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Moravec’s Paradox

Moravec’s Paradox

Last updated on: September 1, 2026 at 6:00 am by Gene Veith

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Moravec’s Paradox 2026-08-23T18:11:17-04:00 Gene Veith

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I quite randomly came across some correspondence by Anmol Arora of Cambridge University in the British medical journal Lancet. It discussed whether AI and robotic technology might someday take the place of human doctors.

In the course of that, Arora mentioned an intriguing concept with wide applicability. It’s called Moravec’s paradox. I’ll let Arora explain it. From Moravec’s paradox and the fear of job automation in health care:

Moravec’s paradox is a phenomenon observed by robotics researcher Hans Moravec, in which tasks that are easy for humans to perform (eg, motor or social skills) are difficult for machines to replicate, whereas tasks that are difficult for humans (eg, performing mathematical calculations or large scale data analysis) are relatively easy for machines to accomplish.

The Wikipedia article on Moravec’s Paradox quotes what Moravec wrote back in 1988: “It is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility.”

The article quotes similar observations by other pioneers in the AI field:

[Marvin] Minsky emphasized that the most difficult human skills to reverse engineer are those that are below the level of conscious awareness. “In general, we’re least aware of what our minds do best”, he wrote, and added: “we’re more aware of simple processes that don’t work well than of complex ones that work flawlessly”. Steven Pinker wrote in 1994 that “the main lesson of thirty-five years of AI research is that the hard problems are easy and the easy problems are hard.”

Why is this? Moravec offered an evolutionary explanation:

Encoded in the large, highly evolved sensory and motor portions of the human brain is a billion years of experience about the nature of the world and how to survive in it. The deliberate process we call reasoning is, I believe, the thinnest veneer of human thought, effective only because it is supported by this much older and much more powerful, though usually unconscious, sensorimotor knowledge. We are all prodigious olympians in perceptual and motor areas, so good that we make the difficult look easy. Abstract thought, though, is a new trick, perhaps less than 100 thousand years old. We have not yet mastered it. It is not all that intrinsically difficult; it just seems so when we do it.

An alternative answer is that our capacity for abstract thought is only a small part of what makes us human. Our consciousness consists of our sensory perceptions, imagination, memory, conscience, emotions, feelings, and much, much more, with “intelligence” being a very small part of the human makeup. This is because we are embodied souls, created in the image of God Himself. Just as He transcends narrow little categories, so do we.

The Wikipedia article also put Moravec’s paradox in the context of AI development:

In the early days of artificial intelligence research, leading researchers often predicted that they would be able to create thinking machines in just a few decades (see history of artificial intelligence). Their optimism stemmed in part from the fact that they had been successful at writing programs that used logic, solved algebra and geometry problems and played games like checkers and chess. Logic and algebra are difficult for people and are considered a sign of intelligence. Many prominent researchers assumed that, having (almost) solved the “hard” problems, the “easy” problems of vision and commonsense reasoning would soon fall into place. They were wrong. . . .

Rodney Brooks explains that, according to early AI research, intelligence was “best characterized as the things that highly educated male scientists found challenging”, such as chess, symbolic integration, proving mathematical theorems and solving complicated word algebra problems. “The things that children of four or five years could do effortlessly, such as visually distinguishing between a coffee cup and a chair, or walking around on two legs, or finding their way from their bedroom to the living room were not thought of as activities requiring intelligence. Nor were any aesthetic judgments included in the repertoire of intelligence-based skills.”

Back to Arora, who applies these limitations to AI-assisted medicine, showing what the technology can do for doctors, but also what it can’t do, suggesting why human doctors will always be important:

For example, a computer-aided diagnostic system might be able to analyse large volumes of images quickly and accurately but might struggle to recognise clinical context or technical limitations that a human radiologist would easily identify. Similarly, a machine learning algorithm might be able to predict a patient’s risk of a specific condition on the basis of their medical history and laboratory results but might not be able to account for the nuances of the patient’s individual case or consider the effect of social and environmental factors that a human physician would consider.

In surgery, there has been great progress in the field of robotics in health care when robotic elements are controlled by humans, but artificial intelligence driven robotic technology has been much slower to develop.

Thus far, research into clinical artificial intelligence has focused on improving diagnosis and predictive medicine. However, this is only a small component of the daily job of a doctor, at any level. Communicating to patients, performing practical procedures, and choosing investigations requires much more time and effort than the interpretation of results.

This is not to say that the use of artificial intelligence to improve the interpretation of investigations is not important. Rather, this use of artificial intelligence augments the delivery of care provided by humans rather than replacing it. Moravec’s paradox highlights the importance of maintaining a human element in the health-care system

To be sure, humans need to be able to handle the “hard” parts. Education dare not farm out rational analysis completely to machines. A woman whose son was in an engineering program at a major university told me that one of his professors told him that he didn’t need to learn calculus, since he can just have AI handle the math problems. No, he needs to learn calculus, if only to direct and make sense of what AI will spit out.

But Moravec’s paradox reminds us that while technology can help human beings, it cannot replace them.

Illustration: Moravec’s Paradox, by Jono Hey, Sketchplanations under a Creative Commons Attribution-NonCommercial 4.0 International License

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