We'll see how it does on its own first, if it can be educated, then perhaps so.
For vision and audio we are not attempting human level abilities, so the frame rate and sampling size And frame size can be greatly reduced. PAM P2 will not be as complex as humans. You can watch the videos for more info on how. Particularly the structures for cognitive systems video. I will consider it successful if it exhibits the comptences of a child, and scheme integration and Differentiation. ~PM > Date: Fri, 8 Nov 2013 21:11:12 -0500 > Subject: Re: [agi] Rules + Big Data > From: [email protected] > To: [email protected] > > On Fri, Nov 8, 2013 at 7:57 PM, Piaget Modeler > <[email protected]> wrote: > > > > The article is free. I think scribd is trying to make some money for > > itself. > > Did not know that. Sign up for a scribd account and you should be able > > to download it for free. or instead I can e-mail it to you personally if > > you prefer. > > Let me know which way you want to go. > > Why don't you just put the PDF file on your website? > > > The goal is to have the infant develop into a more mature general > > intelligence. > > Are you going to raise it like a parent, or do you have some way to > automate or speed up the training? > > > Some compression may be done on the audio video streams but largely > > those percepts will be represented internally as monads. > > The retina compresses a 10^10 bit per second video stream down to > about 10^7 bits per second transmitted over the optic nerve. By the > time our visual perception reaches long term episodic memory, it is > compressed to about 5 bits per second. We know this is the case > because of limits on our ability to recall images or to notice > differences as measured in cognitive tests. But we have no idea how to > do the later stages of this type of lossy compression. > > We do have a pretty good idea of what the retina and lower layers of > the visual cortex are doing. We can use neural networks like Hawkins' > HTM to model the pattern recognition capabilities we have observed in > animal and human experiments. Presumably the higher layers are doing a > similar thing, but with more complex patterns such as faces, words, > and other familiar objects. But to model this, we need a human brain > sized neural network (1 petaflop) and train it on years worth of > video, like 10^9 frames with 10^7 pixels each (10 petabytes). Our most > ambitious experiments, like Google's cat face recognizer, fall far > short of what a toddler can see. And that required 3 days of training > on 8000 CPU cores on 10^-4 as much data. > > So how do you propose doing that with monads? Sure, they are elegant, > but do you expect a 10^6 speedup? > > And, of course, such simple, highly repetitive structures as we > typically use cannot model our genetically programmed fear of heights, > snakes, and spiders. How do you code that into an untrained vision > system? > > > Humans are very complex, PAM-P2 will be less so. > > Are you expecting human level results? If not, then what results would > you consider successful? > > -- > -- Matt Mahoney, [email protected] > > > ------------------------------------------- > AGI > Archives: https://www.listbox.com/member/archive/303/=now > RSS Feed: https://www.listbox.com/member/archive/rss/303/19999924-4a978ccc > 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/303/21088071-f452e424 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-58d57657 Powered by Listbox: http://www.listbox.com
