David, You are correct in that I keep bad company. My approach to NNs is VERY different than other people's approaches. I insist on reasonable math being performed on quantities that I understand, which sets me apart from just about everyone else.
Your "neat" approach isn't all that neat, and is arguably scruffier than mine. At least I have SOME math to back up my approach. Further, note that we are self-organizing systems, and that this process is poorly understood. I am NOT particularly interest in people-programmed systems because of their very fundamental limitations. Yes, self-organization is messy, but it fits the "neat" definition better than it meets the "scruffy" definition. Scruffy has more to do with people-programmed ad hoc approaches (like most of AGI), which I agree are a waste of time. Steve ============ On Wed, Aug 4, 2010 at 12:43 PM, David Jones <davidher...@gmail.com> wrote: > Steve, > > I wouldn't say that's an accurate description of what I wrote. What a wrote > was a way to think about how to solve computer vision. > > My approach to artificial intelligence is a "Neat" approach. See > http://en.wikipedia.org/wiki/Neats_vs._scruffies The paper you attached is > a "Scruffy" approach. Neat approaches are characterized by deliberate > algorithms that are analogous to the problem and can sometimes be shown to > be provably correct. An example of a Neat approach is the use of features in > the paper I mentioned. One can describe why the features are calculated and > manipulated the way they are. An example of a scruffies approach would be > neural nets, where you don't know the rules by which it comes up with an > answer and such approaches are not very scalable. Neural nets require > manually created training data and the knowledge generated is not in a form > that can be used for other tasks. The knowledge isn't portable. > > I also wouldn't say I switched from absolute values to rates of change. > That's not really at all what I'm saying here. > > Dave > > On Wed, Aug 4, 2010 at 2:32 PM, Steve Richfield <steve.richfi...@gmail.com > > wrote: > >> David, >> >> It appears that you may have reinvented the wheel. See the attached >> article. There is LOTS of evidence, along with some good math, suggesting >> that our brains work on rates of change rather than absolute values. Then, >> temporal learning, which is otherwise very difficult, falls out as the >> easiest of things to do. >> >> In effect, your proposal shifts from absolute values to rates of change. >> >> Steve >> =================== >> On Tue, Aug 3, 2010 at 8:52 AM, David Jones <davidher...@gmail.com>wrote: >> >>> I've suddenly realized that computer vision of real images is very much >>> solvable and that it is now just a matter of engineering. I was so stuck >>> before because you can't make the simple assumptions in screenshot computer >>> vision that you can in real computer vision. This makes experience probably >>> necessary to effectively learn from screenshots. Objects in real images to >>> not change drastically in appearance, position or other dimensions in >>> unpredictable ways. >>> >>> The reason I came to the conclusion that it's a lot easier than I thought >>> is that I found a way to describe why existing solutions work, how they work >>> and how to come up with even better solutions. >>> >>> I've also realized that I don't actually have to implement it, which is >>> what is most difficult because even if you know a solution to part of the >>> problem has certain properties and issues, implementing it takes a lot of >>> time. Whereas I can just assume I have a less than perfect solution with the >>> properties I predict from other experiments. Then I can solve the problem >>> without actually implementing every last detail. >>> >>> *First*, existing methods find observations that are likely true by >>> themselves. They find data patterns that are very unlikely to occur by >>> coincidence, such as many features moving together over several frames of a >>> video and over a statistically significant distance. They use thresholds to >>> ensure that the observed changes are likely transformations of the original >>> property observed or to ensure the statistical significance of an >>> observation. These are highly likely true observations and not coincidences >>> or noise. >>> >>> *Second*, they make sure that the other possible explanations of the >>> observations are very unlikely. This is usually done using a threshold, and >>> a second difference threshold from the first match to the second match. This >>> makes sure that second best matches are much farther away than the best >>> match. This is important because it's not enough to find a very likely match >>> if there are 1000 very likely matches. You have to be able to show that the >>> other matches are very unlikely, otherwise the specific match you pick may >>> be just a tiny bit better than the others, and the confidence of that match >>> would be very low. >>> >>> >>> So, my initial design plans are as follows. Note: I will probably not >>> actually implement the system because the engineering part dominates the >>> time. I'd rather convert real videos to pseudo test cases or simulation test >>> cases and then write a psuedo design and algorithm that can solve it. This >>> would show that it works without actually spending the time needed to >>> implement it. It's more important for me to prove it works and show what it >>> can do than to actually do it. If I can prove it, there will be sufficient >>> motivation for others to do it with more resources and man power than I have >>> at my disposal. >>> >>> *My Design* >>> *First, we use high speed cameras and lidar systems to gather sufficient >>> data with very low uncertainty because the changes possible between data >>> points can be assumed to be very low, allowing our thresholds to be much >>> smaller, which eliminates many possible errors and ambiguities. >>> >>> *Second*, *we have to gain experience from high confidence observations. >>> These are gathered as follows: >>> 1) Describe allowable transformations(thresholds) and what they mean. >>> Such as the change in size and position of an object based on the frame rate >>> of a camera. Another might be allowable change in hue and contrast because >>> of lighting changes. With a high frame rate camera, if you can find a match >>> that is within these high confidence thresholds in multiple dimensions(size, >>> position, color, etc), then you have a high confidence match. >>> 2) Find data patterns that are very unlikely to occur by coincidence, >>> such as many features moving together over several frames of a video and >>> over a statistically significant distance. These are highly likely true >>> observations and not coincidences or noise. >>> 3) Most importantly, make sure the matches we find are highly likely on >>> their own and unlikely to be coincidental. >>> 4) Second most importantly, make sure that any other possible matches or >>> alternative explanations are very unlikely in terms of distance( measured in >>> multiple dimensions and weighted by the certainty of those observations). >>> These should also be in terms of the thresholds we used previously because >>> those define acceptable changes in a normalized way. >>> >>> *That is a rough description of the idea. Basically highly likely >>> matches and very unlikely for the matches to be incorrect, coincidental or >>> mistmatched. * >>> >>> Third, We use experience, when we have it, in combination with the >>> algorithm I just described. If we can find unlikely coincidences between our >>> experience and our raw sensory observations, we can use this to look >>> specifically for those important observations the experience predicts and >>> verify them, which will in turn give us higher confidence of inferences. >>> >>> Once we have solved the correspondence problem like this, we can perform >>> higher reasoning and learning. >>> >>> Dave >>> *agi* | Archives <https://www.listbox.com/member/archive/303/=now> >>> <https://www.listbox.com/member/archive/rss/303/> | >>> Modify<https://www.listbox.com/member/?&;>Your Subscription >>> <http://www.listbox.com> >>> >> >> *agi* | Archives <https://www.listbox.com/member/archive/303/=now> >> <https://www.listbox.com/member/archive/rss/303/> | >> Modify<https://www.listbox.com/member/?&;>Your Subscription >> <http://www.listbox.com> >> > > *agi* | Archives <https://www.listbox.com/member/archive/303/=now> > <https://www.listbox.com/member/archive/rss/303/> | > Modify<https://www.listbox.com/member/?&;>Your Subscription > <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=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
