跟讀練習: "재무 모델에도 부담" 저커버그가 털어놓은 메타 AI 올인의 대가 - 通過影片學習英語口語

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96 句

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and i think that that it's both difficult for the people doing it

but i do think that

that also puts some level of strain on the rest of

the company on the financial model of the company actually quite puzzling to me at first the degree to

which you were like willing to make these massive bets is something actually nat

and i would often like talk about i was like wow we're really going big here like

and both of us would be like would you go

this big would you go this big you know

we're taking our compute we're not like peanut buttering across a lot of competing efforts we're basically

Basically, we're investing in the key people, like a lot of compute per researcher, and then they can do their work.

It was an MSL.

He told me to find the message to the king's first message.

He told me to talk about the time, and he told me to talk about it at 44 minutes.

That's the next story.

This was like the core of your strategy, right?

It's basically like you actually don't need a massive number of people.

But what you do need is for like each of the people to be as good as possible,

which kind of informed this kind of crazy recruiting push and like very intense filtering that we had to take,

you know, some of the best people from inside meta and a very intense process to get people from outside meta.

The people of the company are working as a start -up company as a big company.

The online learning team asked how big is the question.

The answer is how big is a pre -training team?

It doesn't need to be that big, but I think if you look at some of the other labs,

it's probably quite big in some other places.

So I think part of the reason why these models are doing well is because you have people who we're betting on,

and they have a lot of resources because we're...

That's what I'm going to say in front of you.

I'm going to compete with each other team.

I'm going to compete with each other.

In general, one of the, one of the bigger challenges of scaling bigger models is it's not just about how many flops

and how much compute you want to be able to scale data with the data.

I mean, this is like part of what when the original kind of scaling laws for pre -training were written.

I mean, this is sort of what came out of that is that.

So if the internet isn't growing that quickly and you want to scale bigger models that can be more intelligent,

I think one of the interesting problems is, all right, where's the data coming from?

Yes.

So, I don't know.

I think maybe when we got started, a lot of people were like, OK, like, how is this going to work?

It's like the kind of like the social media guy is working with the like data guy.

Like, is that like, are we going to build a frontier lab here or what?

But but I mean, but my but I mean, my like really strong conviction, because I talk to like all the leaders of all the labs,

like all the heads of research, like all this stuff.

And I was like, no, Alex is the guy.

Like, he's the fucking guy.

And we're going to basically be able to go pull together a team

and recruit the people that we need to go do this.

Jokerberg은 연구소를 이끌 사람에게 필요한 건 최신 학습기법의 전문성보다 환이 바뀔 때마다 생각을 빠르게 고치는 창업가의 기질이라고 말했습니다.

그런데 왕이 메타에 와서 처음 마주한 분위기는 달랐습니다.

이번엔 왕, 본인의 이야기입니다.

when i joined um i was in this it was in this um

you know meeting with some of the leads of the existing effort

and uh i was like okay i think the first thing

we need to do is train a really good reasoning model um by um

you know by some time frame i don't know when uh

but this is like clearly like the most important milestone

and um one of the the key leads in the group um who no

But it was like, oh, we don't need a reasoning model.

Does any, or any of our, do our consumers need a reasoning model?

Like, I feel like we don't need to do a reasoning model.

We can just do a normal model.

And I was, I had like this heart sinking moment.

Oh no, I've joined the wrong company.

I was like, oh, I've got to take like 10 steps back.

And I think...

The model model is a model model in the summer of the year's open -up.

The model model is a model model.

The model model is a model model model and the other leaders were able to change the AI.

The Instagram leader of Adam Moseri, made a small app in Instagram.

Collis told him that he could use a new idea that he could use a new idea.

The idea of this idea is that it's different.

Obviously, you can't...

A lot of what we had to do here was rotate a lot of the rest of the company and,

like, for things like the infrastructure team that now, you know, it's like building these huge clusters to support this,

I mean, that's obviously a huge thing.

So, huge parts of the company kind of had to pivot to do this,

and obviously you can't do too many of those at the same time, right?

Only one thing can be the top priority.

Yes, yes.

So, how do you handle that burden?

He ended up with a single product, commerce, and commercial sales.

It's been a big deal for years to come.

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本課的詞彙與口語要點

這節口語課以影片“"재무 모델에도 부담" 저커버그가 털어놓은 메타 AI 올인의 대가”為素材。影片中反覆出現的詞有：model, compete, reasoning。這段影片共有 89 個句子、1033 個單詞可供跟讀。講話部分時長為 8:30。說話人語速平穩，每分鐘約 121 個詞，很適合跟讀。 86% 的單詞屬於英語最常用的 3,000 詞，其餘的詞建議在練習前先學一下。

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
compete 動詞	/kəmˈpiːt/	競爭 /競爭, 高下
recruit 動詞	/ɹɪˈkɹut/	招聘, 徵兵 /徵兵
infrastructure 名詞	/ˈɪnfɹəˌstɹʌkt͡ʃə/	基礎設施 /基礎設施
priority 名詞	/pɹaɪˈɒɹɪti/	優先權 /優先權, 更重要的事
butter 名詞	/ˈbʌtɚ/	牛油, bá-tah
resource 名詞	/ɹɪˈzɔːs/	資源 /資源
commerce 名詞	/ˈkɑ.mɚs/	貿易 /貿易, 交易
invest 動詞	/ɪnˈvɛst/	耗費 /耗費, 投資 /投資
intelligent 形容詞	/ɪnˈtɛlɪd͡ʒənt/	聰明 /聰明, 聰慧 /聰慧
filter 名詞	/ˈfɪltɚ/	過濾器 /過濾器, 濾過器 /濾過器
burden 名詞	/ˈbɝdn̩/	負荷 /負荷
sink 動詞	/ˈsɪŋk/	沉, 下沉
inform 動詞	/ɪnˈfɔɹm/	通知, 告知
strain 名詞	/stɹeɪn/	株
conviction 名詞	/kənˈvɪkʃən/	信念

值得反覆跟讀的句子

影片中簡短而完整的句子，可以直接用在日常對話裡：

I'm going to compete with each other team.
I'm going to compete with each other.
I mean, that's obviously a huge thing.

需要注意的發音

說話人用了 32 次縮略和弱讀形式，例如 I'm, we're, don't。請按聽到的簡短形式來說。

長單詞——注意重音位置: infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, priority /pɹaɪˈɒɹɪti/, intelligent /ɪnˈtɛlɪd͡ʒənt/

中文母語者容易讀錯的音：

/v/ — 不要讀成 /w/，上齒輕觸下唇: invest /ɪnˈvɛst/, conviction /kənˈvɪkʃən/, pivot /ˈpɪv.ɪt/
詞尾子音 — 要讀清楚，後面不要加母音: compete /kəmˈpiːt/, lab /læːb/, intense /ɪnˈtɛns/, recruit /ɹɪˈkɹut/, resource /ɹɪˈzɔːs/

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。
用正常速度逐句跟讀，每句重複到你的節奏與說話人一致為止。
錄下自己的聲音並與原聲對比，特別注意 compete, recruit, infrastructure 這類單詞。

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: "재무 모델에도 부담" 저커버그가 털어놓은 메타 AI 올인의 대가 - 通過影片學習英語口語

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