Moravec's Paradox

For machines, high-level reasoning is easy; the perception and movement humans take for granted turn out to be the hard part.

In his 1988 book Mind Children, roboticist Hans Moravec observed that it's comparatively easy to get a computer up to adult level on an intelligence test or at checkers, but hard, or even impossible, to give it the perception and mobility of a one-year-old child. Rodney Brooks, Marvin Minsky, and others voiced similar views in the 1980s; Steven Pinker later summed it up as “the hard problems are easy, and the easy problems are hard.” The usual explanation: perception and motor skills were refined over a vast span of evolution, so they feel effortless to us and we underrate how hard they really are, while abstract reasoning emerged only recently in evolutionary terms — the human brain isn't especially well-suited to it, which is why it feels hard to us, even though it isn't actually that hard for a computer. This paradox is often used to explain why large models can write code and solve math problems while robots still can't fold laundry as fast or as reliably as a person, and it's one reason embodied AI is seen as AI's next major hurdle.

ExampleA large language model can already solve competition-level math problems, but folding laundry or tying a shoelace — things a small child can do — a robot still does far slower and far less reliably than a person.

Related

Embodied AI · Sensorimotor Skills · Embodied Cognition · The Bitter Lesson · Physical Turing Test

Sources

Moravec's paradox (Wikipedia)