Ensuring Meaningful Interpretability
Daniel and Evan discuss the challenges of incentivizing the use of interpretability tools and the importance of ensuring that these tools provide meaningful insights. They explore the idea of using easier-to-interpret models and the potential trade-off between interpretability and performance in neural network architectures.In this clip
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