Replacing manual labor with machines on farms and in factories was one thing, the worriers say. Those machines were dumb and highly specialized, requiring humans to oversee them at every stage. But the 21st century is witnessing the rise of far smarter machines that can perform tasks previously thought to be immune to automation.
Today’s software can answer your calls, organize your calendar, sell you shoes, recommend your next movie, and target you with advertisements. Tomorrow’s software will diagnose your diseases, write your news stories, and even drive your car. When even high-skill “knowledge workers” are at risk of being replaced by machines, what human jobs will be left? Politics, perhaps—and, of course, entrepreneurship and management. The rich will get richer, in other words, and the rest of us will be left behind.
All of which has brought John Maynard Keynes’ concept of “technological unemployment” back into the academic discourse, some 80 years after he coined the phrase.
Deep learning requires that learners get advice from fellow learners and instructors, reflect on their prior learning choices and next options, and then make decisions about their own learning, good and bad ones, and learn from those decisions, whether good or bad, something about the subject of the course as well how they learn.