Jim,
In my prior posts I have listed some of the limitations of Shruiti. The lack of generalized generalizational and compositional hierarchies directly relates to the problems of learning from experience generalized rules that derived from learning in complex environements when the surface representation of many high level concepts are virtually never the same. This relates to your issue about failing to model the complexity of antecedents. But as the Serre paper I have cited multiple times in this thread shows that the type of gen/comp hierarchies need are very complex. His system model a 160x160 pixel greyscale image patch with 23 million models, probably each having something like 256 inputs, for a total about 6 billion links, and this is just to do very quick, feedforward, I-think-I-saw-a-lion uncertain recognition for 1000 objects. So for a Shruity system to capture all the complexities involved in human level perception or semantic reasoning would require much more in the way of computer resources than Shastry had. So although Shuiti's system is clearly very limited, it is amazing how much it does considering how simple it is. But the problem is not just complexity. As I said, Shruiti has some severe architectural limitations. But again, it was smart for Shastri to get his simplified system up and running first before he made all the architectural fixes required to make it more capable of more generalized implication and learning. I have actually spend some time thinking about how to generalize Shruiti. If they, or there equivalent, are not in Ben's new Novamente book I may take the trouble to write them up, but I am expecting a lot form Ben's new book. I did not understand your last sentence Ed Porter -----Original Message----- From: Jim Bromer [mailto:[EMAIL PROTECTED] Sent: Sunday, July 13, 2008 3:47 PM To: agi@v2.listbox.com Subject: Re: FW: [agi] WHAT PORTION OF CORTICAL PROCESSES ARE BOUND BY "THE BINDING PROBLEM"? I have read about half of Shastri's 1999 paper "Advances in Shruti- A neurally motivated model of relational knowledge representation and rapid inference using temporal synchrony" and I see that it he is describing a method of encoding general information and then using it to do a certain kind of reasoning which is usually called inferential although he seems to have a novel way to do this using what he calls "neural circuits". And he does seem to touch on the multiple level issues that I am interested in. The problem is that these kinds of systems, regardless of how interesting they are, are not able to achieve extensibility because they do not truly describe how the complexities of the antecedents would have themselves been achieved (learned) using the methodology described. The unspoken assumption behind these kinds of studies always seems to be that the one or two systems of reasoning used in the method should be sufficient to explain how learning takes place, but the failure to achieve intelligent-like behavior (as is seen in higher intelligence) gives us a lot of evidence that there must be more to it. But, the real problem is just complexity (or complicatedity for Richard's sake) isn't it? Doesn't that seem like it is the real problem? If the program had the ability to try enough possibilities wouldn't it be likely to learn after a while? Well another part of the problem is that it would have to get a lot of detailed information about how good its efforts were, and this information would have to be pretty specific using the methods that are common to most current thinking about AI. So there seem to be two different kinds of problems. But the thing is, I think they are both complexity (or complicatedity) problems. Get a working solution for one, and maybe you'd have a working solution for the other. I think a working solution is possible, once you get beyond the simplistic perception of seeing everything as if they were ideologically commensurate just because you have the belief that you can understand them. Jim Bromer _____ agi | <https://www.listbox.com/member/archive/303/=now> Archives <https://www.listbox.com/member/archive/rss/303/> | <https://www.listbox.com/member/?&; 0> Modify Your Subscription <http://www.listbox.com> ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=108809214-a0d121 Powered by Listbox: http://www.listbox.com
