跟讀練習: The AI Atrophy Problem How CIOs Fight It - 通過影片學習英語口語
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87 句
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(bright curious music) ABBIE LUNDBERG: AI is helping your team work faster, but is it hurting how they think?
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I'm Abbie Lundberg, Editor in Chief at the MIT Sloan Management Review, and we are here at the annual MIT Sloan CIO Symposium asking IT and business leaders what one thing do you do to keep critical thinking sharp as AI takes over more and more work?
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Let's hear what they had to say.
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MONICA CALDAS: The way we think about it is, we put mechanisms in place to enable and remind people what we have to do.
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So for example, when we think about the evolution of technology, we have an emerging technology radar that we incorporate into our ways of working where we talk about what are the emerging techs, where are they going?
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So for example, quantum computing.
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And then we engage in different ways to both go deeper on the topic, but also then extrapolate what is the meaning of that to our business aspirations.
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That's a great example of a way where, yes, you can go and do research on an AI tool, fill in the blank, whatever your preferred chat engagement is, but you also have to have your own point of view.
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So, how do you use a variety of different sources of information to develop your own point of view?
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Critical thinking is now more important than it ever has been because general information is so easily available.
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But how do you actually connect that to the problems that you are trying to solve?
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MICHAEL SCHRAGE: When you get an output or an answer from a Claude or a Grok or a Perplexity that you really, really like, that you really, really resonate with, enjoy it for a moment and then stress it, conflict it, counter it, say, this is terrific, but what are the three most important arguments against what you've said?
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Or are you sure you're not hallucinating?
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When I'm in a more innovative or creative mood, I'll adopt certain kinds of persona.
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What would the biggest criticism of Peter Drucker or a Marshall McLuhan might have for this?
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The point is, you have to build in, forgive me, a dialectical stress test.
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You cannot trust the output to be accurate and precise.
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The overarching takeaway I would urge people to consider is, don't view these outputs as answers.
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View them as insights.
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View them as hypotheses that you should test, and stress test, and yes, iterate around.
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MELISSA SWIFT: So, when I get an AI output, what I do is I actually put on this persona to evaluate it.
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I pretend I'm a super critical outside reviewer, that I'm going to check every detail, right?
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I'm going to vet the ideas, I'm going to look at the sources.
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I put myself in that mindset every time I review AI output.
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And that reengages my critical thinking skills.
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So I don't get lulled by kind of a smooth, shiny output that might not be right.
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KERI PEARLSON: What I've noticed is that there is a marked difference in the activities that people do when they start to use AI.
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The promise is that AI will make our work faster and more streamlined.
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But the flip side is that we don't practice some of the skills that the AI has taken over.
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Now, how can we reverse that?
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Well, first of all, we want to make sure that people who use AI tools actually have the ability to validate the output that comes out of AI.
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If you don't know the basic skills for doing research, for example, and you use AI to do your research, then you have no ability to know if the outcome is valid.
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Is it reasonable? Has it been hallucinated?
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And we want to make sure that workers don't have that outcome.
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So, validating your output, practicing the skills once in a while to make sure you know how to do it.
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Following the thread, even asking AI, how did you come to this conclusion?
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Probing AI as if it were a strong assistant, and not a replacement for the work, is one way we can make sure to keep our skillset strong.
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MEGHNA SHAH: The one thing me and my teams do a lot is we emphasize that AI should accelerate thinking.
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It should not really replace thinking.
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That the output that you're getting from your AI model should really be a starting point.
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It shouldn't be the final answer, right?
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Because creative thinking is no longer going to be about, can you create content from scratch, right?
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It's really going to be, can you ask better questions?
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Can you apply reasoning?
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Can you apply the business context and the judgment that is needed with the response that you're getting from the LLM?
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So, it's going to be a lot of those pieces that feed into the critical thinking part of it.
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I think in today's day and age, when data is available so freely, I don't think having access to intelligence is going to be a differentiator.
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What's going to be a differentiator is how strategically and how responsibly you can actually apply that intelligence is going to be a critical differentiator.
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We also encourage no blind trust flows.
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So, even though we are using AI for coding, our engineers are still checking code and reviewing code before it goes into production.
