So, do you or don't you model uncertainty, contradictory evidence, degree of similarity, and all those good things?
And what is a "CA", or don't i want to know? Edward W. Porter Porter & Associates 24 String Bridge S12 Exeter, NH 03833 (617) 494-1722 Fax (617) 494-1822 [EMAIL PROTECTED] -----Original Message----- From: John G. Rose [mailto:[EMAIL PROTECTED] Sent: Saturday, October 20, 2007 10:39 PM To: agi@v2.listbox.com Subject: RE: [agi] An AGI Test/Prize Hi Edward, I dont see any problems dealing with either discrete or continuous. In fact in some ways itd be nice to eliminate discrete and just operate in continuous mode. But discrete maps very well with binary computers. Continuous is just a lot of discrete, the density depending on resources or defined as ranges in sets, other descriptors, etc. different ways. Im not really well versed on NARS and Novamente so cant comment on them and they are light years down the road. They are basically in implementation stage, closer to realized utility, more than just theories. Oh those 55(80),000 lines of code are an AI product I am making so it is not AGI but the thing has basically stubs for AGI or could be used by AGI. But the methodology I am talking about seems to be very well workable with data from the real world. Its hard for me to find things that it doesnt work with although real tests need to be performed. BTW this type of thinking Im sure is well analyzed by many abstract algebra mathematicians. Computability issues exist and these may make the theory not workable to a certain degree. I actually dont know enough about a lot of this math to really work it through deeply for a feasibility study (yet) and much of it is still up in the air John What I found interesting is that, described at this very general level, what this is saying is actually related to my view of AGI, except that it appears to be based on a totally crisp, 1 or 0 view of the world. If that is correct, it may be very valuable in certain domains, with are themselves totally or almost totally crisp, but it wont work for most human-like thinking, because most human concepts and what they describe in the real world are not crisp. THAT IS, UNLESS, YOU PLAN TO MODEL CONCEPTUAL FLUIDITY, ITSELF, IN A TOTALLY CRISP, UNCERTAINTY-BASED, WAY, which is obviously doable at some level. I guess that is what you are referring to by saying our mind does crisp thinking all the time. Even most of us anti-crispies, plan to implement our fluid system on digital machinery using binary representation, which we hope will be crisp (but at the 22nm node it might be a little less than totally crisp.) But the issue is: do your crisp techniques efficiently learn and represent the fluidity of mental concepts, the non-literal similarity, and the many apparent contradictions, and the uncertainty that dominate in human thinking and sensory information about the real world? And if so, how is your approach different than that of the Novamente/Pei Wang-like approaches? And if so, how well are your (was it) 80,000 lines of code of working at actually representing and making sense of the shadows projected on the walls of your AGIs cave by sensations (or data) from the real world. Ed Porter, P.S. Re CA: maybe I am well versed in them but I dont know what the acronym stands for. If it wouldnt be too much trouble could you please educate me on the subject? _____ This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/? <http://v2.listbox.com/member/?&; > & ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=8660244&id_secret=56007871-ae3472
