 One thing that human cognitive systems do very well is to generalize from a limited number of examples. The difference between humans and machines is particularly striking when we compare how humans learn a complicated skill, such as driving a car, from how a machine learning system learns the same task. As harrowing an experience it is to teach a teenager how to drive a car, they do not need to crash into a utility pole 10,000 times to learn that utility poles are not traversable. What teenagers learn as children about the world through play and observing other people drive lends to them an understanding that utility poles are to be steered around, a piece of commonsense that our current machine learning systems do not have but must learn from scratch on a case-by-case basis. We, unlike our machines, have a remarkable capacity to transfer what we learn from one domain to another domain, a capacity fueled in part by our curiosity (Kidd &amp; Hayden 2015).