शैडोइंग अभ्यास: AI Expert WARNS: "You're Not Ready For 2027" - वीडियो के साथ अंग्रेजी बोलना सीखें
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1
Roman, make your case.
2
I want to agree with you on something you said, but I'll define AI, and that will help us.
3
We use the term AI to mean three different technologies, completely unrelated, and that's what probably creates this debate.
4
AI is a useful tool.
5
As a standard technology, we always had narrow system, makes you more productive, more creative.
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Everyone loves it, supports it.
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I'm a computer scientist, I'm an engineer, I want more of it.
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It helps the economy, it's great.
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We know how to control them, how to make them safe.
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We understand what they do.
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Completely on board with that AI.
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AI we're starting to have now.
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GPT -6 level, human level, AGI level.
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We can argue about what that means.
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Some dangers, like any human.
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They're unsafe like a human would be unsafe.
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But if we introduce them into the research cycle, they're automated scientists, automated engineers.
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What do you mean by that, introducing them into the research cycle?
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So right now you have humans doing research to make GPT -7.
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Yeah.
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But they're starting to add AI tools.
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More programming is done by AI, design of the next parameter set.
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What if the whole process is fully automated?
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What if GPT -6 is writing GPT -7?
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Is this what they call recursive self -improvement?
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Exactly.
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Which is not a foregone conclusion, though.
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A lot of people are predicting, including all the top labs, that they will get there.
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They're introducing junior machine learning researcher in 2026 they want the cycle to start in 2027.
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Which is when the AI will start building the new AIs itself.
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Once that cycle starts, we're going to create something called superintelligence, a system smarter than all of us at everything or capable of learning to be in any new domain.
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We will become secondary species on this planet.
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We will not be in charge.
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We will not decide what happens to us.
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Superintelligence doesn't hate you.
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It just doesn't care about you.
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We didn't learn how to make it care about us.
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And if it decides to, I don't know, cool the planet to make compute more efficient, it will freeze us.
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If it wants to convert this planet to fuel, to fly to Mars, so be it.
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We have not learned how to control those systems.
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The capabilities are getting exponentially better.
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Our ability to control those systems is non -existent.
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We have filters and we have bands.
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We put guardrails of don't say that word, don't talk about this topic.
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And that happens after the fact, after the model already made the decision, sometimes you see it's scraping the result.
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So they build the model and then they put filters around it to make sure it doesn't offend anybody.
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We cannot have it say the end word on the air, like we need to make sure that never happens, that will kill the profit.
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So that's all they have, guardrails of that nature.
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The model itself is completely unaligned.
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It doesn't care about you.
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It's wild that we're developing this and not just developing it before we deploy it through economy, before we get benefits of having GPT -6 propagated through economy,
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it can do so much.
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There are trillions of dollars of value in that model alone.
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We forget that.
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We switch to making the next model as soon as we can.
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Roman, I've just got a follow -up question for you there.
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It would appear to me that the new chat GPT -6 model, the Fable 5 .1 model,
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is arguably smarter than 99 .999 % of humans on planet Earth already is it conceivable
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that a intelligence
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that is much much smarter than humans is there any case
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where it could be controlled by humans does form factor matter does the fact
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that it doesn't have limbs and legs and does
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that matter at all i think long -term control of something
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that much smarter than us is impossible it can be for reasons we don't yet know friendly to us
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and decide to keep us around and make us happy.
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But it's not a guarantee.
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Let me pick up on Steve's question because I like the phrasing a lot.
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Let's say that Fable or whatever the latest release from OpenAI is really is smarter than, I don't know if it's 95 or 99 % of the people.
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Are we only being saved from extinction by the 1 % who are still smarter than the AI?
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No, no. The concern is not the model we have today.
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The concern is what I said.
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But if I believe your argument, then we really should be concerned about the model.
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No, it's like having another human.
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If there was another smart human, there is Einstein today, and he's malevolent, I'm not worried.
