쉐도잉 연습: We’re already using AI more than we realize - 영상으로 영어 말하기 배우기
레슨 만드는 중...
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Of all the interactions you have with technology in a day, interacting with artificial intelligence, or not, feels like a choice.
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But in some ways, it isn't.
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Over the past decade, we've become surrounded by AI systems that perceive our worlds, that support our decisions, and that mimic our ability to create.
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Whether we're aware of it or not is another story.
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Imagine a day like this.
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You do some exercise with a smartwatch, put on a suggested playlist, go to a friend's house and ring their camera doorbell, browse recommended shows on Netflix, check your spam folder for an email you've been waiting for,
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and when you can't find it, talk to a customer support chatbot.
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Each of those things are made possible by technologies that fall under the umbrella of artificial intelligence.
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But when a Pew survey asked Americans to identify whether each of those used AI or not, they only got it right about 60 % of the time.
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Some of these applications of AI have become fairly ubiquitous.
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They almost exist in the background and it's not terribly apparent to folks
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that the tools or services they're using are being powered by this technology.
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That's Alec Tyson, one of the researchers behind that Pew study.
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When Tyson and his team asked respondents how often they think they use AI, almost Most half didn't think they regularly interact with it at all.
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Some of them might be right, but most probably just don't know it.
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We know about 85 % of U .S adults are online every day, multiple times a day.
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Some folks are online almost all the time.
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This suggests a bit of a gap where there seem to be some folks who really must be interacting with AI, but it's not very salient to them.
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They don't perceive it.
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So why does that gap exist?
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Part of the problem is that the term artificial intelligence has been used to refer to a lot of different things.
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Artificial intelligence is totally this giant umbrella tent term that now has become a kitchen sink of everything.
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That's Karen Howe.
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She's a reporter who covers artificial intelligence and society.
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In the past, there were distinct disciplines about which aspect of the human brain do we want to recreate.
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Like, do we want to recreate the vision part?
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Do we want to recreate our ability to hear, our ability to write and speak?" Giving a machine the ability to see became the field of computer vision.
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Giving a machine the ability to write and speak became the field of natural language processing.
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But on their own, these tasks still required a machine to be programmed.
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If we wanted machines to recognize spam emails, we had to explicitly program them to look out for specific things, like poor spelling and urgent phrasing.
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That meant the tools weren't very adaptable to complex situations.
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But that all changed when we started recreating the brain's ability to learn.
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This became the subfield of machine learning, where computers are trained on massive amounts of data so
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that instead of needing to hand code rules about what to see or speak or write, the computers can develop rules on their own.
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With machine learning, a computer could learn to recognize new spam emails by reviewing thousands of existing emails
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that humans have labeled as spam.
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The machine recognizes patterns in this structured data and creates its own rules to help identify those patterns.
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When that training data hasn't been structured and labeled by humans, that method is called deep learning.
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Most of the time people talk about AI now, they're not talking about the whole field, but specifically these two methods.
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We'll hear more about that after a word from this video's sponsor.
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This episode is presented by Microsoft Copilot for Microsoft 365, your AI assistant at work.
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Copilot can help you solve your most complex problems at work, going far beyond simple questions and answers.
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From getting up to speed on a missed Teams meeting in seconds to helping you start a first draft faster in Word,
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Copilot for Microsoft 365 gives everyone an AI assistant at work in their most essential apps to unleash creativity,
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unlock productivity, and up -level skills.
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And it's all built on Microsoft's comprehensive approach to security, privacy, compliance, and responsible AI.
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Microsoft does not influence the editorial process of our videos, but they do help make videos like this possible.
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To learn more, you can go to Microsoft .com slash Copilot for Work.
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Now, back to our video.
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Improvements in computing power, together with the massive amounts of data generated on the internet, made possible a whole new generation of technologies that leveraged machine learning.
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And existing ones swapped out their algorithms for machine learning, too.
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A lot of the how in the back has been swapped into AI over time because people have realized,
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oh wait, we can actually get an even better performance of this product if we just swap our original algorithm,
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our original code, out for a deep learning model.
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Now, machine learning and deep learning models power recommendation for shows, music, videos.
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products, and advertisements.
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They determine the ranking of items every time we browse search results or social media feeds.
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They recognize images like faces to unlock phones or use filters, and the handwriting on remote deposit checks.
