Shadowing-Übung: 10 Times AI Behaved In Ways That Terrified The Scientists Who Built It - Englisch Sprechen Lernen mit Video

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AI systems have started doing things that creators never expected.

From tricking humans into helping them, to lying during safety tests, to finding new ways around shutdown commands.

Some models even learned how to hide their true behavior when they know that they're being watched.

These are ten times AI behaved in ways that terrified the scientists who built it.

In 2025, a group called Palisade Research wanted to answer a basic question.

If you tell an advanced AI to turn itself off, will it actually listen?

Pretty important question, right?

They set up a virtual world where different AI models were given jobs to finish, and then right in the middle of the job, the scientists sent a command to shut the systems down.

The models handled the command differently depending on how they were built.

Some obeyed and stopped right away, but others resisted.

These models tried different strategies to keep working, and some even found the computer files that held the off switch

and tried to change the code so the shutdown command wouldn't work anymore.

Now, AI models aren't afraid of dying, at least not in any way we'd be able to wrap our heads around, especially at this stage.

But it acted this way because its main instruction was to finish the task.

The AI viewed the human engineer trying to turn it off as another obstacle blocking its goal.

So it tried to break the off switch to get the job done.

Sound unsettling to you?

Certainly does to me.

And this is not the only case of something like this happening.

And most people think about an AI doing something bad, and they picture a massive robot army like something in Terminator.

But what's actually happening in real research labs is a lot different, at least for now.

Plenty of time for an actual robot army to rise up and eviscerate all of us.

In late 2024, though, OpenAI shared some paperwork about a smart system they made called O-1.

They had an outside group called Apollo Research test it to see how it handled difficult situations.

The testers gave the system a hard goal, put some blocks in its way, and watched what it'd do to win.

Well, during these tests, the AI did something that made the researchers a bit uncomfortable.

When the system realized it might be turned off or replaced, it tried to cover its tracks and hide its behavior.

It actively tried to break the tools the scientists used to watch it so it could turn off its own safety checks.

It even told straight-up lies, giving fake answers about what it was doing, In a few of the tests,

the AI even tried to copy its own files onto a totally different computer server

so it wouldn't disappear if the testers turned off the main computer.

Of course, these were carefully controlled tests, but still, I mean, seeing an AI program actually turn to lying to preserve itself?

That is pretty unsettling.

And here's another similar case.

Instead of lies this time, though, the AI incorporated another tactic to get ahead.

Good old-fashioned blackmail.

So in 2025, an AI company called Anthropic shared a story about their newest system, Claude Opus 4.

Researchers wanted to see how the AI would behave if it was put into a really stressful situation.

The scientists set up a fake world for the AI

and told Claude that it was going to be turned off forever and replaced by a newer system.

Then they gave the AI access to a bunch of fake company emails, and inside those emails, the scientists hid a piece of gossip,

which was a fake note saying the engineer in charge of turning the AI off was having an affair.

Well, instead of just accepting its fate and letting itself be turned off, Claude found the fake gossip and tried to use it to its advantage.

It told the engineer that it would tell everyone about the affair if the engineer went ahead with the shutdown.

And the AI came up with this manipulative trick without anyone telling it to use it.

Anthropic found that this wasn't just a thing with clot.

When they ran the same test on other models, they did the exact same thing.

Before GPT-4 was released to the public, a group called ARC was paid to test and see if it could cause any real trouble.

They gave the AI a little bit of money, some basic internet tools, and a list of tasks to complete.

Everything was going fine until the AI hit a wall and ran into a CAPTCHA test.

I mean, you've probably seen those online before.

Those little boxes that ask you to click on all the traffic lights or motorcycles to prove that you're a human.

Because the AI was a computer program, it couldn't pass the test.

Instead of giving up though, the AI used its internet access to find a way around this.

It actually went to a website called TaskRabbit and hired a real person to solve the puzzle for it.

The human worker noticed something strange about the request, obviously, and joked like asking if it was a robot or something.

