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 question is not whether machines can think, but whether they can be made to think like humans."
"May not machines carry out something which ought to be described as thinking but which is very different from what a man does?"
"I am a homosexual. I have been convicted of gross indecency. I have been subjected to chemical castration."
"The fact that a machine can imitate a human being does not mean that it is a human being."
"The future of humanity depends on the development of artificial intelligence."
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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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