Thank you Matt. That's very useful input. On Mon, Jun 21, 2010 at 9:57 AM, Matt Mahoney <[email protected]> wrote:
> Your computer monitor flashes 75 frames per second, but you don't notice > any flicker because light sensing neurons have a response delay of about 100 > ms. Motion detection begins in the retina by cells that respond to contrast > between light and dark moving in specific directions computed by simple, > fixed weight circuits. Higher up in the processing chain, you detect motion > when your eyes and head smoothly track moving objects using kinesthetic > feedback from your eye and neck muscles and input from your built in > accelerometer in the semicircular canals in your ears. This is all very > complicated of course. You are more likely to detect motion in objects that > you recognize and expect to move, like people, animals, cars, etc. > > > -- Matt Mahoney, [email protected] > > > ------------------------------ > *From:* David Jones <[email protected]> > *To:* agi <[email protected]> > *Sent:* Mon, June 21, 2010 9:39:30 AM > *Subject:* [agi] Re: High Frame Rates Reduce Uncertainty > > Ignoring Steve because we are simply going to have to agree to disagree... > And I don't see enough value in trying to understand his paper. I said the > math was overly complex, but what I really meant is that the approach is > overly complex and so filled with research specific jargon, I don't care to > try understand it. It is overly converned with copying the way that the > brain does things. I don't care how the brain does it. I care about why the > brain does it. Its the same as the analogy of giving a man a fish or > teaching him to fish. You may figure out how the brain works, but it does > you little good if you don't understand why it works that way. You would > have to create a synthetic brain to take advantage of the knowledge, which > is not a approach to AGI for many reasons. There are a million other ways, > even better ways, to do it than the way the brain does it. Just because the > brain accidentally found 1 way out of a million to do it doesn't make it the > right way for us to develop AGI. > > So, moving on.... > > I can't find references online, but I've read that the Air Force studied > the ability of the human eye to identify aircraft in images that were > flashed on a screen at 1/220th of a second. So, clearly, the human eye can > at least distinguish 220 fps if it operated that way. Of course, it may not > operate on fps second, but that is besides the point. I've also heard other > people say that a study has shown that the human eye takes 1000 exposures > per second. They had no references though, so it is hearsay. > > The point was that the brain takes advantage of the fact that with such a > high exposure rate, the changes between each image are very small if the > objects are moving. This allows it to distinguish movement and visual > changes with extremely low uncertainty. If it detects that the changes > required to match two parts of an image are too high or the distance between > matches is too far, it can reject a match. This allows it to distinguish > only very low uncertainty changes and reject changes that have high > uncertainty. > > I think this is a very significant discovery regarding how the brain is > able to learn in such an ambiguous world with so many variables that are > difficult to disambiguate, interpret and understand. > > Dave > > On Fri, Jun 18, 2010 at 2:19 PM, David Jones <[email protected]>wrote: > >> I just came up with an awesome idea. I just realized that the brain takes >> advantage of high frame rates to reduce uncertainty when it is estimating >> motion. The slower the frame rate, the more uncertainty there is because >> objects may have traveled too far between images to match with high >> certainty using simple techniques. >> >> So, this made me think, what if the secret to the brain's ability to learn >> generally stems from this high frame rate trick. What if we made a system >> that could process even high frame rates than the brain can. By doing this >> you can reduce the uncertainty of matches very very low (well in my theory >> so far). If you can do that, then you can learn about the objects in a >> video, how they move together or separately with very high certainty. >> >> You see, matching is the main barrier when learning about objects. But >> with a very high frame rate, we can use a fast algorithm and could >> potentially reduce the uncertainty to almost nothing. Once we learn about >> objects, matching gets easier because now we have training data and >> experience to take advantage of. >> >> In addition, you can also gain knowledge about lighting, color variation, >> noise, etc. With that knowledge, you can then automatically create a model >> of the object with extremely high confidence. You will also be able to >> determine the effects of light and noise on the object's appearance, which >> will help match the object invariantly in the future. It allows you to >> determine what is expected and unexpected for the object's appearance with >> much higher confidence. >> >> Pretty cool idea huh? >> >> 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 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
