"The automatic encoding of features which previously had to be hand engineered." Yes, that is the main benefit. The drawback is that we are still hand tuning a

"The automatic encoding of features which previously had to be hand engineered." Yes, that is the main benefit. The drawback is that we are still hand tuning architectures, slowly inventing (or incorporating) things like LSTM and the like into the model. One goal would be to achieve a universal building block that can be stacked/repeated without the need for architectural tuning. Maybe something that combines recurrence, one-shot learning, deep learning, and something stolen from AI (like alpha-beta, graph search, or something self-referencing and stochastic with secondary neural networks) into a single "node". Then we won't have to worry about architecture so much.