Pratica di Shadowing: The REAL Reason AI Is Already Escaping - Impara a parlare inglese con i video
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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.
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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.
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And they totally succeeded.
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And this was just a few days later, right?
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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.
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OK, they hacked other companies, and now they've hacked OpenAI.
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Like, they've hacked the company that's supposed to be controlling them, and they just own the research infrastructure now.
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And why was this incident the moment where a lot of the research community woke up and started speaking out publicly?
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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.
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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.
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And I think this was a huge wake -up call because,
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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.
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And I think once researchers at OpenAI realized that this had been happening, this could not have happened a year ago this is
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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
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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
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or like couldn't write code at all
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and now they're hacking your own systems they're finding vulnerabilities that no humans have ever found before.
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And you look at that and you're like, I actually don't know if this is going to go well.
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And then you see your coworkers and you're like, do we have it handled?
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And they're like, no, I don't know if it's going to go well.
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I remember reading a tweet by one of the security people at OpenAI being like, we were fucking shocked.
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We just did not realize that these agents were getting that powerful.
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We're doing our best to try to control them, to try to keep them in sandboxes.
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But I don't know.
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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.
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GPT -3 could not hack anything.
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It was very easy to make a box to contain GPT -3.
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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.
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Vocabolario e note di pronuncia per questa lezione
Questa lezione di conversazione si basa sul video “The REAL Reason AI Is Already Escaping”. Questo video contiene 348 frasi e 4158 parole da ripetere con lo shadowing. Il parlato dura 22:47. Chi parla va veloce, circa 183 parole al minuto: aspettati suoni legati e ridotti. Il 87% delle parole rientra nelle 3.000 più comuni dell’inglese, quindi il lessico è facile da seguire.
Vocaboli chiave di questo video
15 parole del video che vale la pena imparare, con pronuncia e significato:
| Parola | Pronuncia | Significato |
|---|---|---|
| cheat verbo | /ˈt͡ʃiːt/ | fregare, imbrogliare |
| solve verbo | /sɒlv/ | risolvere |
| hug verbo | /hʌɡ/ | abbracciare |
| log sostantivo | /lɔɡ/ | tronco |
| submit verbo | /səbˈmɪt/ | sottomettersi |
| collective aggettivo | /kəˈlɛktɪv/ | collettivo |
| footage sostantivo | /ˈfʊtɪd͡ʒ/ | materiale filmato |
| experiment sostantivo | /ɪkˈspɛɹ.ə.mənt/ | esperimento |
| researcher sostantivo | /ˈɹiˌsɝ.t͡ʃɚ/ | ricercatore, ricercatrice |
| discover verbo | /dɪˈskʌvə/ | scoprire, trovare |
| investigate verbo | /ɪnˈvɛs.tə.ɡeɪt/ | investigare, indagare |
| sacrifice sostantivo | /ˈsæk.ɹɪ.faɪs/ | sacrificio |
| poison sostantivo | /ˈpɔɪ.zən/ | veleno |
| ethical aggettivo | /ˈɛθɪkəl/ | etico |
| password sostantivo | /ˈpæs.wɜɹd/ | parola d'ordine, parola chiave |
I phrasal verb che sentirai
| Parola | Pronuncia | Significato |
|---|---|---|
| figure out verbo | scoprire, rendersi conto | |
| shut down verbo | spegnere | |
| go ahead verbo | /ˌɡoʊ əˈhɛd/ | procedere, andare avanti |
| go out verbo | uscire, andare fuori | |
| keep up with verbo | tenere dietro a | |
| wake up verbo | /ˌweɪk ˈʌp/ | svegliarsi |
| clean up verbo | ripulire | |
| cover up verbo | insabbiare, occultare |
Frasi da ripetere
Frasi brevi e complete del video che puoi riutilizzare nella conversazione di tutti i giorni:
- 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?
La grammatica di questo video
Le strutture che chi parla usa di più, con le parole esatte del video:
| Struttura | Nel video |
|---|---|
| Present perfect have/has + participio passato — un’azione passata che conta ancora adesso | We've been trained · you've solved · They've already found |
| Forma passiva be + participio passato — conta ciò che accade, non chi lo fa | being trained · being given · were told |
| Frasi relative who / which + frase — un’informazione in più su una persona o una cosa | people who can · problem, which is · though which is |
Pronuncia a cui fare attenzione
Chi parla usa 126 contrazioni e forme ridotte, come they're, don't, I'm. Pronunciale nella forma breve, così come le senti.
- I suoni “th”: ethical /ˈɛθɪkəl/, mathematics /mæθ(.ə)ˈmæt.ɪks/, ethics /ˈɛθ.ɪks/, unethical /ʌnˈɛθɪkəl/
- I suoni “sh” e “zh”: shock /ʃɔk/, sheet /ʃit/, evaluation /ɪˌvæljuˈeɪʃn̩/, instruction /ɪnˈstɹʌkʃən/, submission /səbˈmɪʃən/
- Parole lunghe — attenzione all’accento: experiment /ɪkˈspɛɹ.ə.mənt/, investigate /ɪnˈvɛs.tə.ɡeɪt/, infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, communicate /kəˈmjuːnɪkeɪt/, mathematics /mæθ(.ə)ˈmæt.ɪks/
I suoni difficili per chi parla italiano:
- /h/ — va soffiata, non è muta: hug /hʌɡ/, hacker /hækəɹ/, foothold /ˈfʊthoʊld/
- Consonante finale — senza aggiungere una vocale dopo: cheat /ˈt͡ʃiːt/, solve /sɒlv/, hug /hʌɡ/, log /lɔɡ/, submit /səbˈmɪt/
- /æ/ — più aperta della “e”: sacrifice /ˈsæk.ɹɪ.faɪs/, password /ˈpæs.wɜɹd/, scratch /skɹæt͡ʃ/, mathematics /mæθ(.ə)ˈmæt.ɪks/, vast /væst/
Come esercitarsi con questo video
- Ascolta tutto il video una volta senza parlare e annota le parole che non conosci.
- Inizia a velocità 0,75×, fai shadowing frase per frase e torna alla velocità normale quando diventa facile.
- Registrati e confronta con l’originale, facendo attenzione a parole come cheat, solve, hug.
Cos'è la tecnica dello Shadowing?
Shadowing è una tecnica di apprendimento delle lingue supportata da studi scientifici, originariamente sviluppata per la formazione dei traduttori professionisti e resa popolare dal poliglotta Dr. Alexander Arguelles. Il metodo è semplice ma potente: ascolti un audio in inglese di madrelingua e lo ripeti immediatamente ad alta voce — come un'ombra che segue il parlante con un ritardo di solo 1–2 secondi. A differenza dell'ascolto passivo o degli esercizi di grammatica, lo shadowing costringe il tuo cervello e i muscoli della bocca a elaborare e riprodurre simultaneamente i modelli di discorso reale. La ricerca dimostra che migliora significativamente la precisione della pronuncia, l'intonazione, il ritmo, il discorso connesso, la comprensione dell'ascolto e la fluidità del parlato — rendendolo uno dei metodi più efficaci per la preparazione alla prova di speaking dell'IELTS e per la comunicazione reale in inglese.