[sbw1]

Classic non-ML AI research these days sometimes tends to be baked into the background of systems where deep learning is the star-- e.g., all of the search techniques used in AlphaGo are interesting in their own right, but aren't really where the innovation is there.

There are researchers actively looking at more holistic AI systems that aren't just the core learning part (often called cognitive architectures). For instance, take a look at Soar: http://soar.eecs.umich.edu/

It under active development, and has been since the 80s. Soar is very much a spiritual successor to GOFAI (or maybe still is GOFAI), but does incorporate statistical machine learning-- much of the recent work has been around integrating reinforcement learning with symbolic decision-making.

ACT-R is another cognitive architecture under active development, but is more used for cognitive modeling (i.e. psychology research): http://act-r.psy.cmu.edu/

You can find other related systems by searching for "cognitive architecture", but it is unfortunately a field that attracts a lot of proposals/'designs' for things that never get properly implemented (the above being two notable exceptions).