David On Wed, Aug 4, 2010 at 1:16 PM, David Jones <[email protected]> wrote:
> 3) requires manually created training data, which is a major problem. Where did this come from. Certainly, people are ill equipped to create dP/dt type data. These would have to come from sensors. > 4) is designed with biological hardware in mind, not necessarily existing > hardware and software. > The biology is just good to help the math over some humps. So far, I have not been able to identify ANY neuronal characteristic that hasn't been refined to near-perfection, once the true functionality was fully understood. Anyway, with the math, you can build a system anyway you want. Without the math, you are just wasting your time and electricity. The math comes first, and all other things follow. Steve ======================= > > These are my main reasons, at least that I can remember, that I avoid > biologically inspired methods. It's not to say that they are wrong. But they > don't meet my requirements. It is also very unclear how to implement the > system and make it work. My approach is very deliberate, so the steps > required to make it work are pretty clear to me. > > It is not that your approach is bad. It is just different and I really > prefer methods that are not biologically inspired, but are designed > specifically with goals and requirements in mind as the most important > design motivator. > > Dave > > On Wed, Aug 4, 2010 at 3:54 PM, Steve Richfield <[email protected] > > wrote: > >> 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 <[email protected]>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 < >>> [email protected]> 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 <[email protected]>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> >> <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
