I've been baffled by this as well. I can understand why deep learning has done well in fields that can roughly be described as sensory perception, but it has never improved on basic random forests or SVMs for problems in my domain. And we have lots of data (at least more than can fit in an R instance).
Even taking data size out of the picture, functionally it is not there yet for most tasks. Maybe it will be in the future, but the big problem with it is that with n neurons, you have n^n possible topologies, and finding the right neural topology is a major optimization problem that we're only barely learning basic human heuristics for.
I'm willing to bet the deep learning thing is just one more Neat fad that will eventually cause disillusionment at its lack of results, reverting us back to the Scruffy view that intelligence is far too complex to be described holistically by small sets of simple algorithms. The great thing about the Scruffy philosophy is that it isn't derogatory...deep learning will always have a place as a tool in its tool set. It merely doesn't hold unreasonable expectations.
Even taking data size out of the picture, functionally it is not there yet for most tasks. Maybe it will be in the future, but the big problem with it is that with n neurons, you have n^n possible topologies, and finding the right neural topology is a major optimization problem that we're only barely learning basic human heuristics for.
I'm willing to bet the deep learning thing is just one more Neat fad that will eventually cause disillusionment at its lack of results, reverting us back to the Scruffy view that intelligence is far too complex to be described holistically by small sets of simple algorithms. The great thing about the Scruffy philosophy is that it isn't derogatory...deep learning will always have a place as a tool in its tool set. It merely doesn't hold unreasonable expectations.