Why tech needs the humanities | Eric Berridge

159,942 views ・ 2018-05-22

TED


μ•„λž˜ μ˜λ¬Έμžλ§‰μ„ λ”λΈ”ν΄λ¦­ν•˜μ‹œλ©΄ μ˜μƒμ΄ μž¬μƒλ©λ‹ˆλ‹€.

λ²ˆμ—­: Yoonyoung Chang κ²€ν† : JY Kang
00:12
You've all been in a bar, right?
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바에 가보신 적 μžˆμœΌμ‹œμ£ ?
00:14
(Laughter)
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(μ›ƒμŒ)
00:16
But have you ever gone to a bar
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ν•˜μ§€λ§Œ 바에 κ°„ 덕뢄에
00:19
and come out with a $200 million business?
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2μ–΅ λ‹¬λŸ¬μ§œλ¦¬ 계약을 λ”°κ²Œ 된 적은 없을 κ±°μ˜ˆμš”.
00:24
That's what happened to us about 10 years ago.
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10λ…„ 전에 μš°λ¦¬μ—κ²Œ 그런 일이 μžˆμ—ˆμŠ΅λ‹ˆλ‹€.
00:27
We'd had a terrible day.
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λ”μ°ν•œ ν•˜λ£¨μ˜€λ˜ μ–΄λŠ λ‚ μ΄μ—ˆμ–΄μš”.
00:30
We had this huge client that was killing us.
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큰 고객사가 우리λ₯Ό 거의 죽이렀 λ“€μ—ˆκ±°λ“ μš”.
00:34
We're a software consulting firm,
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μš°λ¦¬λŠ” μ†Œν”„νŠΈμ›¨μ–΄ μ»¨μ„€νŒ… νšŒμ‚¬μ˜€λŠ”λ°
00:36
and we couldn't find a very specific programming skill
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νŠΉμ • ν”„λ‘œκ·Έλž˜λ° μŠ€ν‚¬μ΄ μ—†μ–΄μ„œ
00:39
to help this client deploy a cutting-edge cloud system.
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고객이 μ›ν•˜λŠ” μ΅œμ²¨λ‹¨ ν΄λΌμš°λ“œ μ‹œμŠ€ν…œμ„ ꡬ좕할 수 μ—†μ—ˆμŠ΅λ‹ˆλ‹€.
00:43
We have a bunch of engineers,
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μ—”μ§€λ‹ˆμ–΄λŠ” λ§Žμ•˜μ§€λ§Œ
00:45
but none of them could please this client.
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아무도 κ·Έ 고객을 λ§Œμ‘±μ‹œν‚¬ 수 μ—†μ—ˆμŠ΅λ‹ˆλ‹€.
00:49
And we were about to be fired.
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μš°λ¦¬λŠ” 거의 잘릴 μ§€κ²½μ΄μ—ˆμ£ .
00:51
So we go out to the bar,
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κ·Έλž˜μ„œ μš°λ¦¬λŠ” 바에 κ°”μŠ΅λ‹ˆλ‹€.
00:54
and we're hanging out with our bartender friend Jeff,
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κ±°κΈ°μ„œ 바텐더 μ œν”„μ™€ μ΄λŸ°μ €λŸ° μ–˜κΈ°λ₯Ό λ‚˜λˆ΄μ£ .
00:58
and he's doing what all good bartenders do:
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κ·ΈλŠ” 쒋은 바텐더가 ν•  일을 λ‹€ ν•΄μ£Όμ—ˆμŠ΅λ‹ˆλ‹€.
01:00
he's commiserating with us, making us feel better,
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우리 μ–˜κΈ°μ— 곡감해주고, 기뢄을 ν’€μ–΄μ£Όκ³ 
01:03
relating to our pain,
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우리 고좩을 λ‹¬λž˜κΈ°λ„ ν–ˆμ£ .
01:05
saying, "Hey, these guys are overblowing it.
