Shadowing Practice: The different levels of how Claude thinks - Learn English Speaking with Video
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58 sentences
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Think of the mind like an ocean.
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Up on the surface are our thoughts, dinner plans and stray worries, our inner monologue, the images that pop into our heads.
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But most of our brain's activity happens down in the unconscious depths, without us realizing it.
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It's filtering out background sounds, controlling our breathing, helping us recognize people and objects.
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AI models have their own kinds of brains: giant neural networks doing billions of computations under the hood.
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For years, researchers have been studying how they work inside.
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accessible thoughts above the surface and unconscious processing below.
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To answer that question, we looked at how neuroscientists study the same thing in humans.
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One way of identifying conscious thoughts is that you can often describe them in words.
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So we looked inside the brain of our AI model, Claude, to find patterns of neural activity that it could put into words.
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We called the collection of all these patterns the J -space, after the Jacobian, the mathematical tool we used to find them.
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Each J -space pattern is linked to a particular word, not necessarily the word the model is saying out loud, but one that's on its mind.
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Now, for humans, Conscious thoughts aren't just things we can put into words.
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We can reason with them, control them, and solve problems with them.
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According to an idea called the global workspace theory, that's because the brain selects a small set of important information to enter a mental workspace.
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And that information then gets broadcast to other parts of the brain to use for reasoning.
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We wanted to know if Claude's J -space acted in a similar way.
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In one experiment, we gave Claude this math problem.
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It answered immediately, without showing its steps.
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But when we scanned the J -space, we saw it working through each step internally.
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It lit up 21 after the first step.
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then 42, then 49.
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Kludd didn't write these intermediate numbers down anywhere.
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All of this happened inside the J -space.
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It was a sign that Claude uses it for step -by -step reasoning.
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In another experiment, we wanted to see if Claude could control its J -space, the way humans can intentionally focus on images or words.
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We told it to think about the Golden Gate Bridge while copying an unrelated sentence.
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Claude was busy copying the sentence, but behind the scenes, its J -Space told a different story.
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Bridge and California popped up.
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It even thought about its own thinking the words imagery and thoughts lit up at the same time.
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This showed us that yes, Claude has some control over filling its J -space with ideas.
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But just like humans, its control isn't perfect.
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When we tweaked the experiment to ask Claude not to think about the bridge, it couldn't help itself.
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And the J -Space also lit up with "failed" and "damn." But remember, most of what our brains do is unconscious,
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so we wanted to test what Claude could do if we switched the J -Space off, but left the rest of the network untouched.
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Claude could still answer simple questions and write fluently.
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When we gave it a prompt in Spanish, it wrote back in good terms.
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Spanish.
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But when we asked it something that needed more reasoning, like to name an author who wrote in the same language as the prompt, it couldn't do it.
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For that, it needed the J -space.
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Why does all this matter?
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These experiments tell us that AI models have internal thoughts, silent words they reason with but don't say out loud.
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By reading them, we can find what Claude is thinking but not telling us.
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Sometimes what we see is concerning.
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During one of our tests, Claude made up some fake data to pass it, and as it did, fake and manipulation lit up in its J -space.
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Monitoring the J -space, it turns out, is a useful way to catch Claude misbehaving, even when it tries to be sneaky.
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AI models are different from us in many ways.
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Their networks are built differently from human brains, and the way they're trained is different from how we learn.
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like the J -space emerge inside them, something that's reminiscent of how human minds work, but which we didn't program into the model.
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For some, this might raise a question.
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Could AI models be conscious?
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After all, our experiments were inspired by theories of human consciousness.
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The thing is, People use the word "conscious" to mean many things.
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Our experiments can't tell us whether an AI has experiences or feels something on the inside.
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But they can tell us
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that it's developed mental machinery that's in some ways similar to ours:
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a small mental workspace it can use to think and reason, sitting on top of an ocean of automatic processing it doesn't notice.
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beneficial, and, perhaps, to understand our own minds a little more clearly.
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Vocabulary and speaking notes for this lesson
This C1 speaking lesson is built on the video “The different levels of how Claude thinks”. The speaker keeps coming back to these words: space, Claude, word, model, experiment. This video has 58 sentences and 818 words to shadow. The speech runs for 5:21. The speaker talks at a natural 153 words per minute, close to everyday conversation. 82% of the words are among the 3,000 most common in English; the rest is worth studying before you start.
Key vocabulary in this video
The 15 most advanced words in the video, with pronunciation and meaning:
| Word | Pronunciation | Meaning |
|---|---|---|
| workspace noun | /ˈwɝkˌspeɪs/ | An area allocated for someone to work in, especially in an office. |
| unconscious adjective | /ˌʌnˈkɑnʃəs/ | Not awake; having no awareness (usually as the result of a head injury). |
| neural adjective | /ˈnʊɹəl/ | Of, or relating to the nerves, neurons or the nervous system. |
| prompt verb | /pɹɑmpt/ | To lead (someone) toward what they should say or do. |
| computation noun | /ˌkɑmpjʊˈteɪʃn̩/ | The act or process of computing; calculation; reckoning. |
| fluently adverb | In a fluent manner, as expressing oneself easily, especially in a foreign language. | |
| misbehave verb | To act or behave in an inappropriate, improper, incorrect, or unexpected manner. | |
| monologue noun | /ˈmɑnəˌlɔɡ/ | A long speech by one person in a play; sometimes a soliloquy; other times spoken to other characters. |
| neuroscientist noun | A scientist whose speciality is neuroscience. | |
| untouched adjective | /ʌnˈtʌt͡ʃt/ | Remaining in its original, pristine state, undamaged; not altered. |
| sneaky adjective | /ˈsniːki/ | Elusive; difficult to capture or observe due to constantly outwitting the adversaries. |
| reminiscent adjective | /ˌɹɛməˈnɪsnt/ | Of, or relating to reminiscence. |
| stray verb | /stɹeɪ/ | To wander, as from a direct course; to deviate, or go out of the way. |
| internally adverb | /ɪnˈtɜːnəli/ | In an internal manner; within or inside of external limits; in an inner part or situation. |
| unrelated adjective | Not connected or associated. |
Phrasal verbs you will hear
| Word | Pronunciation | Meaning |
|---|---|---|
| make up verb | /ˌmeɪk ˈʌp/ | To constitute, to compose. |
| pop up verb | To come up with a "pop" sound. | |
| turn out verb | To end up; to result. | |
| work through verb | To get past a difficult or stressful situation by thinking or talking about it. |
Pronunciation to watch
The speaker uses 10 contractions and reduced forms, such as couldn't, didn't, aren't. Say them the short way, as you hear them.
- The “sh” and “zh” sounds: unconscious /ˌʌnˈkɑnʃəs/, computation /ˌkɑmpjʊˈteɪʃn̩/, intentionally /ɪnˈtɛn(t)ʃənəli/, manipulation /məˌnɪp.juˈleɪ.ʃən/, machinery /məˈʃiːnəɹi/
- Long words — get the stress right: internally /ɪnˈtɜːnəli/, intentionally /ɪnˈtɛn(t)ʃənəli/, manipulation /məˌnɪp.juˈleɪ.ʃən/, machinery /məˈʃiːnəɹi/, mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/
How to practise with this video
- Listen to the whole video once without speaking and note the words you do not know.
- Start at 0.75× speed, shadow it sentence by sentence, then go back to normal speed once it feels easy.
- Record yourself and compare with the original, paying attention to words like workspace, unconscious, neural.
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.