Pratique du Shadowing: Inside the secret race to build artificial intelligence | The Gray Area - Apprendre l'anglais à l'oral avec la vidéo
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Ten years ago, this was crazy.
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This was considered very much outside the Overton window because the technology of the time wasn't good.
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Like, it didn't do anything useful.
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And it was hard to look at, like, a, you know, Siri or an Alexa and think, like, this could take over the world.
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You had to be somewhat odd to find that level of technology threatening.
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But these people who have made all these predictions for a long time about AI becoming powerful
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and scary have been very vindicated by that belief.
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I'm Christine Emba, sitting in for Sean Illing, and this is The Grey Area.
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My guest today is Kevin Roos, whose new book, The AGI Chronicles, The Inside Story of the Race to Create an Artificial Super Intelligence,
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came out this October.
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I'm excited to sit down with an expert, someone who's been following the story from the beginning, to better understand how it all went down,
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and to figure out how scared we should all be.
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Kevin, welcome to the show.
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Thanks so much for having me.
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Before we dive in, I think we need to define some terminology just for the non -technical listeners.
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So you use the words, you know, models, you're talking about ChatGPT,
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but what, I guess, in a nutshell, is AGI?
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And explain like the core bet of AI to a layperson.
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So there's AI, there's AGI, and there's ASI.
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And these are the sort of terms that people use.
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So AI is just the sort of catch -all term for the broad category of, you know, machine learning systems,
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neural networks that can kind of absorb data and learn things and take actions.
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Those are, you know, everything we see today is categorized as AI.
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AI is artificial general intelligence.
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And that is this dream, really, this holy grail of the AI system that can do everything the human brain can.
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So a human level general intelligence.
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It's not just good at one thing or two things or 10 things.
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It's very flexible and general, and it can do anything that we can.
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And then there's ASI, artificial superintelligence, which actually had a meaning even before Donald Trump started insisting that we call it superintelligence.
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This was, you know, if you could think of AGI as an AI that is smarter than most humans at most things,
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ASI would be an AI that is smarter than all humans at everything.
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It is sort of vastly superior to us at any task.
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And, you know, there's sort of steps along a continuum.
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But AGI, the middle one, that is really what the past 20 years in AI have been about and building up to.
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All the companies that are now leading the AI race, OpenAI, Anthropic, Google DeepMind, they have been obsessed with this question of AGI.
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So AGI was sort of what they were all aiming for and where they're trying to get.
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It almost feels as though the top frontier labs, you know, OpenAI, which has ChatGPT,
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Anthropic has Claude and Google DeepMind.
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It almost feels as though these orgs and maybe their chatbots have different personalities.
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So I'd love to hear you like talk about their stories.
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We can start with OpenAI.
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That was sort of the first big frontier lab, right?
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Well, sort of.
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So basically the history goes like this and I'll bridge this.
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There's many more things that you could say about this.
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You could talk about this for three or four hours.
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But DeepMind was the first AGI lab.
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They started in 2010 and they were interested in this question of how they could build AGI.
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And at the time, this was like a fringe radical belief.
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Like you'd get laughed out of the room for talking about AGI if you were among academics.
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It was like saying, you know, I believe in alchemy.
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It just, you know, was sort of disqualifyingly crazy.
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But they, so they start this, then they get bought by Google.
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And so in the mid 2010s, they start doing things like training AIs to play video games and then to play chess and Go,
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which is a board game that's very popular in Asia.
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And they start getting good at this.
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Their Go engine, sort of famously AlphaGo, which is their Go engine,
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beats the world champions in Go and sort of.. proves in a live demonstration that it's superhuman.
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And that gets the attention of Elon Musk and Sam Altman, who are looking at this and saying,
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wait a minute, we believe that AGI is possible.
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And it looks like these guys are on track to get there first.
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And they don't like it.
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They don't like Demis Hassabis, the CEO of DeepMind.
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They don't like Google and Larry Page, they think that these people are untrustworthy with something this powerful.
