John von Neumann — "When we look at the results of computation, we don't always know what they mean."
When we look at the results of computation, we don't always know what they mean.
When we look at the results of computation, we don't always know what they mean.
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"The greatest value of a picture is when it forces us to notice what we never expected to see."
"The more abstract a thing is, the more real it is."
"It is just as important to know what not to do as it is to know what to do."
"The more precisely the position is determined, the less precisely the momentum is known in this instant, and vice versa."
"Computers are like humans - they do everything except think."
Reflecting on the challenges of interpreting complex computational outputs.
Date: 1950s
GeneralFound in 1 providers: grok
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Computation can produce outputs that are technically correct yet remain opaque or difficult to interpret. Running a calculation or simulation yields numbers or patterns, but understanding what those results actually signify—what they tell us about reality, about the problem, about truth—requires human judgment, intuition, and further analysis that the machine itself cannot provide.
Von Neumann designed the foundational architecture of modern computers and pioneered numerical methods for weapons simulations and fluid dynamics. He regularly confronted results from early computers like ENIAC and IAS that required deep physical intuition to interpret. His game theory work similarly produced mathematical equilibria whose real-world meaning demanded careful translation beyond the raw mathematics.
The 1940s-50s saw the first electronic computers tackling hydrogen bomb calculations, weather prediction, and economic modeling—problems whose outputs were genuinely novel and uncharted. Scientists had no interpretive tradition for machine-generated results. This tension between computational power and human understanding defined early computing culture and foreshadowed modern debates about algorithmic opacity and AI interpretability.
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