Shadowing-Übung: Cambridge Ielts 16 Reading test 1 Passage 3 - Englisch Sprechen Lernen mit Video

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3. The Future of Work According to a leading business consultancy,
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3-14% of the global workforce will need to switch to a different occupation within the next 10-15 years,
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and all workers will need to adapt as their occupations evolve alongside increasingly capable machines.
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Automation, or Embodied Artificial Intelligence, AI, is one aspect of the disruptive effects of technology on the labor market.
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Disembodied L, like the algorithms running in our smartphones, is another.
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Dr. Stella Pacitti from Cambridge Judge Business School believes
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that some of the most fundamental changes are happening as a result of the algorithmication of jobs
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that are dependent on data rather than on production, the so-called knowledge economy.
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Algorithms are capable of learning from data to undertake tasks that previously needed human judgment, such as reading legal contracts,
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analyzing medical scans, and gathering market intelligence.
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In many cases, they can outperform humans, says Pachidi.
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Organizations are attracted to using algorithms because they want to make choices based on what they consider as perfect information, as well as to reduce costs and enhance productivity.
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But these enhancements are not without consequences, says Pachidi.
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If routine cognitive tasks are taken over by AI, how do professions develop their future experts?
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She asks.
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One way of learning about a job is legitimate peripheral participation.
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A novice stands next to experts and learns by observation.
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If this isn't happening, then you need to find new ways to learn.
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Another issue is the extent to which the technology influences or even controls the workforce.
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For over two years, Puccini monitored a telecommunications company.
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The way telecom salespeople work is through personal and frequent contact with clients, using the benefit of experience to assess a situation and reach a decision.
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However, the company had started using an algorithm that defined
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when account managers should contact certain customers about which kinds of campaigns and what to offer them.
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The algorithm, usually built by external designers, often becomes the keeper of knowledge, she explains.
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In cases like this, Pachitti believes,
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a short-sighted view begins to creep into working practices whereby workers learn through the algorithm's eyes and become dependent on its instructions.
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Alternative explorations, where experimentation and human instinct lead to progress and new ideas, are effectively discouraged.
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Pachidi and colleagues even observed people developing strategies to make the algorithm work to their own advantage.
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We are seeing cases where workers feed the algorithm with false
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data to reach their targets she reports it's scenarios like these
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that many researchers are working to avoid their objective is to make ai technologies more trustworthy
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and transparent so that organizations
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and individuals understand how ai decisions are made in the meantime
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says pachidi We need to make sure we fully understand the dilemmas that this new world raises regarding expertise, occupational boundaries, and control.
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Economist Professor Hamish Lowe believes that the future of work will involve major transitions across the whole life course for everyone.
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The traditional trajectory of full-time education followed by full-time work followed by a pension retirement is a thing of the past, says Lowe.
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Instead, he envisages a multi-stage employment life, one where retraining happens across the life course,
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and where multiple jobs and no job happen by choice at different stages.
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On the subject of job losses, Mo believes the predictions are founded on a fallacy, it assumes that the number of jobs is fixed.
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If in 30 years, half of 100 jobs are being carried out by robots,
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that doesn't mean we are left with just 50 jobs for The number of jobs will increase.
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We would expect there to be 150 jobs.
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Dr. Ewan Mitkauhi at Cambridge's Centre for Business Research in King's College London agrees
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that apocalyptic views about the future of work are misguided.
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It's the laws that restrict the supply of capital to the job market, not the advent of new technologies that causes unemployment.
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His recently published research answers the question of whether automation, AI and robotics will mean a jobless future by looking at the causes of unemployment.
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it.
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History is clear that change can mean redundancies, but social policies can tackle this through retraining and redeployment.
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He adds, if there is going to be changed to jobs as a result of AI
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and robotics then I'd like to see governments seizing the opportunity to improve policy to enforce good job security.
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We can reprogram the law to prepare for a fairer future of work and leisure.
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McGauhe's findings are a call to arms to leaders of organizations, governments and banks to preempt for coming changes with bold new policies that guarantee full employment,
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fair incomes and a thriving economic democracy.
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The promises of these new technologies are astounding.
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They deliver humankind the capacity to live in a way that nobody could have once imagined humans.
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Just as the Industrial Revolution brought people past subsistence agriculture and the corporate revolution enabled mass production,
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a third revolution has been pronounced.
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But it will not only be one of technology.
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The next revolution will be social.

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Wortschatz und Sprechhinweise zu dieser Lektion

Diese Sprechübung auf Niveau C1 basiert auf dem Video „Cambridge Ielts 16 Reading test 1 Passage 3“. Diese Wörter kommen am häufigsten vor: algorithm, pachidi, believes, revolution, technologies. Dieses Video enthält 59 Sätze und 839 Wörter zum Nachsprechen. Der gesprochene Teil dauert 5:47. Der Sprecher spricht gleichmäßig mit etwa 145 Wörtern pro Minute – ein angenehmes Tempo fürs Shadowing. Nur 77 % der Wörter gehören zu den 3.000 häufigsten im Englischen, der Wortschatz ist also anspruchsvoll.

