There is another case for avoiding a discussion of a plan. The actual implementation of a plan might be so different from the imagined implementation that in the end the strength of the project might have little to do with the main features of the plan. That is also a good reason to avoid a prolonged contemplation about a plan. The basic nature of day to day work of writing a computer program is pretty consistent, at least across a project that uses one programming language. So they may be more important to the implementation than the inspiration behind the plan. But still some kinds of problems do tend to bubble up in long planned thought which can be seen in the terms of programming problems. For example, I can relate the problem of representing multiplicity of possibilities to my thoughts about cross-categorization and cross-generalization. When the program is trying to 'recognize' a kind of situation from the input, it needs to work from less detail to greater detail. If the features of the (data) 'objects' it has to work with are extensively cross-generalized (if the associations via similar features are extensively cross-related) then the recognition stage of the process might be able to traverse those relations more quickly. However, if recognition is determined one feature at a time then the program will encounter search complexity over and over again. This has been one of the main problems in AI and it can be seen in a wide range of AI and AGI implementations. So instead of traversing from one final recognition object to another via the similarity of features I think it would probably be more efficient to refer to collections of objects that share some sets of features. (I am using a text based method but a reference to a collection does not have to be expressed only using text since I am talking about some kind of internal processing during recognition.) So here I am saying that ideas like cross-categorization can help you get what I mean when I talk about cross-generalization. And the idea of a cross-generalization matrix of features can help you understand what I mean when I talk about a traversal of possibilities via the similarities of features. But we know from the experiences of other programmers that if the program is traversing possible end-product recognition objects then the search process can be so slow as to make the search impossible. In the past people have tried ideas like ideological vectors and weighted references and reasoning in an effort to make more subtle decisions but this hasn't worked because there is still not a straightforward step by step process that can work for all cases (or even most cases). Lists of collections were tried in the early days but these were typically simple step by step elimination methods. What I am saying is that the only way around this is to discover some effective holistic method (neural networks are too inefficient) or to work with intermediate collections of possible objects without resorting to an overly simplistic step-by-step process of elimination. I believe that the efficiency of modern computers can help us develop novel approaches to finding effective solutions that weren't possible in the last 25 years of the twentieth century. So how could I avoid using the elimination approach that did not seem to work in the nineteen seventies? I can't avoid an elimination approach entirely because I need to end up with a final recognition object. But we can take something that Wittgenstein realized to see why the step by step elimination process might not work when dealing with generalization collections. The generalizing principle in language is the use of categorical families where each object in a category shares some familial trait but that does not mean that two objects from the category necessarily have some traits in common. In language we use general terms to describe a specific referent. But since these terms may be based on familial traits we cannot eliminate the possible traits that may apply to the specified object simply because it is not common to two terms used in the description. I'm sorry but this is basic stuff. If you have thought about this then it should be obvious. (I may not have expressed it in the simplest terms but if you have thought about it before you should be able to figure it out. If you haven't thought about it before then you should start now.) And the end product of a recognition does not have to be a specific object because the terms we use in language and in thought are generalizations. So I don't have it all figured out but to make this simple: you cannot use a simplistic step-by-step elimination method to narrow the possibilities down. But there is a way around this.
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