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Our product managers are still reviewing requirements and validating that this is really what the customers need.
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And our architects are also reviewing architecture to see is this really fitting with what the organization needs and the long-term implications.
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So, there's a lot of those pieces to ensure that there's always critical thinking and a human in the loop and the human brain in the loop.
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GEORGE WESTERMAN: So much of what we do is data analysis, writing, summarizing information.
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And so the first thing that my team and I always have to ask is, how do we know that that is correct?
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We're always asking that question, always digging a little bit deeper to test a few things to make sure. But then, you can't ask the LLMs.
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The LLMs will tell you it's correct and they'll give you a really good reasoning, even though that may not be the situation.
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So, that's the first step.
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The other step we're trying to be really careful about is, is this the kind of question, the kind of prompt, that we can expect the engines to be good at?
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Because we don't want to set ourselves up in a situation where it's trying to do something it's not built for, and then we have a giant mess in our hands.
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MAX CHAN: At the end of the day, making sure that there is accountability owned by a human is critical.
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And with that understanding, we make sure that anyone who is leveraging AI, who is using AI to deliver a piece of code, to change a design, they must have the foundation.
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We make sure that employees and leadership do not forget the fact that, you know, being able to do something in AI does not mean that they have the skill to do it, they have the foundation to do it.
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And so that is really one thing that we continue to ensure we keep an eye on.
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And the other thing is with the speed of accelerations, right?
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AI accelerations that is taking place.
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We want to ensure that we have the ability to judge, applying judgment to make sure that we slow down where we have to.
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It's not because everything needs to go so fast.
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It's where do we need to slow down to gain that competitive edge?
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VANESSA ESCRIVÁ GARCÍA: Okay, we believe more than ever that the key is the training of the people and to make sure that people understand that we need to make the right questions.
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And AI, it's just the medium to obtain an objective.
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And one of the examples for that is that we have started with the training with the top of the company.
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The executive level have received this training, and we have a roadmap for it to make sure that 100% of the company is going to receive this training.
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And to understand that the very, very important thing is that the human is going to be the one that decide, and the AI is going to be the medium for this decision.
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KABIR NAGRECHA: You know, I think what we really push for on this team is questioning assumptions.
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Questioning assumptions about what can and cannot be done by AI today.
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What we assumed three months ago is impossible is now completely feasible, and that another part of our workflow could be completely automated.
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That self-questioning, that self introspection, is a big part of critical thinking around AI.
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THOMAS H. DAVENPORT: Hey, I'm a professor.
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I really torture my students to get them to use AI in an appropriate fashion.
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I'd say, "Every assignment that you do with AI," -- and I teach about AI, and of course I have to let them use AI, I've forced them use AI -- but they have to show the different prompts that they use.
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They have to show how they added value to the output, the edits that they made.
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They have to show that they checked the citations on the comments that the language model is making.
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And they can't stand doing this.
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One said to me, "It was easier when I could just paraphrase a Wikipedia article." But I think we all need to learn how to deal with AI in this way.
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And as painful as it might be, as productivity-lowering as these activities are, I think they're critical to our success as a species.
