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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.39 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.40 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 AIs, 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.41
“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

39 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).
40 L. Schulz, “The origins of Inquiry: Inductive inference and exploration in early childhood,” Trends Cog.
Sci., 16:7, 382-89 (2012).
41 A. Gopnik, The Gardener and the Carpenter (New York: Farrar, Straus & Giroux, 2016), chaps. 4 and 5.

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