Ty, Linas! A picture tells more than 1000 words :) Am Dienstag, 2. August 2016 18:20:58 UTC+2 schrieb linas: > > Here this: > > http://ci-journal.net/index.php/ciej/article/viewFile/327/315/2090 > > > https://developingwritersdotorg.files.wordpress.com/2013/07/ee98c-topo_rhizome2.jpg > > > > > On Tue, Aug 2, 2016 at 11:05 AM, Linas Vepstas <[email protected] > <javascript:>> wrote: > >> Hi Andi, >> >> Ben has a good answer, and to emphasize, let me add this: Think of the >> atomspace as being a collection of trees. The atoms are the nodes in the >> tree. Any one atom can appear in many trees, and so the whole thing is in >> fact tangled into a big matt, like a rhizome >> https://www.google.com/search?q=rhizome&tbm=isch >> >> The pattern matcher starts at one atom, and walks the rhizome, exploring >> nearest neighbors, until all the entire neighborhood is explored (and a >> match is found, or some other (local) computation is performed). >> >> The problem is that the atoms are scattered randomly through RAM, so when >> the nearest neighbor walk happens, random locations in RAM get visited. >> I'm guessing that there is a lot of cache-miss going on two: If you have, >> say, a CPU cache that is 8-way, 4-associative, then you could have maybe 32 >> atoms in the cache, but the chance that the 33rd atom will accidentally be >> in one of the existing cache lines is just about zero, and so the graph >> walk will have a 99.9% cache-miss rate. (most graphs that get searched >> have more that 32 atoms in them. ) >> >> >> Hmm, I have an idea -- I guess the atomsapce *could* keep track of >> individual connected components (create a bag of trees, which are >> connected by one or more atoms) -- any given search is guaranteed to stay >> in just one bag, and so maybe one could download the entire bag to the gpu >> before starting a search. Could work if the bags are small enough to fit >> in GPU ram. >> >> Maybe allocation could be changed to improve cache locality: allocate >> atoms so that they are more likely to be on the same cache line if they are >> also connected. But this becomes a hard, fiddly computer-science problem... >> >> --linas >> >> >> On Mon, Aug 1, 2016 at 3:26 PM, Andi <[email protected] <javascript:>> >> wrote: >> >>> Hello All, >>> I do not want to disturb the ongoing work so an answer to this question >>> is not urgent, >>> but it will help me during my investigations within the next month. >>> >>> *What in the hell could prevent me to look at the Atomspace as a certain >>> kind of Neuronal Network?* >>> >>> Please don't tell me:"Because it is a hypergraph" haha.... >>> >>> One of my aims is, try to port the whole thing, or some parts, to >>> hardware. Maybe a bunch of some GPU's, PLD's and a CPU can do it. It seems >>> that they are designing some interesting machines for Deep Learning - so >>> maybe even nothing new has to be invented.. >>> >>> For first steps I think it should at least be possible to use some >>> GPU-power to do some work in parallel or is there really a theoretical >>> barrier for paralleling some work that I cannot see in the moment? >>> >>> Please don't be afraid, I know what kind of challenging task this is >>> and would carry it on my own back. But maybe it is not so much work if the >>> right approach is found... >>> At least I want to investigate this - so any red lights blinking? >>> >>> --Andi >>> >>> >>> Am Sonntag, 24. April 2016 07:07:25 UTC+2 schrieb Ben Goertzel: >>>> >>>> Indeed this is not an OpenCoggy question, but some of us may be able >>>> to help... is this dynamic data or instantaneous data you're trying to >>>> classify? >>>> >>>> >>>> >>>> On Sat, Apr 23, 2016 at 1:46 PM, <[email protected]> wrote: >>>> > Hi >>>> > >>>> > I have a dataset of mocap (motion caption) positions as vectors which >>>> I am >>>> > going to train a DNN for this dataset. >>>> > the sample data would be like a 140-D dimension vector. >>>> > Is it possible to train a CNN for this kind of data? I have >>>> > how to use convolution layers for this kind of data as kernels are >>>> e.g.5x5 >>>> > while the data is a vector? >>>> > >>>> > >>>> > If I make the data in a form of matrix, is it possible to train a >>>> pretrained >>>> > CNN e.g. alexnet for this dataset? >>>> > >>>> > Best >>>> > Majid >>>> > >>>> > -- >>>> > You received this message because you are subscribed to the Google >>>> Groups >>>> > "opencog" group. >>>> > To unsubscribe from this group and stop receiving emails from it, >>>> send an >>>> > email to [email protected]. >>>> > To post to this group, send email to [email protected]. >>>> > Visit this group at https://groups.google.com/group/opencog. >>>> > To view this discussion on the web visit >>>> > >>>> https://groups.google.com/d/msgid/opencog/1a04d763-3dfa-473f-a240-a0e452f6faba%40googlegroups.com. >>>> >>>> >>>> > For more options, visit https://groups.google.com/d/optout. >>>> >>>> >>>> >>>> -- >>>> Ben Goertzel, PhD >>>> http://goertzel.org >>>> >>>> "I am Ubik. Before the universe was, I am. I made the suns. I made the >>>> worlds. I created the lives and the places they inhabit; I move them >>>> here, I put them there. They go as I say, then do as I tell them. I am >>>> the word and my name is never spoken, the name which no one knows. I >>>> am called Ubik, but that is not my name. I am. I shall always be.” -- >>>> Ubik >>>> >>> -- >>> You received this message because you are subscribed to the Google >>> Groups "opencog" group. >>> To unsubscribe from this group and stop receiving emails from it, send >>> an email to [email protected] <javascript:>. >>> To post to this group, send email to [email protected] >>> <javascript:>. >>> Visit this group at https://groups.google.com/group/opencog. >>> To view this discussion on the web visit >>> https://groups.google.com/d/msgid/opencog/b3ca200e-c039-4417-96dc-5ef3f37f38ea%40googlegroups.com >>> >>> <https://groups.google.com/d/msgid/opencog/b3ca200e-c039-4417-96dc-5ef3f37f38ea%40googlegroups.com?utm_medium=email&utm_source=footer> >>> . >>> >>> For more options, visit https://groups.google.com/d/optout. >>> >> >> >
-- You received this message because you are subscribed to the Google Groups "opencog" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at https://groups.google.com/group/opencog. To view this discussion on the web visit https://groups.google.com/d/msgid/opencog/4ae35d62-eb63-4fa3-b180-13ce283f8918%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
