Pratica di Shadowing: Cambridge Ielts 16 Reading test 1 Passage 3 - Impara a parlare inglese con i 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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Vocabolario e note di pronuncia per questa lezione

Questa lezione di conversazione di livello C1 si basa sul video “Cambridge Ielts 16 Reading test 1 Passage 3”. Le parole che ritornano più spesso: algorithm, pachidi, believes, revolution, technologies. Questo video contiene 59 frasi e 839 parole da ripetere con lo shadowing. Il parlato dura 5:47. Chi parla mantiene un ritmo costante di circa 145 parole al minuto, comodo per lo shadowing. Solo il 77% delle parole rientra nelle 3.000 più comuni dell’inglese, quindi il lessico è impegnativo.

Vocaboli chiave di questo video

Le 15 parole più avanzate del video, con pronuncia e significato:

ParolaPronunciaSignificato
algorithm sostantivo/ˈælɡəɹɪðm̩/algoritmo
retrain verbo/ɹiːˈtɹeɪn/riqualificare
robotics sostantivo/ɹoʊˈbɑ.tɪks/robotica
automation sostantivo/ˌɔ.təˈmeɪ.ʃən/automazione, automatizzazione
workforce sostantivo/ˈwɝk.foɹs/forza lavoro, manodopera
consultancy sostantivo/kənˈsʌl.tən.si/consulenza
disembodied aggettivo/dɪsɪmˈbɒdiːd/disincarnato
disruptive aggettivo/dɪsˈɹʌptɪv/frantumante, disgregante
experimentation sostantivo/ɪkˌspɛɹ.ə.mənˈteɪ.ʃən/sperimentazione
fallacy sostantivo/ˈfæl.ə.si/fallacia
redundancy sostantivo/ɹɪˈdʌndən(t)si/ridondanza, copiosità
salesperson sostantivovenditore, venditrice
telecommunication sostantivo/ˌtɛləkəˌmjunɪˈkeɪʃən/telecomunicazioni
trustworthy aggettivo/ˈtɹʌst.wɜː.ði/affidabile, attendibile
discourage verbo/dɪsˈkɝɪd͡ʒ/scoraggiare

I phrasal verb che sentirai

ParolaPronunciaSignificato
carry out verbo/ˈkæ.ɹi aʊt/compiere, realizzare
take over verbousurpare

Pronuncia a cui fare attenzione

Chi parla usa 3 contrazioni e forme ridotte, come doesn't, I'd, isn't. Pronunciale nella forma breve, così come le senti.

  • I suoni “th”: algorithm /ˈælɡəɹɪðm̩/, trustworthy /ˈtɹʌst.wɜː.ði/, thrive /θɹajv/
  • I suoni “sh” e “zh”: automation /ˌɔ.təˈmeɪ.ʃən/, experimentation /ɪkˌspɛɹ.ə.mənˈteɪ.ʃən/, telecommunication /ˌtɛləkəˌmjunɪˈkeɪʃən/, occupational /ɑkjuˈpeɪʃənəl/, leisure /ˈlɛʒə(ɹ)/
  • Parole lunghe — attenzione all’accento: automation /ˌɔ.təˈmeɪ.ʃən/, apocalyptic /əˈpɒ.kə.lɪp.tɪk/, consultancy /kənˈsʌl.tən.si/, disembodied /dɪsɪmˈbɒdiːd/, experimentation /ɪkˌspɛɹ.ə.mənˈteɪ.ʃən/

I suoni difficili per chi parla italiano:

  • /h/ — va soffiata, non è muta: humankind /ˌhjuːmənˈkaɪnd/, enhancement /ɪnˈhæns.mənt/, enhance /ɪnˈhæns/
  • Consonante finale — senza aggiungere una vocale dopo: robotics /ɹoʊˈbɑ.tɪks/, workforce /ˈwɝk.foɹs/, apocalyptic /əˈpɒ.kə.lɪp.tɪk/, astound /əˈstaʊnd/, disembodied /dɪsɪmˈbɒdiːd/
  • /æ/ — più aperta della “e”: algorithm /ˈælɡəɹɪðm̩/, fallacy /ˈfæl.ə.si/, enhancement /ɪnˈhæns.mənt/, advent /ˈæd.vɛnt/, analyze /ˈæn.əˌlaɪz/

Come esercitarsi con questo video

  1. Ascolta tutto il video una volta senza parlare e annota le parole che non conosci.
  2. Fai shadowing frase per frase a velocità normale, ripetendo ognuna finché il tuo ritmo coincide con quello di chi parla.
  3. Registrati e confronta con l’originale, facendo attenzione a parole come algorithm, retrain, robotics.

Cos'è la tecnica dello Shadowing?

Shadowing è una tecnica di apprendimento delle lingue supportata da studi scientifici, originariamente sviluppata per la formazione dei traduttori professionisti e resa popolare dal poliglotta Dr. Alexander Arguelles. Il metodo è semplice ma potente: ascolti un audio in inglese di madrelingua e lo ripeti immediatamente ad alta voce — come un'ombra che segue il parlante con un ritardo di solo 1–2 secondi. A differenza dell'ascolto passivo o degli esercizi di grammatica, lo shadowing costringe il tuo cervello e i muscoli della bocca a elaborare e riprodurre simultaneamente i modelli di discorso reale. La ricerca dimostra che migliora significativamente la precisione della pronuncia, l'intonazione, il ritmo, il discorso connesso, la comprensione dell'ascolto e la fluidità del parlato — rendendolo uno dei metodi più efficaci per la preparazione alla prova di speaking dell'IELTS e per la comunicazione reale in inglese.

Tecnica dello shadowing: leggi la guida completa passo dopo passo →