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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 in 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 AIs 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 machines and allowing them to actively interact with the world might be a route to more realistic and wide-ranging learning. Second, children, unlike existing Als, are social and cultural learners. Humans don’t learn in isolation but avail themselves of the accumulated wisdom of past generations. Recent studies show that even preschoolers learn through imitation and by listening to the testimony of others. But they don’t simply passively obey their teachers. Instead they take in information from others in a remarkably subtle and sensitive way, making complex inferences about where the information comes from and how trustworthy it is and systematically integrating their own experiences with what they are hearing." “Artificial intelligence” and “machine learning” sound scary. And in some ways they are. These systems are being used to control weapons, for example, and we really should be scared about that. Still, natural stupidity can wreak far more havoc than artificial intelligence; we humans will need to be much smarter than we have been in the past to properly regulate the new technologies. But there is not much basis for either the apocalyptic or the utopian visions of AIs replacing humans. Until we solve the basic 3° A. Gopnik, et al., “Changes in cognitive flexibility and hypothesis search across human life history from childhood to adolescence to adulthood,” Proc. Nat. Acad. Sci., 114:30, 7892-99 (2017). “°L. Schulz, “The origins of Inquiry: Inductive inference and exploration in early childhood,” Trends Cog. Sci., 16:7, 382-89 (2012). 4. A. Gopnik, The Gardener and the Carpenter (New York: Farrar, Straus & Giroux, 2016), chaps. 4 and 5. 157 HOUSE_OVERSIGHT_016377