Wikipedia does give a definition of a scientific theory: In modern science <http://en.wikipedia.org/wiki/Science>, the term "theory" refers to scientific theories<http://en.wikipedia.org/wiki/Scientific_theory>, a well-confirmed type of explanation of nature<http://en.wikipedia.org/wiki/Nature>, made in a way consistent <http://en.wikipedia.org/wiki/Consistency> with scientific method <http://en.wikipedia.org/wiki/Scientific_method>, and fulfilling the criteria<http://en.wikipedia.org/wiki/Scientific_theory#Characteristics_of_theories>required by modern science <http://en.wikipedia.org/wiki/Modern_science>. Such theories are described in such a way that any scientist in the field is in a position to understand and either provide empirical support ("verify<http://en.wikipedia.org/wiki/Proof_(truth)>") or empirically contradict ("falsify<http://en.wikipedia.org/wiki/Falsifiability>") it.
But the ancient, more general definition of a theory is: *Theory* is a contemplative <http://en.wikipedia.org/wiki/Contemplation>and rational <http://en.wikipedia.org/wiki/Reason> type of abstract<http://en.wikipedia.org/wiki/Abstraction>or generalizing thinking, or the results of such thinking. Depending on the context, the results might for example include generalized explanations of how nature <http://en.wikipedia.org/wiki/Nature_(philosophy)> works, or even how divine or metaphysical matters are thought to work. The word has its roots in ancient Greek <http://en.wikipedia.org/wiki/Ancient_Greek>, but in modern use it has taken on several different related meanings. Notice that some sciences are still in a speculative stage, so technically the first definition is a little too stuffy. If we were to claim that our discussions are a kind of science appropriate for a nascent technology, then the others in the field are in a position to provide some evidence to support or contradict my theories. The modern scientific theory was developed after some observations of the progress that cart makers made. That fact could be used to support the idea that technological development is a valid scientific field. While a criticism could be leveled at me and many others that we spend too much time talking and not enough time programming, I would point out that this summary is part of an actual testing program that I am planning to start next month. Your criticism that the part of my summary that you have read so far lacks an Operational Definition is nonsense. Since you are not a not an active programmer or programmer analyst in the nascent field of AGI, you are in no position to understand a speculative scientific theory of AGI. Jim Bromer 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
