Hi Gaurav, I think that it should be possible to take advantage of the massiv parallel computing power of a GPU also for the OpenCog system like it is used for NNs.
So, are there any NP-hard problems inside the box? --Andi Am Dienstag, 2. August 2016 03:22:22 UTC+2 schrieb Gaurav Gautam: > > I may be wrong, but as far as I understand one problem may be that neural > networks are not really graphs or hypergraphs. Books show them as a set of > layers and some connecting edges which looks a lot like a graph, but when > they are implemented in code, they mostly are matrix operations. So, as far > as I understand a program implementing a neural network will be doing > matrix operations. If I am right about this, then I don't see how seeing > atomspace as a neural network will help. > > What I am saying is that I don't think the atoms and links can be > connected to make a neural network straightforwardly. Of course, one could > make atoms that represent the coefficients of the model that the CNN > represents and then connect those with links that have weights and then > make a function that can take such a hypergraph and tune the weights. But > wouldn't that be very inefficient? Wouldn't you want to just represent a > feature vector in atomese and then run CNN on it (through an external > library perhaps) and get results in atomese that the other algorithms can > pick up? But then again, I have very little idea what I am talking about, > so I may be way off. > -- 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/067f3e5c-493e-4f68-b7fb-1c2b46a05bb2%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