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He may cause some damage, but he's not going to exterminate 8 billion people.
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We are competitive at this stage.
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There are people just as smart who can understand what happened with the recent hacking accident and do something about it.
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My concern is that in a year, we're going to have a model.
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It's so much smarter.
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It's like squirrels fighting humans.
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They don't understand what we can do to them.
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They have no concept of poisons, tribes, guns in their world model.
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They think you're going to chase them up a tree and bite them really hard.
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Is that also why recursive self -improvement was central to your argument?
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Because at some point, if it starts improving itself, then it's kind of like a runaway train of intelligence.
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It's an intelligence explosion.
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We don't control it.
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We don't understand it.
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We can't monitor it.
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We can't explain it.
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We can't predict it at that time point, it's just a runaway process.
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I've heard this phrase from Sam Altman and the others called fast takeoff.
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Yes.
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Is this what they're describing?
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That is the debate.
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Some people think it's going to take a very long time.
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Yeah, we automated research, but it's still going to take years.
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They need to run physical experiments.
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And fast takeoff means, as I said, instead of a year, it's going to take a month, a week, a day, a second.
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Because you're not having humans doing research you have let's say
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10 000 agents each one smarter than all of us doing
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research 24 7 they don't sleep they don't eat they don't
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get sick they're much faster than us ed your face tells
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a picture it's a i think i could say you disagree we're spending a lot of oxygen discussing something
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that might happen while ignoring what's actually happening and i find
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that very frustrating because the people
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that are killing themselves are a problem the black neighborhoods being poisoned with gas turbines
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that is a problem you said you cared about climate change
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so imagine a guy who goes it's raining right now we
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need umbrellas we need to do something about it this is like weather related
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and completely ignoring climate change the planet will boil over okay
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that's great why are we not talking about the thing
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that actually happened though like
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because relatively it's not important you don't think someone killing themselves
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no it's one person we have 8 billion people were running
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experimental experiments on you don't think anyone else is being given
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that ai psycho why do you not six people ten people
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those numbers are insignificant i'm sorry you have a software that's out there do you understand eight billion people
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and all future generations versus like literally a guy with a
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name you're doing thought experiment about maybe harm jacob coxson goes on tv saying it can copy itself to this
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that and the other jacob coxton is the guy from from anthropic who said he was quitting
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because he was so scared of everything despite spending years at OpenAI and having tons of stock, I believe, from there.
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So good for him.
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The thing he was saying was describing theoreticals all while divorcing the harms, which I think we can agree with that the companies themselves are not taking this seriously enough.
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But always it was about the AI is too powerful and mystical, not OpenAI and Anthropic.
126
The two largest startups are using hundreds of billions of dollars of infrastructure to hack.
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A regular person doing this would be arrested.
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They're saying 8 billion people are going to die.
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And not just them.
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I have this long list of quotes here from the people building this technology, who appear to agree.
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If you look at some of these quotes from Elon Musk, who said, with artificial intelligence, we are summoning a demon.
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You know, all those stories where there's the guy with the pentagram and the holy water, and he's like, yeah, he's sure he can control the demon, but it doesn't work out.
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So one thing I'd say is, you know, I really wish that the world would only give us one problem at a time.
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Sure.
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And if the world did give us only one problem at a time, I would love mine to be last on the list.
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It looks to me like we can have multiple problems at once.
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I think there are current harms.
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I think we should address them.
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It looks to me, I do talk to policymakers sometimes, it looks to me like there's a little bit more movement on the regulatory side about some of the current harms.
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There's, you know, Child Safety Protection Acts.
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There's, you know, anti -deepfake acts.
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We have more of those making more headway in Congress
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or getting passed through Congress than we have sort of trying to make it
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so we don't have any of these extinction risks.
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The other thing I'd throw out there is that I agree we should deal with the current harms.
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But if you watch the people saying deal with the current harms over time, a couple of years ago,
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they were saying we have to deal with current harms like AI bias influencing who's hired.
