Shadowing Practice: "재무 모델에도 부담" 저커버그가 털어놓은 메타 AI 올인의 대가 - Learn English Speaking with Video

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96 משפטים

אם המשפטים קצרים או ארוכים מדי, לחץ על ערוך כדי להתאים אותם.

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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אוצר מילים והערות הגייה לשיעור הזה

שיעור הדיבור הזה מבוסס על הסרטון “"재무 모델에도 부담" 저커버그가 털어놓은 메타 AI 올인의 대가”. המילים שחוזרות הכי הרבה: model, compete, reasoning. בסרטון הזה יש 89 משפטים ו-1033 מילים לתרגול Shadowing. הדיבור נמשך 8:30. הדובר שומר על קצב יציב של כ-121 מילים בדקה, קצב נוח לתרגול Shadowing. 86% מהמילים נמצאות בין 3,000 המילים הנפוצות ביותר באנגלית; כדאי ללמוד את השאר לפני שמתחילים.

אוצר מילים מרכזי בסרטון הזה

14 מילים מהסרטון שכדאי ללמוד, עם ההגייה והמשמעות שלהן:

מילה	הגייה	משמעות
compete פועל	/kəmˈpiːt/	התחרה
infrastructure שם עצם	/ˈɪnfɹəˌstɹʌkt͡ʃə/	תַּשְׁתִּית
butter שם עצם	/ˈbʌtɚ/	חֶמְאָה
resource שם עצם	/ɹɪˈzɔːs/	מַשְׁאָב
commerce שם עצם	/ˈkɑ.mɚs/	סַחַר, מִסְחָר
intelligent שם תואר	/ɪnˈtɛlɪd͡ʒənt/	נָבוֹן
filter שם עצם	/ˈfɪltɚ/	מַסנֵן
burden שם עצם	/ˈbɝdn̩/	נֵטֶל, עֹל
inform פועל	/ɪnˈfɔɹm/	הוֹדִיעַ
conviction שם עצם	/kənˈvɪkʃən/	הרשעה
puzzle שם עצם	/ˈpʌzəl/	חִידָה
peanut שם עצם	/ˈpiˌnʌt/	בוֹטֵן, אגוז אדמה
milestone שם עצם	/ˈmaɪlˌstoʊn/	אבן מיל, אבן דרך
pivot שם עצם	/ˈpɪv.ɪt/	צִיר

משפטים שכדאי לחזור עליהם

משפטים קצרים ושלמים מהסרטון שאפשר להשתמש בהם בשיחה יומיומית:

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.

הגייה ששווה לשים לב אליה

הדובר משתמש ב-32 קיצורים וצורות מקוצרות, כמו I'm, we're, don't. אמרו אותם בצורה הקצרה, כפי שאתם שומעים אותם.

מילים ארוכות — שימו לב להטעמה: infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, priority /pɹaɪˈɒɹɪti/, intelligent /ɪnˈtɛlɪd͡ʒənt/

צלילים שדוברי עברית מתקשים בהם:

/r/ — הלשון לא נוגעת בשום מקום, לא ה-“ר” הגרונית: recruit /ɹɪˈkɹut/, infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, priority /pɹaɪˈɒɹɪti/, resource /ɹɪˈzɔːs/, inform /ɪnˈfɔɹm/

איך לתרגל עם הסרטון הזה

הקשיבו לכל הסרטון פעם אחת בלי לדבר, ורשמו את המילים שאתם לא מכירים.
תרגלו Shadowing משפט אחר משפט במהירות רגילה, וחזרו על כל משפט עד שהקצב שלכם תואם את הדובר.
הקליטו את עצמכם והשוו למקור, תוך תשומת לב למילים כמו compete, infrastructure, butter.

מהי טכניקת השאדואינג?

שאדואינג היא טכניקת למידת שפה מבוססת מדע שפותחה במקור להכשרת מתורגמנים מקצועיים ופופולרית על ידי הפוליגלוט ד"ר אלכסנדר ארגואלס. השיטה פשוטה אך עוצמתית: אתה מקשיב לאודיו באנגלית ילידית וחוזר עליו בקול רם מיד — כמו צל העוקב אחרי הדובר עם עיכוב של 1–2 שניות בלבד. בניגוד להאזנה פסיבית או תרגילי דקדוק, שאדואינג מכריח את המוח והשרירים בפה לעבד ולשחזר דפוסי דיבור אמיתיים בו זמנית. מחקרים מראים שזה משפר משמעותית את דיוק ההגייה, האינטונציה, הקצב, הדיבור המחובר, הבנת השמיעה ושטף הדיבור — מה שהופך אותו לאחת השיטות היעילות ביותר להכנה לIELTS Speaking ולתקשורת באנגלית בעולם האמיתי.

טכניקת Shadowing: קרא את המדריך המלא שלב אחר שלב →

Shadowing Practice: "재무 모델에도 부담" 저커버그가 털어놓은 메타 AI 올인의 대가 - Learn English Speaking with Video

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