases the number of possibilities could increase with the combinations of referents used in a statement. So yes, it could represent a major advancement because it would prove this counter conjecture is wrong. It would also prove that if language was heavily constrained the details of a model could be inferred even if the individual cases of the kinds-of-things and kinds-of-events that were referred to were varied. I think this may be something interesting to test out. Jim Bromer ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-c97d2393 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-2484a968 Powered by Listbox: http://www.listbox.com
