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Sayash Kapoor - How seriously should we take AI X-risk? (ICML 1/13)

Machine Learning Street Talk (MLST)

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Understanding AI Evaluation Metrics

This chapter differentiates between model evaluation and downstream performance in AI systems, stressing the significance of accurate assessments for end-user experiences. It addresses issues of shortcut learning and the importance of proper testing methodologies, particularly for generalist agents. The discussion highlights the role of human feedback in shaping AI performance perceptions and advocates for tailored evaluation benchmarks to ensure reliable assessments.

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