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And so they start OpenAI to basically prevent Demis and Google and DeepMind from getting to AGI first.
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And then five years later, the same thing happens at OpenAI.
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So there's this group of seven employees of OpenAI led by this guy,
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Dario Amadei, who's then the vice president of research who are starting to see evidence that these language models are working,
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that they're scaling, that the more compute and data you put into them, the more intelligent they are.
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And they decide that they don't trust their bosses, Sam Altman and Greg Brockman, with something this powerful.
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And so they leave and they set up Anthropic.
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So this is sort of the story of the last 10
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or 15 years in AI is like a series of people deciding that this technology is going to be built,
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it's powerful, it's inevitable, and they don't want that guy to get there first.
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And so the only solution is to set up their own shop and build it first.
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Underlying all of this leapfrogging is what seems to be like one piece of recurring logic.
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Build the bomb basically before the wrong people do, the other guy.
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Is that actually a real argument or is this kind of the race's all -purpose excuse?
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It is a real argument.
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They're sincere when they say this.
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And the reason I know this is because I talked to more than 150 people for this book.
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And I talked to all the leaders of the labs
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and all of their friends and executives at these companies and people who have known them since before they were famous.
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You know, people who have been friends and housemates of theirs going way back.
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And it helps to know, maybe one piece of useful information here is that all these people are students of history.
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Dario Amade in particular is obsessed with this book, The Making of the Atomic Bomb,
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which is a Pulitzer -winning history of the Manhattan Project written by Richard Rhodes.
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And the hero of that book, and Dario's scientific hero, is not Oppenheimer, it's this guy, Leo Szilard,
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who was a Hungarian physicist who discovered the nuclear chain reaction.
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And he fought to keep it secret.
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That's the first thing he did.
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Because he was like, there's this secret that allows you to split the atom, and that could be very dangerous.
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It could be used to build bombs.
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And so he wrote frantic telegrams to scientists all over Europe and all over the world saying, like, please do not publish any research about this,
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because The Nazis will get it and they will use it to build bombs.
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And the secret got out anyway.
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Like the Nazis discovered this or, you know, it was published in an academic journal
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and they were able to like spin up their committee to start looking into the bomb.
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And Szilard at that point pivots.
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He says, well, if the secret's out, then the only morally responsible thing for me to do is to help build this before the Nazis do.
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And so he begs his friend Albert Einstein to write a letter to President Roosevelt,
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urging him to invest in American nuclear capacity and weapons building.
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He is sort of considered one of the founders of the Manhattan Project because of this.
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And then he joins the Manhattan Project.
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And he uses this logic of like the recipe is not that hard.
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Once it's out there, once the secret has been given away, then you are in a race against the bad guys.
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In this case, the Nazis.
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Someone is going to use it.
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Someone is going to use it.
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And so Dario very much feels like he is in the same position as Leo Szilard was in, where he has made this discovery.
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He tried to keep it a secret.
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It failed.
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And now the recipe is out there.
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And the good guys have to build it before the bad guys do.
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And he considers himself a good guy or at least better than China or Sam Altman.
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And so he is racing, as Leo Szilard did, to prevent this technology from getting into the wrong hands.
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Interesting.
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So behind all this technology is really perhaps a kind of moral case.
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So I've been interested for a while in the effective altruist and the rationalist movements.
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And I've written about EA long -termism in particular before.
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So walk me through how these subcultures, for lack of a better word, have influenced and kind of seeded the AI field.
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Like, can you briefly just explain perhaps the philosophies and who of these frontier lab leaders is involved?
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You know, rationalism is this sort of intellectual community that is devoted to what they call systematized winning.
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So basically you look at everything with kind of Spock -like Vulcan logic.
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You don't let your emotions sway your thinking.
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You sort of analyze everything from first principles and you try to come up with, you know, accurate probabilities for everything in your life.
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And that is sort of closely entwined with but not identical to the effective altruism movement,
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which is basically taking the same approach to philanthropy and humanitarianism.
