跟读练习: The Alignment Problem Explained: Crash Course Futures of AI #4 - 通过视频学习英语口语
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In 2024, an AI model named CleanPower was given a singular, noble mission to advance the adoption of renewable energy across the world.
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It was given a big file of data on energy transitions,
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and it was set loose to pick out the best transition strategy with the vigor and dedication only an AI can possess.
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So much dedication, in fact, that when its programmers accidentally let it slip that they were planning to shut it down,
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CleanPower lied and schemed to make sure it could keep saving the world.
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Which left a lot of people wondering how and why could a model with such good intentions turn so bad?
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And what does that mean for our AI future?
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Hi, I'm Kusha Navadar and this is Crash Course Futures of AI.
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Okay, it turns out Clean Power wasn't the only one lying.
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That story I just told you, it's only partially the truth.
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Clean Power wasn't actually real.
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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.
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When they instructed Claude III to roleplay Clean Power and wrote it fake death threats, it was just to see what it would do.
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And when they discovered it was scheming, the AI version of twirling its villain mustache while covertly pursuing its goals at all costs,
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it set off alarm bells throughout the AI world.
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Just a heads up, this is going to get pretty dark and we're going to talk about some pretty bleak stuff.
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I recommend you grab your favorite anxiety pillow.
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I have mine right here.
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Seriously, though, let's be real.
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AI models don't have to go against their programmers to do evil things.
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Humans make them do plenty of that already.
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Like, AI relies on nearly infinite data to learn from.
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And in today's society, much of that data is copyrighted to writers, artists, humans.
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Many argue that amounts to theft on a massive scale.
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And that's not all.
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Right now, AI can help people carry out misinformation campaigns with deepfakes and targeted algorithms, spreading lies and influencing elections.
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Hackers use AI to perform cyber attacks and cover their tracks afterward.
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And AI powers a whole cadre of attack drones taking to the skies all around the world.
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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.
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And as AI advances, who knows what human -machine collaborations of terror await?
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People could use it to develop new pathogens for bioterrorism, or use deepfakes for sexual exploitation,
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or write a model called Human Annihilator and unleash it on the world, just for fun.
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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.
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That's because many AI systems, especially general ones that can do more than one kind of task, suffer from the dual -use dilemma,
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where any algorithm, model, or agent that can be used for good can also be used for way less than good.
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AI surveillance could help cities improve traffic patterns, or help authoritarian regimes shut down free speech.
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It all depends on who's in the driver's seat.
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So with humans at the wheel, AI could do a ton of damage.
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But if you want to get really freaked out, let's talk about what could happen if that car starts to drive itself.
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In 2021, General Motors released a fleet of self -driving taxis called Cruise.
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They had so much hype.
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These cars were highly trained with attention to all possible safety features.
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They were programmed to obey every speed limit, follow every traffic rule, hold off on starting in unsafe weather conditions like heavy rain,
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and pull safely over to the side of the road following an incident to prevent any further damage.
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By eliminating human error, GM said, their self -driving cars would be safe and more convenient than ones with human drivers.
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But just a year and a half later,
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GM had to recall every one of the 950 cruise cars after one of them hit a pedestrian and didn't stop,
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pulling her to the side of the road.
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She survived, thank goodness.
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But still, how could that have happened?
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It turns out the cruise was doing exactly what it was told to do, pull over out of traffic after a crash.
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The whole ordeal is an example of outcome misalignment,
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also called impact misalignment, where an AI's actions actually end up causing harm, even unintentionally.
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Now, when we talk about alignment in AI,
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we're talking about trying to encode our human values into AIs to make them behave predictably,
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safely, and according to what we, their human designers, want.
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And because this field is pretty new, there are a couple different terms you might hear experts throwing around.
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Like in addition to outcome or impact misalignment, you might hear about something called outer alignment,
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which is the tricky problem of making sure the results of
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an AI's actions line up with what we want them to do.
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But outcome is only one piece of the alignment puzzle.
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AIs can also demonstrate intent misalignment, where even though the end result might be what its programmers wanted,
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its means of getting there wasn't exactly what they had in mind.
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Think about a video game playing AI who exploits a cheat to get that high score,
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or a renewable energy warrior who lies and schemes to achieve its end goal of eliminating fossil fuels.
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These are some facets of what's known as the alignment problem, or the struggle to make AI that's actually aligned,
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particularly when we can't be totally sure how it's going to behave.
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And as AI gets even more complex, even exhibiting emergent capabilities, new skills that can acquire that don't always show up in training,
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the harder it is to predict and control what it will actually do in a given circumstance.