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(bright curious music)
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本課的詞彙與口語要點
這節 C1 級別的口語課以影片“The AI Atrophy Problem How CIOs Fight It”為素材。 影片中反覆出現的詞有：output, critical, thinking。 這段影片共有 87 個句子、1651 個單詞可供跟讀。 講話部分時長為 9:55。 說話人語速自然，每分鐘約 166 個詞，接近日常對話。 83% 的單詞屬於英語最常用的 3,000 詞，其餘的詞建議在練習前先學一下。 全部 87 個句子中有 22 個是問句，適合練習疑問語調和簡短回答。
影片中的重點詞彙
影片中最難的 15 個單詞，附發音和釋義：
| 單詞 | 發音 | 釋義 |
|---|---|---|
| hallucinate 動詞 | /həˈl(j)uːsɪneɪt/ | 幻想 |
| persona 名詞 | /pɝˈsoʊnə/ | 人物 |
| acceleration 名詞 | /əkˌsɛl.əˈɹeɪ.ʃən/ | 加速 |
| emerge 動詞 | /ɪˈmɝd͡ʒ/ | 出現 /出現 |
| assumption 名詞 | /əˈsʌm(p).ʃ(ə)n/ | 假設 /假設 |
| aspiration 名詞 | /ˌæspəˈɹeɪʃən/ | 希望 |
| automate 動詞 | /ˈɔ.təˌmeɪt/ | 自動化 /自動化 |
| compute 動詞 | /kəmˈpjuːt/ | 計算 /計算 |
| dialectical 形容詞 | 辯證的 /辯證的 | |
| extrapolate 動詞 | /ɛkˈstɹæp.əˌleɪt/ | 外推 |
| introspection 名詞 | /ɪntɹəˈspɛkʃən/ | 內省 /內省, 內觀 /內觀 |
| paraphrase 動詞 | /ˈpæɹəfɹeɪz/ | 釋義 /釋義, 意譯 /意譯 |
| reviewer 名詞 | 評論家 /評論家 | |
| streamline 動詞 | /ˈstriːmlaɪn/ | 精簡 /精簡, 簡化 /簡化 |
| summarize 動詞 | /ˈsʌməˌɹaɪz/ | 摘要, 總結 /總結 |
影片中出現的短語動詞
| 單詞 | 釋義 |
|---|---|
| take over 動詞 | 接管, 接手 |
| slow down 動詞 | 減速 /減速, 放慢 |
值得反覆跟讀的句子
影片中簡短而完整的句子，可以直接用在日常對話裡：
- Let's hear what they had to say.
- Or are you sure you're not hallucinating?
- It's really going to be, can you ask better questions?
- Can you apply reasoning?
影片中的語法
說話人最常用的結構，並附上影片中的原話：
| 結構 | 影片中的用法 |
|---|---|
| 現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事 | has been · you've said · I've noticed |
| 被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的 | been hallucinated · is needed · cannot be done |
| 定語從句 who / which + 從句 — 補充說明人或事物 | people who use · anyone who is |
需要注意的發音
說話人用了 29 次縮略和弱讀形式，例如 don't, I'm, you're。請按聽到的簡短形式來說。
- “sh” 和 “zh” 音: differentiator /dɪf.əˈɹɛn.ʃi.eɪ.tə/, acceleration /əkˌsɛl.əˈɹeɪ.ʃən/, assumption /əˈsʌm(p).ʃ(ə)n/, aspiration /ˌæspəˈɹeɪʃən/, introspection /ɪntɹəˈspɛkʃən/
- 長單詞——注意重音位置: differentiator /dɪf.əˈɹɛn.ʃi.eɪ.tə/, hallucinate /həˈl(j)uːsɪneɪt/, acceleration /əkˌsɛl.əˈɹeɪ.ʃən/, aspiration /ˌæspəˈɹeɪʃən/, extrapolate /ɛkˈstɹæp.əˌleɪt/
中文母語者容易讀錯的音：
- /v/ — 不要讀成 /w/，上齒輕觸下唇: validate /ˈvæl.ɪ.deɪt/, overarching /ˌoʊvəɹˈɑɹt͡ʃɪŋ/, leverage /ˈlɛv.(ə.)ɹɪd͡ʒ/, evaluate /ɪˈvaljʊeɪt/
- 詞尾子音 — 要讀清楚，後面不要加母音: validate /ˈvæl.ɪ.deɪt/, distress /dɪˈstɹɛs/, hallucinate /həˈl(j)uːsɪneɪt/, prompt /pɹɑmpt/, emerge /ɪˈmɝd͡ʒ/
- /r/ 和 /l/ 的區別: acceleration /əkˌsɛl.əˈɹeɪ.ʃən/, extrapolate /ɛkˈstɹæp.əˌleɪt/, streamline /ˈstriːmlaɪn/, accelerate /ɪkˈsɛl.əˌɹeɪt/, leverage /ˈlɛv.(ə.)ɹɪd͡ʒ/
如何用這段影片練習
- 先完整聽一遍影片，不要開口，記下不認識的單詞。
- 先用 0.75 倍速逐句跟讀，熟練之後再回到正常速度。
- 錄下自己的聲音並與原聲對比，特別注意 hallucinate, persona, acceleration 這類單詞。
什麼是跟讀法？
跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。