Alan Turing — "We are not interested in the fact that the machine can do well, but in the fact …"
We are not interested in the fact that the machine can do well, but in the fact that it can do badly.
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More from Alan Turing
"The extent to which we regard mind as an attribute of the body, or something separable from it, is largely a matter of convenience."
"I like to think the brain is a kind of pudding, but not a Christmas pudding."
"The problem of creating intelligent machines is one of the most challenging and exciting problems in all of science."
"The works and customs of mankind do not seem to be very suitable material to which to apply scientific induction."
"The human mind is capable of doing many things that a machine cannot, but a machine can do many things that a human cannot."
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Understanding this quote
What it means
A machine that only succeeds proves it was built correctly — that's engineering, not intelligence. What's revealing is how a machine fails: does it err in recognizable, human-like ways, or in cold mechanical ones? Errors expose the underlying logic, assumptions, and gaps in a system. Genuine intelligence is identifiable not by its wins but by the nature and pattern of its mistakes.
Relevance to Alan Turing
Turing's 1950 paper proposed the imitation game specifically to evaluate intelligence through failure — a machine convincingly mimicking human mistakes was his benchmark for thinking. At Bletchley Park, he exploited Enigma operator errors, not the cipher's successes, to crack it open. His framework for artificial intelligence was built on the insight that where a system breaks down tells you more about it than where it performs as designed.
The era
In the early 1950s, computers were room-sized calculators celebrated for deterministic accuracy — their value was eliminating human error. The Cold War demanded reliable, error-free military computation. Claiming that a machine's failures were more interesting than its successes was genuinely subversive. AI as a discipline barely existed; cognition was considered exclusively biological. Turing's focus on machine fallibility anticipated probabilistic AI, neural networks, and decades of research into how systems fail in revealing ways.
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