I should not have said that I did not think that Tintner's commentary was not very useful. I meant that I personally do not usually find his comments very helpful to me because I do not agree with his belief that he understands the problem better than any of the rest of us. But as I said, I have learned something as I tried to think of ways to overcome some challenge that he thought was insurmountable. Even though the super algorithm generator (which I came up with trying to figure how to do something that he said was impossible) might not be something that I will be able to use effectively, it is, at the very least,interesting. Jim Bromer
On Tue, Feb 5, 2013 at 1:44 PM, Jim Bromer <[email protected]> wrote: > So the method of initial recognition that I am going to try with my (very > early version of) image analysis will be to use multiple > analysis algorithms on an image. Then I am going to use the results to > output some kind of result that I choose to study. (The first result is > going to be a modification of the original image which can be used for some > purpose). Then I can select parts of the results to 'reinforce' as cases > where the program achieved the desired goal. Then the test will be to see > if the program can guess which parts should be analyzed by which > algorithms to produce the desired goal. My, point however, is that it is > not that simple. It won't work as described because initial image analysis > is not strong enough to produce the desired results even when there is > exhaustive training. I can predict pretty confidently that the initial > analysis will need to taken through a kind of 'perceptual' stage where > some higher level ideas about the image has to be projected onto the > initial results of the analysis and compared to see if the analysis makes > sense based on previous learning. Often times this comparison of the > projection of high level insight onto the configuration of results of the > initial analysis will not fit perfectly and some 'stretching' of the > relations will need to be made. > > One other thing. Although I think Mike Tintner's commentary has not very > useful, I do feel that he has helped me to discover something important in > the past. He tries to point out that there is no program that can produce > every set of actions or algorithms. That seems reasonable. But most of us > feel that he is missing the point. We can write programs that can learn > new things without being programmed to be able to produce every possible > variation. An AGI program can learn by interacting intelligently with the > IO data world. However, when I once reacted to one of Mike's challenges > and I tried to figure out a way to write an algorithm that could produce > obvious variations of a typological character without being intelligent or > reacting to input I was able to derive an insight about how such a thing > might work. And I could use a variation of that idea on my image analyses > (including analysis through modification) methods. So if I was able to get > somewhere it is theoretically feasible to design a single algorithm that > could create an immense number of image modification algorithms. The one > thing that is left is to harness this process so that a useful analysis or > modification algorithm could be chosen -without running them all- on the > basis that it might produce a result that is required to produce an > insightful result. By examining how the variations on the (single) image > analysis super algorithm affects the result, it might be possible for the > program to learn to 'project' families of characteristics of those > variations and hence make educated guesses that resemble some of ours that > go like, "If I could find this and that kind of thing and test it to see if > it could be related to finding a solution then I might be able to get > closer to figuring this out. Characteristic A and characteristic B are > similar to the this and that therefore by trying variations on the super > algorithm I might be able to detect the this and that event." > Jim Bromer > > > > > > > On Tue, Feb 5, 2013 at 9:07 AM, Jim Bromer <[email protected]> wrote: > >> Oh I forgot. The initial recognition dilemma is this. All good AI methods >> work well in some situations but in others they don't work very well at >> all. The problem is that the situations in which they work well do not >> cover entire situations. So, even in a single particular situation, like a >> scene in visual recognition, the AI algorithm might be spectacular with >> some 'objects' of the scene but fail with others. Now if a programmer was >> looking at the scene he can decide to use different algorithms that work >> well with different parts of the scene and by this method get good initial >> recognition coverage. But an automated system which does not have good >> initial recognition (or understanding) of the scene is not going to be able >> to choose which algorithms it should use for recognition. This assumes that >> the recognition algorithms will both fail to recognize some parts and give >> some false identifications of other parts. >> So the programmer sees that there are some algorithms which work really >> well, and he could select different algorithms to work on different parts >> of a particular input object, but when you try to automate this the problem >> becomes a dilemma. How can a program choose the right algorithm to evaluate >> a part of a scene without first recognizing what parts cannot be identified >> and what algorithms would be best for those parts. >> 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
