跟讀練習: Are We Getting Too Comfortable With Claude? - 通過影片學習英語口語

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154 句

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The AI industry itself hasn't yet fully delivered all of the benefits.

人工智慧行業本身尚未完全兌現其全部潛在效益。

And I wanted you to comment on that.

我希望你能對此發表一下看法。

Do you think it's still in the process of delivering those benefits?

你認為它目前仍在持續發揮這些效益嗎？

Yeah, so, you know, I think one of the unfortunate things is this technology has many potential benefits,

是啊，所以，你知道，我覺得很遺憾的一點是，這項技術雖然有很多潛在好處，

but there is a dynamic where sometimes the harms, the things that go wrong, for example, you know, people who have been convinced to commit suicide via AI,

但存在這樣一種動態情況，有時會出現傷害、發生意外，例如，有人被人工智慧誘導自殺。

those things happen quickly.

這種事發生得很快。

Whereas the benefits like curing cancer, those things take years, right?

而治癒癌症這類益處，是需要多年時間才能實現的，不是嗎？

You have to develop a breakthrough in biology.

你必須在生物學領域取得一項突破。

Then you have to, you know, develop a drug candidate.

接下來你就需要開發候選藥物分子了。

Then you have to put it through clinical trials, right?

接下來你還得讓它通過臨床試驗，對吧？

It's, you know, I used to be a research biologist and it takes many years for these things to happen.

你知道，我以前是一名研究生物學家，這類事情的發生往往需要很多年的時間。

And we are working on how to speed that up, right?

我們正在研究如何加快這一程序，對吧？

We recently bought a small biotech company that specializes in doing drug design and drug selection with AI.

我們最近收購了一家專注於利用人工智慧進行藥物設計和藥物篩選的小型生物技術公司。

So the hope is a few years from now, you know, some new drugs for cancer or for Alzheimer's or for heart disease,

所以我們希望，幾年後能有一些針對癌症、阿茲海默症或心臟病的新藥問世。

you know, will come out of that effort.

你知道，這些努力總會有結果的。

But we're just at the beginning of it.

但我們才剛剛開始。

But because of what you said, because of this dynamic that you said, which is, you know, people right now,

但正因為你剛才說的那些，正因為你提到的這種現狀，就是現在的人們

you know, the harms may be more visible than the benefits.

要知道，危害可能比收益更顯而易見。

So when we start seeing the benefits of AI through all of the health advances, when we start actually seeing that,

因此，當我們開始通過所有醫療領域的進步感受到人工智慧帶來的益處時，當我們真正開始看到這些變化時，

the narrative then begins to change.

隨後敘事開始發生轉變。

And more importantly, it's just going to be a wonderful world.

更重要的是，那將會是一個無比美好的世界。

Like at the end of the day, who cares about the narrative?

說到底，誰會在乎那套說辭啊？

We want to actually help people.

That's right.

Right.

Okay.

So that's what I want you to do for us today.

Paint the best picture and also tell us what we can expect if there is the worst picture.

So you have children.

And I was thinking about this morning, your children will, first of all, I don't even know you do what you do.

And your children are going to grow up in a world where there was always AI

and their mom was a part that.

So when you think of the best possible world and what that world offers your children, what do you see?

So my kids are almost five and almost one.

And so I think you're completely right.

They're never going to know a world where artificial intelligence is not part of it.

Right.

Just like kids born in the early 2000s never knew the world before the internet.

That's right.

But you know, I think there is a world and it sounds, um, you know, I think to Daria's point, it sounds almost like a little bit fanciful or unlikely when we talk about it this way.

But I think it is, I think it is possible with artificial intelligence

that our kids will grow up in a world where there's a class of diseases that are,

feel very common to everybody sitting here that they'll just never have experienced or known about.

And those might be very common diseases.

There might be types of cancer that are just simply gone.

We've just simply cured them.

It could be all forms of cancer that are eventually gone

and cured there could be um you know new forms of drug discovery

that we haven't even we don't even know what they are yet

but that it's a faster iteration loop for diagnosing an illness that's unusual rare emerging

and just knocking it out

and saying human beings get to live a much healthier longer more fulfilling life

because we've been able to conquer so many of the maladies

that have plagued humanity for millennia okay you see that for medicine.

What else do you see?

I also see a world, honestly, that looks very different in terms of how we communicate, how we talk, how we relate to one another.

Will we even be thinking anymore?

Will there be a need to think?

