Note: I am not an expert on ML, ANNs, etc - most of what I have done has been mainly "hobby level" and MOOC learning.
From what I understand, though, what GH is trying to accomplish with capsule networks (which I have tried to understand, and have yet to succeed) is optimizing (or possible removal of) backpropagation.
He has noted in the past how - so far (from what I know) - there isn't a biological equivalent to backpropagation for learning. Backprop is a completely artificial mathematical construct that doesn't happen in natural systems. It is also extremely energy intensive - at least in the manner that it is currently done.
So the question is - that he's hoping to answer I think - what is a proper working framework for implementing an artificial neural network that can learn, without using backpropagation (or using it differently)?
I think whoever can solve this will fundamentally remake the field of ML/AI - much in the same way that backprop (and later "deep learning") did.
From what I understand, though, what GH is trying to accomplish with capsule networks (which I have tried to understand, and have yet to succeed) is optimizing (or possible removal of) backpropagation.
He has noted in the past how - so far (from what I know) - there isn't a biological equivalent to backpropagation for learning. Backprop is a completely artificial mathematical construct that doesn't happen in natural systems. It is also extremely energy intensive - at least in the manner that it is currently done.
So the question is - that he's hoping to answer I think - what is a proper working framework for implementing an artificial neural network that can learn, without using backpropagation (or using it differently)?
I think whoever can solve this will fundamentally remake the field of ML/AI - much in the same way that backprop (and later "deep learning") did.