쉐도잉 연습: "재무 모델에도 부담" 저커버그가 털어놓은 메타 AI 올인의 대가 - 영상으로 영어 말하기 배우기

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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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이 레슨의 어휘와 말하기 포인트

이 말하기 레슨은 영상 “"재무 모델에도 부담" 저커버그가 털어놓은 메타 AI 올인의 대가”을(를) 바탕으로 합니다. 가장 자주 반복되는 단어는 다음과 같습니다: model, compete, reasoning. 이 영상에는 섀도잉할 문장 89개와 단어 1033개가 있습니다. 말하는 구간의 길이는 8:30입니다. 화자는 분당 약 121단어의 일정한 속도로 말해서 섀도잉하기에 편한 속도입니다. 단어의 86%가 영어에서 가장 많이 쓰이는 3,000단어에 속합니다. 나머지는 연습 전에 미리 확인해 두세요.

이 영상의 핵심 어휘

영상에 나오는 익혀 둘 만한 단어 15개를 발음, 뜻과 함께 정리했습니다.

단어	발음	뜻
intense 형용사	/ɪnˈtɛns/	강렬한, 극심한
recruit 동사	/ɹɪˈkɹut/	모집하다
infrastructure 명사	/ˈɪnfɹəˌstɹʌkt͡ʃə/	기반시설, 기간시설
priority 명사	/pɹaɪˈɒɹɪti/	우선, 우선권
butter 명사	/ˈbʌtɚ/	버터, 빠다
resource 명사	/ɹɪˈzɔːs/	자원
commerce 명사	/ˈkɑ.mɚs/	무역, 교역
intelligent 형용사	/ɪnˈtɛlɪd͡ʒənt/	똑똑하다, 똑똑한
filter 명사	/ˈfɪltɚ/	여과기, 필터
burden 명사	/ˈbɝdn̩/	짐, 바리
sink 동사	/ˈsɪŋk/	가라앉다
inform 동사	/ɪnˈfɔɹm/	알리다, 전달하다
frontier 명사	/ˈfɹʌntɪə/	경계, 국경
puzzle 명사	/ˈpʌzəl/	난제, 난문
researcher 명사	/ˈɹiˌsɝ.t͡ʃɚ/	연구원, 연구자

따라 말해 볼 만한 문장

영상에 나오는 짧고 완결된 문장으로, 일상 대화에서 그대로 쓸 수 있습니다:

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.

주의할 발음

화자는 I'm, we're, don't 같은 축약형과 약화된 형태를 32번 사용합니다. 들리는 대로 짧게 발음하세요.

긴 단어 — 강세 위치에 주의: infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, priority /pɹaɪˈɒɹɪti/, intelligent /ɪnˈtɛlɪd͡ʒənt/

한국어 화자가 어려워하는 소리:

/f/ — ㅍ(/p/)으로 바꾸지 말고 윗니를 아랫입술에 대기: infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, filter /ˈfɪltɚ/, inform /ɪnˈfɔɹm/, frontier /ˈfɹʌntɪə/, flop /flɑp/
/v/ — ㅂ(/b/)과 구별하기: invest /ɪnˈvɛst/, conviction /kənˈvɪkʃən/, pivot /ˈpɪv.ɪt/
/z/ — ㅈ이 아니라 성대를 울리는 /s/: resource /ɹɪˈzɔːs/, puzzle /ˈpʌzəl/

이 영상으로 연습하는 방법

먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
보통 속도로 한 문장씩 섀도잉하고, 화자의 리듬과 맞을 때까지 반복합니다.
자신의 목소리를 녹음해 원본과 비교하고, intense, recruit, infrastructure 같은 단어에 특히 주의합니다.

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법

쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.

섀도잉 방법: 단계별 전체 가이드 읽기 →

쉐도잉 연습: "재무 모델에도 부담" 저커버그가 털어놓은 메타 AI 올인의 대가 - 영상으로 영어 말하기 배우기

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