We explore whether this might indeed be the case through an examination of some
quantitative empirical evidence on the evolution of different areas artificial intelligence in terms
of scientific and technical outputs of AI researchers as measured (imperfectly) by the publication
of papers and patents from 1990 through 2015. In particular, we develop what we believe is the
first systematic database that captures the corpus of scientific paper and patenting activity in
artificial intelligence, broadly defined, and divides these outputs into those associated with
robotics, symbolic systems, and deep learning. Though preliminary in nature (and inherently
imperfect given that key elements of research activity in artificial intelligence may not be
observable using these traditional innovation metrics), we find striking evidence for a rapid and
meaningful shift in the application orientation of learning-oriented publications, particularly after
2009.