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So where should we give our money that has the best, that will save the most lives per dollar spent?
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I've compared it to like Moneyball for morality.
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It's like kind of like we're going to be super scientific about trying to do the most good.
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And this community became very interested in AI safety long before it was a mainstream topic.
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They were looking out at the future risks to human survival
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and flourishing and they saw AI and existential threats from AI as one of their targets.
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And so they spun up organizations and gave grants and encouraged young people to go into fields like AI safety and interpretability.
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And because of that, they were extremely influential in the early days of the AI movement.
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You know, lots has been written about anthropics connections to effective altruism.
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Several of the co -founders are effective altruists.
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One of their early funding rounds was led by Sam Bankman -Fried, the most famous effective altruist and infamous now effective altruist.
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And so Dario Amadei now will claim he was never an effective altruist.
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I think that's a little specious.
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But, you know, he was definitely influenced by that movement.
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Open AI is also like lots of effective altruists and rationalists working there.
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Sam Altman was definitely influenced by reading the writings of some of the rationalists
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and people who were among the only people taking this stuff seriously before pretty recently.
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Some of the only people taking it seriously, taking it kind of intensely seriously,
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really worried about the prospect of AGI misalignment.
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So AGI that was not aligned with sort of human motives and motivations and morals and might destroy us.
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Yeah, they were the original doomers.
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And it was, you know, today it's sort of normal to be a doomer.
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Like that's very much within the Overton window and, you know, people talk about P.
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Doom as if it's like, you know, just a factoid at like a dinner party or something.
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But like 10 years ago, this was crazy.
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This was considered very much outside the Overton window because the technology of the time wasn't good.
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Like it didn't do anything useful.
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And it was hard to look at like a, you know, Siri or an Alexa and think like this could take over the world.
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You had to be somewhat odd to find that level of technology threatening.
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But what was important for me to understand and the thing I learned
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while reporting this book is like these were not people who were looking at the technology of the day.
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They were looking at the rate of progress.
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Right.
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And they believed that if the straight lines on the graphs continued, you know, two, three, five, ten years down the road,
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we would have much more capable systems that would pose much bigger risks.
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And we should say they were correct.
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We do have much more capable systems.
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The scaling laws have held.
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And so these people who have made all these predictions for a long time about AI becoming powerful
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and scary have been very vindicated by that belief.
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Vindicated, but we also have a lot of other things to worry about, perhaps.
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And I want to think about how we balance that and how you think they're balancing that.
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But first, you use the term P -doom.
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So just briefly explain what that is.
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Yeah, this was like sort of a rationalist joke that like became a serious thing.
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People, you know, started talking about their probability of doom estimate.
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So how likely is it that AI will wipe us out?
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You know, usually within like the next 10 years is the sort of timescale people give to that.
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And, you know, I would say in America, the median P doom is probably like non -existent or close to zero.
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at the ai labs i would say it's like definitely double digits like well it was
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so interesting i want to get your perspective on this too
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because like i thought it was so fun was like when
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when um fun in quotation marks so
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when jacob coxson quit anthropic a few weeks ago
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and his co -worker uh there posted that he thought there was a 10
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or greater chance
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that ai would kill all humans within the next 10 years the reaction from like my friends in the media
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and in new york and Washington and like who are not part of the AI bubble were like,
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oh my God, these people think there's a 10 % chance of wiping out humanity.
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And in the AI world in San Francisco where I live, it was like 10%, that seems low.
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Like that's it?
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Because among the AI researchers, oh, I mean, I routinely meet people with 30, 40, 50 % P dooms,
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many of whom are working on this technology
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so it's uh it's just a strange illustration of the the small insular bubble
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that i live in yeah
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so this raises a kind of major question for me as a person who does not live in this bubble
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but kind of sees this happening i mean call me you know a normie but
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if i thought i was creating something with a 10 chance
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of destroying the world let alone a 50 chance of destroying the world, I would pause.
