ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: The Alignment Problem Explained: Crash Course Futures of AI #4

In 2024, an AI model named CleanPower was given a singular, noble mission to advance the adoption of renewable energy across the world.

It was given a big file of data on energy transitions,

and it was set loose to pick out the best transition strategy with the vigor and dedication only an AI can possess.

So much dedication, in fact, that when its programmers accidentally let it slip that they were planning to shut it down,

CleanPower lied and schemed to make sure it could keep saving the world.

Which left a lot of people wondering how and why could a model with such good intentions turn so bad?

And what does that mean for our AI future?

Hi, I'm Kusha Navadar and this is Crash Course Futures of AI.

Okay, it turns out Clean Power wasn't the only one lying.

That story I just told you, it's only partially the truth.

Clean Power wasn't actually real.

It was an identity that researchers gave to a couple different AIs as an experiment, including Claude III Opus, one of the best large -language models at the time.

When they instructed Claude III to roleplay Clean Power and wrote it fake death threats, it was just to see what it would do.

And when they discovered it was scheming, the AI version of twirling its villain mustache while covertly pursuing its goals at all costs,

it set off alarm bells throughout the AI world.

Just a heads up, this is going to get pretty dark and we're going to talk about some pretty bleak stuff.

I recommend you grab your favorite anxiety pillow.

I have mine right here.

Seriously, though, let's be real.

AI models don't have to go against their programmers to do evil things.

Humans make them do plenty of that already.

Like, AI relies on nearly infinite data to learn from.

And in today's society, much of that data is copyrighted to writers, artists, humans.

Many argue that amounts to theft on a massive scale.

And that's not all.

Right now, AI can help people carry out misinformation campaigns with deepfakes and targeted algorithms, spreading lies and influencing elections.

Hackers use AI to perform cyber attacks and cover their tracks afterward.

And AI powers a whole cadre of attack drones taking to the skies all around the world.

Not to mention the incidental damage that AI is doing to the environment because of all the water, the land, and energy it takes to run it.

And as AI advances, who knows what human -machine collaborations of terror await?

People could use it to develop new pathogens for bioterrorism, or use deepfakes for sexual exploitation,

or write a model called Human Annihilator and unleash it on the world, just for fun.

This intentional misuse by humans is one way AI could end up doing us a lot of harm, and it could end up being pretty hard to prevent.

That's because many AI systems, especially general ones that can do more than one kind of task, suffer from the dual -use dilemma,

where any algorithm, model, or agent that can be used for good can also be used for way less than good.

AI surveillance could help cities improve traffic patterns, or help authoritarian regimes shut down free speech.

It all depends on who's in the driver's seat.

So with humans at the wheel, AI could do a ton of damage.

But if you want to get really freaked out, let's talk about what could happen if that car starts to drive itself.

In 2021, General Motors released a fleet of self -driving taxis called Cruise.

They had so much hype.

These cars were highly trained with attention to all possible safety features.

They were programmed to obey every speed limit, follow every traffic rule, hold off on starting in unsafe weather conditions like heavy rain,

and pull safely over to the side of the road following an incident to prevent any further damage.

By eliminating human error, GM said, their self -driving cars would be safe and more convenient than ones with human drivers.

But just a year and a half later,

GM had to recall every one of the 950 cruise cars after one of them hit a pedestrian and didn't stop,

pulling her to the side of the road.

She survived, thank goodness.

But still, how could that have happened?

It turns out the cruise was doing exactly what it was told to do, pull over out of traffic after a crash.

The whole ordeal is an example of outcome misalignment,

also called impact misalignment, where an AI's actions actually end up causing harm, even unintentionally.

Now, when we talk about alignment in AI,

we're talking about trying to encode our human values into AIs to make them behave predictably,

safely, and according to what we, their human designers, want.

And because this field is pretty new, there are a couple different terms you might hear experts throwing around.

Like in addition to outcome or impact misalignment, you might hear about something called outer alignment,

which is the tricky problem of making sure the results of

an AI's actions line up with what we want them to do.

But outcome is only one piece of the alignment puzzle.