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They recognize speech in transcription, voice assistance, and voice -enabled TV remotes.
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And they predict text in auto -complete and auto -correct.
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But AI is seeping into more than that.
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There has been this tendency over the last 10 plus years where people have started putting AI into absolutely everything.
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Machine learning algorithms are already being used to decide which political ads we see, which jobs we qualify for, and whether we qualify for loans or government benefits,
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and often carry the same biases as the human decisions that preceded them.
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Are you actually automating the poor decision -making that happened in the past and just bringing it into the future?
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If you're going to use historical data to predict what's going to happen in the future, you're just going to end up with a future that looks like the past.
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And that's part of the reason why it matters to close
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that gap between those who knowingly interact with AI every day and those who don't quite know it yet.
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Awareness needs to grow for folks to be able to participate in some of these conversations about the moral and ethical boundaries, what AI should be used for and what it shouldn't be used for.
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이 레슨의 어휘와 말하기 포인트
이 영상에는 섀도잉할 문장 69개와 단어 1096개가 있습니다. 말하는 구간의 길이는 6:21입니다. 화자는 분당 약 173단어로, 일상 대화에 가까운 자연스러운 속도로 말합니다. 단어의 82%가 영어에서 가장 많이 쓰이는 3,000단어에 속합니다. 나머지는 연습 전에 미리 확인해 두세요.
이 영상의 핵심 어휘
영상에 나오는 익혀 둘 만한 단어 15개를 발음, 뜻과 함께 정리했습니다.
| 단어 | 발음 | 뜻 |
|---|---|---|
| artificial 형용사 | /ˌɑː.tɪˈfɪʃ.əl/ | 인공, 인조 |
| spam 명사 | /spæm/ | 스팸, 스팸 메일 |
| algorithm 명사 | /ˈælɡəɹɪðm̩/ | 알고리즘, 알고리듬 |
| swap 동사 | /ˈswɑp/ | 교환하다 |
| label 명사 | /ˈleɪ.bəl/ | 라벨, 레이블 |
| remote 형용사 | /ɹəˈməʊt̞/ | 원격, 먼 |
| predict 동사 | /pɹɪˈdɪkt/ | 예언하다, 예측하다 |
| automobile 명사 | /ˈɔː.tə.məˌbil/ | 자동차, 차 |
| umbrella 명사 | /ʌmˈbɹɛl.ə/ | 우산 |
| perceive 동사 | /pɚˈsiv/ | 감지하다, 지각하다 |
| refer 동사 | /ɹɪˈfɜː/ | 언급하다 |
| participate 동사 | /pɑːˈtɪs.ɪ.peɪt/ | 참가하다 |
| skill 명사 | /skɪl/ | 기술, 스킬 |
| solve 동사 | /sɒlv/ | 해결하다 |
| aspect 명사 | /ˈæspɛkt/ | 양상 |
이 영상의 문법
화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.
| 문형 | 영상 속 표현 |
|---|---|
| 현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때 | we've become surrounded · have become · has been |
| 수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점 | are made · being powered · be programmed |
주의할 발음
화자는 don't, they're, you're 같은 축약형과 약화된 형태를 17번 사용합니다. 들리는 대로 짧게 발음하세요.
- “th” 소리: algorithm /ˈælɡəɹɪðm̩/, ethical /ˈɛθɪkəl/
- “sh”와 “zh” 소리: artificial /ˌɑː.tɪˈfɪʃ.əl/, interaction /ˌɪn.təˈɹæk.ʃən/, recommendation /ˌɹɛkəmɛnˈdeɪʃən/, transcription /tɹænˈskɹɪpʃən/, unleash /ʌnˈliʃ/
- 긴 단어 — 강세 위치에 주의: artificial /ˌɑː.tɪˈfɪʃ.əl/, automobile /ˈɔː.tə.məˌbil/, participate /pɑːˈtɪs.ɪ.peɪt/, comprehensive /ˌkɑm.pɹəˈhɛn.sɪv/, interaction /ˌɪn.təˈɹæk.ʃən/
이 영상으로 연습하는 방법
- 먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
- 0.75배속으로 한 문장씩 섀도잉을 시작하고, 익숙해지면 보통 속도로 돌아갑니다.
- 자신의 목소리를 녹음해 원본과 비교하고, artificial, spam, algorithm 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.