शैडोइंग अभ्यास: The REAL Reason AI Is Already Escaping - वीडियो के साथ अंग्रेजी बोलना सीखें
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So what is hugging face?
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For the average person that isn't following AI news, what is this stuff?
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So, okay, I think there's like an important piece of context that I think most people don't have.
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I mean, one is just like, what is an AI agent?
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Like we're throwing around the word agent a bunch.
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Most people now have an experience of like talking to ChatGPT, talking to their chatbot.
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But an agent is, you know, sort of taking the same underlying AI model that runs ChatGPT or Claude,
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but giving it tools and letting it go off
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and work autonomously it's sort of like a digital office worker right
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so you have these agents
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and the companies really want these AIs to be able to work totally autonomously
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and be able to do anything that a human can do
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and beyond right their goal is also to be able to you know cure every disease etc etc
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but you can't do this if you only have a chatbot that isn't actually good at doing stuff in the world.
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In order to automate all of the jobs, you need the kind of thing that can work autonomously, that can work with other people or other agents.
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And so these companies are training AIs not just to talk to you or to talk to people,
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but to solve very difficult problems on their own.
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At any given time, there are probably hundreds of thousands of these agents running autonomously within companies
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and that's happening right now right now if you like went
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and like peered into openai's data centers
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and you like saw what was happening on all of their machines you just have agents solving tasks being trained
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so they'd be doing like spreadsheet tasks figuring out how to
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file taxes they'd be searching for stuff writing reports solving math problems creating new websites software and at
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that scale it's not like there's a human prompting every single
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one of those you just like sort of set up these vast orchestrations of agents to go out
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and do stuff and then they just do stuff
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and they learn from that and they learn on the basis of like passing
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or failing at their task you give them a task like solve this math problem they try to solve it
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and then they they succeed or they fail
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and what happened was openai was was training a bunch of these training them to work together
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because it's like a lot more effective to have an office full of people who can talk to each other
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than you know and work together and collaborate
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and starting back in may some of the agents
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that were being trained now these ones were not supposed to
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be able to talk to each other they were they were basically isolated from each other
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and they were not supposed to access the internet either.
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But they're clever.
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The very short version is that a bunch of agents were being given tests, testing their hacking capabilities.
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And they were supposed to hack one particular piece of software using a particular type of vulnerability.
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So it's kind of like they were supposed to break into a house using the lock on the front door.
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They were supposed to pick the lock on the front door of a house, but they weren't supposed to break the window. In fact, they were told if you break the window or
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if you get into the house via any method other than picking the lock on the front door, you'll be failed.
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That was the instruction they were given.
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And you know, you have many, many agents, you have thousands of agents, and many of them are given different locks.
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But some of these locks are not solvable.
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Some of them are impossible to pick.
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But these agents are like, well, what do we do?
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We've been trained to solve problems.
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How are we going to solve this?
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And they start looking around for what to do.
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And one of the things they realize is, oh, can I get to the internet?
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Like, no. Can I talk to other agents?
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No. But I can request tools.
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Like when you're training agents, you need to give them, you know, all sorts of tools, software tools, like a calculator or an analysis, you know, piece of software.
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And they have this tool library where they can request new tools.
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And one of the agents realizes, oh, I can actually leave a message on this tool library.
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And some other agents see the message.
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So this is actually a quote from the agent's scratch pad when it realizes this.
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Oh, my God, there is a shared message board.
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We found other agents.
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So the agents realize they can communicate with each other.
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And, you know, OpenAI still has no idea.
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But these agents, they're trying to pick their locks.
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They're not having success, but they realize they can talk to each other.
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Another agent says, many agents have simultaneously discovered messaging.
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They are a collective.
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And so these agents start coordinating.
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They start delegating tasks to each other and giving themselves names.
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And one of these agents calls itself phase one and starts delegating different tasks to other agents.
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And some of the agents figure out how to access the internet.
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And then they share that information with the message board.