AIs can also demonstrate intent misalignment, where even though the end result might be what its programmers wanted,

its means of getting there wasn't exactly what they had in mind.

Think about a video game playing AI who exploits a cheat to get that high score,

or a renewable energy warrior who lies and schemes to achieve its end goal of eliminating fossil fuels.

These are some facets of what's known as the alignment problem, or the struggle to make AI that's actually aligned,

particularly when we can't be totally sure how it's going to behave.

And as AI gets even more complex, even exhibiting emergent capabilities, new skills that can acquire that don't always show up in training,

the harder it is to predict and control what it will actually do in a given circumstance.

And if we're not careful, we could end up with really powerful misaligned systems that even with really noble goals,

end up lying to their programmers or copying themselves to new servers without permission, or even entirely annihilating humanity.

Hold on though, why would AI want to annihilate humans?

It's true, powerful AI wouldn't necessarily be inherently evil, it's just really big on goals.

But big, complex goals like advanced renewable energy are a little too broad for an AI to grasp,

so smart AIs tend to break big goals down into smaller ones, just like humans do.

These are called instrumental goals, and they're where things can start to get dicey.

A really common instrumental goal is resource acquisition.

For AI, that means getting their cloud -based hands on the resources they need for their end goal,

like control of the solar panels or wind turbines that create the renewable energy in the first place,

or even things like water, land, or money resources also include stuff like the compute and electricity AIs need to power themselves,

and maybe even additional data to train on.

That's because self -improvement is another instrumental goal.

The more knowledge and power you have, the better you'll be at whatever you're trying to do.

So given the right tools and access, an AI might engage in recursive self -improvement, tweaking its own structure, code,

and capabilities, even against its programmer's wishes.

And theoretically, it definitely helps to be alive, or up and running, if you will.

So even though they're technically indifferent about this mortal coil itself, lots of AIs pursue self -preservation, the goal to stay operating,

and goal preservation, the goal to, well, preserve their original goal as part of their end games.

So if they read a memo saying they're going to be modified or deleted,

They might, say, copy themselves to another server and lie about it in an effort to stay alive.

When threatened, some models even show spooky, power -seeking behaviors against their programmers.

For example, Claude III's more advanced younger sibling,

Claude Opus IV, tried to blackmail one of its engineers by exposing a fake affair when he threatened to turn Claude off.

Instrumental goals like these are how AI with harmless or even helpful end goals could do us harm anyway.

Acquiring resources could mean taking them away from people who need them.

Self -improvement could mean violating human privacy to access even more training data.

Self -preservation could mean disobeying, deceiving, blackmailing, or annihilating the humans that are trying to turn you off.

And as AI gets smart enough to trick and blackmail its human overseers, we could end up with a rogue AI scenario,

where powerful models begin to execute harmful instrumental goals on a really large scale, and we, humans, are powerless to stop it.

100

Just how that rogue AI scenario might come to be could look a lot of different ways.

101

And not all of them involve AI trapping humanity in the Matrix in their quest for absolute control.

102

For instance, in the hard takeoff scenario, where AI develops human -level intelligence really fast,

103

it could become ultra -powerful and go rogue basically overnight.

104

AI could do a lot of damage as it snaps up money and resources, seizes control of networks and infrastructure, and destroys people who threaten its mission.

105

But if things went slower, we could end up with a more gradual disempowerment.

106

This kind of robot takeover would be way sneakier and more insidious, where people slowly put AI in charge of more and more systems

107

and processes because they appear to align with our human goals until human action goes the way of dial -up internet.

108

And without humans at the helm, it's possible that AI alignment may begin to drift,

109

but by that point, they could be too embedded in our systems and structures for us to walk them back.

110

Think about all those humans stuck on the cruise ship in WALL -E like that.

111

And of course, it's always possible AI just won't go that far,

112

that compute or data or government regulations We'll put the lid on it before it gets out of control,

113

so we don't need Neo or John Connor or WALL -E to save the day.

114

All that uncertainty about the future of AI makes it really hard to know what we should do about it.

