Steve, I replace your need for math with my need to understand what the system is doing and why it is doing it. It's basically the same thing. But you are approaching it at an extremely low level. It doesn't seem to me that you are clear on how this "math" makes the system work the way we want it to work. So, make the math as perfect as you like, if you don't understand why you need the math and how it makes the system do what you want, then it's not going to do you any good.
Understanding what you are trying to accomplish and how you want the system to work comes first, not math. If your neural net doesn't require training data, I don't understand how it works or why you expect it to do what you want it to do if it is "self organized". How do you tell it how to process inputs correctly? What guides the processing and analysis? Dave On Wed, Aug 4, 2010 at 4:33 PM, Steve Richfield <steve.richfi...@gmail.com>wrote: > David > > On Wed, Aug 4, 2010 at 1:16 PM, David Jones <davidher...@gmail.com> 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 < >> steve.richfi...@gmail.com> 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 <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> >>> <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