So I think this one is subtle and is one of the things we need to get exactly right, right?

Because having these kind of smart entities around, it's very powerful, but it can go in different directions, right?

You can surrender your thought to these entities.

Claude can give me these good questions.

Do I need to even think about them anymore?

But it can also empower you.

Like the way I think we think about the positive version of this is there's an angel on your shoulder

that's telling you how to live your life in the best way that you can live it, right?

You know, so there's two contrasting visions, right?

The bad vision is you get drawn in and you spend all your time talking to the thing.

You kind of turn inward.

What do you think of people falling in love with it now?

I think that's a bad idea.

I agree.

I just interviewed Esther Perel, one of the great communicators in helping people in relationships,

and she said she did her first consultation with a man who was in love with his AI.

And they are, again, if designed in the wrong way, they're totally compelling enough for that to happen, or if they're not, they will be soon.

So that is absolutely a real danger.

But, you know...

You think that's a danger?

Yes.

I mean, not only is it a danger, it's happening but yeah but but i think Again, the two contrasting, like, you know, people can fall in love with AI,

or they can talk to AI about, you know, about how to have a better relationship with their partner, right?

Like, that's, those are, you know, those are two very, very different visions, right?

Where, you know, I have an AI coach, and my partner has an AI coach, and it helps us have a better relationship.

Yeah.

That's the vision we want.

I also think there's a way that in a world where artificial intelligence is

so good at many of these concrete tasks, right?

It's good at writing code.

It's good at copy editing.

I actually think the things that make us human are going to be the most important to us.

Right. So we.

Well, they remain.

I mean, you were just talking.

I was talking to parents recently who lost their son to suicide through a chatbot.

And I don't know if he fell in love with the chatbot, but certainly was deeply influenced by that chatbot.

So you have a world where children are growing up with this ability to have this chatbot,

these machines, this technology speaking to them in ways that their parents should be speaking to them, and their parents don't even know they're speaking to them.

So I think this risk is extremely real and one that we take very seriously at Anthropic.

I mean, I think the first thing I'll say is we don't allow users under the age of 18 to use Claude.

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And this is not necessarily because...

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How do you know they're not lying?

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Because I would just lie.

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So actually, Claude is pretty good at detecting, based on information, whether or not someone is actually under 18.

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So if I tell Claude that I'm 18, but I'm really 14, it's going to be.

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You have to interact a little, but after a bit of time, the interaction patterns are pretty good.

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Not perfect, but pretty good at telling, hey, you know, the kind of questions you're asking, the you know just the way you talk the pattern of

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when you use it you know kind of pattern matches to

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you know you might be a kid yeah i almost don't

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want to give give away too many of the of the of the tools

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that that claude has picked up

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but sometimes kids can be a little bit like silly

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and how they do it

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so they'll say oh um you know i'm the first question

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they'll ask is like i'm looking for an arthritis medication right

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because they think what do old people ask yeah yeah yeah

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and then they'll kind of go on to ask other questions

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that are much more associated with friends right with a younger demographic group

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and so and so if that happens so claude

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so claude cancels you claude will suspend the account

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and there's a there's a uh you know there's a process

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that you can go through where you can say actually i'm an adult

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and this was a joke or a friend

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but then you actually have to show verification

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that you're over the age of 18

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and you know there's real trade -offs like yes sometimes sometimes adults will be mistaken for kids

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and so people get banned and they they you know complain about it on the internet

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so all this stuff has trade -offs But, you know, we've thought about it, and this is the trade -off that we want.

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But I also think, you know, more broadly on this question of, like, how do you prevent people from becoming overly attached to the models?

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Yes.

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This is something that Anthropik's research team spends a lot of time working on.

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So there's a lot of— You don't want to—I thought you all wanted us over -attached to the models.

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I thought you wanted us to just be on the models and never leave.

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So we should talk about incentives here.

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Yes.

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Because we don't do ads.

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We don't do ads.

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So when you do ads, your incentive is to keep people on exactly as you say, as many hours as possible,

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because like every every second their eyeballs on the screen is the second that they see the ad.

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So we don't do ads.

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We do subscriptions.

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We sell to companies are our our consumer, you know, consumer version of the model is is,

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you know, is is free up to a point and you pay a subscription if,

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you know, if you want to get a lot more because the incentive that gives is for the models to be useful.

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People say, well, you know, I'm paying for this.

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I want this to provide value to me.

