Barely at all. Long Short-Term Memory network blocks, for example, are basically perceptrons with feedback controlled by two or three completely conventional no

Barely at all. Long Short-Term Memory network blocks, for example, are basically perceptrons with feedback controlled by two or three completely conventional non-linear nets running off the same inputs. Deep learning networks are an assembly of existing connectionist parts, enabled by Moore's Law. There's nothing bad about this: the progress being made is real and substantial, but it's being made in ways that are true to Edison's prescribed ratio of inspiration and perspiration, although a lot of the perspiring is being done by machines. The architectural tweaks, while small, are still very important. But the core learning algorithm is still backprop deployed over the new architectures.