To study the phenomenon of the Moravec's Paradox

TL;DR

Moravec's paradox highlights that tasks easy for humans, like perception and motor skills, are difficult for machines, whereas complex reasoning tasks are easier for AI, revealing fundamental differences in AI development challenges.

Contribution

This paper discusses Moravec's paradox, emphasizing the disparity between AI's proficiency in logical reasoning and its struggles with perceptual and motor tasks.

Findings

AI excels in calculations and chess but struggles with perception tasks.

Perceptual and motor skills are more challenging for AI than complex reasoning.

Humans rely on unconscious sensorimotor knowledge for everyday tasks.

Abstract

"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 powerful, though usually unconscious, sensor motor 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."- Hans Moravec Moravec's paradox is involved with the fact that it is the seemingly easier day to day problems that are harder to implement in a machine, than the seemingly complicated logic based problems of today. The results prove that most artificially intelligent machines are as adept if not more than us at under-taking long calculations or even play chess, but their logic brings them nowhere when it comes to carrying out everyday tasks like walking, facial gesture recognition or speech recognition.