Jim Bromer Theory

the mindset of Jim ... Part 1 points 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 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. Jim's start The core of AGI is not going to be found in the peripherals. IO is necessary for AI/AGI and this abstraction is a probably more appropriate basis for the requirements of AGI. 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. 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. 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. I am going to use weighted reasoning and probability but only to a limited extent. Part 2 Points it takes a great deal of knowledge to 'understand' one thing. 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. 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. The program has to create new theories about statements or reactions it is considering. A single experiment does not 'prove' a new theory in science. Part 3 Points The program will make extensive use of generalizations and cross-generalization. The program will need to be able to discover abstractions So the program will be able to develop abstractions of relations and then build categorizations from these relations. Part 4 Points Artificial imagination is also necessary for AGI. An association between concepts or (concept objects) which cannot be interpreted as meaningful is not usually very useful. 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. 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. Part 5 Points 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. 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. 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. back to Jim Bromer Theory