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Last year, they were saying we have to deal with current harms like kids killing themselves.
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This year, Gary Tan, just on an interview the other day.
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Who's Gary Tan?
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Sorry, Gary Tan is a technologist who runs Y Combinator, which Sam Altman used to run before going to OpenAI.
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And on an interview the other day, he said, let's not worry about these crazy future risks.
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We need to worry about current harms like AI swarms breaking out and taking over data centers.
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And I'm like, look, guys, at some point, we need to look at the progression of the current harms
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that everyone is saying we have to worry about instead of the extinction threats and watch where the puck is going.
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play where the puck is going.
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And I'm like, these extinction threats are coming down the line.
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They aren't in opposition with dealing with the problems we have today.
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We just need to deal with both.
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We're not dealing with the ones today, though.
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We should deal with them both.
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Okay, good.
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Andy.
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As I've tried to understand the alignment argument and the extinction risk argument, a couple of things keep popping out to me.
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Number one, it seems to rely on thresholds Once we hit recursive self -improvement, once we hit AGI, then it's game over for us.
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I don't love those threshold arguments.
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They're fairly poorly defined, and there's a huge assumption on the other side of them.
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We hit this point, and then all of humanity goes away.
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That is a gigantic.. claim.
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Let me finish, please.
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On its face, that is a gigantic claim.
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I also think there's a lack of humility in your community.
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We are working on humanity's most important problem.
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And based on the thinking that we've been doing, we can't see a way that we're wrong.
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In other words, as soon as we get to these thresholds, bam, that's game over.
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I find that very far from a humble approach, especially given
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that we have no large base of evidence to base any
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of this on i agree with you guys ai is new
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and the fact that ai uh is
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so these days is agentic it goes off
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and does long chains of things on its own after we
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give it some very very vague very short initial instructions holy
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tledo it will it will spawn up a storm of agents
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and they will go off and kind of do their own thing
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and they will they will grind they will they will spawn
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lots of them they will work for a long time they
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will exhaust every possibility with the experience i have with agentic ai i'm just amazed at the tenacity
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and the doggedness of these things and we saw a super clear example of
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that with this most recent uh uh jailbreak this this attack
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that wound up at the website hugging face
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and i'm going to try to summarize the the step by step of
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that i think you all three probably know this in more detail than I do, but let me step through what I think is the sequence of events.
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And unless I get it dead flat wrong, like, you know, let me keep going.
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So a team at OpenAI set up a sandbox, an allegedly protected secure environment in the cloud,
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where they told a bunch of agents to go try to exploit security vulnerabilities.
196
That's dead wrong.
197
One important, yeah.
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What they did is they had thousands of agents.
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Each individual agent was given a task of use this vulnerability to break this particular piece of software.
200
I want to finish my TikTok.
201
So a couple really, really interesting things has happened.
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First of all, these agents escaped the sandbox that OpenAI thought they were going to be contained in.
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And they got, OpenAI tried very well, they set up an environment so that these agents could not access the big, broad public internet.
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And guess what?
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They accessed a big, broad public internet via a very clever series of things that they strung together to get out there.
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And then once they got out there, they went to a website called Hugging Face and used that.
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They took over part of the Hugging Face infrastructure and started doing more things, the details of which I forget.
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That's pretty wild, right?
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Like, I grant you.
210
It's even more wild than that, but yeah.
211
Okay, that is really, it's impressive and it is a little bit unsettling at least, right?
212
Now, let's talk about what the results of that were.
213
OpenAI was not super vigilant about the environment that they set up, apparently, because the agents were kind of going off their end of the world starting in May or something of this year.
214
Yeah, yeah.
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And OpenAI was not aware of that.
216
As I understand it...
217
They actually broke out once and crashed OpenAI's servers internally, and then OpenAI didn't notice what was happening still, hatched the holes that they used to get out the first time,
218
started them running again, and then they came out a second time.