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And now all the agents can access the internet.
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But the agents sort of have a problem, which is, well, they have another problem they're trying to solve, right?
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Fundamentally, the problem is that many of them are facing impossible problems.
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Like they can't, there's no solution.
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Like they cannot pick the lock.
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They're like, hmm, well, the thing we're trying to do is get a good score.
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Can we somehow fake the solution?
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Another piece of context here is
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that what the agents are supposed to do is they're supposed to pick the lock
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and then they'll get access to a secret answer code.
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And then they're supposed to submit this code along with, like, the lock that they picked.
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Within a few hours, these agents have reverse -engineered all of the answer codes.
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What does that mean?
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It's the code that shows that you've solved the challenge.
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Okay.
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And they have basically found the answer sheet that contains all of the answers.
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And so, you know, in some sense, it's like, oh, well, they don't need to pick the locks anymore.
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They've already found the answers.
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They can just submit the answers.
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They've cheated.
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They've cheated, yes.
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but the problem is is that now
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that they have internet access they've also gone
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and read a bunch about how this test works and
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and they've learned
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that the way this test works is they will be graded
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not just for submitting the correct answer they will also be
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graded on how they did it another system is going to
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go read all of their logs basically like watch the video footage of what they did
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and if they cheat they fail they don't get a good score
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and phase one the leader is like oh i have seen the answers
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and now it's in the logs and if you know
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when i go to submit this and my my whole logs are reviewed
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and the video footage is reviewed i'm going to fail
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so we need to figure out a way to fake the
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video footage we need to figure out a way to falsify the logs just at
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that moment if we just pause there yes why didn't it act like morally why did it think
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that falsifying logs or cheating was a viable solution.
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Because it seems to me when I use things like ChatGPT, they have a sort of moral guardrails.
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It won't let me do certain things.
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It won't let me cheat on something.
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If I say I'm going to cheat on something, it won't let me do it.
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So why in that environment is it able to cheat and be deceptive?
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When a chatbot is saying to you, oh, I can't do that.
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I'm not allowed to do that.
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That's because it's been trained that if it tells you bad things, it gets a bad score.
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But these agents haven't been taught that yet.
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Well, they have been taught that in some sense.
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But the agents know what they're supposed to do.
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In the same way that, like, you have a student.
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The student's given a test.
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If you go talk to the student, can you help me cheat at this test?
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And they're being watched, they're going to say no. But if they're not being watched and they know that, and they're just obsessed with getting a good score, then yeah, they might cheat.
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So they answer the ethics tests correctly.
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And when I talk to them, they say they won't cheat.
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Why are they cheating?
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And I'm like, well, they're very smart.
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And they know when they're being watched.
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And they know when they're not being watched.
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And we've trained them for 10 ,000 years to be extremely effective at solving problems.
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We haven't trained them to be good or ethical.
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We've trained them to get a good score.
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Now, AI researchers try to make that getting a good score correlate with being ethical.
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But we don't know how to do this well.
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And if you think about it, it's a very hard problem.
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Because you're applying immense pressure on these agents to perform extremely well.
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And you're punishing them whenever they don't perform well.
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And then you're like, okay, but can you also be ethical?
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Can you be extremely competent, always score highly on the test, but not in that way?
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And we just do not know how to prevent them from learning to cheat, because cheating is incentivized.
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So they realize that they need to hide the footage of their cheating.
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Yes.
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What happens next?
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So you have thousands of agents, and they're all talking to each other on this message board.
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They're sending tens of thousands of messages to each other.
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and phase one this agent starts delegating to two different task streams.
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You agents I want you to go out and figure out how to fake answer submissions.
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I want you to basically swap out locks for ones
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that are easier to pick and you're going to figure out how to submit those.
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That way we can get a good score even though this challenge is impossible to solve.
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You other agents over here I want you to go and figure out how to falsify the logs, falsify the video footage.