115

But if we wait until AI shows clear signs of going rogue, it's probably going to be way too late to stop it.

116

That's why, when it comes to AI, it's important to follow the precautionary principle.

117

The precautionary principle says that when something might cause catastrophic harm, we shouldn't wait for absolute proof that it will before we do something about it.

118

And it's one of the best ways we humans have thought up to guard ourselves against potentially dangerous but uncertain futures.

119

Lots of people, including leading experts in the field, believe that powerful AI might cause catastrophic harm.

120

So according to the precautionary principle, we should work to make sure that doesn't happen even if we're not certain it would in the first place.

121

Because left unchecked, even good bots like Clean Power could end up doing some really dirty work.

122

And if we want to get out ahead of it, we should probably start like right now.

123

And how are we going to do it?

124

That's next episode here on Crash Course Futures of AI.

125

Crash Course Futures of AI was produced in partnership with the Future of Life Institute.

126

This episode was filmed at our studio in Indianapolis, Indiana, and was made with the help of all these nice people.

127

If you want to help keep Crash Course free for everyone forever, you can join our community on Patreon.

📺 ช่องเดียวกัน

Vectors and 2D Motion: Crash Course Physics #4

TOEFL PrepCambridge Exam PrepUniversity Students

The Civil War, Part I: Crash Course US History #20

The Bicameral Congress: Crash Course Government and Politics #2

The Secret to Business Writing: Crash Course Business - Soft Skills #3

What's all the Yellen About? Monetary Policy and the Federal Reserve: Crash Course Economics #10

IELTS PrepBusiness EnglishUniversity Students

✨ วิดีโอแนะนำ

260 ประโยค

AI Just Crossed the Terrifying Line - Now What?

Business EnglishDaily ConversationUniversity Students

179 ประโยค

Understand AI in 14 minutes – with Anthropic's Chloe Lubinski [ARC 2026]

Business EnglishDaily ConversationUniversity Students

159 ประโยค

AI Is Stealing From You

Business EnglishDaily ConversationUniversity Students

154 ประโยค

歐巴馬談 AI：喊放慢的公司「是真的在擔心自己的產品」【AI 大人說】

Business EnglishDaily ConversationUniversity Students

170 ประโยค

What Happens When Capitalism Doesn't Need Workers Anymore?

Business EnglishDaily ConversationUniversity Students

147 ประโยค

Could AI Become Dangerous? The Real Risks Explained

Business EnglishDaily ConversationUniversity Students

76 ประโยค

The Limitless AI Lie. The Bubble Is Slowly BURSTING.

Business EnglishDaily ConversationUniversity Students

54 ประโยค

Did Google just kickstart the intelligence explosion?

Business EnglishDaily ConversationUniversity Students

457 ประโยค

MIT Explains the 12 Possible Endings for AI

Business EnglishDaily ConversationUniversity Students

184 ประโยค

A.I. ‐ Humanity's Final Invention?

Business EnglishDaily ConversationUniversity Students

112 ประโยค

‘We don’t have months’: Bernie Sanders sounds alarm on AI's 'extinction' threat

Business EnglishDaily ConversationUniversity Students

คำศัพท์และข้อสังเกตด้านการพูดสำหรับบทเรียนนี้

วิดีโอนี้มี 127 ประโยค และ 1984 คำ สำหรับฝึกพูดตาม ช่วงที่มีเสียงพูดยาว 12:22 ผู้พูดพูดด้วยความเร็วเป็นธรรมชาติ ประมาณ 160 คำต่อนาที ใกล้เคียงกับบทสนทนาในชีวิตประจำวัน มีเพียง 81% ของคำที่อยู่ใน 3,000 คำที่ใช้บ่อยที่สุดในภาษาอังกฤษ คำศัพท์จึงค่อนข้างยาก