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"κ·Έ 고객듀이 λ„ˆλ¬΄ ν–ˆλ„€μš”. 신경쓰지 λ§ˆμ„Έμš”."
01:07
Don't worry about it."
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01:08
And finally, he deadpans us and says,
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κ·ΈλŸ¬λ‹€ κ·ΈλŠ” μ§„μ§€ν•˜κ²Œ μ΄λ ‡κ²Œ λ§ν–ˆμŠ΅λ‹ˆλ‹€.
01:11
"Why don't you send me in there?
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"μ €λ₯Ό κ·Έ κ³ κ°μ‚¬λ‘œ 보내 μ£Όμ„Έμš”.
01:13
I can figure it out."
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μ œκ°€ 방법을 μ°Ύμ•„ λ³Όκ²Œμš”."
01:15
So the next morning, we're hanging out in our team meeting,
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λ‹€μŒλ‚  μ•„μΉ¨, μš°λ¦¬λŠ” νŒ€ 회의λ₯Ό ν–ˆμŠ΅λ‹ˆλ‹€.
01:19
and we're all a little hazy ...
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λ¬Όλ‘  λ‹€λ“€ 술이 덜 κΉ¬ μƒνƒœμ˜€μ£ ...
01:22
(Laughter)
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(μ›ƒμŒ)
01:24
and I half-jokingly throw it out there.
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μ €λŠ” λ°˜λ†λ‹΄μ‘°λ‘œ λ§ν–ˆμŠ΅λ‹ˆλ‹€.
01:26
I say, "Hey, I mean, we're about to be fired."
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뭐 μ–΄μ°¨ν”Ό 잘릴 νŒμ΄μ—ˆμœΌλ‹ˆκΉŒμš”.
01:29
So I say,
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μ΄λ ‡κ²Œ λ§ν–ˆμ£ .
01:30
"Why don't we send in Jeff, the bartender?"
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"바텐더 μ œν”„λ₯Ό 보내면 μ–΄λ–¨κΉŒμš”?"
01:32
(Laughter)
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(μ›ƒμŒ)
01:35
And there's some silence, some quizzical looks.
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μž μ‹œ 침묡이 흐λ₯΄κ³  λ‹€λ“€ λ†€λΌλŠ” λ“― ν•˜λ”λ‹ˆ
01:39
Finally, my chief of staff says, "That is a great idea."
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λ§ˆμΉ¨λ‚΄ νŒ€μž₯이 λ§ν–ˆμŠ΅λ‹ˆλ‹€. "쒋은 μ•„μ΄λ””μ–΄κ΅°μš”."
01:43
(Laughter)
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(μ›ƒμŒ)
01:45
"Jeff is wicked smart. He's brilliant.
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"μ œν”„λŠ” 맀우 λ˜‘λ˜‘ν•˜κ³  λ›°μ–΄λ‚œ μ‚¬λžŒμ΄μ£ .
01:48
He'll figure it out.
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λ­”κ°€ 방법을 찾을 κ²λ‹ˆλ‹€.
01:50
Let's send him in there."
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κ·Έλ₯Ό κ³ κ°μ—κ²Œ λ³΄λƒ…μ‹œλ‹€."
01:52
Now, Jeff was not a programmer.
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μ œν”„λŠ” ν”„λ‘œκ·Έλž˜λ¨Έκ°€ μ•„λ‹™λ‹ˆλ‹€.
01:54
In fact, he had dropped out of Penn as a philosophy major.
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사싀, νŽœμ‹€λ² λ‹ˆμ•„ λŒ€ν•™μ—μ„œ 철학을 κ³΅λΆ€ν•˜λ‹€ μ€‘ν‡΄ν–ˆμ£ .
01:59
But he was brilliant,
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ν•˜μ§€λ§Œ λ˜‘λ˜‘ν–ˆμŠ΅λ‹ˆλ‹€.
02:01
and he could go deep on topics,
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문제λ₯Ό 깊이 λ³Ό λŠ₯λ ₯이 μžˆμ—ˆμ£ .
