쉐도잉 연습: Cambridge 16 IELTS Reading Test 4 Passage 3 - 영상으로 영어 말하기 배우기
레슨 만드는 중...
1
Attitudes towards artificial intelligence.
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A artificial intelligence, AI, can already predict the future.
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Police forces are using it to map when and where crime is likely to occur.
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Doctors can use it to predict when a patient is most likely to have a heart attack or stroke.
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Researchers are even trying to give AI imagination so it can plan for unexpected consequences.
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Many decisions in our lives require a good forecast, and AI is almost always better at forecasting than we are.
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Yet for all these technological advances, we still seem to deeply lack confidence in AI predictions.
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Recent cases show that people don't like relying on AI and prefer to trust human experts, even if these experts are wrong.
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If we want AI to really benefit people, we need to find a way to get people to trust it.
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To do that, we need to understand why people are so reluctant to trust AI in the first place.
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They take the case of Watson for Oncology, one of technology giant IBM's supercomputer programs.
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Their attempt to promote this program to cancer doctors was a PR disaster.
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The AI promised to deliver top-quality recommendations on the treatment of 12 cancers that accounted for 80% of the world's cases.
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But when doctors first interacted with Watson, they found themselves in a rather difficult situation.
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On the one hand, if Watson provided guidance about a treatment that coincided with their own opinions, physicians did not see much point in Watson's recommendations.
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The supercomputer was simply telling them what they already knew, and these recommendations did not change the actual treatment.
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On the other hand, if Watson generated a recommendation that contradicted the expert's opinion, doctors would typically conclude that Watson wasn't competent.
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And the machine wouldn't be able to explain why its treatment was plausible
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because its machine learning algorithms were simply too complex to be fully understood by humans.
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Consequently, this has caused even more suspicion and disbelief,
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leading many doctors to ignore the seemingly outlandish AI recommendations and stick to their own expertise.
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See this as just one example of people's lack of confidence in AI
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and their reluctance to accept what Al has to offer.
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Trust in other people is often based on our understanding of how others think and having experience of their reliability.
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This helps create a psychological feeling of safety.
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Al, on the other hand, is still fairly new and unfamiliar to most people.
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Even if it can be technically explained, and that's not always the case, AI's decision-making process is usually too difficult for most people to comprehend.
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And interacting with something we don't understand can cause anxiety and give us a sense that we're losing control.
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Many people are also simply not familiar with many instances of AI actually working because it often happens in the background.
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Instead, they are acutely aware of instances where AI goes wrong.
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Embarrassing AI failures receive a disproportionate amount of media attention, emphasizing the message that we cannot rely on technology.
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Machine learning is not foolproof, in part because the humans who design it aren't.
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Deep feelings about AI run deep.
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In a recent experiment, people from a range of backgrounds were given various sci-fi films about AI to watch
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and then asked questions about automation in everyday life.
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It was found that, regardless of whether the film they watched depicted Al in a positive or negative light,
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simply watching a cinematic vision of our technological future polarize the participants' attitudes.
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Optimists became more extreme in their enthusiasm for AI and skeptics became even more guarded.
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This suggests people use relevant evidence about AI in a biased manner to support their existing attitudes,
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a deep-rooted human tendency known as confirmation bias.
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As L is represented more and more in media and entertainment, it could lead to a society split between those who benefit from AI and those who reject it.
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More pertinently, refusing to accept the advantages offered by AI could place a large group of people at a serious disadvantage.
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Unfortunately, we already have some ideas about how to improve trust in AI.
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Simply having previous experience with AI can significantly improve people's opinions about the technology, as was found in the study mentioned above.
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Evidence also suggests the more you use other technologies such as the internet, the more you trust them.
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Another solution may be to reveal more about the algorithms which our users and the purposes they serve.
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Several high-profile social media companies and online marketplaces already released transparency reports about government requests and surveillance disclosures.
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A similar practice for AI could help people have a better understanding of the way algorithmic decisions are made.
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If research suggests that allowing people some control over AI decision-making could also improve trust
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and enable AI to learn from human experience.
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For example, one study showed that when people were allowed the freedom to slightly modify an algorithm, they felt more satisfied with its decisions,
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more likely to believe it was superior and more likely to use it in the future.
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We don't need to understand the intricate inner workings of AI systems, but if people are given a degree of responsibility for how they are implemented,
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they will be more willing to accept GAL into their lives.
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이 클립의 영어 회화 연습 목표
이 비디오는 IELTS 스피킹 준비생들에게 특히 유용한 유창성 향상을 목표로 합니다. AI에 대한 논의를 통해 복잡한 주제를 명확히 표현하는 능력을 기르고, 논리적인 흐름을 유지하는 연습을 할 수 있습니다. 또한 shadow speak 기법을 활용하여 자연스러운 발음과 리듬을 익히는 데 도움이 됩니다.
핵심 표현 모음
- predict the future (미래를 예측하다)
- account for (차지하다, 설명하다)
- coincide with (일치하다)
- contradict the expert's opinion (전문가의 의견과 모순되다)
- confirmation bias (확증 편향)
- place a large group of people at a disadvantage (많은 사람들에게 불리한 위치에 놓다)
이 표현들은 IELTS 스피킹 뿐만 아니라 일상 회화에서도 자주 사용되므로 shadow speech를 통해 반복 연습하세요.
연습 포인트: 발음과 리듬
이 비디오의 주요 연습 포인트는 긴 문장에서의 리듬 조절과 강세 위치입니다. 특히 "machine learning algorithms were simply too complex to be fully understood by humans"와 같은 복잡한 문장에서는 단어 간의 연결(linking)과 약어(contractions)를 주의깊게 들여보세요. shadow speak 기법을 사용하여 원본 음성과 동시에 말하면서 자연스러운 발음 습관을 길러보세요. 또한, "consequently", "however"와 같은 전환어의 발음을 명확히 하여 논리적인 흐름을 강조할 수 있습니다.
영어 회화 연습을 위해 매일 10분씩 shadowspeak를 실천하면 발음과 유창성이 크게 향상됩니다. IELTS 스피킹 시험에서 높은 점수를 받으려면 복잡한 주제를 논리적으로 전달하는 능력뿐만 아니라 자연스러운 리듬도 중요합니다. 이 비디오를 활용하여 지속적으로 연습하세요!
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.