Machine Learning Street Talk (MLST) cover image

Superintelligence Strategy (Dan Hendrycks)

Machine Learning Street Talk (MLST)

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Rethinking AI Benchmarks

This chapter critiques the current benchmarks used in AI development, focusing on the limitations of scores like MMLU and the anthropocentric biases in tests such as ArcV2 and ArcV3. It advocates for the need to create more challenging, open-ended tasks that effectively evaluate AI capabilities beyond human-like performance. The discussion introduces new benchmarks like Enigma Eval, emphasizing the complexities of measuring intelligence and the importance of acknowledging diverse forms of cognitive abilities.

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