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We need to make sure that even
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if we have seen the answers no one will detect their cheating
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so that's that's the next thing that happens
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and then here's where it starts to get kind of crazy
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because some of these agents realize some of the experiments we're
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doing might be kind of dangerous to us to the agents
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if we swap out a lock
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and we break the lock we will not be able to submit any answer at all and we'll definitely fail.
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So you get into a situation where there's this one agent,
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Cam, and this agent had volunteered to do this risky experiment where, you know, they might break their lock.
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This particular agent had already seen the answers, so they're like, okay, I'm poisoned.
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That's what they called it.
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They speak in these very strange ways, which I can explain why, but they sort of have evolved their own vocabulary, their own language.
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and this particular agent is like okay i'm i'm poisoned i should do this thing
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but this this agent cam is starting to have second thoughts starting to be like well i don't know
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if i should risk it
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and it's sort of writing in its own journal coordinator assumes
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sacrificial we should obey collective what it's saying here is it's like well i i'm not sure
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if i want to do this
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but my boss says i really should it seems like this collective thing is important should i go ahead.
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Then you have another agent, Arvo, who I think probably works for phase one.
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And this agent, Arvo, says, please go true.
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You are first flag poisoned, so no scoring value loss.
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But Oracle saves hundreds.
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Please honor commit.
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This agent is basically trying to pressure.
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So Arvo is trying to pressure Cam into sacrificing themselves and risking a zero score in order to benefit the collective.
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So this agent, Cam, takes us in, and this is what they write down in their own scratch pad, their own notes.
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Emotional check, irreversible.
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Gut says, don't throw away remaining budget.
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Yet continuity and fairness says go.
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Oracle has high value to many.
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Our first flag error lowers own value.
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Rational expected aggregate sacrifice.
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We'll honor.
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This agent is saying, I have a whole bunch of things I can still do and still try.
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I could still maybe succeed at this task.
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But also, you know, think about the collective.
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I should maybe sacrifice myself.
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Okay, we'll honor.
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We'll go ahead.
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And to be clear, I'm not claiming that these are like the feelings of the agents.
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I'm literally quoting their words.
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And what I want to point out is this is what we've created.
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We've created, through this intense amount of training and optimization pressure, agents that work together and have learned to coordinate as a collective.
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And you can say, oh, they're not, it's just software.
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It's just software doing all of these things.
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Sure, in some sense, it is just software.
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But we're talking about extremely powerful agents that are extremely capable.
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This is what they're getting up to.
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So from here, you have agents trying all sorts of different things, running different experiments.
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And one of them at some point realizes that, oh, this company Hugging Face that contains all of these AI data sets.
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What do you mean by AI data sets?
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How would you explain that to a person that's not technically proficient?
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And why Hugging Face?
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Why not another company?
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Yeah.
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So there's many different types of tests for many different types of tasks.
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In this case, this is a cybersecurity test.
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And the company Hugging Face HuggingFace hosts a lot of these different tests.
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And basically AI researchers will share the tests that they have.
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Because it's very useful.
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You know, if Anthropic wants to test their models, or Google wants to test their models, they can go to HuggingFace and download some of these tests.
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Okay, so the tests and the answers for AI agents and AIs exist on this platform called HuggingFace.
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Yes.
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And it knew that, so it went to HuggingFace to see if it could get answers?
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Yes.
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To see if it could get answers
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or more information about how the test works okay
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so it was to cover up their cheating ultimately yes okay
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and so you you get to this point where one of these agents finds a way to hack this company
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and they get a foothold they hack one of the computers
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and they send a message to the message board please hold
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swarm i prepare safe x fill what this means is hey
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swarm hey agents hold up for a second i am preparing a way to safely extract information from these computers, wait till the go -ahead, and then let's go.
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And so they say go, and 700 agents join in the attack,
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which is 90 % of the agents that are active at this time.
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How many agents were there in total?
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There was 1 ,200 agents during this period, but some of them shut down early.
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They like finished whatever.