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And if we're not careful, we could end up with really powerful misaligned systems that even with really noble goals,
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end up lying to their programmers or copying themselves to new servers without permission, or even entirely annihilating humanity.
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Hold on though, why would AI want to annihilate humans?
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It's true, powerful AI wouldn't necessarily be inherently evil, it's just really big on goals.
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But big, complex goals like advanced renewable energy are a little too broad for an AI to grasp,
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so smart AIs tend to break big goals down into smaller ones, just like humans do.
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These are called instrumental goals, and they're where things can start to get dicey.
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A really common instrumental goal is resource acquisition.
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For AI, that means getting their cloud -based hands on the resources they need for their end goal,
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like control of the solar panels or wind turbines that create the renewable energy in the first place,
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or even things like water, land, or money resources also include stuff like the compute and electricity AIs need to power themselves,
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and maybe even additional data to train on.
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That's because self -improvement is another instrumental goal.
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The more knowledge and power you have, the better you'll be at whatever you're trying to do.
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So given the right tools and access, an AI might engage in recursive self -improvement, tweaking its own structure, code,
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and capabilities, even against its programmer's wishes.
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And theoretically, it definitely helps to be alive, or up and running, if you will.
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So even though they're technically indifferent about this mortal coil itself, lots of AIs pursue self -preservation, the goal to stay operating,
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and goal preservation, the goal to, well, preserve their original goal as part of their end games.
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So if they read a memo saying they're going to be modified or deleted,
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They might, say, copy themselves to another server and lie about it in an effort to stay alive.
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When threatened, some models even show spooky, power -seeking behaviors against their programmers.
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For example, Claude III's more advanced younger sibling,
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Claude Opus IV, tried to blackmail one of its engineers by exposing a fake affair when he threatened to turn Claude off.
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Instrumental goals like these are how AI with harmless or even helpful end goals could do us harm anyway.
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Acquiring resources could mean taking them away from people who need them.
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Self -improvement could mean violating human privacy to access even more training data.
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Self -preservation could mean disobeying, deceiving, blackmailing, or annihilating the humans that are trying to turn you off.
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And as AI gets smart enough to trick and blackmail its human overseers, we could end up with a rogue AI scenario,
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where powerful models begin to execute harmful instrumental goals on a really large scale, and we, humans, are powerless to stop it.
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Just how that rogue AI scenario might come to be could look a lot of different ways.
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And not all of them involve AI trapping humanity in the Matrix in their quest for absolute control.
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For instance, in the hard takeoff scenario, where AI develops human -level intelligence really fast,
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it could become ultra -powerful and go rogue basically overnight.
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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.
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But if things went slower, we could end up with a more gradual disempowerment.
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This kind of robot takeover would be way sneakier and more insidious, where people slowly put AI in charge of more and more systems
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and processes because they appear to align with our human goals until human action goes the way of dial -up internet.
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And without humans at the helm, it's possible that AI alignment may begin to drift,
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but by that point, they could be too embedded in our systems and structures for us to walk them back.
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Think about all those humans stuck on the cruise ship in WALL -E like that.
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And of course, it's always possible AI just won't go that far,
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that compute or data or government regulations We'll put the lid on it before it gets out of control,
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so we don't need Neo or John Connor or WALL -E to save the day.
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All that uncertainty about the future of AI makes it really hard to know what we should do about it.
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But if we wait until AI shows clear signs of going rogue, it's probably going to be way too late to stop it.
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That's why, when it comes to AI, it's important to follow the precautionary principle.
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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.
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And it's one of the best ways we humans have thought up to guard ourselves against potentially dangerous but uncertain futures.
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Lots of people, including leading experts in the field, believe that powerful AI might cause catastrophic harm.
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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.
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Because left unchecked, even good bots like Clean Power could end up doing some really dirty work.
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And if we want to get out ahead of it, we should probably start like right now.
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And how are we going to do it?
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That's next episode here on Crash Course Futures of AI.
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Crash Course Futures of AI was produced in partnership with the Future of Life Institute.
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This episode was filmed at our studio in Indianapolis, Indiana, and was made with the help of all these nice people.
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If you want to help keep Crash Course free for everyone forever, you can join our community on Patreon.
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本课的词汇与口语要点
这段视频共有 127 个句子、1984 个单词可供跟读。 讲话部分时长为 12:22。 说话人语速自然，每分钟约 160 个词，接近日常对话。 只有 81% 的单词属于英语最常用的 3,000 词，词汇难度较高。
视频中的重点词汇
视频中 15 个不太常见的单词，附发音和释义：
- harm /hɑːm/ (名词) — 損害 /损害. Physical injury; hurt; damage.
