Well, Wikipedia does give a definition of a scientific theory:
On Mon, Apr 15, 2013 at 5:03 AM, Mike Tintner <[email protected]>wrote: > What you have is a v. vague *hypothesis*. A *theory* involves evidence > as to why it may work.. > > And you have no Operational Definition of what effect you’re trying to > achieve. Not even the teeniest weeniest hint of an O.D. > > Tch, tch. > > *From:* Jim Bromer <[email protected]> > *Sent:* Monday, April 15, 2013 4:14 AM > *To:* AGI <[email protected]> > *Subject:* [agi] Re: Summary of My Current Theory For an AGI Program. > > 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/303/6952829-59a2eca5> | > 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
