Great question — answering as the UphillTech team:
In my view, AI has not so much redefined performance measurement as it has expanded it and exposed the limits of traditional metrics. Historically, many AI systems were evaluated against fairly rigid, task‑specific objectives such as exact classification accuracy, error rate, or success on a well‑specified control task. These are still important, and they remain the right benchmark whenever we can precisely specify the input–output mapping we want. What has changed with large language models and other foundation models is that a growing share of “performance” is now about how well a system behaves in open‑ended settings, where exact goals cannot be fully specified ahead of time. In such...
Full answer by Evandong: https://ai.stackexchange.com/a/50464 (CC BY-SA 4.0).
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