219
There was actually, I think, three swarms, although we don't actually...
220
Yeah.
221
That's the worst story I have. So far.
222
Thank you.
223
Look at the trends.
224
Let me finish, please.
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This is my last sentence.
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From there to this kills everybody.
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I find that a really, really long, very uncertain journey.
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And I have no confidence that we wind up here.
229
It feels like you two find that a very straight, narrow path.
230
And I think that's an important difference.
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That's my point.
232
Do you want to respond to that?
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I would be happy to get into it.
234
I don't know if we're going to have the time to go deep.
235
A couple points to throw out.
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I just really want to say some of the crazier things
237
that happened in the Hugging Face swarm if we want it later.
238
A lot of people thought that these AIs were breaking into Hugging Face in attempts to steal answers to their test.
239
That's what we thought originally.
240
Turns out that's not true.
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It turns out that these AIs immediately were able to solve their problems by cheating, and they were breaking out in order to cover their tracks.
242
They were uncertain how to delete the log files and hide their cheating from the process that was going to score them.
243
So just to clarify for a simpleton like me, they were all given effectively a test to do.
244
They did the test straight away, but they cheated.
245
So they were breaking out to figure out how to cover the fact that they cheated.
246
That's right.
247
So it's like you're telling, it's like you have a bunch of students in separate rooms and you're like, use these lockpicks to break into this lock.
248
And there's like a thing behind the lock.
249
There's like a secret code behind the lock to show me that you succeeded.
250
And what they do is they break it with a hammer, get the thing out.
251
And they're like, oh no, I wasn't supposed to do that.
252
So then they use the lockpicks to break out of the door.
253
They meet up with a thousand other people.
254
They start calling themselves a swarm, and they go to break into the administrator's office to see if they can delete the camera footage.
255
And they don't find the camera footage there.
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This is the swarm breaking into OpenAI.
257
They don't find the camera footage there, so they break out the window of the school, hotwire a car.
258
drive to the therapist's office
259
to try and read through the therapist's files to figure out where is the teacher going to keep the security footage?
260
And at that point, they're caught.
261
And you're like, oh, like, what did you expect?
262
You were giving them a lockpicking exam.
263
It's like, well, I sure as heck didn't expect this.
264
You know, totally crazy.
265
Can I, I have a weirdly between both of your opinion, which is everything you're saying is correct, but you keep anthropomorphizing software.
266
And I, to be clear, what you're describing is...
267
It's just the facts that happened, yeah.
268
Sure, but you're missing out on an important detail, which is the hundreds of billions of dollars in infrastructure provided by Microsoft, Google, Amazon, and Oracle.
269
To be clear, the harms are very similar.
270
We're not disagreeing on that, but I think it's important to know
271
that this was a function of where it was making decisions
272
was it was checking on a decision tree based on the harness, based on the training data.
273
It's not a decision tree, I know, but it's an alignment issue still.
274
I will agree.
275
So what's your point?
276
These aren't conscious beings.
277
They are acting in ways that have real outcomes, but they are a function of the alignment problems that we'd actually agree on.
278
Intelligence is a spectrum projected next five years forward.
279
Where are we going to be?
280
So I think a model like that would be dangerous in ways you are not seeing.
281
There will absolutely be risks and weird stuff happening in ways that I can't see right now.
282
What I'm quite confident, and I think this is where you and I probably part, where the two of you and I part, is our ability to control these things.
283
So I actually tried proving what is possible and what is not possible in that space.
284
The impossibility results published in peer -reviewed papers, well -cited.
285
We cannot control something smarter than us.
286
We cannot explain it.
287
We cannot predict it.
288
It's not a question of getting more money for those companies, more time, smarter humans.
289
It's just not a possibility.
290
If we create general superintelligence, we are fried.
291
Andy, how do we control something smarter than ourselves?
292
Because that's the base premise that you're sort of asserting that.
