跟读练习: "재무 모델에도 부담" 저커버그가 털어놓은 메타 AI 올인의 대가 - 通过视频学习英语口语

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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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本课的词汇与口语要点

这节口语课以视频“"재무 모델에도 부담" 저커버그가 털어놓은 메타 AI 올인의 대가”为素材。视频中反复出现的词有：model, compete, reasoning。这段视频共有 89 个句子、1033 个单词可供跟读。讲话部分时长为 8:30。说话人语速平稳，每分钟约 121 个词，很适合跟读。 86% 的单词属于英语最常用的 3,000 词，其余的词建议在练习前先学一下。

视频中的重点词汇

视频中 15 个值得学习的单词，附发音和释义：

单词	发音	释义
compete 动词	/kəmˈpiːt/	競爭 /竞争, 高下
recruit 动词	/ɹɪˈkɹut/	招聘, 徵兵 /征兵
infrastructure 名词	/ˈɪnfɹəˌstɹʌkt͡ʃə/	基礎設施 /基础设施
priority 名词	/pɹaɪˈɒɹɪti/	優先權 /优先权, 更重要的事
butter 名词	/ˈbʌtɚ/	牛油, bá-tah
resource 名词	/ɹɪˈzɔːs/	資源 /资源
commerce 名词	/ˈkɑ.mɚs/	貿易 /贸易, 交易
invest 动词	/ɪnˈvɛst/	耗費 /耗费, 投資 /投资
intelligent 形容词	/ɪnˈtɛlɪd͡ʒənt/	聰明 /聪明, 聰慧 /聪慧
filter 名词	/ˈfɪltɚ/	過濾器 /过滤器, 濾過器 /滤过器
burden 名词	/ˈbɝdn̩/	負荷 /负荷
sink 动词	/ˈsɪŋk/	沉, 下沉
inform 动词	/ɪnˈfɔɹm/	通知, 告知
strain 名词	/stɹeɪn/	株
conviction 名词	/kənˈvɪkʃən/	信念

值得反复跟读的句子

视频中简短而完整的句子，可以直接用在日常对话里：

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.

需要注意的发音

说话人用了 32 次缩略和弱读形式，例如 I'm, we're, don't。请按听到的简短形式来说。

长单词——注意重音位置: infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, priority /pɹaɪˈɒɹɪti/, intelligent /ɪnˈtɛlɪd͡ʒənt/

中文母语者容易读错的音：

/v/ — 不要读成 /w/，上齿轻触下唇: invest /ɪnˈvɛst/, conviction /kənˈvɪkʃən/, pivot /ˈpɪv.ɪt/
词尾辅音 — 要读清楚，后面不要加元音: compete /kəmˈpiːt/, lab /læːb/, intense /ɪnˈtɛns/, recruit /ɹɪˈkɹut/, resource /ɹɪˈzɔːs/

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。
用正常速度逐句跟读，每句重复到你的节奏与说话人一致为止。
录下自己的声音并与原声对比，特别注意 compete, recruit, infrastructure 这类单词。

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。

影子跟读法: 阅读完整分步指南 →

跟读练习: "재무 모델에도 부담" 저커버그가 털어놓은 메타 AI 올인의 대가 - 通过视频学习英语口语

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