02:04
and we were about to be fired.
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μš°λ¦¬λ„ 잘리기 직전이라
02:06
So we sent him in.
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κ·Έλž˜μ„œ κ·Έλ₯Ό λ³΄λƒˆμŠ΅λ‹ˆλ‹€.
02:09
After a couple days of suspense,
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κΈ΄μž₯의 λͺ‡ 일이 μ§€λ‚œ ν›„
02:11
Jeff was still there.
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μ œν”„λŠ” 계속 고객사에 μžˆμ—ˆμŠ΅λ‹ˆλ‹€.
02:15
They hadn't sent him home.
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고객은 κ·Έλ₯Ό 돌렀 보내지 μ•Šμ•˜μ–΄μš”.
02:17
I couldn't believe it.
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μ „ 믿을 수 μ—†μ—ˆμŠ΅λ‹ˆλ‹€.
02:19
What was he doing?
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뭘 μ–΄λ–»κ²Œ ν–ˆμ„κΉŒμš”?
02:21
Here's what I learned.
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내막은 μ΄λŸ¬ν–ˆμŠ΅λ‹ˆλ‹€.
02:23
He had completely disarmed their fixation on the programming skill.
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κ·ΈλŠ” 고객의 ν”„λ‘œκ·Έλž˜λ° μŠ€ν‚¬μ— λŒ€ν•œ 집착을 μ™„μ „νžˆ λ²„λ¦¬κ²Œ λ§Œλ“€μ—ˆμŠ΅λ‹ˆλ‹€.
02:29
And he had changed the conversation,
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그리고 λ…Όμ˜ 주제λ₯Ό λ°”κΎΈμ—ˆμ£ .
02:31
even changing what we were building.
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심지어 μš°λ¦¬κ°€ λ§Œλ“€λ €λ˜ λŒ€μƒκΉŒμ§€ λ°”κΎΈμ–΄λ²„λ ΈμŠ΅λ‹ˆλ‹€.
02:33
The conversation was now about what we were going to build and why.
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무얼 λ§Œλ“€μ§€μ™€ λ§Œλ“œλŠ” λͺ©μ μ΄ λ…Όμ˜ μ£Όμ œκ°€ λ˜μ—ˆμŠ΅λ‹ˆλ‹€.
02:41
And yes, Jeff figured out how to program the solution,
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λ¬Όλ‘ , μ œν”„λŠ” 해결책을 ν”„λ‘œκ·Έλž˜λ°ν•˜λŠ” 방법을 μ°Ύμ•„λƒˆκ³ 
02:46
and the client became one of our best references.
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κ·Έ κ³ κ°μ‚¬λŠ” μ£Όμš” μ°Έκ³  사둀 쀑 ν•˜λ‚˜κ°€ λ˜μ—ˆμŠ΅λ‹ˆλ‹€.
02:50
Back then, we were 200 people,
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λ‹Ήμ‹œ 200λͺ…μ˜ 직원이 μžˆμ—ˆκ³ 
02:52
and half of our company was made up of computer science majors or engineers,
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μ ˆλ°˜μ€ 컴퓨터 κ³Όν•™μ΄λ‚˜ 곡학 μ „κ³΅μžμ˜€μŠ΅λ‹ˆλ‹€.
02:59
but our experience with Jeff left us wondering:
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μ œν”„μ˜ μ‚¬λ‘€λ‘œ ν•œκ°€μ§€ 의문점이 λ‚¨μ•˜μ£ .
03:02
Could we repeat this through our business?
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우리 업무에 λ‹€μ‹œ μ μš©ν•  수 μžˆμ„κΉŒ?
03:06
So we changed the way we recruited and trained.
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κ·Έλž˜μ„œ μš°λ¦¬λŠ” μ±„μš©κ³Ό κ΅μœ‘λ°©μ‹μ„ λ°”κΎΈμ—ˆμŠ΅λ‹ˆλ‹€.