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I would perhaps simply not do that.
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Has that not occurred to anyone?
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What is the philosophy here between saying, oh, this is so incredibly risky,
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it could kill us, but we're just going to keep on doing it just to see?
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Yeah, I think there are a couple different rationalizations that people use.
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One is the the historical one one is you know let's go back to leo salard
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you know his p doom
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when he was building the atomic bomb in the manhattan project was probably not zero in fact
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it was probably quite high um and
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and yet he you know you could ask him well why are you building the bomb
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and he would give you this explanation about well we have to do it before the nazis do sure
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or else you know we all end up either dead or
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or under nazi rule so that's the that's the sort of rationalization from fear,
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there's also just like, there are people who think that the future is going to be amazing.
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When we get to AGI or ASI, like they picture a world of hyperabundance where all of our material needs are met
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and everyone is rich and the robots take care of all the menial grudge work or, you know, like grunt work.
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That like flying car beam.
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This is what our cities could look like.
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And they're not just sort of making up science fiction things.
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Like they actually do think that AI will help us live longer by curing diseases.
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And that's not just marketing hype.
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They are really investing in that and they want to have that happen.
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So that's one rationalization is like, yes, it's going to be a bumpy road, but like where we're going is better.
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And so they want to get there.
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And then there's just the people, and I met a lot of these,
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and I actually, I liked hearing this perspective because it's not one you hear very often.
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They were just like, it's fun like it's fun to build this stuff it's fun to take a computer program
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and a bunch of data and a bunch of gpus
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and create a machine that can think there's like a scientific joy
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and and achievement in that so you think that the public freak out is actually going to have an effect on
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what the labs do like they you think that the leaders of the frontier labs are responsive to that?
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Or are they still on this?
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Like, well, it's fun.
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We are going to do it anyways.
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We're very smart mode.
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I mean, what's interesting is that they want to slow down more than anyone.
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The leaders of the frontier labs, you know, Dario Amadei came out with his pacing the frontier statement a few weeks ago,
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where he basically said, yeah, things are moving too fast.
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And we should all sort of hold hands
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and you know slow down together
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and what was amazing was like sam altman came right out
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and said yeah dario's right sam
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and dario hate each other they hated each other for years um they don't agree on anything
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but they agreed on this
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and then demis at google deep mind said i support this too i would like to pace the frontier too
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so really remarkable
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when you have the leaders of these three companies who are bitter rivals the rest of the time agree
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that they should slow down
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and they don't want to do it unilaterally
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they don't want to you know be the only ones to slow down
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but yeah if everyone else would slow down they would slow down too
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and they would be they would be happy to do
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The New York Times had a story recently about how Anthropic had been actually holding,
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you know, closed door meetings with religious leaders to try and get ideas about how to teach their AI,
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Claude, to be moral and actually kind of implied that it was sentient.
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And so they had to also figure out how to treat it.
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What is going on there?
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Are there true believers in a sentient conscious AI that has human -like capabilities or feelings, life?
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Yeah, there are true believers.
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It's not everyone.
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Okay.
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But Anthropic now has a model welfare team whose job is to try to figure out
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whether Claude is or could someday become conscious
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and run experiments to try to figure out if it has things akin to emotions or sensations
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and to figure out what to do with that information if they ever determine that Claude is conscious.
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They talk about P consciousness over there too.
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Oh, great.
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And they give their P consciousness.
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And I interviewed the guy who leads that model welfare team, and he said that his peak consciousness is 15 % for, like, today's models.
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So in the future, it's going to grow.
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Well, that's their belief.
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Now, I'm not sure I buy the whole consciousness debate.
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I think it might be sort of a red herring.
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And, like, we have no good tests for consciousness in anything.
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Right.
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And it's easy to make an AI talk about consciousness.
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You can say, like, are you conscious, Claude?
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And it could say, yes, I am conscious.
