Shadowing-Übung: We’re already using AI more than we realize - Englisch Sprechen Lernen mit Video
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Of all the interactions you have with technology in a day, interacting with artificial intelligence, or not, feels like a choice.
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But in some ways, it isn't.
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Over the past decade, we've become surrounded by AI systems that perceive our worlds, that support our decisions, and that mimic our ability to create.
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Whether we're aware of it or not is another story.
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Imagine a day like this.
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You do some exercise with a smartwatch, put on a suggested playlist, go to a friend's house and ring their camera doorbell, browse recommended shows on Netflix, check your spam folder for an email you've been waiting for,
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and when you can't find it, talk to a customer support chatbot.
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Each of those things are made possible by technologies that fall under the umbrella of artificial intelligence.
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But when a Pew survey asked Americans to identify whether each of those used AI or not, they only got it right about 60 % of the time.
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Some of these applications of AI have become fairly ubiquitous.
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They almost exist in the background and it's not terribly apparent to folks
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that the tools or services they're using are being powered by this technology.
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That's Alec Tyson, one of the researchers behind that Pew study.
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When Tyson and his team asked respondents how often they think they use AI, almost Most half didn't think they regularly interact with it at all.
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Some of them might be right, but most probably just don't know it.
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We know about 85 % of U .S adults are online every day, multiple times a day.
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Some folks are online almost all the time.
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This suggests a bit of a gap where there seem to be some folks who really must be interacting with AI, but it's not very salient to them.
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They don't perceive it.
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So why does that gap exist?
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Part of the problem is that the term artificial intelligence has been used to refer to a lot of different things.
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Artificial intelligence is totally this giant umbrella tent term that now has become a kitchen sink of everything.
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That's Karen Howe.
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She's a reporter who covers artificial intelligence and society.
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In the past, there were distinct disciplines about which aspect of the human brain do we want to recreate.
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Like, do we want to recreate the vision part?
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Do we want to recreate our ability to hear, our ability to write and speak?" Giving a machine the ability to see became the field of computer vision.
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Giving a machine the ability to write and speak became the field of natural language processing.
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But on their own, these tasks still required a machine to be programmed.
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If we wanted machines to recognize spam emails, we had to explicitly program them to look out for specific things, like poor spelling and urgent phrasing.
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That meant the tools weren't very adaptable to complex situations.
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But that all changed when we started recreating the brain's ability to learn.
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This became the subfield of machine learning, where computers are trained on massive amounts of data so
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that instead of needing to hand code rules about what to see or speak or write, the computers can develop rules on their own.
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With machine learning, a computer could learn to recognize new spam emails by reviewing thousands of existing emails
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that humans have labeled as spam.
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The machine recognizes patterns in this structured data and creates its own rules to help identify those patterns.
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When that training data hasn't been structured and labeled by humans, that method is called deep learning.
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Most of the time people talk about AI now, they're not talking about the whole field, but specifically these two methods.
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We'll hear more about that after a word from this video's sponsor.
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This episode is presented by Microsoft Copilot for Microsoft 365, your AI assistant at work.
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Copilot can help you solve your most complex problems at work, going far beyond simple questions and answers.
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From getting up to speed on a missed Teams meeting in seconds to helping you start a first draft faster in Word,
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Copilot for Microsoft 365 gives everyone an AI assistant at work in their most essential apps to unleash creativity,
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unlock productivity, and up -level skills.
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And it's all built on Microsoft's comprehensive approach to security, privacy, compliance, and responsible AI.
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Microsoft does not influence the editorial process of our videos, but they do help make videos like this possible.
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To learn more, you can go to Microsoft .com slash Copilot for Work.
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Now, back to our video.
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Improvements in computing power, together with the massive amounts of data generated on the internet, made possible a whole new generation of technologies that leveraged machine learning.
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And existing ones swapped out their algorithms for machine learning, too.
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A lot of the how in the back has been swapped into AI over time because people have realized,
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oh wait, we can actually get an even better performance of this product if we just swap our original algorithm,
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our original code, out for a deep learning model.
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Now, machine learning and deep learning models power recommendation for shows, music, videos.
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products, and advertisements.
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They determine the ranking of items every time we browse search results or social media feeds.
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They recognize images like faces to unlock phones or use filters, and the handwriting on remote deposit checks.
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They recognize speech in transcription, voice assistance, and voice -enabled TV remotes.
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And they predict text in auto -complete and auto -correct.
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But AI is seeping into more than that.
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There has been this tendency over the last 10 plus years where people have started putting AI into absolutely everything.
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Machine learning algorithms are already being used to decide which political ads we see, which jobs we qualify for, and whether we qualify for loans or government benefits,
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and often carry the same biases as the human decisions that preceded them.
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Are you actually automating the poor decision -making that happened in the past and just bringing it into the future?
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If you're going to use historical data to predict what's going to happen in the future, you're just going to end up with a future that looks like the past.
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And that's part of the reason why it matters to close
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that gap between those who knowingly interact with AI every day and those who don't quite know it yet.