03:11
And while we still sought after computer engineers and computer science majors,
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컴퓨터 κ³Όν•™μ΄λ‚˜ 곡학 μ „κ³΅μžλ₯Ό 계속 μ°ΎμœΌλ©΄μ„œλ„
03:17
we sprinkled in artists, musicians, writers ...
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μ˜ˆμˆ κ°€, μŒμ•…κ°€, μž‘κ°€λ₯Ό 간간이 ν¬ν•¨μ‹œμΌ°μŠ΅λ‹ˆλ‹€.
03:24
and Jeff's story started to multiply itself throughout our company.
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그리고, μ œν”„μ™€ 같은 κ²½μš°κ°€ λ°˜λ³΅ν•΄μ„œ λ‚˜νƒ€λ‚˜κΈ° μ‹œμž‘ν–ˆμŠ΅λ‹ˆλ‹€.
03:29
Our chief technology officer is an English major,
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우리 졜고 기술 μ±…μž„μž(CTO)λŠ” μ˜μ–΄ μ „κ³΅μžμž…λ‹ˆλ‹€.
03:34
and he was a bike messenger in Manhattan.
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λ§¨ν•΄νŠΌμ—μ„œ μžμ „κ±° 택배λ₯Ό ν•˜κΈ°λ„ ν–ˆμ£ .
03:38
And today, we're a thousand people,
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ν˜„μž¬λŠ” 직원이 1,000λͺ…이 λ˜μ—ˆκ³ 
03:41
yet still less than a hundred have degrees in computer science or engineering.
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μ—¬μ „νžˆ 100λͺ… μ΄ν•˜μ˜ 컴퓨터 κ³Όν•™μ΄λ‚˜ 곡학 μ „κ³΅μžκ°€ 있고
03:48
And yes, we're still a computer consulting firm.
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μ—¬μ „νžˆ 컴퓨터 μ»¨μ„€νŒ… νšŒμ‚¬μž…λ‹ˆλ‹€.
03:52
We're the number one player in our market.
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μš°λ¦¬λŠ” μ‹œμž₯μ—μ„œ 일등 기업이고
03:54
We work with the fastest-growing software package
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κΈ‰ μ„±μž₯ν•˜λŠ” μ†Œν”„νŠΈμ›¨μ–΄ νŒ¨ν‚€μ§€λ₯Ό λ§Œλ“€κ³ 
03:56
to ever reach 10 billion dollars in annual sales.
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μ—°λ§€μΆœμ΄ 100μ–΅ λ‹¬λŸ¬μ— κ°€κΉμŠ΅λ‹ˆλ‹€.
04:01
So it's working.
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νš¨κ³Όκ°€ μžˆμ—ˆλ˜ κ±°μ£ .
04:05
Meanwhile, the push for STEM-based education in this country --
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반면 μš°λ¦¬λ‚˜λΌμ—μ„œ STEM 기반 κ΅μœ‘μ— λŒ€ν•œ κ°•μš”λŠ” --
04:11
science, technology, engineering, mathematics --
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κ³Όν•™, 기술, 곡학, μˆ˜ν•™--
04:14
is fierce.
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κ·Ήμ‹¬ν•©λ‹ˆλ‹€.
04:15
It's in all of our faces.
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μ΄λŠ” 우리 λͺ¨λ‘μ˜ λͺ¨μŠ΅μž…λ‹ˆλ‹€.
04:18
And this is a colossal mistake.
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그리고 μ—„μ²­λ‚œ μ‹€μˆ˜μž…λ‹ˆλ‹€.
04:21
Since 2009, STEM majors in the United States
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2009λ…„ 이후, 미ꡭ의 STEM μ „κ³΅μžλŠ”
04:25
have increased by 43 percent,
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43% μ¦κ°€ν–ˆμŠ΅λ‹ˆλ‹€.
04:28
while the humanities have stayed flat.
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반면 인문학 μ „κ³΅μž μˆ˜λŠ” λ™μΌν•©λ‹ˆλ‹€.
