Shadowing Practice: Cambridge Ielts 16 Reading test 1 Passage 3 - Learn English Speaking with 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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Vocabulario y notas de pronunciación para esta lección

Esta lección de conversación de nivel C1 se basa en el vídeo “Cambridge Ielts 16 Reading test 1 Passage 3”. Las palabras que más se repiten: algorithm, pachidi, believes, revolution, technologies. Este vídeo tiene 59 frases y 839 palabras para practicar shadowing. La parte hablada dura 5:47. El hablante mantiene un ritmo constante de unas 145 palabras por minuto, cómodo para el shadowing. Solo el 77 % de las palabras está entre las 3.000 más comunes del inglés, por lo que el vocabulario es exigente.

Vocabulario clave de este vídeo

Las 15 palabras más avanzadas del vídeo, con su pronunciación y significado:

PalabraPronunciaciónSignificado
algorithm sustantivo/ˈælɡəɹɪðm̩/algoritmo
retrain verbo/ɹiːˈtɹeɪn/capacitar
robotics sustantivo/ɹoʊˈbɑ.tɪks/robótica
automation sustantivo/ˌɔ.təˈmeɪ.ʃən/automatización
workforce sustantivo/ˈwɝk.foɹs/mano de obra, fuerza laboral
apocalyptic adjetivo/əˈpɒ.kə.lɪp.tɪk/apocalíptico
astound verbo/əˈstaʊnd/asombrar, pasmar
disruptive adjetivo/dɪsˈɹʌptɪv/perturbante, disruptivo
envisage verbo/ɛnˈvɪzɪd͡ʒ/visualizar, concebir
experimentation sustantivo/ɪkˌspɛɹ.ə.mənˈteɪ.ʃən/experimentación
fallacy sustantivo/ˈfæl.ə.si/falacia
misguided adjetivo/ˌmɪsˈɡaɪdɪd/desacertado, desatinado
novice adjetivo/ˈnɑvɪs/novicio, novicia
redundancy sustantivo/ɹɪˈdʌndən(t)si/redundancia, superabundancia
reprogram verboreprogramar

Phrasal verbs que vas a escuchar

PalabraPronunciaciónSignificado
carry out verbo/ˈkæ.ɹi aʊt/llevar a cabo, llevar al cabo
take over verboapoderarse, enseñorearse

Pronunciación a tener en cuenta

El hablante usa 3 contracciones y formas reducidas, como doesn't, I'd, isn't. Dilas en su forma corta, tal como las oyes.

  • Los sonidos de “th”: algorithm /ˈælɡəɹɪðm̩/, trustworthy /ˈtɹʌst.wɜː.ði/, thrive /θɹajv/
  • Los sonidos de “sh” y “zh”: automation /ˌɔ.təˈmeɪ.ʃən/, experimentation /ɪkˌspɛɹ.ə.mənˈteɪ.ʃən/, telecommunication /ˌtɛləkəˌmjunɪˈkeɪʃən/, occupational /ɑkjuˈpeɪʃənəl/, leisure /ˈlɛʒə(ɹ)/
  • Palabras largas — cuida el acento: automation /ˌɔ.təˈmeɪ.ʃən/, apocalyptic /əˈpɒ.kə.lɪp.tɪk/, consultancy /kənˈsʌl.tən.si/, disembodied /dɪsɪmˈbɒdiːd/, experimentation /ɪkˌspɛɹ.ə.mənˈteɪ.ʃən/

Sonidos difíciles para hispanohablantes:

  • s + consonante al inicio — sin añadir una “e” delante: smartphone /ˈsmɑɹtfoʊn/, scan /skæn/
  • /v/ — no es /b/: los dientes tocan el labio inferior: disruptive /dɪsˈɹʌptɪv/, envisage /ɛnˈvɪzɪd͡ʒ/, novice /ˈnɑvɪs/, advent /ˈæd.vɛnt/, thrive /θɹajv/
  • /z/ — sonora, no /s/: envisage /ɛnˈvɪzɪd͡ʒ/, seize /ˈsiːz/, analyze /ˈæn.əˌlaɪz/, observe /əbˈzɜːv/, expertise /ˌɛkspɜːˈtiːz/

Cómo practicar con este vídeo

  1. Escucha el vídeo entero una vez sin hablar y anota las palabras que no conoces.
  2. Haz shadowing frase por frase a velocidad normal, repitiendo cada una hasta que tu ritmo coincida con el del hablante.
  3. Grábate y compara con el original, prestando atención a palabras como algorithm, retrain, robotics.

¿Qué es la Técnica de Shadowing?

Shadowing es una técnica de aprendizaje de idiomas respaldada por la ciencia, desarrollada originalmente para la formación de intérpretes profesionales y popularizada por el políglota Dr. Alexander Arguelles. El método es simple pero poderoso: escuchas audio en inglés nativo y lo repites en voz alta de inmediato, como una sombra que sigue al hablante con solo 1-2 segundos de retraso. A diferencia de la escucha pasiva o los ejercicios de gramática, el shadowing obliga a tu cerebro y músculos de la boca a procesar y reproducir simultáneamente patrones de habla reales. Las investigaciones muestran que mejora significativamente la precisión de la pronunciación, la entonación, el ritmo, el habla conectada, la comprensión auditiva y la fluidez al hablar, convirtiéndola en una de las metodologías más efectivas para la preparación del IELTS Speaking y la comunicación en inglés en el mundo real.

Técnica de shadowing: lee la guía completa paso a paso →