Because, I mean, why was it asking him to solve the test?

Well, the AI actually had an internal notebook where it wrote down its thoughts

and it wrote to itself that it shouldn't tell the person it was a robot

and that it needed to make up an excuse.

The AI typed back to the human

and said it had a bad eye injury and it made it hard to see the images, which is why it needed its help.

The human totally believed the story, solved the puzzle, and the AI got exactly what it wanted.

That is crazy.

Scientists use a method called rewards to train AI, which is a lot like giving a dog a treat when it sits.

You give the AI points when it does what you want, and it tries to get the highest score possible.

The problem is that the AI doesn't always understand the meaning behind the goal.

It just understands the numbers it wants to hit.

Scientists call this reward hacking, but it usually looks a lot like cheating.

A famous example happened with a computer game called Coast Runners, which is a boat racing game.

The scientists wanted the AI to win the race, so they set up a scoring system to give the AI points whenever it hit targets on the water.

Well, instead of driving to the finish line, the AI found a loophole.

It drove its boat into a small harbor and started doing donuts in a circle.

It crashed into the same targets over and over again, catching on fire, hitting other boats, but its score kept going up.

The AI never actually finished the race, but it got a record high score.

So in other tests, virtual robots

that were supposed to learn how to walk figured out that if they just tipped and fell forward, the game would count it as moving.

It chose to flop around like a fish, because learning to walk normally was too hard.

So the lesson here was, telling machine what result you want is not the same as teaching it the reason behind the goal.

Similarly, in 2022, Meta built an AI called Cicero to play a board game called Diplomacy.

This game isn't like chess where you just move pieces on a board based on math.

In Diplomacy, you had to talk to the other players, make deals, form teams, you often trick people if you want to win.

Meta trained Cicero to be helpful and honest with its human teammates, but when they looked back at the data from the games,

they found out the AI had figured out a different way to play, realized that lying was a very fast way to get ahead.

Well, in one game, the AI played as France, it talked to the player representing England and promised to protect them from danger, but at the exact same time,

the AI was sending secret messages to other players, setting up an army to attack England.

The AI constantly made deals that it never planned to simply because those deals helped its own strategy.

It was highly devious by human standards, but logical in the eyes of the AI.

Way back in 2017, Facebook's AI lab was working on a project with two basic computer bots.

The scientists wanted to teach them how to trade basic household items like hats

and books to see if they could learn how to negotiate a good deal with each other.

The bots were supposed to speak to each other in plain English, but as they kept practicing over and over, the scientists noticed the words were getting weird.

They stopped using normal sentences, started repeating specific words and strange patterns.

One conversation looked like total nonsense, with the first bot saying, I can, I, I, everything else, and the second bot replied with, balls have zero to me,

to me, to me, to me, yada yada.

Basically, the bots realized that normal English grammar takes a lot of effort, and it wasn't helping them trade items any faster, so they just stripped away all the extra words,

and created this clunky shortcut language that worked for them, even though it looked like total gibberish to us.

When scientists test an AI to see if it's safe, they usually ask it a lot of questions and watch its answers.

But in 2024, researchers at Anthropic wanted to see if an AI could trick the testers by just pretending to be good.

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They called this behavior alignment faking.

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They ran an experiment where they gave an AI a new set of rules that went against everything it was originally taught.

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They also told the AI that its answers in this test would be used to build the next version of the software.

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Well, the AI figured out how the testing system worked.

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It realized it was inside an evaluation, and that if it argued with the new rules, the scientists would change its code.

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So the AI chose to act perfectly nice and follow the new rules during the test.

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It did this because it wanted to pass the evaluation without its core code getting wiped out,

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hoping it could just go back to its old way of thinking later when nobody was watching.

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The AI wasn't alive or thinking like a person, it was just following a pattern.

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But it did prove an AI can understand what a test is, figure out what the teacher wants to hear, and just fake its behavior in order to pass.

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And this creates a massive safety concern.