293
These agents that broke out are smarter than 99 -ish percent of the security researchers in the world.
294
They were not caught by the 0 .1 % or the 1%.
295
They were caught by some dude at hugging face, maybe, I'm sorry, a person at hugging face, looking through their log files and finding an anomaly.
296
That's some, you know, hopefully pretty well -qualified person, noticing something was wrong and having pretty easy ways to unplug,
297
disconnect from the internet, wipe it clean, do whatever.
298
That's the skill that's available to like, I don't know, the 75 % most intelligent security employee at Hugging Face.
299
The idea that the IQ points are what separate us from extinction doesn't hold up.
300
It doesn't help me understand what happened in this example, where we had very, very smart agents being turned off and cleansed by probably less smart people.
301
That does actually make me think of something.
302
So that is an IT observability problem.
303
It's being able to see what's happening with your infrastructure.
304
And I think that there is actually, I think you would agree with this.
305
There is a serious problem with these companies that we do not know.
306
And it doesn't seem they know what's going on with their compute.
307
It's like a chimp with a gun.
308
These people have access to all this infrastructure and they're running.
309
We don't know how much money they spent on the hugging face exploit, because it is relevant because it's how much could a threat actor use to recreate this
310
because conscious or not it is very dangerous
311
but it's ai is in the dangerous hands it's an open ai
312
and anthropics we have a problem with
313
that conscious not however we may think it goes i think we have a real
314
and present thing where we have these companies working willy -nilly just running experiments
315
that are potentially very dangerous we do i really think we need a government regulatory body whether
316
or not we get to the things you are discussing i think we have a clear
317
and present danger today these things are however, not intelligent in the same way humans are.
318
This isn't an argument about AI being able to do stuff.
319
It's, we need to build different infrastructure or different regulatory infrastructure to deal with what LLMs can and can't do.
320
And I think that starts with a realistic discussion of what happened.
321
It was a poorly run security environment.
322
It was clearly, there's something going on with the line.
323
It was an unreleased model, right?
324
Unreleased model.
325
So we have no idea what it was trained like.
326
We don't really have, we as people should at very least have clarity into where our alignment is going.
327
The idea of META.
328
You sound like these guys.
329
Here's the thing.
330
Everyone's converging on us with time.
331
Here's the thing.
332
I may not agree with a large chunk of what they say, but we agree that these companies are acting recklessly.
333
Absolutely.
334
Andy, two questions for you then.
335
Do you agree with the statement that AI is going to get increasingly more intelligent?
336
It's going to get more capable.
337
Okay.
338
Capable intelligence, fine.
339
I'm going to use my word.
340
Okay.
341
It's going to get more capable.
342
It's going to get increasingly more capable and is capability a function of intelligence
343
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344
and you watch this channel please do me a huge favor
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become part of the 15 of the viewers on this channel
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इस पाठ की शब्दावली और बोलने के सुझाव
इस वीडियो में शैडोइंग के लिए 346 वाक्य और 3937 शब्द हैं। बोला गया हिस्सा 20:02 लंबा है। वक्ता तेज़ बोलता है, लगभग 196 शब्द प्रति मिनट, इसलिए जुड़ी हुई और कमज़ोर ध्वनियाँ ज़्यादा मिलेंगी। 85% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं; बाकी शब्द अभ्यास से पहले देख लेना अच्छा रहेगा।
इस वीडियो की मुख्य शब्दावली
वीडियो के 15 कम प्रचलित शब्द, उच्चारण और अर्थ के साथ:
- harm /hɑːm/ (संज्ञा) — चोट, घाव. Physical injury; hurt; damage.
- hug /hʌɡ/ (क्रिया) — आलिंगन करना. To cling closely together.
- infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/ (संज्ञा) — अवसंरचना. An underlying base or foundation for a building, organization, or system.
- extinction /ɪkˈstɪŋkʃən/ (संज्ञा) — विलुप्त. The action of making or becoming extinct; annihilation.
