쉐도잉 연습: Cambridge Ielts 16 Reading test 1 Passage 3 - 영상으로 영어 말하기 배우기

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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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이 레슨의 어휘와 말하기 포인트

이 C1 수준 말하기 레슨은 영상 “Cambridge Ielts 16 Reading test 1 Passage 3”을(를) 바탕으로 합니다. 가장 자주 반복되는 단어는 다음과 같습니다: algorithm, pachidi, believes, revolution, technologies. 이 영상에는 섀도잉할 문장 59개와 단어 839개가 있습니다. 말하는 구간의 길이는 5:47입니다. 화자는 분당 약 145단어의 일정한 속도로 말해서 섀도잉하기에 편한 속도입니다. 영어에서 가장 많이 쓰이는 3,000단어에 속하는 단어가 77%뿐이라 어휘가 어려운 편입니다.

이 영상의 핵심 어휘

영상에서 가장 어려운 단어 15개를 발음, 뜻과 함께 정리했습니다.

단어발음뜻
algorithm 명사/ˈælɡəɹɪðm̩/알고리즘, 알고리듬
robotics 명사/ɹoʊˈbɑ.tɪks/로봇공학, 로봇학
automation 명사/ˌɔ.təˈmeɪ.ʃən/자동화, 오토메이션
workforce 명사/ˈwɝk.foɹs/노동력, 로동력
disruptive 형용사/dɪsˈɹʌptɪv/산만한
redundancy 명사/ɹɪˈdʌndən(t)si/중복성
salesperson 명사판매원, 점원
trajectory 명사/tɹəˈd͡ʒɛktəɹi/궤도, 탄도
undertake 동사/ˌʌndəˈteɪk/맡다
dilemma 명사/daɪˈlɛmə/딜레마
seize 동사/ˈsiːz/붙잡다
restrict 동사/ɹɪˈstɹɪkt/제한하다
analyze 동사/ˈæn.əˌlaɪz/분석하다
instinct 명사/ˈɪn.stɪŋkt/본능
economist 명사/iˈkɑ.nə.mɪst/경제학자

영상에 나오는 구동사

단어발음뜻
carry out 동사/ˈkæ.ɹi aʊt/실행하다, 행하다

주의할 발음

화자는 doesn't, I'd, isn't 같은 축약형과 약화된 형태를 3번 사용합니다. 들리는 대로 짧게 발음하세요.

  • “th” 소리: algorithm /ˈælɡəɹɪðm̩/, trustworthy /ˈtɹʌst.wɜː.ði/, thrive /θɹajv/
  • “sh”와 “zh” 소리: automation /ˌɔ.təˈmeɪ.ʃən/, experimentation /ɪkˌspɛɹ.ə.mənˈteɪ.ʃən/, telecommunication /ˌtɛləkəˌmjunɪˈkeɪʃən/, occupational /ɑkjuˈpeɪʃənəl/, leisure /ˈlɛʒə(ɹ)/
  • 긴 단어 — 강세 위치에 주의: automation /ˌɔ.təˈmeɪ.ʃən/, apocalyptic /əˈpɒ.kə.lɪp.tɪk/, consultancy /kənˈsʌl.tən.si/, disembodied /dɪsɪmˈbɒdiːd/, experimentation /ɪkˌspɛɹ.ə.mənˈteɪ.ʃən/

한국어 화자가 어려워하는 소리:

  • /f/ — ㅍ(/p/)으로 바꾸지 말고 윗니를 아랫입술에 대기: workforce /ˈwɝk.foɹs/, fallacy /ˈfæl.ə.si/, outperform /ˌaʊt.pɚˈfɔɹm/, peripheral /pəˈɹɪf(ə)ɹ(ə)l/, enforce /ɪnˈfoɹs/
  • /v/ — ㅂ(/b/)과 구별하기: disruptive /dɪsˈɹʌptɪv/, envisage /ɛnˈvɪzɪd͡ʒ/, novice /ˈnɑvɪs/, advent /ˈæd.vɛnt/, thrive /θɹajv/
  • /z/ — ㅈ이 아니라 성대를 울리는 /s/: envisage /ɛnˈvɪzɪd͡ʒ/, seize /ˈsiːz/, analyze /ˈæn.əˌlaɪz/, observe /əbˈzɜːv/, expertise /ˌɛkspɜːˈtiːz/

이 영상으로 연습하는 방법

  1. 먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
  2. 보통 속도로 한 문장씩 섀도잉하고, 화자의 리듬과 맞을 때까지 반복합니다.
  3. 자신의 목소리를 녹음해 원본과 비교하고, algorithm, robotics, automation 같은 단어에 특히 주의합니다.

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법

쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.

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