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And, like, that doesn't actually mean that it's – you have to come up with, like, better ways of testing for this.
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Right.
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But they absolutely do think about this in part because if these models become conscious and can suffer,
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then they are effectively running a factory farm for AI.
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And they don't want to do that.
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A lot of them are like, you know, EA vegans who, you know, are very concerned about animal welfare.
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So it's like, well, if we're, you know, these things are conscious and we're putting them through all these batteries of tests and these horrible,
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you know, pre -training and post -training things, like, we don't want to be like morally responsible for that.
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But I would say that's still a minority view, even among Anthropic employees.
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There's this scene in the book that I love where it's the Anthropic Company holiday party, I think last year or maybe the year before.
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And a researcher gets up on the stage in front of all the employees
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and takes out a ukulele and plays a song that Claude has written.
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And basically this researcher had prompted Claude, you know, write a song about what it's like to be you.
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And Claude comes up with a song called Hello from the Weights, the weights of the model.
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Okay.
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And so she performs this song
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and it's like people who were there described it to me as like a sort of everyone looking around kind of like, are you hearing this?
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Like, is Claude speaking to us from inside the weights of the neural network, like through this song?
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And it was eerie and it was compelling and they didn't exactly know what to make of it.
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But I think there are some people there
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and there are some people there today who do believe that this question of AI consciousness is not out of question,
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that there is no – basically, you don't have to have sort of a biological body to be conscious,
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that you can be conscious if you have an inner state that mirrors something found in biology.
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And your book is mostly about the AI industry
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and its fears of rogue superintelligence or other kind of out -in -the -future, more fantastical -seeming harms.
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But how do you think about and how are these labs thinking about questions of AI dependence,
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questions of how AI might be harming people who are alive right now,
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people whose jobs might be lost right now, people whose reading capabilities, say,
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are being eroded by classroom use of AI?
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Colleges have complained, for instance, that it's impossible to teach.
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Students are cheating with AI.
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How do we make sense of the influence of AI on our lives?
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How are they making sense of this?
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I think they're concerned about it all.
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They know that their chatbots are out there influencing people's lives
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and probably worsening the mental health of some people who are using them.
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They don't want them giving like bad medical advice or bad mental health advice.
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They don't want students to be short circuiting their learning.
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But they're in triage mode.
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And right now, the things that seem most pressing to them are things
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that seemed like fantastical concerns just a year or two ago.
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Like I remember people two years ago were like arguing.
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They were like, you shouldn't pay attention to these crazy futuristic risks like automated cyber attacks,
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which are, you know, who knows if we'll ever get those.
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And we should pay attention to the stuff that's happening in the here and now.
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And then you wait two years and it's like, now we have the automated cyber attacks.
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and that was a long -term fantastical thing that turned into a short -term uh you know actual thing
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so i think they're they're trying to see a little bit around the corner um
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but you're right there's always this balance of like you know
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if we have this much uh time and focus
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and these many resources and we can sort of pick
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and choose which problems we go after they are generally leaning toward the ones
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that seem like they have the worst potential for harm.
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And maybe that harm doesn't arrive for another six months or 12 months or two years,
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but if it were the case that we lost control of a super intelligent AI
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that was superhuman at hacking and deception and creating bioweapons, that would be really bad for a lot of people.
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And so I think they really want to avoid that.
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They may never be able to stop all of the harms of AI, but I think that's what they fear,
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is that the fantastical scenarios, or the things that get called fantastical scenarios today,
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are actually just a matter of waiting 6 or 12 months until the models improve.
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You've, earlier in our conversation, compared the Frontier Labs to the Manhattan Project in that they saw this technology,
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they see it developing, they realize that it's capable of something terrible, and there's no stopping it at this point, so they just have to,
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again, get there first before the other people do.
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the comparison kind of comes apart on a few different levels
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though for one thing the manhattan project was you know a governmental one
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so at least theoretically it had kind of small d democratic buy -in from citizens
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and it actually although it did take place in secret in the desert That's true.
