43:I[185,["/_next/static/chunks/2ys9ev5m3_24i.js","/_next/static/chunks/3pomb8r7ja54n.js","/_next/static/chunks/42spz6paskvb6.js","/_next/static/chunks/178kgr5-cejk9.js","/_next/static/chunks/1v5ewlr55okcj.js","/_next/static/chunks/3jnlvw-emqotx.js"],"SendEmailButton"] 45:I[63625,["/_next/static/chunks/2ys9ev5m3_24i.js","/_next/static/chunks/3pomb8r7ja54n.js","/_next/static/chunks/42spz6paskvb6.js","/_next/static/chunks/178kgr5-cejk9.js","/_next/static/chunks/1v5ewlr55okcj.js","/_next/static/chunks/3jnlvw-emqotx.js"],"AddToLibraryButton"] 46:I[29721,["/_next/static/chunks/2ys9ev5m3_24i.js","/_next/static/chunks/3pomb8r7ja54n.js","/_next/static/chunks/42spz6paskvb6.js","/_next/static/chunks/178kgr5-cejk9.js","/_next/static/chunks/1v5ewlr55okcj.js","/_next/static/chunks/3jnlvw-emqotx.js"],"ShareButton"] 48:I[45269,["/_next/static/chunks/2ys9ev5m3_24i.js","/_next/static/chunks/3pomb8r7ja54n.js","/_next/static/chunks/42spz6paskvb6.js","/_next/static/chunks/178kgr5-cejk9.js","/_next/static/chunks/1v5ewlr55okcj.js","/_next/static/chunks/3jnlvw-emqotx.js"],"SignupCTA"] 3b:["$","h1",null,{"className":"max-w-[22ch] font-display text-[clamp(32px,4.5vw,48px)] font-medium leading-[1.05] tracking-[-0.02em] text-[color:var(--fg-1)] text-balance","children":"Understanding the inner thoughts of AI"}] 3c:["$","div",null,{"className":"flex flex-wrap items-center gap-x-4 gap-y-1 font-mono text-[12px] uppercase tracking-[0.1em] text-[color:var(--fg-3)]","children":[["$","time",null,{"dateTime":"2026-07-10T15:50:06.000Z","children":"July 10, 2026"}],["$","span",null,{"aria-hidden":true,"children":"·"}],["$","span",null,{"children":"00:53:06"}],false]}] 3d:["$","p",null,{"className":"max-w-[60ch] text-[18px] leading-[1.5] text-[color:var(--fg-2)]","children":"What if you were to peer inside the ‘mind’ of AI? You wouldn't find fully formed thoughts, just vast arrays of numbers. In this episode, Professor Hannah Fry is joined by Neel Nanda, to shine a light on an ongoing open area of research, interpretability."}] 44:T538,

Neel and his team are trying to do something phenomenally difficult: understand an intelligence that didn't come with a manual. Together, they explore the cutting-edge "neuroscience" of artificial intelligence—revealing the surprising, elegant structures being discovered inside these networks (like spare autoencoders), the inherent limits of looking under the hood, and why interpretability is absolutely essential if we are to build safe, aligned and trustworthy AI as we move towards AGI. Learn more about this area of research via https://deepmind.google/

Timecodes

00:00 Introduction
02:41 Motivation for interpretability research
04:01 Mechanistic interpretability
08:14 Chain of thought monitoring
18:14 Interpretability techniques
35:00 Auditing models for safety
48:53 What comes next for interpretability

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