쉐도잉 연습: NVIDIA CEO on Elon Musk, xAI, Colossus supercomputer and systems engineering | Jensen Huang - YouTube로 영어 말하기 배우기

C2
you've highly lauded Elon and XAI's accomplishment in Memphis in building Colossus supercomputer,
⏸ 일시 정지
97 문장
문장이 너무 짧거나 길면 Edit를 눌러 조정하세요.
1
you've highly lauded Elon and XAI's accomplishment in Memphis in building Colossus supercomputer,
2
probably in record time in just four months.
3
It's now at 200,000 GPUs and growing very quickly.
4
Is there something that you could speak to,
5
understand about his approach that's instructive broadly to all the data center creators that enable that kind of accomplishment?
6
his approach to engineering, his approach to the whole management of construction, everything.
7
First of all, Elon is deep in so many different topics,
8
yet he's also a really good systems thinker.
9
And so he's able to think through multiple disciplines.
10
And he obviously pushes things,
11
questions everything, whether number one, is it necessary?
12
number two does it have to be done this way
13
and in other words you know does it have does it have to take this long
14
and and so
15
so he he has he has the he has the ability
16
uh to question everything uh to the point where everything is
17
down to its minimal amount that's necessary you can't take anything else out
18
and and yet yet the the the the the necessary um capabilities of the product retains you know
19
and so he's he is as minimalist as you could possibly imagine
20
and he does it at a system system scale um i i also love the fact
21
that he he is um he is represented he he is he is present at the point of action.
22
You know, he'll just go there.
23
If there's a problem, he'll just go there and show me the problem.
24
You know, when you do all of this in combination,
25
you overcome a lot of previous,
26
this is just the way we do it.
27
You know, I'm waiting for them.
28
You know, I mean, it's just,
29
everybody has a lot of excuses.
30
And so, and then the last thing is,
31
When you act personally with so much urgency,
32
it causes everybody else to act with urgency.
33
Every supplier has a lot of customers going on.
34
Every supplier has a lot of projects going on.
35
He makes it his business that he's the top priority of everybody else's projects.
36
He does that by demonstrating it.
37
I've been in a bunch of those meetings.
38
It's fun to watch because really not enough people ask the question like,
39
okay, so can this be done a lot faster and how?
40
Why does it have to take this long?
41
Yeah, right.
42
And then that becomes an engineering question often.
43
And yes, I think when you get the ground truth of actually,
44
I remember one of the times I was hanging out with him,
45
he literally is going through the entire process of how to plug in cables into a rack.
46
He's working with an engineer on the ground that's doing that task,
47
and he's just trying to understand what does that process look like so it can be less error-prone.
48
And just building up that intuition from every single task involved in putting together the data center,
49
you start to immediately get a sense at the detailed scale and at the broad system scale of where the inefficiencies are.
50
And so you can make it more and more and more efficient.
51
Plus, you have the big hammer of being able to say,
52
let's do it totally different and remove all possible blockers.
53
That's right.
54
is there parallels in the nvidia extreme systems code design approach that you see in the way Elon approaches systems engineering?
55
Well, first of all, co-design is an ultimate systems engineering problem.
56
And so we approach the work that we do from that principle.
57
The other thing that we do,
58
and this is a philosophy that,
59
a thought, a state of mind,
60
I guess, a method that I started 30 years ago,
61
and it's called the speed of light.
62
A speed of light is not just about the speed.
63
A speed of light is my shorthand for what's the limit of what physics can do.
64
And so everything that we do is compared against the speed of light.
65
Memory speed, math speed, power,
66
cost, time, effort, number of people, manufacturing cycle time.
67
and when you think about latency versus throughput,
68
when you think about cost versus throughput,
69
cost versus capacity, all of these things,
70
you test against the speed of light to achieve all of these different constraints separately.
71
And then when you consider it together,
72
you know you have to make compromises because a system that achieves extremely low latency
73
versus a system that achieves very high throughput are architected fundamentally differently.
74
But you want to know what's the speed of light of a system that achieves high throughput?
75
What's the speed of light of a system that achieves low latency?
76
And then when you think about the total system, you could make trade-offs.
77
And so I force everybody to think about what's the first principles,
78
the limits the physical limits um for everything before we you know before we uh do anything
79
and and we test everything against that and
80
so that's a good frame of mind i don't love the other methods which is continuous improvement
81
the problem with continuous improvement it first of all you should
82
engineer something from first principles at the speed you know with speed of light thinking limited only by physical limits
83
and and physics limits and um after
84
that of course you would improve it over time um
85
but i don't like going into a problem
86
and somebody says hey you know it takes 74 days to do this today right now
87
and we can do it for you in 72 days you know i'd rather strip it all back to zero
88
and say first of all explain to me why it's 74 days in the first place
89
and let's think about what's possible today.
90
And if I were to build it completely from scratch,
91
how long would it take?
92
Oftentimes you'd be surprised and might come to six days.
93
Now the rest of the six days to 74
94
could be very well-reasoned and compromises and cost reductions and all kinds of different things,
95
but at least you know what they are.
96
And then now that you know that six days is possible,
97
Then the conversation from 74 to 6 Surprisingly much more effective

