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The Utility of Interpretability — Emmanuel Amiesen

Latent Space: The AI Engineer Podcast

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Unpacking AI Interpretability and Model Behavior

This chapter explores the complexities of model architectures, specifically in their ability to identify and label features through unsupervised learning. The speakers discuss attribution graphs, challenges in model transparency, and the intricate decision-making process behind publishing AI research. Additionally, they highlight the importance of effective data visualization and team collaboration in communicating complex AI concepts.

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