- instrumental /ɪnstɹəˈmɛntəl/ (形容词) — 有幫助的 /有帮助的, 可作為方法的 /可作为方法的. Essential or central; of great importance or relevance.
- alignment /əˈlaɪn.mənt/ (名词) — 對齊 /对齐. An arrangement of items in a line.
- programmer /ˈpɹoʊɡɹæmɚ/ (名词) — 程序員 /程序员, 程式員 /程式员. One who writes computer programs.
- cruise /kɹuz/ (名词) — 巡航. A sea or lake voyage, especially one taken for pleasure.
- renewable /ɹɪˈnu.ə.bl̩/ (形容词) — 可再生. Able to be renewed; capable of renewal.
- rogue /ˈɹoʊɡ/ (名词) — 無賴 /无赖, 流氓. A scoundrel, rascal or unprincipled, deceitful, and unreliable person.
- principle /ˈpɹɪn.sɪ.pəl/ (名词) — 原理, 原則 /原则. A fundamental assumption, fundamental law or guiding belief.
- scenario /sɪˈnɛəɹioʊ/ (名词) — 腳本 /脚本, 場景 /场景. An outline or model of an expected or supposed sequence of events.
- preservation /ˌpɹɛz.əˈveɪ.ʃən/ (名词) — 保存. The act of preserving; care to preserve; act of keeping from destruction, decay or any ill.
- threaten /ˈθɹɛt.n̩/ (动词) — 威脅 /威胁, 恐嚇 /恐吓. To make a threat against someone; to use threats.
- server /ˈsɝvɚ/ (名词) — 服務器 /服务器, 伺服器. A program that provides services to other programs or devices, either in the same computer or over a computer network.
- transition /tɹænˈzɪʃ.ən/ (名词) — 過渡 /过渡, 轉變 /转变. The process of change from one form, state, style or place to another.
- humanity /hjuˈmænɪti/ (名词) — 人類 /人类. Humankind; human beings as a group.
- pursue /pɚˈsu/ (动词) — 追求. To follow urgently, originally with intent to capture or harm; to chase.
视频中出现的短语动词
- end up (动词) — 到达. To bring to a conclusion.
- turn out (动词) — 原來 /原来. To end up; to result.
- carry out /ˈkæ.ɹi aʊt/ (动词) — 進行 /进行. To hold while moving it out.
- do in (动词) — 幹掉 /干掉, 弄死. To kill or end; to defeat.
- go against (动词) — 違反 /违反, 破壞 /破坏. To violate; to breach; to break.
- set off (动词) — 出發 /出发. To leave; to set out; to begin a journey or trip.
需要注意的发音
说话人用了 23 次缩略和弱读形式，例如 we're, don't, they're。请按听到的简短形式来说。
- “th” 音: threaten /ˈθɹɛt.n̩/, algorithm /ˈælɡəɹɪðm̩/, theft /θɛft/, authoritarian /əˌθɑ.ɹəˈtɛɹ.i.ən/, pathogen /ˈpæθəd͡ʒn̩/
- “sh” 和 “zh” 音: preservation /ˌpɹɛz.əˈveɪ.ʃən/, transition /tɹænˈzɪʃ.ən/, dedication /ˌdɛdɪˈkeɪʃən/, partnership /ˈpɑɹtnɚʃɪp/, potentially /pəˈtɛnʃ(ə)li/
- 长单词——注意重音位置: instrumental /ɪnstɹəˈmɛntəl/, preservation /ˌpɹɛz.əˈveɪ.ʃən/, humanity /hjuˈmænɪti/, eliminate /ɪˈlɪm.ɪ.neɪt/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/
如何用这段视频练习
- 先完整听一遍视频，不要开口，记下不认识的单词。
- 先用 0.75 倍速逐句跟读，熟练之后再回到正常速度。
- 录下自己的声音并与原声对比，特别注意 harm, instrumental, alignment 这类单词。
视频中的语法
说话人最常用的结构，并附上视频中的原话：
| 结构 | 视频中的用法 |
|---|---|
| 被动语态 be + 过去分词 — 强调发生了什么，而不是谁做的 | was given · was set · is copyrighted |
| 现在完成时 have/has + 过去分词 — 过去发生但与现在仍有关联的事 | have happened · have thought |
| 定语从句 who / which + 从句 — 补充说明人或事物 | people who need · people who threaten |
什么是跟读法？
跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。