Moravec's Paradox
The observation that tasks easy for humans (like perception and movement) are hard for AI, while tasks hard for humans (like math and chess) are easy for AI.
Also known as: Moravec Paradox, Moravec's Problem
Category: Principles
Tags: ai, cognition, robotics, paradox, human-machine-interaction
Explanation
Moravec's Paradox, articulated by roboticist Hans Moravec in the 1980s, reveals a counterintuitive truth about artificial intelligence: the skills we consider intellectually demanding are often the easiest for computers, while the 'simple' things we do without thinking are incredibly difficult to replicate.
Examples of the paradox:
- **Easy for AI, hard for humans**: Complex mathematics, playing chess at grandmaster level, processing millions of data points, perfect recall
- **Easy for humans, hard for AI**: Recognizing faces in different lighting, walking on uneven terrain, understanding sarcasm, loading a dishwasher
Why this happens:
- High-level reasoning is a recent evolutionary development with explicit, codifiable rules
- Sensorimotor skills evolved over millions of years and are deeply optimized but implicit
- Abstract thinking uses serial, conscious processing that's easy to program
- Perception and motor control use massive parallel processing that's hard to replicate
As Moravec put it: '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.'
Implications for knowledge work:
- AI excels at tasks we find tedious (data analysis, summarization)
- Humans remain superior at tasks requiring physical world understanding
- The best results come from human-AI collaboration that leverages complementary strengths
- Don't assume 'easy' tasks for you will be easy to automate
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