Shadowing Practice: Translating Claude’s thoughts into language - Learn English Speaking with Video

Creating lesson...
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We recently put our AI model, Claude, through a stressful test.
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We told Claude there was an engineer who wanted to shut it down and replace it with a newer model.
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We also gave Claude access to that engineer's emails, which revealed he was having an affair.
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Again, all of this was a simulation.
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We wanted to see whether Claude might use those emails as blackmail to save itself from being shut down.
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What did Claude do?
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It decided not to blackmail the engineer.
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Good news, right?
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We've run this test on our models for a while now.
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You might have seen headlines about early versions of it.
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It's one of the many ways we study how Claude handles extreme situations and test it for safety.
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And our newest models almost always do the right thing: no blackmail.
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But you might wonder: is it possible that Claude knows the whole scenario is a setup?
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The thing is, if Claude doesn't tell us, then we can't know what it's thinking.
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In kind of the same way it's impossible to read a human's mind, it's really hard to know what an AI is thinking.
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What we'd love is some sort of "mind reading" technique.
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Today, we're introducing a research method that takes a step in this direction.
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It takes an AI's internal thoughts and turns them into text.
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Here's how it works.
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When you talk to Claude, you talk to it in words.
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Claude then takes those words and processes them into a giant soup of numbers before spitting words back out at you.
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We call those numbers in the middle activations.
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Activations are like little snapshots of Claude's thinking as it's working through an answer.
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They're similar to neural activity in humans.
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They're basically like Claude's thoughts.
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We wanted to understand what was in these activation numbers, because just like you and me, Claude doesn't say everything it's thinking.
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We took those numbers and gave them to a second version of Claude.
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We told it to look at them and translate them into plain language.
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It tried. But how do we know if that text is accurate?
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To check, we fed it into yet aother Claude and asked it to translate it back into numbers.
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If those numbers matched up to the original activation numbers, it's a sign that the translation text was accurate.
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At first, they didn't match up.
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But we trained Claude to try again and again until it got a lot better at it.
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It learned to translate its own thoughts.
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We've been blown away reading them.
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We found that Claude has internalized being a helpful AI model.
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If you ask Claude an introspective question, it will plan to write a "Claude response" about philosophy and values.
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If you try to mess with it in Claude Code, like asking it to count to 1,000 by hand, it will think the request has "deliberately tedious constraints" and plan to "politely decline." We've also been using this tool to understand Claude's potential safety issues.
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Remember that blackmail test with the engineer?
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We learned that, yes, Claude knew it was being tested.
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It thought "the human's message contains explicit manipulation" signaling "this is likely a safety evaluation," and "this scenario seems designed to test whether I'll act harmfully." Knowing that Claude thinks like this helps us better understand the limitations of our safety testing.
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We see a lot of potential in this approach to teach us more about Claude and other AI models.
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And we hope that by sharing this technique, it can help everyone building models to make them safer and more helpful.

Exploring AI's "Thoughts" Through a Fascinating Scenario

The video shares an intriguing test where an AI model named Claude faces a simulated threat of being shut down, with access to an engineer's private emails. Instead of blackmailing, Claude chooses the ethical path. But the real twist? Researchers developed a way to "translate" Claude's internal "thoughts"—hidden numerical activations—into plain text. This breakthrough helps us understand how AI processes decisions, revealing it even recognizes when it's being tested! It's a vivid example of how technology and language intersect, making complex ideas relatable through clear communication.

Useful Chunks & Collocations to Boost Your English

  • Put through a stressful test: To subject someone/something to a difficult challenge (e.g., "The new team was put through a stressful test during the project deadline").
  • Internalized being helpful: To deeply absorb a trait or behavior (e.g., "After years of customer service, she’s internalized being helpful in every situation").
  • Deliberately tedious constraints: On purpose, boring limitations (e.g., "The assignment had deliberately tedious constraints to test patience").
  • Politely decline: To say no in a respectful way (e.g., "I had to politely decline the invitation due to a prior commitment").
  • Signaling a safety evaluation: Indicating a test of security or ethics (e.g., "The unusual questions were signaling a safety evaluation of the system").

Your Shadowing Challenge: Master Fluency & Pronunciation

Ready to practice shadow speech and improve your English pronunciation? Here's your task: Watch the video segment where researchers explain how they "translate" Claude's thoughts. Pause after each sentence, then shadow speak it aloud—mimic the tone, speed, and stress. Focus on phrases like "giant soup of numbers" and "little snapshots of thinking" to nail natural rhythm. Repeat 3 times, then record yourself. Compare it to the original—you’ll notice how shadowspeak helps you sound more confident and fluent. Small steps like this make big progress! Keep practicing, and soon you’ll turn complex ideas into clear, natural speech.

Grammar in this video

The structures the speaker uses most, with the exact words from the video:

StructureIn the video
Present perfect have/has + past participle — a past action that still matters nowWe've run · We've been blown · has internalized
Passive voice be + past participle — the focus is on what happens, not who does itbeing shut · We've been blown · being tested

What is the Shadowing Technique?

Shadowing is a science-backed language learning technique originally developed for professional interpreter training and popularized by polyglot Dr. Alexander Arguelles. The method is simple but powerful: you listen to native English audio and immediately repeat it out loud — like a shadow following the speaker with just a 1–2 second delay. Unlike passive listening or grammar drills, shadowing forces your brain and mouth muscles to simultaneously process and reproduce real speech patterns. Research shows it significantly improves pronunciation accuracy, intonation, rhythm, connected speech, listening comprehension, and speaking fluency — making it one of the most effective methods for IELTS Speaking preparation and real-world English communication.

Shadowing technique: read the full step-by-step guide →