Symbolic reasoning and related fields are just areas to were important to be researched back in the day. There are some very interesting results that came of that family of research, especially on the intersection of traditional math and CS, i.e. logic stuff, proof assistants, various solvers, etc.
Machine learning and its flavours were always there. What is new in deep learning is the scale of everything: amount of compute made possible by clusters of GPU-centric machines and amount of data made available by the Internet.
The shocking thing here how almost boring the core of recent advances is: do more of the same, much, much more, and quantity will turn into new qualities yet again.