The future of work looks grim for many people. A recent study from Forrester estimated that 10% of U.S. jobs would be automated this year, and another from McKinsey estimates that close to half of all U.S. jobs may be automated in the next decade.
After reflecting on characteristics of numerous jobs and professions, two non-routine kinds of work seem to me to be particularly common, and difficult to automate: First, emotion. Emotion plays an important role in human communication (think about that physician sitting with the family, or that bartender interacting with customers). It is critically involved in virtually all forms of nonverbal communication and in empathy.
Second, context. Humans can easily take context into account when making decisions or having interactions with others. Context is particularly interesting because it is open ended — for instance, every time there’s a news story, it changes the context (large or small) in which we operate. Moreover, changes in context (e.g., the election of a maverick President) can change not just how factors interact with each other, but can introduce new factors and reconfigure the organization of factors in fundamental ways. This is a problem for machine learning, which operates on data sets that by definition were created previously, in a different context. Thus, taking context into account (as a congenial bartender can do effortlessly) is a challenge for automation.