04:30
Our past president
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우리의 μ „ λŒ€ν†΅λ Ήμ€
04:33
dedicated over a billion dollars towards STEM education
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10μ–΅ λ‹¬λŸ¬ 이상을 STEM κ΅μœ‘μ— νˆ¬μžν–ˆμŠ΅λ‹ˆλ‹€.
04:36
at the expense of other subjects,
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λ‹€λ₯Έ κ³Όλͺ©μ˜ μ˜ˆμ‚°μ„ μ€„μ΄λ©΄μ„œ 말이죠.
04:39
and our current president
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그리고 ν˜„ λŒ€ν†΅λ Ήμ€
04:42
recently redirected 200 million dollars of Department of Education funding
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졜근 κ΅μœ‘λΆ€ μ˜ˆμ‚° 쀑 2μ–΅ λ‹¬λŸ¬λ₯Ό 컴퓨터 κ³Όν•™ μͺ½μ— νˆ¬μžν–ˆμŠ΅λ‹ˆλ‹€.
04:47
into computer science.
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04:49
And CEOs are continually complaining about an engineering-starved workforce.
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κΈ°μ—… CEO듀은 늘 곡학전곡 인λ ₯이 λΆ€μ‘±ν•˜λ‹€κ³  λΆˆν‰ν•©λ‹ˆλ‹€.
04:57
These campaigns,
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이런 μ›€μ§μž„μ€
05:00
coupled with the undeniable success of the tech economy --
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기술 경제의 λͺ…λ°±ν•œ 성곡과 맞물리게 λ˜μ—ˆμŠ΅λ‹ˆλ‹€.
05:04
I mean, let's face it,
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κ·ΈλŸ¬λ‹ˆκΉŒ, μ†”μ§νžˆ λ§ν•΄μ„œ
05:05
seven out of the 10 most valuable companies in the world by market cap
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μ‹œκ°€μ΄μ•‘μœΌλ‘œ λ”°μ Έμ„œ 세계 10λŒ€ κΈ°μ—… 쀑 7κ°œλŠ”
05:10
are technology firms --
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기술 κ΄€λ ¨ νšŒμ‚¬μž…λ‹ˆλ‹€.
05:13
these things create an assumption
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μ΄λ‘œλΆ€ν„° μ˜ˆμΈ‘λ˜λŠ” 것은
05:16
that the path of our future workforce will be dominated by STEM.
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미래 인λ ₯의 κ²½λ ₯은 STEM으둜 μ±„μ›Œμ§ˆ κ±°λΌλŠ” κ²ƒμž…λ‹ˆλ‹€.
05:24
I get it.
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μ’‹μ•„μš”.
05:26
On paper, it makes sense.
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이둠적으둜 λ§žμŠ΅λ‹ˆλ‹€.
05:29
It's tempting.
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맀λ ₯적이기도 ν•˜μ£ .
05:33
But it's totally overblown.
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ν•˜μ§€λ§Œ μ™„μ „νžˆ κ³ΌλŒ€ν‰κ°€λ˜μ—ˆμŠ΅λ‹ˆλ‹€.
05:35
It's like, the entire soccer team chases the ball into the corner,
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μΆ•κ΅¬νŒ€ μ„ μˆ˜ λͺ¨λ‘κ°€ 곡을 따라 ν•œκ³³ κ΅¬μ„μœΌλ‘œ λͺ¨μ΄λŠ” λͺ¨μ–‘μƒˆμž…λ‹ˆλ‹€.
05:41
because that's where the ball is.
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곡이 κ±°κΈ° μžˆμœΌλ‹ˆκΉŒμš”.
05:44
We shouldn't overvalue STEM.
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STEM에 λ„ˆλ¬΄ κ°€μΉ˜λ₯Ό λΆ€μ—¬ν•΄μ„œλŠ” μ•ˆλ©λ‹ˆλ‹€.
05:48
We shouldn't value the sciences any more than we value the humanities.
