On Sat, Apr 13, 2013 at 6:39 AM, Jim Bromer <[email protected]> wrote:
> Part 1 > > I feel that complexity is a major problem facing contemporary AGI. It is > true, that for most human reasoning we do not need to figure out > complicated problems precisely in order to take the first steps toward > competency but so far AGI has not been able to get very far beyond the > narrow-AI barrier. > > I am going to start with a text-based AGI program. I agree that more > kinds of IO modalities would make an effective AGI program better. However, > I am not aware of any evidence that sensory-based AGI or multi-modal > sensory based AGI or robotic based AGI has been able to achieve something > greater than other efforts. The core of AGI is not going to be found in the > peripherals. And it is clear that starting with complicated IO > accessories would make AGI programming more difficult. It seems obvious > that IO is necessary for AI/AGI and this abstraction is a probably more > appropriate basis for the requirements of AGI. > > My AGI program is going to be based on discreet references. I feel that > the argument that only neural networks are able to learn or are able to > incorporate different kinds of data objects into an associative field is > not accurate. I do, however, feel that more attention needs to be paid to > concept integration. And I think that many of us recognize that a good > AGI model is going to create an internal reference model that is a kind of > network. The discreet reference model more easily allows the program to > retain the components of an agglomeration in a way in which the traditional > neural network does not. This means that it is more likely that the > parts of an associative agglomeration can be detected. On the other > hand, since the program will develop its own internal data objects, these > might be formed in such a way so that the original parts might be difficult > to detect. With a more conscious effort to better understand concept > integration I think that the discreet conceptual network model will prove > itself fairly easily. > Yep, so can do concept integration, by representing concepts with sentences. It works, it's simple, it can show association, it maintains original parts. > I am going to use weighted reasoning and probability but only to a limited > extent. > On Sat, Apr 13, 2013 at 7:34 AM, Jim Bromer <[email protected]> wrote: Part 2 > I believe that it takes a great deal of knowledge to 'understand' one > thing. A statement has to be integrated into a greater collection of > knowledge in order for the relations of understanding to be formed. > Just like how a sentence can be integrated into a story. > And the knowledge of a single statement has to be integrated into a > greater field of knowledge concerning the central features of the subject > for the intelligent entity to truly understand the statement. While > conceptual integration, by some name, has always been a primary subject in > AI/AGI, I think it was relegated to a subservient position by those who > originally stressed the formal methods of logic, linguistics, psychology, > numerics, probability, and neural networks. Thinking that the details of > how ideas work in actual thinking was either part of some > predawn-of-science-philosophy or the turn-of-the-crank production of the > successful application of formal methods, a focus on the details of how > ideas work in actual problems was seen as naïve. > Many people understand things through story. Indeed it is the way in which our brains are designed to operate and interpret new information, since the cave paintings at the very least. It's also the most effective way of transmitting information from one person to another, as it often bypasses much of the conscious criticality, and simply subsumes into the subconscious background. Even computers understand things through story, even thought the typical programming language may make this hard to see, however the setting or variables are declared intially, the rising action is the preperation of the variables for interaction, then the conflict or change is the actual transmutation of the variables, and resolution is the returning of the result. > This problem, where the smartest thinkers would spend lives pursuing the > abstract problems without wasting their time carefully examining many real > world cases occurs often in science. It is amplified by ignorance. If > no one knows how to create a practical application then the experts in the > field may become overly pre-occupied with the proposed formal methods that > had been presented to them. Formal methods are important - but they are > each only one kind of thing. It takes a great deal of knowledge about > many different things to 'understand' one kind of thing. A reasonable > rule of thumb is that formal methods have to be tried and shaped based on > exhaustive applications of the methods to real world problems. > In order to integrate new knowledge the new idea that is being introduced > usually has to be verified using many steps to show that it holds. > well there is always parsing, and compiling, if it works it works. Though factual information about the world could be statistically cross-referenced. > Since there is no absolute insight into truth for this kind of thing, > knowledge has to be integrated in a more thorough trial and error manner. > truth is personal experience, so each perspective has it's own truth. knowledge is past-experience, there is true knowledge, and real knowledge. real is the set of beliefs that are in common amongst a group. exist is anything that can be imagined/compiled. > The program has to create new theories about statements or reactions it > is considering. This would extend to interpretations of observations for > systems where other kinds of sensory systems were used. A single > experiment does not 'prove' a new theory in science. A large number of > experiments are required and most of those experiments have to demonstrate > that the application of the theory can lead to better understanding of > other related effects. > you know it all depends, are we making little scientists, or AGI's? Is there purpose to "prove" stuff, or rather simply to "do" stuff. Sure they could use some scientific method, and statistical verification, however it's more important to actually get stuff done i.e. result of experiment, than have it published in a peer-reviewed journal, or get a bunch of peers to re-do the experiments. The experiment and results could be shared with other AGI's and people over the internet, likely in the form of a story, if others come across similar issues they may wish to try it themselves, and comment if it works out for them. > It takes a knowledge of a great many things to verify a statement about > one thing. In order for the knowledge represented by a statement to be > verified and comprehended it has to be related to, and integrated with, a > great many other statements concerning the primary subject matter. It is > necessary to see how the primary subject matter may be used in many > different kinds of thoughts to be able to understand it. > I disagree, as you don't have to know all the ways to add stuff, to simply add some numbers together. You can easily learn new things later on, like how to perform addition amongst new types of things, like for instance arrays, or ingredients. Gotta start somewhere, and arithmetic addition is a sufficient place to do so. > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/5037279-a88c7a6d> | > Modify<https://www.listbox.com/member/?&;>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/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
