This is cool. From what I understand from paper, its DSL has set of algorithms as building blocks that learn the input/output function. Deep learning algos are trying to do the same but with more generic blocks where assumption is that a lot of these blocks will be able to learn algorithms too. Deep learning is trying to build with a more generic approach in which transfer learning is helping to reduce number of examples needed by reusing algorithms learned.
