Pratique du Shadowing: AI & Inequality | Daron Acemoglu: Big Tech poses risks as AI reshapes society - Apprendre l'anglais à l'oral avec la vidéo
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74 phrases
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My name is Dharan Acemoglu.
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I'm an institute professor at MIT.
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I am an economist and I study inequality, political economy, and technology.
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And my book, Power and Progress, Our Thousand Year Struggle Over Technology and Prosperity with Simon Johnson, is really at the center of these topics,
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but especially in the context of new digital technologies and AI and who will benefit from them.
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You know, AI is completely unique.
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Its effects on the economy are going to be unique.
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But on the other hand, we have so much knowledge from history about how similar breakthroughs in technology have worked out,
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under what conditions they have been foundations for shared prosperity, when they have been tools in the hands of a narrow elite to exploit the rest of the population.
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So there's a lot we can learn.
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And in fact, the British Industrial Revolution,
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where our prosperity and great technological improvements originate provides ample evidence on how sometimes things went very wrong
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and poverty and inequality was created out of new technological breakthroughs
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and how sometimes new technologies together with new institutions became the basis of shared prosperity.
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When people hear my book, my general arguments from the tech industry, I get a love and hate message.
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There are some people who think, you know, questioning what the tech industry is doing is sacrilege,
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that, you know, we are killing the golden goose that's going to deliver prosperity to the United States.
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And other people are clearly worried about what their industry their own firms are doing
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and they're grappling towards a framework to make sense of it and that's that's what i'm hoping that we contribute to
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ai machine learning
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and all sorts of other really impressive digital technologies are going
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to be as good as what we make of them
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so it's wrong to ask the question what will ai do
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to labor The right question is what we will decide to do with AI and how this will impact inequality.
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There is tremendous potential to use AI for good, but that's not the path we're going.
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And on our current path, I think AI and ML will be used for monitoring workers, reducing their autonomy.
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They will be used for automation, sidelining labor, and perhaps disempowering the already weaker members of our society.
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And if you look at over the last 40 years, we see many digital technologies deepen inequality because they automated work and displaced workers,
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and they weren't used for creating new jobs for the same workers who were displaced from their jobs.
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So I think those are the pitfalls that we should try to avoid with AI, and I think, again, we are not likely to avoid them unless we do a course correction.
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I think today we have a more open conversation about how we can use new technologies for workers.
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Labor unions have become more awake to the issues of digital technologies and AI.
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And I think there's a broader openness in the U .S society
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that was absent just four years ago about regulation and reducing the power of big tech.
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I think all of those are useful developments.
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I mean, I think it's impossible to predict what these amazingly impressive technologies are going to bring
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but I would venture the following guesses.
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I think we're going to see a lot of investment in AI and related technologies
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and I think that they are going to be rolled out very quickly in some businesses
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and there's going to be a lot of disappointment.
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There's going to be a lot of disappointment
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because in the middle of the hype many businesses are going to try to implement the technologies too fast.
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In many cases, I think they're going to underappreciate what humans were doing
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and overestimate what AI can do which is going to lead to disappointing results.
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That doesn't mean that we're not going to see some impressive applications, we will, but I think on the whole,
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we're not going to see the sort of amazing productivity promises become realized.
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I am a big believer in the market process.
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I don't think there's any alternative but the market to bring more innovation and better prosperity.
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Central planning is not going to be our future, but that doesn't mean that the market is going to get everything right.
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And one of the things that it doesn't get right is exactly distributional issues and issues related to the direction of technology.
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If we leave it to the market, whether we're going to use AI for automating work or monitoring workers or creating new tasks, what does that mean?
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It's not the market.
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It's Google and Facebook and Amazon and Microsoft and OpenAI.
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That's not the market.
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We already live in a world in which there are very, very big corporations.
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that have an enormous influence on our society, on the future of work, and they're not the market, and they have their own ideology, they have their own priorities,
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they have their own profit motives, they have their own business models.
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I don't think we can just think that there is a miraculous market that's going to solve all problems.
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When you talk about inequality and worker control, people may get the impression that it's just businesses versus workers.
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There's an element of that.
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We live in a society in which there is conflict.
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It's inevitable.
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But I also think that there's a path both in the tech sector and in the broader corporate sector where
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businesses can flourish while doing things that are good for their workers.
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And I would say a key distinction is whether you see your workers as a cost or as a resource.
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If they are a cost, you're going to just introduce more and more conflict, you're going to try to cut those costs.