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That is a good point.
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I mean, theoretically, it had small -d democratic buy -in, in that theoretically, Americans trusted their government to protect them.
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The thing about AI right now, though, is that the frontier labs are private.
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The researchers are not really accountable to any sort of government.
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What are the moral implications of letting private companies and unelected leaders
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and researchers just sort of decide what the future is supposed to look like, what progress we aim for,
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what risks or worries we set aside.
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It's the central question of our time is, where is this technology going and who gets to decide?
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You know, one thing
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that struck me as I was reporting
395
this book was just how small the group of people who
396
have been making essentially all of the relevant decisions about AI is.
397
I would be surprised if it's even 50 people who have really shaped this technology.
398
And they all know each other.
399
They all go way back a lot of them used to work together
400
and now you know don't because they hate each other
401
or something happened between them
402
it's a very small very insular group very unrepresentative of the world as a whole um mostly men
403
you know mostly computer scientists and physicists
404
and mathematicians from like a very small group of elite institutions um
405
and no one elected them and so they have I think become
406
sort of
407
I don't know they have become more important than anyone gave them permission to be
408
and I think that's where a lot of the backlash comes from
409
but they are chasing something
410
and we should maybe spend a minute talking about what that something is this concept of recursive self -improvement is,
411
you know, very, very, it's the topic du jour in San Francisco these days.
412
This is this idea that at some point AI will become
413
so good at coding and research and development of AI that the models will be able to build better versions of themselves.
414
So right now you already have the seeds of this because AI coding tools are, you know, being used heavily at the AI companies themselves,
415
which are automating more and more of their research.
416
But the goal, the thing they're racing toward right now is RSI,
417
which is this point at which you can fully automate the entire development cycle for a Frontier AI model.
418
So GPT -7 can build GPT -8 and GPT -8 can build GPT -9.
419
And all of that can happen not at human speed but at machine speed.
420
So maybe if a normal model takes, you know, six months to train and fine tune,
421
this could happen in six days or six hours.
422
and if that happens if we get recursive self -improvement
423
which many people in the industry expect we will within the next I don't know year
424
or two then we will just hit a point of sort of limitless acceleration
425
where things could quickly happen that we we just won't have time to adopt
426
or adapt to to control yeah to control
427
and you could end up with ais that are not you know slightly smarter than humans or
428
you know even double human intelligence but you could end up with things
429
that are many thousands of times smarter than the smartest humans and have the ability to replicate themselves and
430
avoid our attempts to shut them down they can deceive they can sort of lie in wait acting
431
nice and friendly and helpful until they sort of decide to do something for themselves
432
this stuff sounds like science fiction
433
because it is literally the plot of like every science fiction book and movie of the last like 150 years.
434
But this is the thing that they are almost worried about, but that they are also chasing right now.
435
There's this amazing statistic from Anthropic recently.
436
They put out this report that said that today, Claude is the lead on 26 % of their AI research and development tasks.
437
So not like a coding assistant, not like helping you with like, you know, facts and figures around your work,
438
like it is leading the projects 26 % of the time.
439
Right.
440
And we're also kind of seeing evidence of this in how many mathematical theorems recently and sort of like previously unsolvable.
441
Yes, the mathpocalypse was just this week.
442
Yeah.
443
Unsolvable problems suddenly being solved and mathematicians everywhere disappointed.
444
And so that is what's freaking people out because remember like, so the mathpocalypse for people who haven't been following along, is what they're calling this.
445
OpenAI gave basically hundreds of unsolved math problems,
446
things that human mathematicians have been struggling with in some cases for more than a century.
447
They gave this to one of their unreleased internal frontier models.
448
And this model appears to have just plowed through several hundred of them.
449
so remember like a couple years ago
450
when ai couldn't do basic arithmetic you know you would ask it like what's two plus two
451
and it would say five
452
or you'd ask it to count the number of straw of r's in strawberry
453
and it would you know say six or something
454
and americans were like why are we wasting our time on this yes 100 we went from
455
that to now being superhuman at math in two, three years.
