Jim, We will eventually stumble upon this conceptual complexity, namely a few algorithms that exceed the results that human intelligence uses (the algorithms created through slow evolution and relatively fast learning). we would have a smarter machine that exhibits advanced intelligence in many ways... maybe capable of self learning to ever higher levels and then nothing else if needed, except that:
today, we dont know how to extract sufficient patterns yet from natural language without additional training/trainers because languages reflect the unique histories of the respective races. Your conceptual complexity laden program full of insights would need to be trained in these cases anyway, no matter how insightful it became (think Wolfram's Computational Equivalence theory, where some things are really not pattern matching but must be simulated to the last detail to be fully understood due to their complex nature). So why start out with something that goes back to training issues anyway and is not even available today. Alternatively, semantic webs from expert systems will become more available every year, the permutations of the objects contained therein will not be exhaustive searches of the truly unrealistic search space that would result, but are more like Deep Blue's solutions using trade offs of time and quality of knowledge. Many permutations would never even be attempted because objects are in different classes and context rules determine areas with a high potential for valuable insights that would be favored. The constant self organization of the program and its database according to the rules of maximal lossless compression would insure that a given set of computational resources becomes intelligent over time. Letting such a system "read" CYC type databases will further reduce the search space of interest. The benefit is this can be done sooner with the knowledge we have today. t On Sat, May 31, 2008 at 4:38 PM, Jim Bromer <[EMAIL PROTECTED]> wrote: > Suppose that an advocate of behaviorism and reinforcement was able to make > a successful general AI program that was clearly far in advance of any other > effort. At first I might argue that his advocacy of behaviorism and > reinforcement was only an eccentricity, that his program must be coded with > some greater complexity than simple reinforcement to produce true learning. > Now imagine that everyone got to examine his code, and after studying it I > discovered that it was amazingly simple in concept. For example, suppose > the programmer only used 32 methods to combine or integrate referenced data > objects and these 32 methods were used randomly in the program to combine > behaviors that were to be reinforced by training. At first, I might argue > that the 32 simple internal methods of combining data or references wasn't > truly behaviorist because behaviorism was only concerned with the observable > gross behavior of an animal. My criticism would be somewhat valid, but it > would quickly be seen as petty quibbling and non-instructive because, in > this imagined scenario, the efficacy of the program is so powerful, and the > use of 32 simple integration methods along with a reinforcement of > observable 'behaviors' so simple, that my criticism against the programmer's > explanation of the paradigm would be superficial. I might claim that it > would be more objective to drop the term behaviorist in favor of the use of > some more objective explanation using familiar computational terms, but even > this would be a minor sub-issue compared to the implications of the success > of the paradigm. > > > > The programmer in my fictional story could claim that the simplest > explanation for the paradigm could qualify as the most objective > description. While he did write 32 simple internal operations, the > program had to be trained through the reinforcement of its observable > 'behavior', so it would qualify as a true behavioral-reinforcement method. > People could make the case that they could improve on the program by > including more sophisticated methods in the program, but the simplest > paradigm that could produce the desired effects would still suffice as an > apt description of the underlying method. > > > > Now there are a number of reasons why I do not think that a simple > reinforcement scheme, like the one I mentioned in my story, will be first to > produce higher intelligence or even feasible as a model for general use. The > most obvious one, is that the number of combinations of data objects that > are possible when strung together would be so great, that it would be very > unlikely that the program would stumble on insight through a simplistic > reinforcement method as described. And it would be equally unlikely that > the trainer would have the grasp of the complexity of possible combinations > to effectively guide the program toward that unlikely goal. To put it > another way, the simplistic reinforcement paradigm is really only a > substitute for insightful programming. The paradigm, even if conceptually > possible, only moves the complicated job of acquiring deeper insight into > how a computer can be programmed to exhibit human-like intelligence from the > programmer to the trainer. > > > > So I am skeptical when people argue about various simplistic paradigms > without ever going into the deeper problems of conceptual complexity. > > > Jim Bromer > > > ------------------------------ > *agi* | Archives <http://www.listbox.com/member/archive/303/=now> > <http://www.listbox.com/member/archive/rss/303/> | > Modify<http://www.listbox.com/member/?&;>Your Subscription > <http://www.listbox.com> > ------------------------------------------- agi Archives: http://www.listbox.com/member/archive/303/=now RSS Feed: http://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: http://www.listbox.com/member/?member_id=8660244&id_secret=103754539-40ed26 Powered by Listbox: http://www.listbox.com
