Практика Shadowing: AI & Inequality | Daron Acemoglu: Big Tech poses risks as AI reshapes society - Изучайте разговорный английский по видео
Создание урока...
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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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О данном уроке
В этом уроке ты познакомишься с интервью экономиста Дарана Аcemoglu о влиянии ИИ на общество, попрактикуешься в воспроизведении речи на английском с помощью техники shadowing и улучшишь понимание сложных тем. Ты научишься воспринимать естественную речь, работать с интонацией и выражать свои мысли на темы технологий и неравенства.
Ключевой вокабуляр и фразы
- AI (Artificial Intelligence) — искусственный интеллект (основной тема видео)
- inequality — неравенство (ключевое понятие в дискуссии)
- automation — автоматизация (технология, которая заменяет людей на машины)
- displace workers — вытеснять рабочих (фраза, описывающая последствия автоматизации)
- course correction — корректировка курса (меры, чтобы изменить направление развития технологий)
- distributional issues — вопросы распределения (проблемы с равным разделением благ)
Советы по практике
Для эффективного shadowing английский в этом видео обратите внимание на умеренный темп речи Дарана Аcemoglu — он четко Artikuliert, но не спешит. Рекомендуем следующий подход:
- Сначала прослушай отрывок длиной 5-7 секунд, затем сразу повтори его, пытаясь скопировать интонацию и ритм. Это поможет улучшить shadow speech и чувство языка.
- Определите ключевые слова (например, "AI", "inequality") и обратите внимание, как они акцентируются. Это поможет в воспроизведении естественной речи.
- Используйте паузы между репликами, чтобы анализировать структуру предложений. Это полезно для практики разговорного английского и построения собственных высказываний.
Если нужно больше материалов, ищи shadowing site с аналогичными видео — это отличный способ тренироваться регулярно. Помни: учить английский по видео — это не только понимание, но и умение говорить так, как говорят носители. Пора практиковаться!
Грамматика в этом видео
Конструкции, которые говорящий использует чаще всего, с точными словами из видео:
| Конструкция | В видео |
|---|---|
| Пассивный залог be + причастие прошедшего времени — важно, что происходит, а не кто это делает | was created · were displaced · be rolled |
| Present Perfect have/has + причастие прошедшего времени — прошлое действие, важное сейчас | have worked · have been · have become |
Что такое техника Shadowing?
Shadowing — это научно обоснованная техника изучения языка, изначально разработанная для подготовки профессиональных переводчиков и популяризированная полиглотом доктором Александром Аргуэльесом. Метод прост, но эффективен: вы слушаете аудио на английском от носителей языка и немедленно повторяете вслух — как тень, следующая за говорящим с задержкой в 1–2 секунды. В отличие от пассивного прослушивания или грамматических упражнений, Shadowing заставляет мозг и мышцы рта одновременно обрабатывать и воспроизводить реальные речевые паттерны. Исследования показывают, что это значительно улучшает точность произношения, интонацию, ритм, связную речь, понимание на слух и беглость речи — что делает его одним из самых эффективных методов для подготовки к IELTS Speaking и реального общения на английском.