Leo Gao
Researcher and writer focused on mechanistic interpretability and AI alignment, author of the essay 'An Ambitious Vision for Interpretability' narrated in this episode.
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Dec 6, 2025 • 9min
“An Ambitious Vision for Interpretability” by leogao
Leo Gao, a researcher in mechanistic interpretability and AI alignment, dives into the ambitious vision of fully understanding neural networks. He discusses why mechanistic understanding is crucial for effective debugging, allowing us to untangle complex behaviors like scheming. Gao shares insights on the progress made in circuit sparsity and challenges faced in the interpretability landscape. He envisions future advancements, suggesting that small interpretable models can provide insights for scaling up to larger models. Expect thought-provoking ideas on enhancing AI transparency!
Dec 5, 2025 • 9min
“An Ambitious Vision for Interpretability” by leogao
Leo Gao, a researcher in mechanistic interpretability and AI alignment, discusses his ambitious vision for understanding neural networks. He highlights the importance of mechanistic understanding, likening it to switching from print statement debugging to using an actual debugger for clearer diagnostics. Gao shares recent advances in circuit sparsity, making circuits simpler and more interpretable. He also outlines future research directions, emphasizing that ambitious interpretability, although challenging, is crucial for safer AI development.
