I often find the discussions in these AI groups to be distracting because although they are related to ideas that I feel strongly about, they are rarely inline with what I think is of central importance. But even though the other people in the group are not interested in the exact same kinds of ideas that I am interested in, the diversity of the interests does sometimes help me to examine ideas in new ways.
For example, I think that complexity, in the more general sense, is of great significance to the advancement of AI, but the specifics of the conversations here on the subject during the past months were not within what I felt should have been at the focus of the discussions. But I still derived some new insights as I considered the comments that were made. One of those new ideas is that extensibility of complexity is itself of fundamental importance to solving some of the contemporary problems of AI. Although I had thought about something similar before, the newly placed significance on the centrality of this concept per se has inspired me to reconsider a simple starting off point for a possible AI program. One of the problems with AI research during the past 60 years is that controlled models or prototypes of future programs will sometimes work well up to a point of complexity that seems to be just beyond the researchers horizon. As a result, the early researchers often got people excited by their discoveries but after a few years it became clear to the skeptics that the new breakthroughs had been overstated. Thinking about it from another vantage however, it is nearly impossible to conceive of a truly novel and effective program without first testing component ideas out in controlled environments. What I am thinking about now is that I should start with a simple model that can produce extensible complexity of reference. One of the most important advantages that a simple model possesses is that you can make controlled experiments on it. (It is also simple of course which helps with the feasibility thing.) But the problem with the simple AI models of the past has been that they tended to fail as they became more complicated. My thinking now is that if the design goal of the experimentation is to focus the study on extensible complexity (in the general sense) then perhaps some insights into solving some of those problems may be achieved. I have been rejecting the idea of starting with some overly simplistic AI method for years because of the failures of such models to deal with greater complexity. But, this argument may not be as meaningful if the simplification is directed toward the centrality of devising methods of extensible complexity. So I am seriously thinking about trying some ideas I have had about dealing with complexity and ambiguity. By using simplified artificial problems, and making complexity extensibility a central goal I think I may (if I am lucky) be able to discover something of importance in this field. Although I considered doing something very similar to this years ago, the discussions about complexity here during the last few months have helped me to refocus my efforts specifically onto this question of complexity extensibility and how it relates to ambiguity in controlled natural language problems. My point is: I often get something out of these conversations even though other people's thinking is usually very different from mine. 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/?member_id=8660244&id_secret=108809214-a0d121 Powered by Listbox: http://www.listbox.com
