Jim My understanding is that a Novamente-like system would have a process of natural selection that tends to favor the retention and use of patterns (perceptive, cognative, behaviors) prove themselves useful in achieving goals in the word in which it is embodied.
It seems to me t such a process of natural selection would tend to naturally put some sort of limit on how out-of-touch many of an AGI's patterns would be, at least with regard to patterns about things for which the AGI has had considerable experience from the world in which it is embodied. However, we humans often get pretty out of touch with real world probabilities, as the recent bubble in housing prices, and the commonly said, although historically inaccurate, statement of several years ago --- that housing prices never go down on a national --- shows. It would be helpful to make AGI's be a little more accurate in their evaluation of the evidence for many of their assumptions --- and what that evidence really says --- than we humans are. Ed Porter -----Original Message----- From: Jim Bromer [mailto:[EMAIL PROTECTED] Sent: Saturday, November 29, 2008 10:49 AM To: agi@v2.listbox.com Subject: [agi] Mushed Up Decision Processes One of the problems that comes with the casual use of analytical methods is that the user becomes inured to their habitual misuse. When a casual familiarity is combined with a habitual ignorance of the consequences of a misuse the user can become over-confident or unwisely dismissive of criticism regardless of how on the mark it might be. The most proper use of statistical and probabilistic methods is to base results on a strong association with the data that they were derived from. The problem is that the AI community cannot afford this strong a connection to original source because they are trying to emulate the mind in some way and it is not reasonable to assume that the mind is capable of storing all data that it has used to derive insight. This is a problem any AI method has to deal with, it is not just a probability thing. What is wrong with the AI-probability group mind-set is that very few of its proponents ever consider the problem of statistical ambiguity and its obvious consequences. All AI programmers have to consider the problem. Most theories about the mind posit the use of similar experiences to build up theories about the world (or to derive methods to deal effectively with the world). So even though the methods to deal with the data environment are detached from the original sources of those methods, they can still be reconnected by the examination of similar experiences that may subsequently occur. But still it is important to be able to recognize the significance and necessity of doing this from time to time. It is important to be able to reevaluate parts of your theories about things. We are not just making little modifications from our internal theories about things when we react to ongoing events, we must be making some sort of reevaluation of our insights about the kind of thing that we are dealing with as well. I realize now that most people in these groups probably do not understand where I am coming from because their idea of AI programming is based on a model of programming that is flat. You have the program at one level and the possible reactions to the data that is input as the values of the program variables are carefully constrained by that level. You can imagine a more complex model of programming by appreciating the possibility that the program can react to IO data by rearranging subprograms to make new kinds of programs. Although a subtle argument can be made that any program that conditionally reacts to input data is rearranging the execution of its subprograms, the explicit recognition by the programmer that this is useful tool in advanced programming is probably highly correlated with its more effective use. (I mean of course it is highly correlated with its effective use!) I believe that casually constructed learning methods (and decision processes) can lead to even more uncontrollable results when used with this self-programming aspect of advanced AI programs. The consequences then of failing to recognize that mushed up decision processes that are never compared against the data (or kinds of situations) that they were derived from will be the inevitable emergence of inherently illogical decision processes that will mush up an AI system long before it gets any traction. Jim Bromer ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?&; Powered by Listbox: http://www.listbox.com ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=120640061-aded06 Powered by Listbox: http://www.listbox.com