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더 이상 인문학 μ΄μƒμœΌλ‘œ 과학에 κ°€μΉ˜λ₯Ό λΆ€μ—¬ν•˜λ©΄ μ•ˆλ©λ‹ˆλ‹€.
05:52
And there are a couple of reasons.
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μ—¬κΈ°μ—λŠ” λͺ‡ 가지 μ΄μœ κ°€ μžˆμŠ΅λ‹ˆλ‹€.
05:55
Number one, today's technologies are incredibly intuitive.
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첫째, μ˜€λŠ˜λ‚  κΈ°μˆ μ€ λ„ˆλ¬΄λ‚˜λ„ μ§κ΄€μ μž…λ‹ˆλ‹€.
06:01
The reason we've been able to recruit from all disciplines
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μš°λ¦¬κ°€ λ‹€μ–‘ν•œ μ „κ³΅μžλ₯Ό μ±„μš©ν•  수 μžˆμ—ˆκ³ 
06:05
and swivel into specialized skills
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λ‹€λ₯Έ μ „λ¬Έ 뢄야에 λˆˆμ„ 돌릴 수 μžˆμ—ˆλ˜ μ΄μœ λŠ”
06:08
is because modern systems can be manipulated without writing code.
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μ΅œμ‹  μ‹œμŠ€ν…œμ€ ν”„λ‘œκ·Έλž˜λ° 기술이 없어도 λ‹€λ£° 수 있기 λ•Œλ¬Έμž…λ‹ˆλ‹€.
06:13
They're like LEGO: easy to put together, easy to learn, even easy to program,
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레고처럼 μ‰½κ²Œ μ‘°λ¦½ν•˜κ³ , 배우기 쉽고, ν”„λ‘œκ·Έλž˜λ°λ„ μ‰½μŠ΅λ‹ˆλ‹€.
06:19
given the vast amounts of information that are available for learning.
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ν•™μŠ΅μ— ν•„μš”ν•œ κ΄‘λ²”μœ„ν•œ 정보가 주어지기 λ•Œλ¬Έμ΄μ£ .
06:23
Yes, our workforce needs specialized skill,
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λ¬Όλ‘ , 우리 인λ ₯은 전문적인 λŠ₯λ ₯이 ν•„μš”ν•©λ‹ˆλ‹€.
06:27
but that skill requires a far less rigorous and formalized education
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ν•˜μ§€λ§Œ κ·Έ λŠ₯λ ₯은 νœ μ”¬ 덜 μ—„κ²©ν•˜κ³  덜 μ •ν˜•ν™”λœ ꡐ윑만 있으면 λ©λ‹ˆλ‹€.
06:32
than it did in the past.
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과거에 λΉ„ν•΄μ„œ 말이죠.
06:34
Number two, the skills that are imperative and differentiated
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λ‘˜μ§Έ, λ°˜λ“œμ‹œ ν•„μš”ν•˜κ³  μ°¨λ³„ν™”λ˜λŠ” λŠ₯λ ₯은
06:40
in a world with intuitive technology
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직관적인 기술의 μ„Έκ³„μ—μ„œ
06:43
are the skills that help us to work together as humans,
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μΈκ°„μœΌλ‘œμ„œ ν•¨κ»˜ μΌν•˜λŠ” 데에 도움을 μ£ΌλŠ” λŠ₯λ ₯μž…λ‹ˆλ‹€.
06:49
where the hard work is envisioning the end product
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μ—΄μ‹¬νžˆ μΌν•œλ‹€λŠ” 것은 μ΅œμ’… μ‚°μΆœλ¬Όμ„ κ΅¬μƒν•˜λŠ” 것이죠.
06:54
and its usefulness,
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κ·Έ νš¨μš©μ„±λ„ ν¬ν•¨ν•΄μ„œμš”.
06:55
which requires real-world experience and judgment and historical context.
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μ΄λŠ” μ‹€μ œ μ„Έκ³„μ˜ κ²½ν—˜κ³Ό νŒλ‹¨, 역사적 λ§₯락이 μš”κ΅¬λ©λ‹ˆλ‹€.
