I am not sure that anyone is actually interested in my opinion on this but to repeat my views on this argument again. I am not actually interested in designing a human clone. I am interested in writing an AGI program with an actual electronic computer. While having real world events from sensors which were similar to human senses to tie a referent to would be nice once in a while I just do not think that approach would simplify the problem of designing a more advanced AGI program at this time. So far there has been no outstanding evidence that multiple sensory modalities would actually solve the contemporary problems of AGI. As I have said, I believe that the main problem is one of complexity and while the use of multiple sensory types might make the problem simpler in some cases, I do not think it would not make it simpler in general. There is however, outstanding evidence that text-only AGI is possible, and that is the success of Watson. Watson may not have been a true AGI program, but it represented a major milestone in AI. Few people foresaw that an encyclopedic AI program would be achieved before an AI program exhibited more typical human like reasoning but in retrospect most insightful futurists did foresee a computer program that would have encyclopedic knowledge. So Watson may not fully explain human reasoning but it does provide a part in the stream of evidence that text-only AGI is possible.
Reasoning is not something that relies only on real world facts. The idea that visual input would somehow give an AI program reliable stream of real world facts is surprisingly naïve. If it did, then visual based AI would have already worked and the problem already solved. The problems of understanding how thinking takes place has not been sidestepped by declaring that visual-based AI and robotic AI is necessary for advanced AGI. I realize that progress has been made in visual AI and robotic AI but those problems are as difficult as text based reasoning has been. The problem as I see it is that insight must be based on a complicated process that is a little beyond our skills at this time. Is there some kind of short cut that we might use. Ok, maybe. By using trial and error methods, perhaps some future AGI program will be able to achieve genuine general learning. But at this time there is no, or very little, evidence that some simplistic system - like a traditional logical system - is sufficient. So instead, I am wondering if perhaps a great deal of specialization is what is needed to provide the basis for simplifying the complexity of AGI. Perhaps by keeping track of all the specializations that the program can conjecture it might be able to advance without getting bogged down in complexity. Jim Bromer ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