Wichtiger Wortschatz in diesem Video

Die 15 anspruchsvollsten Wörter aus dem Video, mit Aussprache und Bedeutung:

WortAusspracheBedeutung
algorithm Substantiv/ˈælɡəɹɪðm̩/Algorithmus
robotics Substantiv/ɹoʊˈbɑ.tɪks/Robotik
automation Substantiv/ˌɔ.təˈmeɪ.ʃən/Automatisierung
workforce Substantiv/ˈwɝk.foɹs/Belegschaft, Arbeitskräfte
apocalyptic Adjektiv/əˈpɒ.kə.lɪp.tɪk/apokalyptisch
astound Verb/əˈstaʊnd/verblüffen, überraschen
disembodied Adjektiv/dɪsɪmˈbɒdiːd/körperlos, geisterhaft
disruptive Adjektiv/dɪsˈɹʌptɪv/störend
envisage Verb/ɛnˈvɪzɪd͡ʒ/vorstellen
experimentation Substantiv/ɪkˌspɛɹ.ə.mənˈteɪ.ʃən/Experimentieren
fallacy Substantiv/ˈfæl.ə.si/Täuschung
misguided Adjektiv/ˌmɪsˈɡaɪdɪd/fehlgeleitet
novice Adjektiv/ˈnɑvɪs/Novize, Novizin
preempt Verb/ˌpɹiːˈɛmpt/zuvorkommen, vorgreifen
redeployment SubstantivVerlegung, Verlegen

Phrasal Verbs, die du hören wirst

WortAusspracheBedeutung
carry out Verb/ˈkæ.ɹi aʊt/raus tragen
take over Verbübernehmen

Aussprache, auf die du achten solltest

Der Sprecher verwendet 3 Kurzformen und abgeschwächte Formen, zum Beispiel doesn't, I'd, isn't. Sprich sie in der kurzen Form, so wie du sie hörst.

  • Die „th“-Laute: algorithm /ˈælɡəɹɪðm̩/, trustworthy /ˈtɹʌst.wɜː.ði/, thrive /θɹajv/
  • Die Laute „sh“ und „zh“: automation /ˌɔ.təˈmeɪ.ʃən/, experimentation /ɪkˌspɛɹ.ə.mənˈteɪ.ʃən/, telecommunication /ˌtɛləkəˌmjunɪˈkeɪʃən/, occupational /ɑkjuˈpeɪʃənəl/, leisure /ˈlɛʒə(ɹ)/
  • Lange Wörter – auf die Betonung achten: automation /ˌɔ.təˈmeɪ.ʃən/, apocalyptic /əˈpɒ.kə.lɪp.tɪk/, consultancy /kənˈsʌl.tən.si/, disembodied /dɪsɪmˈbɒdiːd/, experimentation /ɪkˌspɛɹ.ə.mənˈteɪ.ʃən/

Laute, die Deutschsprachigen schwerfallen:

  • /w/ — mit runden Lippen, nicht wie das deutsche „w“ (/v/): workforce /ˈwɝk.foɹs/, trustworthy /ˈtɹʌst.wɜː.ði/, whereby /wɛə(ɹ)ˈbaɪ/, consequence /ˈkɑn.sɪ.kwəns/
  • Stimmhafter Auslaut — /b/, /d/, /g/, /z/, /v/ am Wortende nicht verhärten: astound /əˈstaʊnd/, disembodied /dɪsɪmˈbɒdiːd/, disruptive /dɪsˈɹʌptɪv/, envisage /ɛnˈvɪzɪd͡ʒ/, humankind /ˌhjuːmənˈkaɪnd/
  • /æ/ — offener als „ä“: algorithm /ˈælɡəɹɪðm̩/, fallacy /ˈfæl.ə.si/, enhancement /ɪnˈhæns.mənt/, advent /ˈæd.vɛnt/, analyze /ˈæn.əˌlaɪz/

So übst du mit diesem Video

  1. Höre dir das ganze Video einmal an, ohne zu sprechen, und notiere die Wörter, die du nicht kennst.
  2. Sprich Satz für Satz in normaler Geschwindigkeit nach und wiederhole jeden, bis dein Rhythmus zum Sprecher passt.
  3. Nimm dich auf und vergleiche mit dem Original; achte dabei auf Wörter wie algorithm, robotics, automation.

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.

Shadowing-Technik: die vollständige Schritt-für-Schritt-Anleitung lesen →