07:03
What Jeff's story taught us
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μ œν”„μ˜ μ‚¬λ‘€μ—μ„œ 배운 것은
07:05
is that the customer was focused on the wrong thing.
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고객이 잘λͺ»λœ 곳에 μ§‘μ€‘ν–ˆλ‹€λŠ” κ²ƒμž…λ‹ˆλ‹€.
07:10
It's the classic case:
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μ „ν˜•μ μΈ ν˜•νƒœλŠ” μ΄λ ‡μŠ΅λ‹ˆλ‹€.
07:12
the technologist struggling to communicate with the business and the end user,
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κΈ°μˆ μžλŠ” κΈ°μ—…μ΄λ‚˜ μ΅œμ’… μ‚¬μš©μžμ™€ μ†Œν†΅ν•˜κΈ° μœ„ν•΄ μ• μ“°κ³ 
07:16
and the business failing to articulate their needs.
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기업은 μžμ‹ μ˜ μš”κ΅¬ 사항을 λΆ„λͺ…ν•˜κ²Œ μ„€λͺ…ν•˜μ§€ λͺ»ν•©λ‹ˆλ‹€.
07:22
I see it every day.
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μ €λŠ” 이런 경우λ₯Ό 맀일 λ΄…λ‹ˆλ‹€.
07:25
We are scratching the surface
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μš°λ¦¬λŠ” ν‘œλ©΄λ§Œ κ²‰λŒκ³  μžˆμŠ΅λ‹ˆλ‹€.
07:27
in our ability as humans to communicate and invent together,
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μΈκ°„μœΌλ‘œμ„œ μ„œλ‘œ μ†Œν†΅ν•˜κ³  κ°œλ°œν•˜λŠ” 우리의 λŠ₯λ ₯에 μžˆμ–΄μ„œ λ§μž…λ‹ˆλ‹€.
07:32
and while the sciences teach us how to build things,
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과학이 무언가λ₯Ό μ–΄λ–»κ²Œ λ§Œλ“€μ§€μ— λŒ€ν•΄ κ°€λ₯΄μ³μ£ΌλŠ” λ°˜λ©΄μ—
07:36
it's the humanities that teach us what to build and why to build them.
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인문학은 무엇을 λ§Œλ“€μ–΄μ•Ό ν•˜κ³  μ™œ λ§Œλ“€μ–΄μ•Ό ν•˜λŠ”μ§€λ₯Ό μ•Œλ €μ€λ‹ˆλ‹€.
07:43
And they're equally as important,
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이 λ‘˜ λͺ¨λ‘ λ˜‘κ°™μ΄ μ€‘μš”ν•˜κ³ 
07:46
and they're just as hard.
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λ‘˜ λ‹€ μ–΄λ ΅μŠ΅λ‹ˆλ‹€.
07:50
It irks me ...
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μ œκ°€ λΆˆνŽΈν•¨μ„ λŠλΌλŠ” λ•ŒλŠ”
07:54
when I hear people treat the humanities as a lesser path,
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μ‚¬λžŒλ“€μ΄ 인문학을 덜 μ€‘μš”ν•˜κ³  더 μ‰¬μš΄ κ²½λ ₯으둜 μ·¨κΈ‰ν•  λ•Œμž…λ‹ˆλ‹€.
08:00
as the easier path.
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08:01
Come on!
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μ—¬λŸ¬λΆ„!
08:04
The humanities give us the context of our world.
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인문학은 μš°λ¦¬μ—κ²Œ μ„Έμƒμ˜ λ§₯락을 μ œκ³΅ν•©λ‹ˆλ‹€.
08:10
They teach us how to think critically.
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μš°λ¦¬μ—κ²Œ λΉ„νŒμ μœΌλ‘œ μ‚¬κ³ ν•˜λŠ” 방법을 μ•Œλ €μ€λ‹ˆλ‹€.