앱 다운로드

당신이 말하는 모든 문장을 AI가 채점

스캔하여 다운로드
스캔하여 다운로드
Download on theApp Store
Get it onGoogle Play
TRENDING

인기 동영상

맥락 및 배경

이번 영상에서는 NVIDIA CEO인 젠슨 황(Jensen Huang)이 일론 머스크(Elon Musk)와 xAI의 성과에 대해 이야기하며, 콜로서스 슈퍼컴퓨터(Colossus supercomputer)의 구축 과정에서의 공학적 접근 방식과 시스템 공학의 중요성을 강조합니다. 젠슨은 특히 일론 머스크의 시스템 사고 방식이 데이터 센터 구축자들에게 어떤 교훈이 될 수 있는지를 설명하며, 문제 해결을 위한 실제적인 행동과 효율성을 강조합니다. 이러한 대화를 통해 학생들은 기술적 주제에 대해 깊이 있는 영어 표현을 익힐 수 있습니다.

일상 통신을 위한 5가지 주요 문구

  • “이 과정에서의 문제를 보여줘.”
  • “이렇게 해야 할 필요가 있을까?”
  • “이렇게 오래 걸려야 하는 이유는 무엇인가요?”
  • “효율성을 높이기 위해 무엇을 할 수 있을까요?”
  • “이것을 더 신속하게 수행할 수 있는 방법은 무엇인가요?”

단계별 쉐도잉 가이드

영상의 내용이 어렵게 느껴질 수 있지만, 영어 쉐도잉을 통해 효과적으로 개선할 수 있습니다. 다음 단계에 따라 연습해보세요:

  1. 듣기 연습: 영상의 내용을 한 번 전체적으로 듣고, 이해가 되는 부분과 이해가 잘 안 되는 부분을 구분합니다.
  2. 쉐도잉 시작: 짧은 문장이나 구문을 선택하여 반복적으로 따라 읽습니다. 이때, 나의 발음과 리듬을 체크합니다. 그리고 shadow speech를 통해 나의 발음 교정이 요구되는 부분을 집중적으로 연습합니다.
  3. 문맥 이해: 문장에 포함된 단어와 문구의 의미를 파악합니다. 특히, 영어 발음 교정에 도움이 되는 발음 팁을 메모합니다.
  4. 반복 연습: 처음에 어려웠던 문장을 여러 번 반복하여 익숙해지도록 합니다. 문장을 입에 붙도록 연습합니다.
  5. 자기 피드백: 녹음하여 자신의 음성을 듣습니다. 이를 통해 shadowspeaks의 원리에 따라 개선점을 찾고 피드백을 적용합니다.

이 과정을 통해 영상에서 다룬 기술적이고 복잡한 내용에 대한 영어 표현력을 향상시키고, 일상적인 대화에서도 자연스럽게 사용할 수 있는 능력을 기를 수 있습니다.

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.

☕ 커피 한 잔 사주기