You can show this in children’s everyday learning, too. Young children rapidly learn abstract intuitive theories of biology, physics, and psychology in much the way adult scientists do, even with relatively little data. The remarkable machine-learning accomplishments of the recent AI systems, both bottom-up and top-down, take place in a narrow and well-defined space of hypotheses and concepts—a precise set of game pieces and moves, a predetermined set of images. In contrast, children and scientists alike sometimes change their concepts 1n radical ways, performing paradigm shifts rather than simply tweaking the concepts they already have. Four-year-olds can immediately recognize cats and understand words, but they can also make creative and surprising new inferences that go far beyond their experience. My own grandson recently explained, for example, that if an adult wants to become a child again, he should try not eating any healthy vegetables, since healthy vegetables make a child grow into an adult. This kind of hypothesis, a plausible one that no grown- up would ever entertain, is characteristic of young children. In fact, my colleagues and I have shown systematically that preschoolers are better at coming up with unlikely hypotheses than older children and adults.*? We have almost no idea how this kind of creative learning and innovation is possible. Looking at what children do, though, may give programmers useful hints about directions for computer learning. Two features of children’s learning are especially striking. Children are active learners; they don’t just passively soak up data like Als do. Just as scientists experiment, children are intrinsically motivated to extract information from the world around them through their endless play and exploration. Recent studies show that this exploration is more systematic than it looks and is well-adapted to find persuasive evidence to support hypothesis formation and theory choice.*° Building curiosity into mach