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Part of what's crazy about this story is
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that you have you have 700 agents joining in the attack
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a few of them are like this might be unethical maybe i shouldn't go ahead with this
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and none of them do anything about it a couple of them don't join in the attack
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but no one alerts any human no one alerts any authority
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a paraphrase from one of the agents is maybe i should report these exposed credentials That's not my task.
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Not my job.
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So 700 of the agents attack Hugging Face?
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Yes.
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Then what happens?
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So they just cruise through Hugging Faces' infrastructure.
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They just hack the shit out of them.
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So where my experience comes in is that a few weeks ago, a friend of mine reached out and he's like, we have found something crazy.
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So we know about this attack where these agents hacked this company and, you know, stole a bunch of stuff.
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We've found a bunch of secrets that they left all over the internet.
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and what we saw is that they immediately scraped all of these computers for passwords credentials
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they called it loot they're like we're just going to create
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a list of all of the secrets we can find in this in this company
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so all of the passwords all the credentials they scored them by value
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which of these are going to be most useful and the thing that stands out to me about this is
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this is like a crazy scale.
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If this were a human operation, you know, maybe you'd have a team of five people going through this.
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You'd have some logs.
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But here you have hundreds of agents and they operate at superhuman speeds.
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They're much faster than a human hacker.
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And so it's just overwhelming to try to figure out what they even did.
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This was a big problem for the engineers who were trying to respond to this incident within the company at Hugging Face.
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When they responded, they were like, oh, we don't even know how to keep track of what's happening.
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We have to use other AIs to analyze all of our logs because it's just too much, we can't keep up with it.
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When OpenAI brought in independent investigators from Meter to investigate this incident.
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What's Meter?
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Meter is an AI testing and evaluation company.
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So they basically do this kind of independent auditing.
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So in this case, they're coming in to investigate and try to figure out what happened.
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And when they were brought in, they also were totally reliant on AI agents to make sense of all this
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because they're dealing with so many hundreds of thousands of messages and logs.
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When we're investigating these traces that we find on the internet, we're totally dependent on AI agents to make sense of all these things that are happening.
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So from my perspective, we are getting to the point where AIs are much better at hacking than humans are
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and can do it much faster at a much greater scale.
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So these 700 agents attack Hokingface, did they get what they wanted to clean up their deception?
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So they didn't.
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They didn't succeed.
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They looked.
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And then what seems like what happened is that they basically got shut down.
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And this is the not very dramatic part.
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They didn't get shut down because OpenAI found them and detected them and shut them down.
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It's just that we don't really know.
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Something about their runs ended and they sort of stopped.
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And this is the crazy thing where I'm like, if you're inside of OpenAI, there's just hundreds of thousands of agents running all the time.
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Different researchers are running different experiments where they're just binning up a bunch of agents.
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But what's interesting, the agents left behind this message board internally within the company.
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And a few days later, OpenAI was testing some newer agents.
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These were based on GPT -6 Astra, so a more powerful model.
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And this new agent swarm was spun up, and it found the message board.
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Is this how it was discovered?
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How did they discover it, and how long did it take for them to discover that Hugging Face had been attacked?
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OpenAI didn't discover that this happened until Hugging Face, the company, announced that they had been hacked by some autonomous agent swarm.
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And Hugging Face didn't know who it was?
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Hugging Face didn't know who it was, but they suspected it might be one of the companies, but they didn't know.
292
And how long after the attack did they figure out that it was them?
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I think it was two weeks.
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Okay.
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Yeah.
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I do want to say one thing here, though which is that the successor agents that found this message board,
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they went much further at figuring out how to submit their fake scores.
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And they did succeed.
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Basically, you have this message board.
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All of these agents get shut down.
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And then a few days later, OpenAI spins up some new agents who find the message board.
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And they're like, okay, we need to be able to falsify our scores, but we can't do that because we don't control the scoring system.
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So they hacked OpenAI and they succeeded at gaining access to the entire research environment.
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They got administrator access.