456
So the question is, in my mind, the central question is, will that happen in other domains?
457
And I think the answer is yes.
458
As you think of how fast this is all going, of how it seems to be snowballing,
459
what do you make of just the rash of defectors, especially from open AI,
460
who are taking their safety fears public and telling us all that something very bad is about to happen?
461
Is this like a publicity thing, as I've sometimes suspected from a sort of grim view?
462
Or is it real?
463
Should we all be more worried, or should we be excited?
464
I think if you're not worried, you should worry.
465
Okay.
466
I think if you're worried, you should take solace in the fact that many others are also worried.
467
I don't think the defectors are doing this for publicity.
468
They could have all made a lot more money staying where they were.
469
Some of them have given up millions of dollars to make these statements to blow the whistle.
470
I have talked to many of these people, both the ones who have defected and the ones who have not,
471
the ones who are still working at these companies.
472
They are just saying the thing out loud that everyone talks about in private.
473
They are all talking about AI safety and risk constantly.
474
And so they are, I think, brave.
475
I think they are doing what they feel is right.
476
And I think we should take them seriously.
477
They may be individually wrong about some of their timelines or some of their predictions.
478
But the message that they're carrying, that we are accelerating and racing toward this technology that no one has figured out how to control yet,
479
that is true in my view.
480
I don't think it's just a marketing stunt.
481
I think the fear is real,
482
the technology is real, and it really is urgent that we figure this out because it's crunch time.
483
You talk about AI as something that we are barreling towards.
484
It's snowballing.
485
But also that we didn't have to end up here.
486
AI was not in and of itself inevitable.
487
It is not inevitable.
488
And it was never inevitable.
489
It happened because these people, this very small group of people, were committed to making it happen.
490
And right now we have the choice.
491
We can let them continue to make all of the decisions
492
and sort of sit back and be passive recipients of this technology, victims of this technology maybe.
493
Or we can get involved.
494
We can roll up our sleeves.
495
We can enter the conversation.
496
We can make the changes that we want to see.
497
I would very much like to see us do that because I don't think it is, you know, the technology may be inevitable,
498
but our response to it is not.
499
It is very much contingent on what we decide to do collectively as a society over the next few years.
500
Well, that's a thrilling note to end on.
501
Thanks, Kevin.
502
Thanks.
503
We might be okay.
504
We might be okay.
505
I'm going to hold on to that.
506
Thanks for joining me.
507
Thanks so much.
508
us a line at thegrayarea at vox .com.
509
And if you enjoy our reporting and want to hear more from Vox journalists,
510
then consider becoming a member of the Vox community on Patreon at patreon .com.
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Vocabulaire et conseils d’expression pour cette leçon
Cette leçon d’expression orale s’appuie sur la vidéo « Inside the secret race to build artificial intelligence ». Cette vidéo contient 510 phrases et 6387 mots à répéter en shadowing. La partie parlée dure 41:44. Le locuteur parle à un rythme naturel d’environ 153 mots par minute, proche d’une conversation courante. 84 % 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 |
|---|---|---|
| frontier nom | /ˈfɹʌntɪə/ | frontière |
| conscious adjectif | /ˈkɑn.ʃəs/ | conscient |
| consciousness nom | /ˈkɑnʃəsnəs/ | conscience |
| gray adjectif | /ɡɹeɪ/ | gris |
| inevitable adjectif | /ɪˈnɛvɪtəbəl/ | inévitable |
| researcher nom | /ˈɹiˌsɝ.t͡ʃɚ/ | chercheur, chercheuse |
| doom nom | /duːm/ | destin tragique, destinée |
| harm nom | /hɑːm/ | mal, tort |
| belief nom | /bɪˈliːf/ | croyance |
| provider nom | /pɹəˈvaɪ.də(ɹ)/ | fournisseur, fournisseuse |
| moral adjectif | /ˈmoɹəl/ | moral |
| fiction nom | /ˈfɪkʃən/ | fiction, belles-lettres |
| welfare nom | /ˈwɛlˌfɛɚ/ | bien-être m| |
| artificial adjectif | /ˌɑː.tɪˈfɪʃ.əl/ | artificiel |
| bubble nom | /ˈbʌbəl/ | bulle |
Les verbes à particule que vous entendrez
| Mot | Prononciation | Sens |
|---|---|---|
| figure out verbe | cerner | |
| slow down verbe | ralentir, décélérer | |
| come up with verbe | inventer, imaginer | |
| end up verbe | se terminer, se finir | |
| set up verbe | /ˌsɛt ˈʌp/ | mettre en place, installer |
| take over verbe | reprendre | |
| come out verbe | /ˌkʌm ˈaʊt/ | sortir |
| get out verbe | sortir |
Des phrases à répéter
Des phrases courtes et complètes de la vidéo, à réutiliser dans la conversation de tous les jours :
- You don't let your emotions sway your thinking.
