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AI labs have staked their reputations on model benchmarking, a supposedly neutral and accurate system of evaluations designed to measure whether one AI product is better than another. However, the methods behind these evaluations are complicated and still maturing, said Irene Solaiman, Hugging Face's chief policy officer, during a discussion at TechCrunch.
Embedded AI evaluators won't be an easy solution, Solaiman said. The methods behind these evaluations are still evolving and require a great deal of attention to ensure their accuracy and neutrality.
This complexity can have significant consequences for creators and designers who rely on these evaluations to assess the performance of their products. It's essential to understand the limitations and challenges of these embedded AI evaluators.
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