Learning from humans: what is inverse reinforcement learning?

One of the goals of AI research is to teach machines how to do the same things people do, but better. In the early 2000s, this meant focusing on problems like flying helicopters and walking up flights of stairs. However, there's still a massive list of problems where humans outperform machines. Although we can no longer claim to beat machines at tasks like Go and image classification, we have a distinct advantage in solving problems that aren't as well-defined, like judging a well-executed backflip, cleaning a room while preventing accidents, and perhaps the most human problem of all: reasoning about people's values. Since all these tasks contain some degree of subjectivity, machines need information about the world as well as a way to learn about the people within it in order to solve these problems.