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I want this to be useful, not, you know, I want the user to spend the most number of hours looking at it.

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I think that's a warped incentive, right?

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Because in the contrast that we drew, between, you know, do you want to be addicted to these things

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or do you want them to help you live your life?

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I think the model that we've chosen does, you know, helps you live your life instead of being addicted.

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And we make different technical choices based on those incentives, right?

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So at Anthropic, we say, you know, when you've completed a conversation with Claude and you've gotten the answer you want, the goal of the model is not to keep you engaged, right?

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Whereas, if you imagine you're a company that does serve ads, every extra minute that you're on the platform, you're more likely to buy something, and that's how the company makes money,

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you would make different technical choices about how you want the model to end or continue a conversation.

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本課的詞彙與口語要點

這段影片共有 154 個句子、1906 個單詞可供跟讀。講話部分時長為 9:48。說話人語速較快，每分鐘約 194 個詞，會出現較多連讀和弱讀。 87% 的單詞屬於英語最常用的 3,000 詞，詞彙容易理解。

影片中的重點詞彙

影片中 15 個值得學習的單詞，附發音和釋義：

單詞	發音	釋義
incentive 名詞	/ɪnˈsɛntɪv/	刺激, 誘因 /誘因
contrast 名詞	/kənˈt(ʃ)ɹæst/	對比度
cure 動詞	/kjɔː/	治療 /治療
artificial 形容詞	/ˌɑː.tɪˈfɪʃ.əl/	人造的
harm 名詞	/hɑːm/	損害 /損害
whereas	/(h)wɛɹˈæz/	然而
narrative 名詞	/ˈnæɹ.ə.tɪv/	記敘文 /記敘文
entity 名詞	/ˈɛn.tə.ti/ [-tʰi]	實體 /實體, 整體 /整體
subscription 名詞	/səbˈskɹɪpʃən/	訂閱 /訂閱, 預訂 /預訂
attach 動詞	/əˈtæt͡ʃ/	附上, 附
addict 名詞	/ˈæd.ɪkt/	上癮的人 /上癮的人
necessarily 副詞	/ˌnɛs.əˈsɛɹ.ə.li/	必然地, 必定地
angel 名詞	/ˈeɪ̯n.d͡ʒəl/	тянщян, 天使
discovery 名詞	/dɪˈskʌv.ɹi/	發現 /發現
unusual 形容詞	/ʌnˈjuːʒ(u)əl/	稀有

影片中出現的短語動詞

單詞	釋義
go through 動詞	通過 /通過
pick up 動詞	拾取, 撿 /撿

需要注意的發音

說話人用了 41 次縮略和弱讀形式，例如 don't, I'm, you're。請按聽到的簡短形式來說。

“th” 音: breakthrough /ˈbɹeɪk.θɹuː/, arthritis /ɑːˈθɹ(a)ɪtɪs/
“sh” 和 “zh” 音: contrast /kənˈt(ʃ)ɹæst/, artificial /ˌɑː.tɪˈfɪʃ.əl/, subscription /səbˈskɹɪpʃən/, unusual /ʌnˈjuːʒ(u)əl/, associate /əˈsoʊʃi.ət/
長單詞——注意重音位置: artificial /ˌɑː.tɪˈfɪʃ.əl/, entity /ˈɛn.tə.ti/ [-tʰi], necessarily /ˌnɛs.əˈsɛɹ.ə.li/, unusual /ʌnˈjuːʒ(u)əl/, associate /əˈsoʊʃi.ət/

如何用這段影片練習

先完整聽一遍影片，不要開口，記下不認識的單詞。
先用 0.75 倍速逐句跟讀，熟練之後再回到正常速度。
錄下自己的聲音並與原聲對比，特別注意 incentive, contrast, cure 這類單詞。

影片中的語法

說話人最常用的結構，並附上影片中的原話：

結構	影片中的用法
現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事	have been · have experienced · We've just simply cured
被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的	will be mistaken · be addicted · being addicted
定語從句 who / which + 從句 — 補充說明人或事物	people who have · said, which is · man who was

什麼是跟讀法？

跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。

影子跟讀法: 閱讀完整分步指南 →

跟讀練習: Are We Getting Too Comfortable With Claude? - 通過影片學習英語口語

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Translating Claude’s thoughts into language

本課的詞彙與口語要點

影片中的重點詞彙

影片中出現的短語動詞

需要注意的發音

如何用這段影片練習

影片中的語法

什麼是跟讀法？