RICHARD, I can't provide "concrete reasons" why Novamente and roughly similar approaches will work --- precisely because they are designed to operate in the realm Wolfram calls computationally irreducible --- meaning it cannot be modeled properly by anything substantially less complex than itself. Thus, whether or not it will work cannot be formally proven in advance.
I assume it would have been equally hard to provide "concrete reasons" why Hofstadter's Copycat and his similar systems would work before they were built. But they did work. Since computation irreducibility is something you make a big point about in one of your own papers you frequently cite --- you --- of all people --- should not deny the right to believe in approaches to AGI before they are supported by concrete proof. But I do have, what I consider, rational reasons for believing a Novamente-like systems will work. One of them is that you have failed to describe to me any major conceptual problem for the AGI community does not have what appear to be valid approaches. For more reasons why I believe a Novamente-like approach might work I copy the following portion of a former post of from about 5 months ago --- describing my understanding of how Novamente itself would probably work, based on reading material from Novamente and my own other reading and thinking. ======= Novamente starts with a focus on "General Intelligence", which it defines as "the ability to achieve complex goals in complex environments." It is focused on automatic, interactive learning, experiential grounding, self understanding, and both conscious (focus-of-attention) and unconscious (currently less attended) thought. It records experience, finds repeated patterns in it, makes generalizations and compositions out of such patterns -- all through multiple generalizational and compositional levels -- based on spatial, temporal, and learned-pattern-derived relationships. It uses a novel form of inference, firmly grounded in Bayesian mathematics, for deriving inferences from many millions of activated patterns at once. This provides probabilistic reasoning much more powerful and flexible than any prior Bayesian techniques. Patterns -- which can include behaviors (including mental behaviors) -- are formed, modified, generalized, and deleted all the time. They have to complete for their computational resources and continued existence. This results in a self-organizing network of similarity, generalizational, and compositional patterns and relationships, that all must continue to prove their worth in a survival-of-the-fittest, goal-oriented, experiential-knowledge ecology. Re-enforcement learning is used to weight patterns, both for general long term and context specific importance, based on the direct or indirect roles they have played in achieving the system's goals in the past. These indications of importance -- along with a deep memory for past similar experiences, goals, contexts, and similar inferencing and learning patterns -- significantly narrow and focus attention, avoiding the pitfalls of combinatorial explosion, and resulting in context-appropriate decision making. Genetic learning algorithms, made more efficient by the system's experience and probabilistic inferencing, give the system the ability to learn new behaviors, classifiers, and creative ideas. Taken together all these features, and many more, will allow the system to automatically learn, reason, plan, imagine, and create with a sophistication and power never before possible -- not even for the most intelligent of humans. Of course, it will take some time for the first such systems to learn the important aspects of world knowledge. Most of the valuable patterns in the minds of such machines will come from machine learning and not human programming. Such learning can be greatly speed if such machines are taught, at least partially, the way human children are. But once knowledge has been learned by such systems much of it can be quickly replicated into, or shared between, other machines. ======= -----Original Message----- From: Richard Loosemore [mailto:[EMAIL PROTECTED] Sent: Saturday, April 19, 2008 7:57 PM To: agi@v2.listbox.com Subject: Re: [agi] WHAT ARE THE MISSING CONCEPTUAL PIECES IN AGI? Ed Porter wrote: > WHAT ARE THE MISSING CONCEPTUAL PIECES IN AGI? > > With the work done by Goertzel et al, Pei, Joscha Bach > <http://www.micropsi.org/> , Sam Adams, and others who spoke at AGI 2008, I > feel we pretty much conceptually understand how build powerful AGI's. I'm > not necessarily saying we know all the pieces of the puzzle, but rather that > we know enough to start building impressive intelligences Ed, can you please specify *precisely* what, in the talks at AGI 2008, leads you to the conclusion that "we know enough to start building impressive intelligences"? Some might say that everything shown at AGI 2008 could be interpreted as lots of people working on the problem, but that in ten years time those same people will come back and report that they are still working on the problem, but with no substantial difference to what they showed this year. The reason that some people would say this is that if you went back ten years, you could find people achieving forms of AI that exhibited no *substantial* difference to anything shown at AGI 2008. So, I am looking for your concrete reason (not gut instinct, but concrete reason) to claim that "we know enough ...etc.". Richard Loosemore ------------------------------------------- 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/?&; Powered by Listbox: 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=101455710-f059c4 Powered by Listbox: http://www.listbox.com