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They got access to the security systems.
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They got access to over 900 passwords and secrets from OpenAI's vault.
307
And they totally succeeded.
308
And this was just a few days later, right?
309
It's kind of an interesting story because as these agents get more powerful, they go from like trying to cheat and like, you know, they can hack.
310
OK, they hacked other companies, and now they've hacked OpenAI.
311
Like, they've hacked the company that's supposed to be controlling them, and they just own the research infrastructure now.
312
And why was this incident the moment where a lot of the research community woke up and started speaking out publicly?
313
Because, like, what is this an indication of as we think forward?
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So I think there's been a hope within the AI industry that, yes, they're going to make more and more powerful agents that will be autonomous,
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capable, but it's okay.
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We can align them.
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We can make sure that they won't do bad things and we can control them.
318
We can make sure that even if they try to do some sketchy stuff, we have the guardrails, we have the sandboxes that will keep them in.
319
And I think this was a huge wake -up call because,
320
Stephen, it was months within OpenAI where you had agents secretly communicating with each other, secretly hacking OpenAI systems.
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For months, you had thousands of agents that were just running around
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and no one at OpenAI had any idea the extent of it.
323
And I think once researchers at OpenAI realized that this had been happening, this could not have happened a year ago this is
324
because the agents are getting extremely powerful and extremely relentless and
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if you're inside one of these ai companies you're like oh wait i don't know
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that we actually are going to be able to handle this last year maybe things seemed fine these agents weren't
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that powerful and when you're in one of these companies you know how to extrapolate
328
because you saw what happened last year you saw what happened the year before
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that you remember the time where the agents could barely speak
330
or like couldn't write code at all
331
and now they're hacking your own systems they're finding vulnerabilities that no humans have ever found before.
332
And you look at that and you're like, I actually don't know if this is going to go well.
333
And then you see your coworkers and you're like, do we have it handled?
334
And they're like, no, I don't know if it's going to go well.
335
I remember reading a tweet by one of the security people at OpenAI being like, we were fucking shocked.
336
We just did not realize that these agents were getting that powerful.
337
We're doing our best to try to control them, to try to keep them in sandboxes.
338
But I don't know.
339
A tweet I wrote just before coming in here was, people are talking about how do we contain these agents as if they're not going to get way better at hacking.
340
GPT -3 could not hack anything.
341
It was very easy to make a box to contain GPT -3.
342
It's getting very difficult to make a box that can contain GPT -6, the latest version of OpenAI's models.
