We are almost finished with two weeks of the new year. I said that I was going to make a prediction about being able to get an AGi-Lite program working within a year in order to demonstrate how an actual prediction like this can be used as the basis for drawing conclusions of the effectiveness of one's own theories. I agree that you cannot expect results in set period of time but I was able to create other useful theories based on using different kinds of reasoning on the prediction. The question now is whether or not I can accept the results of my own experiments.
For instance, I said that if I was truly confident that I knew how to create an AGI program (even an AGi-Lite program or AGi as I called it) then I would be extremely motivated to get going on this project. So then, I reasoned, if two weeks went by and I did not even have the user interface done then this would be indicative that I wasn't quite as motivated as my hubris would suggest. Well, I took the database definitions and the user interface from the remains of an old program that I had abandoned then salvaged a number of years ago and started working on it. It was much more complicated than I remembered and even though I haven't been able to save any data with it yet, it is slowly coming back to me. So yes, I had a fundamental user interface within two weeks, and while that does not show anything about whether my AGi ideas will work or not, it does show that I have a fundamental enthusiasm and confidence in my theories. I have proven nothing about my AGi theories, but I did take one fast indication of a potential problem off the table. My conscious and my unconscious or semi-conscious impressions of what I am doing are in sync. On the other hand, since I did just grab a program out of the attic I should have made more progress than I have. Two weeks is1/26 of the way to my predicted goal. I also realized that I could begin making some very basic AGi experiments using the text on the web and I feel that I should have started that by now. So prediction - (including prediction as a nexus of potential progress overlayed with the nexus of dynamically developing plans) -is useful to me. I can now use the goals - the original one and the new one produced by a realization that I could start some initial testing using the web - to create a new schedule that will provide me at a tiny bit more insight into how my plans are starting to unfold. At this stage I haven't gotten any results on any AI / AGI theories but if I am able to start testing one or two of my ideas within the next two weeks I should have some kind of results to examine. One thing I did learn was that to get an advantage on the preliminaries it is nice to have something - that had been salvaged for just such a situation - to use to jump into the fun part of the puzzle a little faster. And that is a strategy that I can use in the next stage of my planning. Instead of writing a web-crawler (which is what I would like to do) I can just copy some text from various web pages and then paste them into the user interface on my salvaged program and test some elementary text searches to see if one of my ideas can actually be made to work. So based on my experiences during the first two weeks of working with a schedule I have developed a more efficient method of getting to the lowest levels of the game. I still want to write a web crawler but I can do that when I learn more about it. So the next two weeks: That will be 1/13 of the way to the end of the year. I better get going on this. (That is a familiar example of how prediction and using the actual results of your efforts can drive insight by the way). So I will like to get the bugs out of my salvaged program and begin testing the database operations with automated methods during the next 2 weeks. And I would like to conduct my preliminary text searches on text that I can take from selected web pages to test my theories about the relative words and count of those words associated with some key words based on whether or not the key word was (according to my opinion) a primary subject word or not. For instance, the word "flight" is found in many types of texts, but by developing frequency of use records of the words used along with the word "flight" I should be able to identify whether the text is primarily about birds or about about airplanes or about other sub-categories. (Although this is not a very exciting AI theory in this day and age, I am using it as an example of how I am able to jump into AI testing even though my program will take a some months to get up to speed.) Explanation of the Project I got so tired of hearing people use the word "prediction" as a basis of their AI/AGI theories that I decided to try using prediction in real life to see if was an effective method. I found, that like most other AI theories it worked really well in a few cases and not so well in most cases. However, the use of formal (declared) predictions gave me a surprising ability to crystallize new insight around the predicted events when they were compared to the event. So I tried to get the guys who like to use the term "prediction" in their discussions of AI to try this experiment themselves. Of course they could not be bothered with such a mundane experiment. So I challenged them: Why not use your predictions about your own AI/AGI projects as the basis for developing new insights about your theories? They would have to be able to accept the results of their experiments regardless of how well it worked out for them because it just does not make sense to believe that you have it all figured out if you cannot get your ideas to work year after year after year. But even with this aggressive challenge they still could not be bothered. So I decided to try it myself to show them how it might actually work. I predicted that I would be able to get a limited AGI program (I called it AGi) working within a year. I pointed out that some skeptics would not be convinced no matter how the program turned out but that many of my peers (like the less delusionally narcissistic guys in these groups) would be able to see that it was working if I was actually able to get it working. On the other hand, I explained, if I could not get my program working then I would have to accept the results of my experiments and recognize that I did not and do not have it all figured out. I do agree that an experimenter has to be given some leeway and there is always the possibility of a life-changing event interfering with the goal, but if I still have nothing to show after 2 years then I have to accept that there must be something important that I haven't figured out. It was explained to me that the concept of "prediction" is used in different theoretical ways in this group, but I was already aware of that. If you are using "prediction" as a basis of any kind of AI/AGI learning models, then you have to be able to know how to accept the results of an actual experiment to compare it against the predicted experiment. In an advanced AGI models (including the use of Bayesian Nets which I consider to be mundane) the potential for generating new structured relationships based on actual experiments is essential to the effective utilization of the model. This kind of effective utilization is directly related to my personal experience of having new insights crystallize around the predicted event when compared with an actual experiment. My interest though, is not in prediction per se but in how new structured insights crystallize. Jim Bromer ------------------------------------------- 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