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Awareness needs to grow for folks to be able to participate in some of these conversations about the moral and ethical boundaries, what AI should be used for and what it shouldn't be used for.
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Wortschatz und Sprechhinweise zu dieser Lektion
Dieses Video enthält 69 Sätze und 1096 Wörter zum Nachsprechen. Der gesprochene Teil dauert 6:21. Der Sprecher spricht in natürlichem Tempo, etwa 173 Wörter pro Minute, ähnlich wie im Alltagsgespräch. 82 % der Wörter gehören zu den 3.000 häufigsten im Englischen; den Rest solltest du dir vor dem Üben ansehen.
Wichtiger Wortschatz in diesem Video
15 lernenswerte Wörter aus dem Video, mit Aussprache und Bedeutung:
| Wort | Aussprache | Bedeutung |
|---|---|---|
| artificial Adjektiv | /ˌɑː.tɪˈfɪʃ.əl/ | künstlich |
| folk Substantiv | /foʊk/ | Volk |
| interact Verb | /ɪn.təˈɹækt/ | interagieren |
| spam Substantiv | /spæm/ | Spam |
| algorithm Substantiv | /ˈælɡəɹɪðm̩/ | Algorithmus |
| swap Verb | /ˈswɑp/ | tauschen, austauschen |
| label Substantiv | /ˈleɪ.bəl/ | Etikett, Etikette |
| remote Adjektiv | /ɹəˈməʊt̞/ | fern, entfernt |
| qualify Verb | /ˈkwɑ.lɪ.faɪ/ | qualifizieren, sich qualifizieren |
| predict Verb | /pɹɪˈdɪkt/ | prophezeien |
| automobile Substantiv | /ˈɔː.tə.məˌbil/ | Automobil, Auto |
| unlock Verb | /ʌnˈlɑk/ | aufschließen, entriegeln |
| umbrella Substantiv | /ʌmˈbɹɛl.ə/ | Schirm, Regenschirm |
| perceive Verb | /pɚˈsiv/ | wahrnehmen |
| browse Verb | /bɹaʊz/ | blättern, durchsuchen |
Phrasal Verbs, die du hören wirst
| Wort | Bedeutung |
|---|---|
| get up to Verb | begehen |
Grammatik in diesem Video
Die Strukturen, die der Sprecher am häufigsten verwendet, mit den genauen Worten aus dem Video:
| Struktur | Im Video |
|---|---|
| Present Perfect have/has + Partizip Perfekt – eine vergangene Handlung, die jetzt noch wichtig ist | we've become surrounded · have become · has been |
| Passiv be + Partizip Perfekt – wichtig ist, was geschieht, nicht wer es tut | are made · being powered · be programmed |
Aussprache, auf die du achten solltest
Der Sprecher verwendet 17 Kurzformen und abgeschwächte Formen, zum Beispiel don't, they're, you're. Sprich sie in der kurzen Form, so wie du sie hörst.
- Die „th“-Laute: algorithm /ˈælɡəɹɪðm̩/, ethical /ˈɛθɪkəl/
- Die Laute „sh“ und „zh“: artificial /ˌɑː.tɪˈfɪʃ.əl/, interaction /ˌɪn.təˈɹæk.ʃən/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, transcription /tɹænˈskɹɪpʃən/, unleash /ʌnˈliʃ/
- Lange Wörter – auf die Betonung achten: artificial /ˌɑː.tɪˈfɪʃ.əl/, automobile /ˈɔː.tə.məˌbil/, participate /pɑːˈtɪs.ɪ.peɪt/, comprehensive /ˌkɑm.pɹəˈhɛn.sɪv/, interaction /ˌɪn.təˈɹæk.ʃən/
So übst du mit diesem Video
- Höre dir das ganze Video einmal an, ohne zu sprechen, und notiere die Wörter, die du nicht kennst.
- Beginne mit 0,75-facher Geschwindigkeit, sprich Satz für Satz nach und wechsle zur normalen Geschwindigkeit, sobald es leichtfällt.
- Nimm dich auf und vergleiche mit dem Original; achte dabei auf Wörter wie artificial, folk, interact.
Was ist die Shadowing-Technik?
Shadowing ist eine wissenschaftlich fundierte Sprachlerntechnik, die ursprünglich für die professionelle Dolmetscherausbildung entwickelt und durch den Polyglotten Dr. Alexander Arguelles populär gemacht wurde. Die Methode ist einfach aber wirkungsvoll: Du hörst englisches Audio von Muttersprachlern und wiederholst es sofort laut — wie ein Schatten, der dem Sprecher mit nur 1–2 Sekunden Verzögerung folgt. Anders als passives Hören oder Grammatikübungen zwingt Shadowing dein Gehirn und deine Mundmuskulatur, gleichzeitig echte Sprachmuster zu verarbeiten und zu reproduzieren. Studien zeigen, dass es Aussprachegenauigkeit, Intonation, Rhythmus, verbundene Sprache, Hörverständnis und Sprechflüssigkeit signifikant verbessert — was es zu einer der effektivsten Methoden für die IELTS Speaking-Vorbereitung und reale englische Kommunikation macht.