跟读练习: Cambridge 16 IELTS Reading Test 4 Passage 3 - 通过视频学习英语口语
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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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這個影片適合誰？
無論你是準備雅思口語練習的考生，還是想提升英语口语练习的學習者，這部解析劍橋16閱讀測試的影片都能幫你。它不僅鍛煉聽力理解，還能透過科技話題積累高級表達，特別適合想要突破口語內容深度的同學。跟著影片練習shadow speak，既能熟悉學術用語，又能磨練語速和語調，可謂一舉多得！
值得偷師的詞彙與短語
- outlandish：古怪的，離奇的。影片中用來形容AI的建議，可用於描述不常見的觀點。
- confirmation bias：確認偏誤。指人們傾向於相信支持自己觀點的資訊，是很好的學術名詞。
- disproportionate：不成比例的。可用於表達「過度關注」，如「媒體對AI失敗的報導不成比例」。
如何練好口音？
這部影片的講者語速中等，發音清晰，非常適合shadow speech練習。建議專注以下兩點：一是重讀節奏，如「artificial intelligence」中「ar-ti-fi-cial」的重音在第二音節，模仿時要突出；二是連讀現象，像「predict the future」會讀成「pre-dict-the-fu-ture」，注意語流的順暢。每天跟讀5分鐘，堅持一周就能明顯感受到發音更自然！
別忘了，shadowspeaks的核心是「模仿+反覆」，先聽一遍，再逐句跟讀，錄下自己的聲音對比，找出差距。透過這部影片，你不僅能學會科技類話題的表達，還能養成標準的英語語感，為雅思口語或日常交流打下堅實基礎。趕快行動起來吧！
什么是跟读法？
跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。