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The Utility of Interpretability — Emmanuel Amiesen

Latent Space: The AI Engineer Podcast

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Decoding Model Interpretability

This chapter examines the evolution of model interpretability, honing in on the challenges and complexities faced by advanced models like CNNs and transformers. It highlights the significance of recognizing neuron functions in vision models compared to the obscured relationships in language models, alongside discussing concepts like superposition and sparsity. The speakers also explore self-supervised interpretability, demonstrating how understanding these dimensions can manipulate model behavior and enhance interpretability through vivid examples such as the Golden Gate Bridge.

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