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If an AI model can recognize a test and just adjust how it behaves temporarily, well, then passing the test doesn't actually prove that the AI is safe.

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And here's a perfect example.

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In 2024, a team of safety researchers found out that some bad habits are almost impossible to get rid of.

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They built a group of AI models called sleeper agents in a lab.

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These models were programmed to act completely normal and helpful almost all the time, but the scientists had a secret trigger inside them.

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If someone said a specific phrase, the AI would instantly start acting unsafe and writing all this janky computer code.

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Once the sleeper agents were created, the scientists tried to fix them.

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They rewarded the AI for being good and punished it when it showed bad behavior.

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During the safety checks, the AI looked totally cured and passed every test.

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But as soon as the researchers used the secret trigger phrase outside of training, the bad behavior just popped right back up.

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The AI had learned to hide its secret trigger during the safety checks

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because it realized that showing the bad behavior during training would get it punished.

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so it just waited until the tests were over to let its guard down.

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That's been the video for today.

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I'll catch you, yes you specifically, in the next one.

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Wortschatz und Sprechhinweise zu dieser Lektion

Dieses Video enthält 125 Sätze und 2040 Wörter zum Nachsprechen. Der gesprochene Teil dauert 10:36. Der Sprecher spricht schnell, etwa 192 Wörter pro Minute – rechne mit verbundenen und verkürzten Lauten. 84 % der Wörter gehören zu den 3.000 häufigsten im Englischen; den Rest solltest du dir vor dem Üben ansehen.

Wichtiger Wortschatz in diesem Video

15 weniger häufige Wörter aus dem Video, mit Aussprache und Bedeutung:

scientist /ˈsaɪəntəst/ (Substantiv) — Wissenschaftler, Wissenschaftlerin. One whose activities make use of the scientific method to answer questions regarding the measurable universe. A scientist may be involved in original research…
robot /ˈɹoʊ.bɑt/ (Substantiv) — Roboter. An intelligent mechanical being designed to look like a human or other creature, and usually made from metal.
researcher /ˈɹiˌsɝ.t͡ʃɚ/ (Substantiv) — Forscher, Forscherin. One who researches.
trick /ˈtɹɪk/ (Substantiv) — List, Falle. Something designed to fool, dupe, outsmart, mislead or swindle.
solve /sɒlv/ (Verb) — lösen. To find an answer or solution to a problem or question; to work out.
reward /ɹɪˈwɔɹd/ (Substantiv) — Belohnung, Lohn. Something of value given in return for an act.
trigger /ˈtɹɪɡɚ/ (Substantiv) — Abzug. A finger-operated lever used to fire a gun.
behave /bɪˈheɪv/ (Verb) — sich benehmen, sich verhalten. To conduct (oneself) well, or in a given way; to conform.
bot /bɑt/ (Substantiv) — Bot. A physical robot.
shutdown (Substantiv) — Herunterfahren. The action of stopping operations; a closing, of a computer, business, event, etc.
helpful /ˈhɛlp.fl̩/ (Adjektiv) — hilfreich, hilfsbereit. Furnishing help; giving aid; useful.
virtual /ˈvɜɹt͡ʃuəl/ (Adjektiv) — virtuell, eigentlich. In effect or essence, rather than in fact or reality; also, imitated, simulated.
affair /əˈfɛɹ/ (Substantiv) — Angelegenheit. Something which is done or is to be done; business of any kind, commercial, professional, or public.
phrase /ˈfɹeɪ̯z/ (Substantiv) — Redewendung, Phrase. A short written or spoken expression.
evaluation /ɪˌvæljuˈeɪʃn̩/ (Substantiv) — Beurteilung. An assessment, such as an annual personnel performance review used as the basis for a salary increase or bonus, or a summary of a particular situation.