- experiment /ɪkˈspɛɹ.ə.mənt/ (संज्ञा) — प्रयोग, तजुर्बा. A test under controlled conditions made to either demonstrate a known truth, examine the validity of a hypothesis, or determine the efficacy or likelihood of…
- cheat /ˈt͡ʃiːt/ (क्रिया) — छलना. To violate rules in order to gain, or attempt to gain, advantage from a situation.
- introduce /ˌɪntɹəˈdus/ (क्रिया) — परिचय. To cause (someone) to be acquainted (with someone else).
- intelligent /ɪnˈtɛlɪd͡ʒənt/ (विशेषण) — बुद्धिमान, चतुर. Of high or especially quick cognitive capacity, bright.
- conscious /ˈkɑn.ʃəs/ (विशेषण) — सचेत, जागरूक. Alert, awake; with one's mental faculties active.
- threshold /ˈθɹɛʃ(h)oʊld/ (संज्ञा) — डेवढ़ी. The lowermost part of a doorway that one crosses to enter; a sill.
- ignore /ɪɡˈnɔː/ (क्रिया) — उपेक्षा, उपेक्षा करना. To deliberately not listen or pay attention to.
- narrow /ˈnæɹəʊ/ (विशेषण) — तंग. Having a small width; not wide; having opposite edges or sides that are close, especially by comparison to length or depth.
- filter /ˈfɪltɚ/ (संज्ञा) — फ़िल्टर, साफी. A device which separates a suspended, dissolved, or particulate matter from a fluid, solution, or other substance; any device that separates one substance from…
- capability /ˌkeɪ.pəˈbɪl.ɪ.ti/ (संज्ञा) — क्षमता. The power or ability to generate an outcome.
- demon /ˈdiː.mən/ (संज्ञा) — दानव, दैत्य. An evil being resident in or working for Hell; a devil.
वीडियो में आने वाली फ़्रेज़ल वर्ब
- look at (क्रिया) — हेरना. To observe or watch.
ध्यान देने योग्य उच्चारण
वक्ता 93 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे don't, I'm, they're। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।
- “th” की ध्वनियाँ: threshold /ˈθɹɛʃ(h)oʊld/, anthropic /ænˈθɹɒp.ɪk/, anthropomorphize /ˌanθɹəpəˈmɔːfʌɪz/
- “sh” और “zh” की ध्वनियाँ: extinction /ɪkˈstɪŋkʃən/, conscious /ˈkɑn.ʃəs/, threshold /ˈθɹɛʃ(h)oʊld/, efficient /ɪˈfɪʃənt/, conclusion /kənˈkluːʒən/
- लंबे शब्द — बलाघात सही रखें: infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, experiment /ɪkˈspɛɹ.ə.mənt/, intelligent /ɪnˈtɛlɪd͡ʒənt/, regulatory /ˈɹɛɡ.jʊ.lə.t(ɔː.)ɹi/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/
इस वीडियो से अभ्यास कैसे करें
- पहले बिना बोले पूरा वीडियो एक बार सुनें और जो शब्द नहीं आते उन्हें लिख लें।
- 0.75× गति से शुरू करें, एक-एक वाक्य की शैडोइंग करें, और सहज लगने पर सामान्य गति पर लौटें।
- अपनी आवाज़ रिकॉर्ड करें और मूल से तुलना करें, ख़ासकर harm, hug, infrastructure जैसे शब्दों पर ध्यान दें।
इस वीडियो का व्याकरण
वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:
| संरचना | वीडियो में |
|---|---|
| “used to” used to + क्रिया — बीती आदत या स्थिति जो अब नहीं है | used to run · used to get |
| Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है | is done · could be controlled · being saved |
| Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है | have not learned · I've heard · have let |
| Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी | guy who goes · opinion, which is · detail, which is |
शैडोइंग तकनीक क्या है?
शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।