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AI evaluations uncover failures beyond traditional benchmarks

microsoft.com by Chad Atalla, Jennifer Neville 28 min read Editor score 6/10

Jennifer Neville discusses testing AI systems beyond standard benchmarks, practical ways to work with current models, and examining data when results surprise. Engineers designing evaluations can use these perspectives to look for failures benchmarks miss.

What

AI evaluation tests models beyond traditional benchmarks to expose surprising failures and assess whether systems meet user needs.

How

Jennifer Neville emphasizes examining data when results defy expectations and discusses evaluation’s role in advancing real-world AI.

Why it matters

For engineers, evaluation beyond standard benchmarks can reveal failures that affect user-facing AI behavior.

Read on microsoft.com →

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