シャドーイング練習: Cambridge 16 IELTS Reading Test 4 Passage 3 - 動画で英語スピーキングを学ぶ

レッスンを作成中...
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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.

この動画は誰におすすめですか?

英語スピーキング練習をしている中級レベル以上の学習者に最適です。IELTSリーディングのパッセージを題材に、AIに関する議論を通して、論理的な文章構造や高度な語彙、ネイティブスピーカーの発音パターンを学ぶことができます。特に、英語の発音を良くすることや、シャドーイング(shadow speak)の練習によってスピーキング力を向上させたい人におすすめです。

覚えておきたい単語とイディオム

この動画には、論述文でよく使われる表現がたくさん含まれています。以下はそのハイライトです:

  • outlandish:風変わりな、常識外れの。「AIの奇妙な推奨事項」という文脈で使われています。
  • confirmation bias:確認バイアス。既存の信念を支持する情報を優先的に受け入れる傾向のこと。
  • foolproof:万无一失の、失敗しない。「機械学習は万无一失ではない」という文脈で使われています。
  • stick to one's expertise:自分の専門知識に固執する。医師がAIの意見を無視して自分の判断を信じる様子を表しています。

発音を磨くコツ

この動画のスピーカーは、明確な発音と適切なリズムを持って話しています。英語の発音を良くするためには、以下のポイントに注目してシャドーイング(shadowing)を行いましょう:

「shadowspeak」の技術を使う

シャドーイングとは、スピーカーの発音を聞いた直後に同じように繰り返す練習法で、「shadow speak」とも呼ばれます。この動画では、algorithm(アルゴリズム)やtransparency(透明性)などの長い単語が多いので、スピーカーの発音のリズムを追いながら、発音の強弱やアクセントの位置を覚えましょう。

リズムとイントネーションに注目

英語のスピーキングでは、リズムとイントネーションが重要です。このスピーカーは、文の中で重要な単語(例えば「trust」「confidence」)を強調するために、イントネーションを上げたり、発音を伸ばしたりしています。これらのポイントを意識して繰り返し練習することで、自然な発音が身につきます。

弱音と省略形に慣れる

日常的な会話では、don't(ドント)やcan't(カント)などの省略形がよく使われます。この動画でも、「they don't like relying on AI」のような表現があります。これらの弱音や省略形を正確に聞き取り、再現することで、英語スピーキング練習の効果が上がります。

以上のポイントを踏まえて、動画を何度も繰り返し聞き、シャドーイングを行うことで、発音だけでなく、リスニング力とスピーキング力を同時に向上させることができます。

シャドーイングとは?英語上達に効果的な理由

シャドーイング(Shadowing)は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的:ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影(shadow)」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。

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