Prática de Shadowing: Cambridge Ielts 16 Reading test 1 Passage 3 - Aprenda a falar inglês com vídeo

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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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Vocabulário e dicas de fala para esta lição

Esta aula de conversação de nível C1 usa o vídeo “Cambridge Ielts 16 Reading test 1 Passage 3”. As palavras que mais se repetem: algorithm, pachidi, believes, revolution, technologies. Este vídeo tem 59 frases e 839 palavras para praticar shadowing. A fala dura 5:47. O falante mantém um ritmo estável de cerca de 145 palavras por minuto, confortável para o shadowing. Apenas 77% das palavras estão entre as 3.000 mais comuns do inglês, por isso o vocabulário é exigente.

Vocabulário principal deste vídeo

As 15 palavras mais avançadas do vídeo, com pronúncia e significado:

PalavraPronúnciaSignificado
algorithm substantivo/ˈælɡəɹɪðm̩/algoritmo
robotics substantivo/ɹoʊˈbɑ.tɪks/robótica
automation substantivo/ˌɔ.təˈmeɪ.ʃən/automação, automatização
workforce substantivo/ˈwɝk.foɹs/mão de obra
apocalyptic adjetivo/əˈpɒ.kə.lɪp.tɪk/apocalíptico, apocalítico
astound verbo/əˈstaʊnd/surpreender
consultancy substantivo/kənˈsʌl.tən.si/consultoria
disruptive adjetivo/dɪsˈɹʌptɪv/diruptivo, disruptivo
envisage verbo/ɛnˈvɪzɪd͡ʒ/imaginar, conjeturar
experimentation substantivo/ɪkˌspɛɹ.ə.mənˈteɪ.ʃən/experimentação
fallacy substantivo/ˈfæl.ə.si/ilusão
novice adjetivo/ˈnɑvɪs/noviço, noviça
redundancy substantivo/ɹɪˈdʌndən(t)si/redundância, repetição
salesperson substantivovendedor, vendedora
telecommunication substantivo/ˌtɛləkəˌmjunɪˈkeɪʃən/telecomunicação

Phrasal verbs que você vai ouvir

PalavraPronúnciaSignificado
carry out verbo/ˈkæ.ɹi aʊt/carregar
take over verbotomar posse de, apoderar-se de

Pronúncia para ficar de olho

O falante usa 3 contrações e formas reduzidas, como doesn't, I'd, isn't. Diga-as na forma curta, do jeito que você ouve.

  • Os sons de “th”: algorithm /ˈælɡəɹɪðm̩/, trustworthy /ˈtɹʌst.wɜː.ði/, thrive /θɹajv/
  • Os sons de “sh” e “zh”: automation /ˌɔ.təˈmeɪ.ʃən/, experimentation /ɪkˌspɛɹ.ə.mənˈteɪ.ʃən/, telecommunication /ˌtɛləkəˌmjunɪˈkeɪʃən/, occupational /ɑkjuˈpeɪʃənəl/, leisure /ˈlɛʒə(ɹ)/
  • Palavras longas — acerte a sílaba tônica: automation /ˌɔ.təˈmeɪ.ʃən/, apocalyptic /əˈpɒ.kə.lɪp.tɪk/, consultancy /kənˈsʌl.tən.si/, disembodied /dɪsɪmˈbɒdiːd/, experimentation /ɪkˌspɛɹ.ə.mənˈteɪ.ʃən/

Sons difíceis para falantes de português:

  • Consoante final — sem acrescentar um “i” depois: apocalyptic /əˈpɒ.kə.lɪp.tɪk/, astound /əˈstaʊnd/, disembodied /dɪsɪmˈbɒdiːd/, humankind /ˌhjuːmənˈkaɪnd/, misguided /ˌmɪsˈɡaɪdɪd/
  • /l/ final — a língua toca o céu da boca, não vira “u”: peripheral /pəˈɹɪf(ə)ɹ(ə)l/, occupational /ɑkjuˈpeɪʃənəl/
  • /h/ — um sopro suave, diferente do “r”: humankind /ˌhjuːmənˈkaɪnd/, enhancement /ɪnˈhæns.mənt/, enhance /ɪnˈhæns/

Como praticar com este vídeo

  1. Ouça o vídeo inteiro uma vez sem falar e anote as palavras que você não conhece.
  2. Faça shadowing frase por frase na velocidade normal, repetindo cada uma até o seu ritmo ficar igual ao do falante.
  3. Grave a sua voz e compare com o original, prestando atenção a palavras como algorithm, robotics, automation.

O que é a Técnica de Shadowing?

Shadowing é uma técnica de aprendizado de idiomas com base científica, originalmente desenvolvida para o treinamento de intérpretes profissionais. O método é simples, mas poderoso: você ouve áudio em inglês nativo e repete imediatamente em voz alta — como uma sombra seguindo o falante com 1-2 segundos de atraso. Pesquisas mostram melhora significativa na precisão da pronúncia, entonação, ritmo, sons conectados, compreensão auditiva e fluência na fala.

Técnica de shadowing: leia o guia completo passo a passo →