Dreyfus's critique of AI
In plain wordsThe philosopher Hubert Dreyfus's argument, from the 1960s on, that rule-based AI could never match human intelligence, because much human skill rests on bodily know-how and intuition rather than explicit rules.
The critique of symbolic AIsymbolic AI, n.: An approach to AI that writes knowledge down as human-readable symbols and rules, such as 'every bird has wings', and reasons by manipulating them step by step instead of learning patterns from data. developed by the philosopher Hubert DreyfusHubert Dreyfus, prop. n.: An American philosopher who argued from the 1960s that computers following explicit rules could never match human intelligence, which rests on bodily, intuitive know-how., drawing on Martin Heidegger, Maurice Merleau-Ponty and Ludwig Wittgenstein. Dreyfus argued that the field rested on four mistaken assumptions — biological (that the brain works by discrete, switch-like operations), psychological (that the mind follows formal rules over bits of information), epistemological (that everything known can be made explicit and formal) and ontological (that the world divides into independent, context-free facts) — whereas human expertise depends on embodied, situated skills, a background of tacit understanding and an intuitive grasp of what is relevant. He predicted the stalls of good old-fashioned AIgood old-fashioned AI, n.: A nickname for the classic style of AI, dominant from the 1950s to the 1980s, that tried to make computers intelligent by writing knowledge and rules down by hand. and expert systemsexpert system, n.: A program that stores a human specialist's know-how as many if–then rules and uses them to give advice or make decisions in a narrow field, such as diagnosing infections or configuring computers., and anticipated themes of embodied cognitionembodied cognition, n.: The idea that thinking is not something the brain does on its own: the body, its senses and movements, and its surroundings play a real part in how we think and understand., the frame problemframe problem, n.: The difficulty of getting an AI to work out what does and does not change when something happens, without spelling out every one of the countless things that stay the same. and Polanyi's paradoxPolanyi's paradox, n.: The observation that we know how to do many things — recognize a face, ride a bike — that we cannot explain in words. That made such skills hard to program until machines learned them from examples.. He was more sympathetic to neural networks, though he doubted they could generalize as humans do without a humanlike body and form of life.
Origin
First set out in Hubert Dreyfus's RAND Corporation paper 'Alchemy and Artificial Intelligence' (1965), then in What Computers Can't Do (1972), its revision What Computers Still Can't Do (1992) and, with his brother Stuart Dreyfus, Mind over Machine (1986). AI researchers' early reactions were hostile; many later conceded parts of the critique.
Related
- Hubert DreyfusHubert Dreyfus, prop. n.: An American philosopher who argued from the 1960s that computers following explicit rules could never match human intelligence, which rests on bodily, intuitive know-how.
- good old-fashioned AIgood old-fashioned AI, n.: A nickname for the classic style of AI, dominant from the 1950s to the 1980s, that tried to make computers intelligent by writing knowledge and rules down by hand.
- embodied cognitionembodied cognition, n.: The idea that thinking is not something the brain does on its own: the body, its senses and movements, and its surroundings play a real part in how we think and understand.
- frame problemframe problem, n.: The difficulty of getting an AI to work out what does and does not change when something happens, without spelling out every one of the countless things that stay the same.
- Polanyi's paradoxPolanyi's paradox, n.: The observation that we know how to do many things — recognize a face, ride a bike — that we cannot explain in words. That made such skills hard to program until machines learned them from examples.
- Moravec's paradoxMoravec's paradox, n.: The observation that what is hard for people, like chess or algebra, is often easy for computers, while what toddlers do effortlessly, such as seeing, walking and picking things up, is very hard for machines.
- symbolic AIsymbolic AI, n.: An approach to AI that writes knowledge down as human-readable symbols and rules, such as 'every bird has wings', and reasons by manipulating them step by step instead of learning patterns from data.
Sources
- Dreyfus, H. L. (1965). Alchemy and Artificial Intelligence. RAND Corporation Paper P-3244.
- Dreyfus, H. L. (1972). What Computers Can't Do: A Critique of Artificial Reason. Harper & Row.
Mentioned in
embodied cognitionembodied cognition, n.: The idea that thinking is not something the brain does on its own: the body, its senses and movements, and its surroundings play a real part in how we think and understand., frame problemframe problem, n.: The difficulty of getting an AI to work out what does and does not change when something happens, without spelling out every one of the countless things that stay the same., Hubert DreyfusHubert Dreyfus, prop. n.: An American philosopher who argued from the 1960s that computers following explicit rules could never match human intelligence, which rests on bodily, intuitive know-how., philosophy of artificial intelligencephilosophy of artificial intelligence, n.: The branch of philosophy that asks what AI is and could be: whether machines can think, understand or be conscious, what intelligence is, and what we owe to and should expect from intelligent machines., physical symbol system hypothesisphysical symbol system hypothesis, n.: The 1976 claim by two AI pioneers that a machine which stores and rearranges symbols according to rules has everything needed for general intelligence, and that anything generally intelligent must be such a machine., Polanyi's paradoxPolanyi's paradox, n.: The observation that we know how to do many things — recognize a face, ride a bike — that we cannot explain in words. That made such skills hard to program until machines learned them from examples.