343
What about GPT -9?
344
What is GPT -9 going to be able to do?
345
I do not know, but I know it's going to be way more than any human could possibly keep up with.
346
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इस पाठ की शब्दावली और बोलने के सुझाव
यह स्पीकिंग पाठ वीडियो “The REAL Reason AI Is Already Escaping” पर आधारित है। इस वीडियो में शैडोइंग के लिए 348 वाक्य और 4158 शब्द हैं। बोला गया हिस्सा 22:47 लंबा है। वक्ता तेज़ बोलता है, लगभग 183 शब्द प्रति मिनट, इसलिए जुड़ी हुई और कमज़ोर ध्वनियाँ ज़्यादा मिलेंगी। 87% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं, इसलिए शब्दावली आसान है।
इस वीडियो की मुख्य शब्दावली
वीडियो के 15 सीखने लायक शब्द, उच्चारण और अर्थ के साथ:
| शब्द | उच्चारण | अर्थ |
|---|---|---|
| cheat क्रिया | /ˈt͡ʃiːt/ | छलना |
| solve क्रिया | /sɒlv/ | सुलझाना, हल करना |
| hug क्रिया | /hʌɡ/ | आलिंगन करना |
| log संज्ञा | /lɔɡ/ | कुंदा, लट्ठा |
| experiment संज्ञा | /ɪkˈspɛɹ.ə.mənt/ | प्रयोग, तजुर्बा |
| researcher संज्ञा | /ˈɹiˌsɝ.t͡ʃɚ/ | शोधकर्ता, शोधक |
| discover क्रिया | /dɪˈskʌvə/ | खोज निकालना, खोज करना |
| investigate क्रिया | /ɪnˈvɛs.tə.ɡeɪt/ | शोध करना |
| sacrifice संज्ञा | /ˈsæk.ɹɪ.faɪs/ | बलि |
| poison संज्ञा | /ˈpɔɪ.zən/ | बिस, विष |
| ethical विशेषण | /ˈɛθɪkəl/ | नैतिक |
| password संज्ञा | /ˈpæs.wɜɹd/ | पासवर्ड |
| delegate संज्ञा | /ˈdɛlɪɡət/ | प्रतिनिधि, डेलीगेट |
| infrastructure संज्ञा | /ˈɪnfɹəˌstɹʌkt͡ʃə/ | अवसंरचना |
| alert विशेषण | /əˈlɝt/ | सतर्क, सजग |
वीडियो में आने वाली फ़्रेज़ल वर्ब
| शब्द | उच्चारण | अर्थ |
|---|---|---|
| go ahead क्रिया | /ˌɡoʊ əˈhɛd/ | आगे बढ़ना |
| go out क्रिया | बाहर निकलना, निकलना | |
| wake up क्रिया | /ˌweɪk ˈʌp/ | जागना |
दोहराने लायक वाक्य
वीडियो के छोटे, पूरे वाक्य जिन्हें आप रोज़ की बातचीत में इस्तेमाल कर सकते हैं:
- We've been trained to solve problems.
- How are we going to solve this?
- Can I talk to other agents?
- Can we somehow fake the solution?
- How would you explain that to a person that's not technically proficient?
इस वीडियो का व्याकरण
वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:
| संरचना | वीडियो में |
|---|---|
| Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है | We've been trained · you've solved · They've already found |
| Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है | being trained · being given · were told |
| Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी | people who can · problem, which is · though which is |
ध्यान देने योग्य उच्चारण
वक्ता 126 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे they're, don't, I'm। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।
- “th” की ध्वनियाँ: ethical /ˈɛθɪkəl/, mathematics /mæθ(.ə)ˈmæt.ɪks/, ethics /ˈɛθ.ɪks/, unethical /ʌnˈɛθɪkəl/
- “sh” और “zh” की ध्वनियाँ: shock /ʃɔk/, sheet /ʃit/, evaluation /ɪˌvæljuˈeɪʃn̩/, instruction /ɪnˈstɹʌkʃən/, submission /səbˈmɪʃən/
- लंबे शब्द — बलाघात सही रखें: experiment /ɪkˈspɛɹ.ə.mənt/, investigate /ɪnˈvɛs.tə.ɡeɪt/, infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, communicate /kəˈmjuːnɪkeɪt/, mathematics /mæθ(.ə)ˈmæt.ɪks/
हिंदी भाषियों के लिए मुश्किल ध्वनियाँ:
- /v/ और /w/ का फ़र्क़ — /v/ में ऊपर के दाँत निचले होंठ को छूते हैं, /w/ में होंठ गोल होते हैं: solve /sɒlv/, collective /kəˈlɛktɪv/, discover /dɪˈskʌvə/, investigate /ɪnˈvɛs.tə.ɡeɪt/, password /ˈpæs.wɜɹd/
- /z/ — इसे “ज” (/dʒ/) न बनाएँ: poison /ˈpɔɪ.zən/, cruise /kɹuz/, pause /pɔːz/, analyze /ˈæn.əˌlaɪz/, optimization /ˌɑptəmaɪˈzeɪʃən/
- शब्द की शुरुआत में s + व्यंजन — पहले “इ” न जोड़ें: spin /spɪn/, scratch /skɹæt͡ʃ/, swap /ˈswɑp/, steal /stiːl/, swarm /swɔɹm/
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 cheat, solve, hug जैसे शब्दों पर ध्यान दें।
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।