I'm reading about the retina motion processing. Maybe the brain does only get 24 frames per second, but the retina may send it information about hypothesized or likely movements. The brain can then do some further processing, such as using including kinesthetic feedback that tells the brain about how the body moved during the time that the images were captured, which Matt mentioned (thanks again).
Maybe the important question here is could we potentially create a real camera system to capture extremely high frame rates for the purposes of generating vast amounts of very low uncertainty training data that could be used to create algorithms that do not require such high frame rates. With such training data, you could automatically test algorithms much more quickly. You could potentially even use genetic algorithms to find the right solution, although I'm not a big fan of such an approach. To solve screenshot computer vision, I could potentially generate screenshots with very high frame rates for training data also. Even if the process is supervised, it could probably generate vastly more training data than we've ever had before. Dave On Mon, Jun 21, 2010 at 10:30 AM, deepakjnath <[email protected]> wrote: > The brain does not get the high frame rate signals as the eye itself > only gives brain images at 24 frames per second. Else u wouldn't be > able to watch a movie. > Any comments? > > On 6/21/10, 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 | Modify Your Subscription > > > > > > ------------------------------------------- > > 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/?&; > > Powered by Listbox: http://www.listbox.com > > > > -- > Sent from my mobile device > > cheers, > Deepak > > > ------------------------------------------- > 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/?&; > Powered by Listbox: 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
