Shadowing Practice: The different levels of how Claude thinks - Learn English Speaking with Video
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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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Woordenschat en spreektips bij deze les
Deze spreekles op niveau C1 is gebaseerd op de video “The different levels of how Claude thinks”. Deze woorden komen het vaakst terug: space, Claude, word, model, experiment. Deze video bevat 58 zinnen en 818 woorden om na te spreken. Het gesproken deel duurt 5:21. De spreker praat in een natuurlijk tempo van ongeveer 153 woorden per minuut, dicht bij een alledaags gesprek. 82% van de woorden hoort bij de 3.000 meest gebruikte Engelse woorden; de rest kun je beter vooraf bekijken.
Belangrijke woorden in deze video
De 15 moeilijkste woorden uit de video, met uitspraak en betekenis:
| Woord | Uitspraak | Betekenis |
|---|---|---|
| unconscious bijvoeglijk naamwoord | /ˌʌnˈkɑnʃəs/ | bewusteloos |
| neural bijvoeglijk naamwoord | /ˈnʊɹəl/ | neuraal |
| prompt werkwoord | /pɹɑmpt/ | aansporen, aanmoedigen |
| computation zelfstandig naamwoord | /ˌkɑmpjʊˈteɪʃn̩/ | berekening |
| fluently bijwoord | vloeiend | |
| monologue zelfstandig naamwoord | /ˈmɑnəˌlɔɡ/ | monoloog, alleenspraak |
| sneaky bijvoeglijk naamwoord | /ˈsniːki/ | geniepig, gluiperig |
| stray werkwoord | /stɹeɪ/ | dwalen |
| manipulation zelfstandig naamwoord | /məˌnɪp.juˈleɪ.ʃən/ | manipulatie, gebruik |
| emerge werkwoord | /ɪˈmɝd͡ʒ/ | opduiken |
| researcher zelfstandig naamwoord | /ˈɹiˌsɝ.t͡ʃɚ/ | onderzoeker |
| inspire werkwoord | /ɪnˈspaɪə(ɹ)/ | inspireren |
| machinery zelfstandig naamwoord | /məˈʃiːnəɹi/ | machinery, machinepark |
| mathematical bijvoeglijk naamwoord | /ˌmæθ(.ə)ˈmæt.ɪ.kəl/ | wiskundig |
| intermediate bijvoeglijk naamwoord | /ɪn.tə(ɹ)ˈmi.di.ət/ | middelmatig |
Phrasal verbs die je zult horen
| Woord | Betekenis |
|---|---|
| pop up werkwoord | opduiken, te voorschijn komen |
| turn out werkwoord | uitvallen, resulteren |
Uitspraak om op te letten
De spreker gebruikt 10 samentrekkingen en verkorte vormen, zoals couldn't, didn't, aren't. Spreek ze kort uit, zoals je ze hoort.
- De klanken “sh” en “zh”: unconscious /ˌʌnˈkɑnʃəs/, computation /ˌkɑmpjʊˈteɪʃn̩/, intentionally /ɪnˈtɛn(t)ʃənəli/, manipulation /məˌnɪp.juˈleɪ.ʃən/, machinery /məˈʃiːnəɹi/
- Lange woorden — let op de klemtoon: internally /ɪnˈtɜːnəli/, intentionally /ɪnˈtɛn(t)ʃənəli/, manipulation /məˌnɪp.juˈleɪ.ʃən/, machinery /məˈʃiːnəɹi/, mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/
Klanken die Nederlandstaligen lastig vinden:
- Stemhebbende eindmedeklinker — /b/, /d/, /g/, /z/, /v/ niet verscherpen: monologue /ˈmɑnəˌlɔɡ/, emerge /ɪˈmɝd͡ʒ/
- /æ/ — opener dan de Nederlandse “e”: mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/, scan /skæn/
Zo oefen je met deze video
- Luister de hele video één keer zonder te spreken en noteer de woorden die je niet kent.
- Begin op 0,75× snelheid, spreek zin voor zin na en ga terug naar normale snelheid zodra het makkelijk gaat.
- Neem jezelf op en vergelijk met het origineel; let daarbij op woorden als unconscious, neural, prompt.
Wat is de Shadowing-techniek?
Shadowing is een wetenschappelijk onderbouwde taalleermethode die oorspronkelijk is ontwikkeld voor professionele tolkentraining en gepopulariseerd door polyglot Dr. Alexander Arguelles. De methode is eenvoudig maar krachtig: je luistert naar native Engelse audio en herhaalt het onmiddellijk hardop — als een schaduw die de spreker volgt met slechts 1–2 seconden vertraging. In tegenstelling tot passief luisteren of grammaticadrills, dwingt shadowing je hersenen en mondspieren om echte spraakpatronen tegelijkertijd te verwerken en te reproduceren. Onderzoek toont aan dat het de uitspraaknauwkeurigheid, intonatie, ritme, verbonden spraak, luisterbegrip en spreekvaardigheid aanzienlijk verbetert — waardoor het een van de meest effectieve methoden is voor IELTS Speaking-voorbereiding en echte Engelse communicatie.