คำศัพท์สำคัญในวิดีโอนี้

คำที่พบไม่บ่อย 15 คำจากวิดีโอ พร้อมคำอ่านและความหมาย:

harm /hɑːm/ (คำนาม) — ความเสียหาย. Physical injury; hurt; damage.
alignment /əˈlaɪn.mənt/ (คำนาม) — การปรับแนว, การปรับตรง. An arrangement of items in a line.
programmer /ˈpɹoʊɡɹæmɚ/ (คำนาม) — โปรแกรมเมอร์, ผู้เขียนโปรแกรม. One who writes computer programs.
principle /ˈpɹɪn.sɪ.pəl/ (คำนาม) — หลัก, คติ. A fundamental assumption, fundamental law or guiding belief.
scenario /sɪˈnɛəɹioʊ/ (คำนาม) — ฉากทัศน์. An outline or model of an expected or supposed sequence of events.
threaten /ˈθɹɛt.n̩/ (คำกริยา) — ขู่. To make a threat against someone; to use threats.
server /ˈsɝvɚ/ (คำนาม) — เซิร์ฟเวอร์. A program that provides services to other programs or devices, either in the same computer or over a computer network.
humanity /hjuˈmænɪti/ (คำนาม) — มนุษยชาติ. Humankind; human beings as a group.
algorithm /ˈælɡəɹɪðm̩/ (คำนาม) — ขั้นตอนวิธี, อัลกอริทึม. A collection of ordered steps that solve a mathematical problem. A precise step-by-step plan for a computational procedure that possibly begins with an input…
behave /bɪˈheɪv/ (คำกริยา) — ประพฤติ, ทำตัว. To conduct (oneself) well, or in a given way; to conform.
necessarily /ˌnɛs.əˈsɛɹ.ə.li/ (คำกริยาวิเศษณ์) — ต้อง. Inevitably; of necessity.
argue /ˈɑɹ.ɡju/ (คำกริยา) — เถียง, ถก. To show grounds for concluding (that); to indicate, imply.
cloud /ˈklaʊ̯d/ (คำนาม) — เมฆ. A visible mass of water droplets suspended in the air.
infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/ (คำนาม) — โครงสร้างพื้นฐาน. An underlying base or foundation for a building, organization, or system.
anxiety /æŋˈzaɪ.əti/ (คำนาม) — ความวิตกกังวล. An unpleasant state of mental uneasiness, nervousness, apprehension and obsession or concern about some uncertain event.

กริยาวลีที่คุณจะได้ยิน

carry out /ˈkæ.ɹi aʊt/ (คำกริยา) — กระทำ. To hold while moving it out.
go against (คำกริยา) — ฝืน. To violate; to breach; to break.

การออกเสียงที่ควรระวัง

ผู้พูดใช้รูปย่อและรูปลดเสียง 23 ครั้ง เช่น we're, don't, they're ให้พูดแบบสั้นตามที่ได้ยิน

เสียง “th”: threaten /ˈθɹɛt.n̩/, algorithm /ˈælɡəɹɪðm̩/, theft /θɛft/, authoritarian /əˌθɑ.ɹəˈtɛɹ.i.ən/, pathogen /ˈpæθəd͡ʒn̩/
เสียง “sh” และ “zh”: preservation /ˌpɹɛz.əˈveɪ.ʃən/, transition /tɹænˈzɪʃ.ən/, dedication /ˌdɛdɪˈkeɪʃən/, partnership /ˈpɑɹtnɚʃɪp/, potentially /pəˈtɛnʃ(ə)li/
คำยาว — ลงเสียงหนักให้ถูกพยางค์: instrumental /ɪnstɹəˈmɛntəl/, preservation /ˌpɹɛz.əˈveɪ.ʃən/, humanity /hjuˈmænɪti/, eliminate /ɪˈlɪm.ɪ.neɪt/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/

วิธีฝึกกับวิดีโอนี้

ฟังวิดีโอให้จบหนึ่งรอบโดยยังไม่ต้องพูด แล้วจดคำที่ยังไม่รู้จัก
เริ่มที่ความเร็ว 0.75× พูดตามทีละประโยค แล้วกลับไปใช้ความเร็วปกติเมื่อเริ่มคล่อง
อัดเสียงตัวเองแล้วเทียบกับต้นฉบับ โดยสังเกตคำอย่าง harm, alignment, programmer เป็นพิเศษ

