Post by Google DeepMind

A model’s chain of thought acts like a scratchpad, offering a window into its inner reasoning. But how do we know if an AI is showing its true workings? In the latest episode of our podcast, host Hannah Fry sits down with our research scientist Neel Nanda🔸 to explore interpretability - the science of reverse-engineering how neural networks actually learn and think. Neel breaks down critical safety techniques, sharing how our team tracks a model's logic to debug issues, flag potential risks early, and research deeper methods to ensure this transparency continues as AI evolves.

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