Earlier quoted context omitted.
As a computer vision researcher, I'm not at all convinced that deep learning methods will be "final" in any sense. I know that in the past, neural networks were "final", and then graphical models were "final", and so on. And while deep learning methods have indeed shown remarkable improvements recently, they're not yet state-of-the-art on the most important/relevant computer vision benchmarks.
As a computer vision researcher it must be pain you to see that all your learnings are for nought when faced with deep learning methods which can get amazing performances from raw pixels (see mnist results for example). Also see ronan collobert's natural language processing from scratch paper where handily beats the past few decades of nlp research in parsing (in terms of efficiency, and probably performances soon to…
But I don't think it is. MNIST data is not particularly challenging. It's great that deep learning methods work there -- they must be doing something right.
Come back and taunt me when deep learning methods start getting state-of-the-art results on, e.g., Pascal VOC: http://pascallin.ecs.soton.ac.uk/challenges/VOC/