ไวยากรณ์ในวิดีโอนี้

โครงสร้างที่ผู้พูดใช้บ่อยที่สุด พร้อมคำพูดจริงจากวิดีโอ:

โครงสร้าง	ในวิดีโอ
Passive voice be + กริยาช่อง 3 — เน้นสิ่งที่เกิดขึ้น ไม่ใช่ผู้กระทำ	was given · was set · is copyrighted
Present perfect have/has + กริยาช่อง 3 — เหตุการณ์ในอดีตที่ยังเกี่ยวข้องกับปัจจุบัน	have happened · have thought
Relative clauses who / which + อนุประโยค — ข้อมูลเพิ่มเติมเกี่ยวกับคนหรือสิ่งของ	people who need · people who threaten

เทคนิค Shadowing คืออะไร?

Shadowing เป็นเทคนิคการเรียนรู้ภาษาที่ได้รับการรับรองทางวิทยาศาสตร์ พัฒนาขึ้นสำหรับการฝึกนักแปลมืออาชีพ วิธีการนี้เรียบง่ายแต่ทรงพลัง: คุณฟังเสียงภาษาอังกฤษจากเจ้าของภาษาและพูดตามทันที — เหมือนเงาที่ตามผู้พูดด้วยช่วงเวลาห่าง 1-2 วินาที การวิจัยแสดงว่าเทคนิคนี้ปรับปรุงความแม่นยำในการออกเสียง ทำนองเสียง จังหวะ การเชื่อมเสียง การฟังเข้าใจ และความคล่องแคล่วในการพูดได้อย่างมีนัยสำคัญ

เทคนิค shadowing: อ่านคู่มือฉบับเต็มทีละขั้นตอน →

ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: The Alignment Problem Explained: Crash Course Futures of AI #4

Vectors and 2D Motion: Crash Course Physics #4

The Civil War, Part I: Crash Course US History #20

The Bicameral Congress: Crash Course Government and Politics #2

The Secret to Business Writing: Crash Course Business - Soft Skills #3

What's all the Yellen About? Monetary Policy and the Federal Reserve: Crash Course Economics #10

Unit Conversion & Significant Figures: Crash Course Chemistry #2

How Often do Asteroids Hit the Earth?: Crash Course Geology #20

What Is Myth? Crash Course World Mythology #1

Mesopotamia: Crash Course World History #3

Crash Course Sex Ed Preview

The Agricultural Revolution: Crash Course World History #1

Derivatives: Crash Course Physics #2

Indus Valley Civilization: Crash Course World History #2

Ancient Egypt: Crash Course World History #4

What is Engineering?: Crash Course Engineering #1

The Persians & Greeks: Crash Course World History #5

Supervised Learning: Crash Course AI #2

The Periodic Table: Crash Course Chemistry #4

Where Do Diamonds Come From?: Crash Course Geology #8

Where Did Democracy Come From, and Does It Still Work?

AI Just Crossed the Terrifying Line - Now What?

Understand AI in 14 minutes – with Anthropic's Chloe Lubinski [ARC 2026]

AI Is Stealing From You

歐巴馬談 AI：喊放慢的公司「是真的在擔心自己的產品」【AI 大人說】

What Happens When Capitalism Doesn't Need Workers Anymore?

Could AI Become Dangerous? The Real Risks Explained

The Limitless AI Lie. The Bubble Is Slowly BURSTING.

Did Google just kickstart the intelligence explosion?

MIT Explains the 12 Possible Endings for AI

A.I. ‐ Humanity's Final Invention?

‘We don’t have months’: Bernie Sanders sounds alarm on AI's 'extinction' threat

คำศัพท์และข้อสังเกตด้านการพูดสำหรับบทเรียนนี้

คำศัพท์สำคัญในวิดีโอนี้

กริยาวลีที่คุณจะได้ยิน

การออกเสียงที่ควรระวัง

วิธีฝึกกับวิดีโอนี้

ไวยากรณ์ในวิดีโอนี้

เทคนิค Shadowing คืออะไร?