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If they are a resource, which I think they are, then businesses can try to flourish while increasing the productivity of their businesses, providing better tools to their workers,
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improving their trainings, their skills.
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Every political regime depends on information.
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Every political regime depends on some degree of who controls information, how information is presented, who manipulates whom what agendas are encouraged
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and AI is changing that completely and it's changing
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that again in a very lopsided way
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because AI is very much concentrated in the hands of a few people
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and a few companies and that raises a big threat to democracy.
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Vocabulaire et conseils d’expression pour cette leçon
Cette vidéo contient 74 phrases et 1080 mots à répéter en shadowing. La partie parlée dure 6:37. Le locuteur parle à un rythme naturel d’environ 163 mots par minute, proche d’une conversation courante. 82 % des mots font partie des 3 000 mots les plus courants en anglais ; le reste mérite d’être vu avant de commencer.
Vocabulaire clé de cette vidéo
15 mots de la vidéo à retenir, avec leur prononciation et leur sens :
| Mot | Prononciation | Sens |
|---|---|---|
| worker nom | /ˈwɝ.kɚ/ | travailleur, travailleuse |
| prosperity nom | /pɹɑˈspɛɹ.ɪ.ti/ | prospérité |
| inequality nom | /ˌɪn.ɪˈkwɑ.lɪ.ti/ | inégalité |
| impressive adjectif | /ɪmˈpɹɛsɪv/ | impressionnant, épatant |
| resource nom | /ɹɪˈzɔːs/ | ressource |
| monitor nom | /ˈmɑ.nɪ.tɚ/ | moniteur, monitrice |
| regime nom | /ɹəˈʒiːm/ | régime |
| depend verbe | /dɪˈpɛnd/ | dépendre |
| productivity nom | /ˌpɹɒdʌkˈtɪvəti/ | productivité |
| technological adjectif | /ˌtɛk.nəˈlɑ.dʒɪ.kəl/ | technologique |
| disappointment nom | /ˌdɪsəˈpɔɪntmənt/ | déception |
| breakthrough nom | /ˈbɹeɪk.θɹuː/ | percée, trouée |
| flourish verbe | /ˈflʊ̈ɹ.ɪʃ/ | fleurir |
| topic nom | /ˈtɑpɪk/ | sujet, thème |
| element nom | /ˈɛləmənt/ | élément |
Les verbes à particule que vous entendrez
| Mot | Prononciation | Sens |
|---|---|---|
| roll out verbe | déployer, sortir | |
| work out verbe | /ˌwɝk ˈaʊt/ | calculer |
Prononciation à surveiller
Le locuteur utilise 18 contractions et formes réduites, comme we're, doesn't, they're. Prononcez-les sous leur forme courte, telles que vous les entendez.
- Les sons « sh » et « zh »: regime /ɹəˈʒiːm/, flourish /ˈflʊ̈ɹ.ɪʃ/, institution /ˌɪn.stɪˈtʃuː.ʃən/, regulation /ˌɹɛɡjʊˈleɪʃən/, impression /ɪmˈpɹɛʃn̩/
- Mots longs — placez bien l’accent: prosperity /pɹɑˈspɛɹ.ɪ.ti/, inequality /ˌɪn.ɪˈkwɑ.lɪ.ti/, productivity /ˌpɹɒdʌkˈtɪvəti/, technological /ˌtɛk.nəˈlɑ.dʒɪ.kəl/, disappointment /ˌdɪsəˈpɔɪntmənt/
Comment s’entraîner avec cette vidéo
- Écoutez la vidéo en entier une fois sans parler et notez les mots que vous ne connaissez pas.
- Commencez à la vitesse 0,75×, répétez phrase par phrase, puis revenez à la vitesse normale quand cela devient facile.
- Enregistrez-vous et comparez avec l’original, en faisant attention à des mots comme worker, prosperity, inequality.
La grammaire de cette vidéo
Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :
| Structure | Dans la vidéo |
|---|---|
| Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le fait | was created · were displaced · be rolled |
| Present perfect have/has + participe passé — une action passée qui compte encore maintenant | have worked · have been · have become |
Qu'est-ce que la technique du Shadowing ?
Le Shadowing est une technique d'apprentissage des langues fondée sur la science, développée à l'origine pour la formation des interprètes professionnels. Le principe est simple mais puissant : vous écoutez de l'anglais natif et le répétez immédiatement à voix haute — comme une ombre suivant le locuteur avec un décalage de 1 à 2 secondes. Les recherches montrent une amélioration significative de la précision de la prononciation, de l'intonation, du rythme, des liaisons, de la compréhension orale et de la fluidité.