Neural nets are Turing complete, so anything any computer program can do, can be done in principle by a (recurrent) neural network...
The power of specific NN architectures and learning algorithms, is a different issue... ben On Wed, Jun 27, 2012 at 11:59 AM, Jim Bromer <jimbro...@gmail.com> wrote: > I did not read the original paper. I see this as a pure extrapolation of > other neural networks. There is nothing unexpected - or was there? > > The problem is that neural networks are not able to recognize > cross-categorical features (like seeing eyes both in humans and in other > animals). (This example may be too fussy because the paper discussed an > untrained model that only sampled still images but I just wanted to find an > important example.) Another example is that folds of cloth might look > like limbs and bodies and so they might be cross categorized (in another > sample). But what happens to this kind of cross-categorization that a > neural network can produce? The features could be confused as well as be > used to recognize a type of thing in an image. I believe that types of > things that can be cross-categorized (and used to significantly detect > similarities and differences during recognition) will only tend to blur > those similarities and differences when done in a neural network. However, > I am not that familiar with neural networks. > Jim Bromer > > On Wed, Jun 27, 2012 at 9:53 AM, Matt Mahoney <mattmahone...@gmail.com>wrote: > >> On Wed, Jun 27, 2012 at 2:09 AM, bfrs <bfrs1...@gmail.com> wrote: >> > nytimes article on this paper: >> > >> https://www.nytimes.com/2012/06/26/technology/in-a-big-network-of-computers-evidence-of-machine-learning.html?_r=1 >> >> Original paper here: >> http://arxiv.org/pdf/1112.6209v3.pdf >> >> To summarize, a 9 layer neural network with 10^9 connections is >> trained unsupervised for 3 days on 1000 16-core CPUs on 10^7 unlabeled >> 200x200 images, each a random frame from a different Youtube video. >> When the resulting top level neurons are examined, it turns out that >> there are detectors for (among other things) human faces, human >> bodies, and cats. >> >> It was not told to look for these things. This is just a compression >> problem. If you want to encode an image efficiently, then you do so by >> describing its high level features (e.g. a person holding a cat). The >> learning problem is to find a set of useful features, knowing nothing >> about the world or what these arrays of pixels might represent. >> >> It does not achieve human level accuracy, but is still better than >> anything else. The equivalent problem for human vision would be to >> train 10^13 synapses for a decade on 10^9 images of 10^8 pixels each. >> >> -- >> -- Matt Mahoney, mattmahone...@gmail.com >> >> >> ------------------------------------------- >> AGI >> Archives: https://www.listbox.com/member/archive/303/=now >> RSS Feed: >> https://www.listbox.com/member/archive/rss/303/10561250-164650b2 >> 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> > <https://www.listbox.com/member/archive/rss/303/212726-11ac2389> | > Modify<https://www.listbox.com/member/?&;>Your Subscription > <http://www.listbox.com> > -- Ben Goertzel, PhD http://goertzel.org "My humanity is a constant self-overcoming" -- Friedrich Nietzsche ------------------------------------------- AGI Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/21088071-c97d2393 Modify Your Subscription: https://www.listbox.com/member/?member_id=21088071&id_secret=21088071-2484a968 Powered by Listbox: http://www.listbox.com
