The distinction between harder AI tests and more realistic ones
The post argues that more realistic tests often make answers harder to judge: real work involves deciding what problem to solve, which constraints matter and what a useful answer looks like.
TLDR
A post about AI evaluation explores a tradeoff: tests that better reflect real work often become harder to grade confidently. It suggests science may offer a way around that problem, because predictions—such as how much drag a wing produces—can be checked against physical measurements. But the author notes that those measurements are expensive. Using a fluid-dynamics simulation instead introduces a distinction the post highlights: verification asks whether the chosen equations are solved correctly; validation asks whether the model adequately represents the actual flow for its intended use.
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