Neel Nanda on Avoiding an AI Catastrophe with Mechanistic Interpretability

Neel Nanda joins the podcast to talk about mechanistic interpretability and how it can make AI safer. Neel is an independent AI safety researcher. You can find his blog here: https://www.neelnanda.io Timestamps: 00:00 Introduction 00:46 How early is the field mechanistic interpretability? 03:12 Why should we care about mechanistic interpretability? 06:38 What are some successes in mechanistic interpretability? 16:29 How promising is mechanistic interpretability? 31:13 Is machine learning analogous to evolution? 32:58 How does mechanistic interpretability make AI safer? 36:54 36:54 Does mechanistic interpretability help us control AI? 39:57 Will AI models resist interpretation? 43:43 Is mechanistic interpretability fast enough? 54:10 Does mechanistic interpretability give us a general understanding? 57:44 How can you help with mechanistic interpretability? Social Media Links: ➡️ WEBSITE: https://futureoflife.org ➡️ TWITTER: https://twitter.com/FLIxrisk ➡️ INSTAGRAM: https://www.instagram.com/futureoflifeinstitute/ ➡️ META: https://www.facebook.com/futureoflifeinstitute ➡️ LINKEDIN: https://www.linkedin.com/company/future-of-life-institute/

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The Future of Life Institute (FLI) is a nonprofit working to reduce global catastrophic and existential risk from powerful technologies. In particular, FLI focuses on risks from artificial intelligence (AI), biotechnology, nuclear weapons and climate change. The Institute's work is made up of three main strands: grantmaking for risk reduction, educational outreach, and advocacy within the United Nations, US government and European Union institutions. FLI has become one of the world's leading voices on the governance of AI having created one of the earliest and most influential sets of governance principles: the Asilomar AI Principles.