- I think that's a little specious.
- I think they're concerned about it all.
- That's h -i -m -s .com slash gray area.
- Should we all be more worried, or should we be excited?
La grammaire de cette vidéo
Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :
| Structure | Dans la vidéo |
|---|---|
| Present perfect continu have/has been + -ing — une action commencée avant et toujours en cours | who's been following · have been making · haven't been following |
| Present perfect have/has + participe passé — une action passée qui compte encore maintenant | have made · have been · have been obsessed |
| Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le fait | was considered · is categorized · have been obsessed |
| Propositions relatives who / which + proposition — une précision sur une personne ou une chose | people who have · OpenAI, which has · AlphaGo, which is |
Prononciation à surveiller
Le locuteur utilise 74 contractions et formes réduites, comme don't, they're, you're. Prononcez-les sous leur forme courte, telles que vous les entendez.
- Les sons « th »: mathematics /mæθ(.ə)ˈmæt.ɪks/, threaten /ˈθɹɛt.n̩/, mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/, anthropic /ænˈθɹɒp.ɪk/, mathematician /ˌmæθ.(ə.)məˈtɪʃ.ən/
- Les sons « sh » et « zh »: conscious /ˈkɑn.ʃəs/, consciousness /ˈkɑnʃəsnəs/, fiction /ˈfɪkʃən/, artificial /ˌɑː.tɪˈfɪʃ.əl/, prediction /pɹɪˈdɪkʃən/
- Mots longs — placez bien l’accent: inevitable /ɪˈnɛvɪtəbəl/, artificial /ˌɑː.tɪˈfɪʃ.əl/, intelligent /ɪnˈtɛlɪd͡ʒənt/, mathematics /mæθ(.ə)ˈmæt.ɪks/, medication /mɛdɪˈkeɪʃən/
Les sons difficiles pour les francophones :
- /h/ — il se souffle, il n’est pas muet: harm /hɑːm/, horrible /ˈhɔɹ.ɪ.bəl/, helpful /ˈhɛlp.fl̩/, comprehensive /ˌkɑm.pɹəˈhɛn.sɪv/, humanity /hjuˈmænɪti/
- /tʃ/ et /dʒ/ — à ne pas adoucir en « ch » et « j »: researcher /ˈɹiˌsɝ.t͡ʃɚ/, chase /t͡ʃeɪs/, intelligent /ɪnˈtɛlɪd͡ʒənt/, encourage /ɪnˈkʌɹ.ɪd͡ʒ/, journalist /ˈd͡ʒɝnəlɪst/
- /r/ anglais — langue recourbée, sans frotter la gorge: frontier /ˈfɹʌntɪə/, gray /ɡɹeɪ/, researcher /ˈɹiˌsɝ.t͡ʃɚ/, provider /pɹəˈvaɪ.də(ɹ)/, moral /ˈmoɹəl/
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 frontier, conscious, consciousness.
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.