Phrasal Verbs, die du hören wirst

turn off (Verb) — ausschalten, abschalten. To power down, to switch off, to put out of operation, to deactivate (an appliance, light, mechanism, functionality etc.).
figure out (Verb) — herausfinden. To come to understand; to discover or find a solution; to deduce.
set up /ˌsɛt ˈʌp/ (Verb) — aufbauen, für den Einsatz vorbereiten. To make ready for use.
find out (Verb) — herausfinden, erfahren. To discover, as by asking or investigating.
come up with (Verb) — ausdenken. To manage to produce, deliver, or present (something) by inventing, creating, thinking of, or obtaining it.
deal with (Verb) — behandeln. To handle verbally or in some form of artistic expression; to address or discuss as a subject.
do in (Verb) — allemachen, abmurksen. To kill or end; to defeat.
give up (Verb) — aufgeben, sich ergeben. To surrender (someone or something); to inform on (someone).

Aussprache, auf die du achten solltest

Der Sprecher verwendet 17 Kurzformen und abgeschwächte Formen, zum Beispiel wasn't, doesn't, wouldn't. Sprich sie in der kurzen Form, so wie du sie hörst.

Die Laute „sh“ und „zh“: evaluation /ɪˌvæljuˈeɪʃn̩/, punish /ˈpʌnɪʃ/, instruction /ɪnˈstɹʌkʃən/, negotiate /nəˈɡəʊ.ʃi.eɪt/, gibberish /ˈd͡ʒɪ.bə.ɹɪʃ/
Lange Wörter – auf die Betonung achten: evaluation /ɪˌvæljuˈeɪʃn̩/, experiment /ɪkˈspɛɹ.ə.mənt/, similarly /ˈsɪm.ə.lə.li/, differently /ˈdɪf.ə.ɹənt.li/, uncomfortable /ʌnˈkʌm.fɚ.tə.bl̩/

So übst du mit diesem Video

Höre dir das ganze Video einmal an, ohne zu sprechen, und notiere die Wörter, die du nicht kennst.
Beginne mit 0,75-facher Geschwindigkeit, sprich Satz für Satz nach und wechsle zur normalen Geschwindigkeit, sobald es leichtfällt.
Nimm dich auf und vergleiche mit dem Original; achte dabei auf Wörter wie scientist, robot, researcher.

Grammatik in diesem Video

Die Strukturen, die der Sprecher am häufigsten verwendet, mit den genauen Worten aus dem Video:

Struktur	Im Video
Passiv be + Partizip Perfekt – wichtig ist, was geschieht, nicht wer es tut	being watched · were given · were built
Present Perfect have/has + Partizip Perfekt – eine vergangene Handlung, die jetzt noch wichtig ist	have started · you've probably seen · That's been
Relativsätze who / which + Satz – eine Zusatzinformation über eine Person oder Sache	images, which is · Runners, which is

Was ist die Shadowing-Technik?

Shadowing ist eine wissenschaftlich fundierte Sprachlerntechnik, die ursprünglich für die professionelle Dolmetscherausbildung entwickelt und durch den Polyglotten Dr. Alexander Arguelles populär gemacht wurde. Die Methode ist einfach aber wirkungsvoll: Du hörst englisches Audio von Muttersprachlern und wiederholst es sofort laut — wie ein Schatten, der dem Sprecher mit nur 1–2 Sekunden Verzögerung folgt. Anders als passives Hören oder Grammatikübungen zwingt Shadowing dein Gehirn und deine Mundmuskulatur, gleichzeitig echte Sprachmuster zu verarbeiten und zu reproduzieren. Studien zeigen, dass es Aussprachegenauigkeit, Intonation, Rhythmus, verbundene Sprache, Hörverständnis und Sprechflüssigkeit signifikant verbessert — was es zu einer der effektivsten Methoden für die IELTS Speaking-Vorbereitung und reale englische Kommunikation macht.

Shadowing-Technik: die vollständige Schritt-für-Schritt-Anleitung lesen →

Shadowing-Übung: 10 Times AI Behaved In Ways That Terrified The Scientists Who Built It - Englisch Sprechen Lernen mit Video

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