The Misguided Quest for Mechanistic AI Interpretability | AI Frontiers

In March this year, Google DeepMind announced it was deprioritizing its work on mechanistic interpretability. The following month, Anthropic CEO Dario Amodei published an essay advocating for greater focus on “mechanistic interpretability” and expounding his optimism about achieving “MRI for AI” in the next 5-10 years. While policymakers and the public tend to assume interpretability would be a good thing, there has recently been intensified debate among experts about the value of research in this field. ‍Mechanistic interpretability aims to reverse-engineer AI systems. Mechanistic interpretability research, which has been going on for over a decade, aims to uncover the specific neurons and circuits in a model that are responsible for given tasks. In so doing, it hopes to trace the model’s reasoning process and offer a “nuts-and-bolts” explanation of its behavior. This is an understandable impulse: knowledge is power; to name is to know, and to know is to control. As such, many assume

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