enhancing the efficiency of research activity but in creating a new “playbook” for innovation
itself. We then turn in Section III to briefly contrasting three key technological trajectories
within AI—robotics, symbolic systems, and deep learning. We propose that these often
conflated fields will likely play very different roles in the future of innovation and technical
change. Work in symbolic systems appears to have stalled and is likely to have relatively little
impact going forwards. And while developments in robotics have the potential to further displace
human labor in the production of many goods and services, innovation in robotics technologies
per se has relatively low potential to change the nature of innovation itself. By contrast, deep
learning seems to be an area of research that is highly general-purpose and that has the potential
to change the innovation process itself.
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