What is with your diagrams?
On Mon, Apr 15, 2013 at 12:19 PM, Piaget Modeler <[email protected]>wrote: > Can you draw some diagrams to illustrate your theory? > > ~PM > > ------------------------------ > Date: Mon, 15 Apr 2013 11:40:35 -0400 > Subject: [agi] Re: Summary of My Current Theory For an AGI Program. > From: [email protected] > To: [email protected] > > > Part 5 > > Gradual methods seem to be called for. However, by utilizing structural > verification and integration, the gradual method can be augmented by > structural advancements where key pieces of knowledge seem to be able to > better explain a variety of related fragments of knowledge. Of course > even these methods are not absolute so there will always be the problem of > inaccurate knowledge being mixed in with the good. One of the key > problems with contemporary AGI is that ineffective knowledge (in some form) > will interfere with the effort to build even the foundations for an AGI > program. Since I do not believe that there is any method that will work > often enough to allow for a solid foundation to be easily formed, a way to > work with and around inaccurate and inadequate knowledge has to be found. > Structural integration can sometimes enhance a cohesive bunch of > inaccurate fragments of knowledge. But I believe that there are a few > things that can be done to deal with this problem. First of all, the > method of (partial) verification through structural knowledge should > usually work better with effective fragments of knowledge then it would > with inaccurate fragments. Secondly, a few kinds of flaws can often be > found in inaccurate theories. One is that they are often 'circular' or > what I call 'loopy'. Although good paradigms (mini-paradigms) are often > strongly interdependent, nonsensical paradigms do not fit well into systems > external to the central features of the paradigm. This fitting can be > done through the cross-categorization networks across transcendent > boundaries and it is an important part of understanding how good theories > work. The idea of the transcendent boundary is a solvent for the fact > that we don't really form our understanding of the world based on perfect > logic. So by being able to examine cross-categorical relations we should > be able to deal with small logical systems that can be related to other > small systems even though they may not be perfectly integrated. I think > most interested people should be able to get some idea of what I am saying > about this problem and they should be able to find examples of methods to > find flaws in simple systems of theories from real life. But there is > another problem that my theory of the transcendent boundary system would > tend to create. It would be pretty easy to build small systems that > overlay an 'insightful' bounded system and these could even be integrated > with other transcendent systems that were built to overlay other insightful > bounded systems. So a well developed fantasy system could be created on > top of related insightful systems using the methods that I have in mind. This > problem does have a solution. These systems which overlay the insightful > systems can be carefully examined to see if a viable method to tie these > into some IO observations that are directly related to the insightful > systems could be created. If a transcendent system is truly insightful, > it should typically be useful in explaining and predicting some basic > observations. Of course systems like this are not perfect and during the > initial stages of learning the program might create some elaborate systems > of nonsense. And an exhaustive search for inaccurate theories can > interfere with learning since inaccuracies that do not play key roles for > paradigms can act to support the weight of the paradigm while the 'student' > is first learning. For instance, the good student will be aware that the > fact that he does not fully understand the supporting structures (and > transcendent relations) of a paradigm does not mean that he can use his > ignorance to knock the theory down. Similarly, the fantasy that a system > (like an axiomatic system) is sufficient to support an application of the > system would not ruin that student's work with system unless he tried to > apply it to a field where the naive application was not effective (like > trying to use traditional logic to produce AGI). > > > > > On Sun, Apr 14, 2013 at 11:14 PM, Jim Bromer <[email protected]> wrote: > > Part 4 > > Artificial imagination is also necessary for AGI. Imagination can take > place simply by creating associations between concepts but obviously the > best forms of imagination are going to be based on rational meaningfulness. > An association between concepts or (concept objects) which cannot be > interpreted as meaningful is not usually very useful. So it seems that if > the relationship is both imaginative and potentially meaningful it would be > advantageous. An association formed by a categorical substitution is > more likely to be meaningful so I consider this a rational form of > imagination. However, you can find many examples where a categorical > substitution does not produce a meaningful association, so perhaps my claim > that it is a rational process is dependent on the likelihood that the > process will turn up a greater proportion of meaningful relations than > purely random associations. Some imaginative relations may exist just as > entertainment, but I believe that the application of the imagination is one > of the more important steps toward understanding. In fact, I believe > that all understanding is essentially a form of imaginative projection, > where you project previously formed ideas onto an ongoing situation which > is recognized or thought to share some characteristics with the projected > ideas. So from this point of view, the reliance of previously learned > knowledge is really an application of the imagination. Perhaps it is a > special form of imagination but the imagination none the less. Anyway, > once an imaginative association or relation is created it has to be tested. > I feel that relations of understanding cannot be appreciated out of context. > The basic rule of thumb is that it takes knowledge of many things to > understand one thing. This creates a problem when trying to test or > validate an insight which was partially produced by the imagination or > which had to be fitted using imaginative projection. The only way an AGI > program is going to be able to validate a new idea is by seeing how well it > fits and how well it works in a variety of related contexts. This is > what I call a structural integration. It not only represents a single > concept but it also carries a lot of other information with it that can > seemingly explain a lot of other small facts as well. A new idea seems > to make sense if it fits in with a number of insights that were previously > acquired. > > > > > On Sun, Apr 14, 2013 at 3:30 PM, Jim Bromer <[email protected]> wrote: > > Part 3 > > The program will make extensive use of generalizations and > cross-generalization. The program will need to be able to discover > abstractions. These abstractions typically may be used to develop > generalizations. A generalization may be formed from a group in which all > the members share some common characteristics. However, generalizations may > also be formed by various arbitrary processes. And, if the program works, > generalizations may be formed in response to some educational instruction. > The most typical example of cross-generalization may be the consideration > of similarities across individual systems of taxonomies or classes or > subclasses. In this broad definition of generalization, the collections > do not have to be grouped by any common characteristic and the same can go > for cross-categorizations. Although this might be a misuse of the term > generalization, the generalizations that my program will create may not be > trees because they can potentially branch off in different directions. > Indexes > into data for internal searches may be formed in a similar way but I will > have to think about whether the variety of branching makes sense as I am > developing the program. I believe that because of the variety of forms > of generalization or categorization that the program will use it is > necessary for the program to keep track of the different kinds of > categorization and generalization that it develops. And it will put > transcendent boundaries around portions of the generalizations that it > develops as it uses them in particular ways. These boundaries are > transcendent in that overlapping relations may be considered across them > (as in cross-generalization or cross-categorization). Perhaps the terms > relations and categorization are more abstract than the terms of > generalization. So the program will be able to develop abstractions of > relations and then build categorizations from these relations. The > categories that I have in mind may be somewhat free-wheeling. > Cross-categorization > will be important because they will help the program find and consider > similarities across the categorical structures. These categorical > structures may need to be bounded, but since bounded categories may still > be related across a relatively dominant categorical relation that means > that they can be transcended by other associative relations. > > > > > > > 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. 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. 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. 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. 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. 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. > > > 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. > > I am going to use weighted reasoning and probability but only to a limited > extent. > > > > > > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/19999924-5cfde295> | > Modify <https://www.listbox.com/member/?&;> Your Subscription > <http://www.listbox.com> > *AGI* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/10561250-470149cf> | > 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