08:14
They are purposely unstructured,
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인문학은 μ˜λ„μ μœΌλ‘œ κ΅¬μ‘°ν™”λ˜μ§€ μ•Šμ•˜μŠ΅λ‹ˆλ‹€.
08:16
while the sciences are purposely structured.
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반면 과학은 μ˜λ„μ μœΌλ‘œ κ΅¬μ‘°ν™”λ˜μ—ˆμŠ΅λ‹ˆλ‹€.
08:19
They teach us to persuade, they give us our language,
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인문학은 섀득을 κ°€λ₯΄μΉ˜κ³  μ–Έμ–΄λ₯Ό μ œκ³΅ν•©λ‹ˆλ‹€.
08:23
which we use to convert our emotions to thought and action.
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μš°λ¦¬λŠ” 이λ₯Ό 톡해 감성을 사고와 ν–‰λ™μœΌλ‘œ μ „ν™˜ν•©λ‹ˆλ‹€.
08:32
And they need to be on equal footing with the sciences.
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인문학은 κ³Όν•™κ³Ό λ™λ“±ν•˜κ²Œ μ·¨κΈ‰λ˜μ–΄μ•Ό ν•©λ‹ˆλ‹€.
08:36
And yes, you can hire a bunch of artists
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μ—¬λŸ¬λΆ„λ„ λ§Žμ€ μ˜ˆμˆ κ°€λ₯Ό μ±„μš©ν•˜μ—¬
08:40
and build a tech company
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기술 νšŒμ‚¬λ₯Ό 섀립할 수 μžˆμŠ΅λ‹ˆλ‹€.
08:43
and have an incredible outcome.
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그러면 μ—„μ²­λ‚œ κ²°κ³Όλ₯Ό 얻을 κ²ƒμž…λ‹ˆλ‹€.
08:46
Now, I'm not here today to tell you that STEM's bad.
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μ €λŠ” 였늘 여기에 STEM이 λ‚˜μ˜λ‹€κ³  λ§ν•˜λ €κ³  온 것은 μ•„λ‹™λ‹ˆλ‹€.
08:52
I'm not here today to tell you that girls shouldn't code.
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μ—¬μ„± ν”„λ‘œκ·Έλž˜λ¨Έλ₯Ό λ°˜λŒ€ν•˜λ €λŠ” 것도 μ•„λ‹ˆμ—μš”.
08:57
(Laughter)
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(μ›ƒμŒ)
08:58
Please.
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제발
09:00
And that next bridge I drive over
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μ œκ°€ μš΄μ „ν•΄ 갈 λ‹€μŒ 닀리
09:02
or that next elevator we all jump into --
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ν˜Ήμ€ μš°λ¦¬κ°€ νƒˆ λ‹€μŒ μ—˜λ¦¬λ² μ΄ν„°...
09:07
let's make sure there's an engineer behind it.
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κ·Έ μ΄λ©΄μ—λŠ” 곡학이 μžˆμŒμ„ λΆ„λͺ…νžˆ ν•©μ‹œλ‹€.
09:09
(Laughter)
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(μ›ƒμŒ)
09:14
But to fall into this paranoia
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κ·ΈλŸ¬λ‚˜ 이런 νŽΈμ§‘μ¦μ— 잘λͺ» 빠지면
09:17
that our future jobs will be dominated by STEM,
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우리의 미래 직업을 STEM이 지배할 κ²ƒμž…λ‹ˆλ‹€.
09:22
that's just folly.
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그건 어리석은 κ²ƒμž…λ‹ˆλ‹€.
09:24
If you have friends or kids or relatives or grandchildren
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μ—¬λŸ¬λΆ„μ΄ 친ꡬ, μžλ…€, μΉœμ²™, 손주
09:28
or nieces or nephews ...
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ν˜Ήμ€ μ—¬μž μ‘°μΉ΄, λ‚¨μž μ‘°μΉ΄κ°€ μžˆλ‹€λ©΄
09:30
encourage them to be whatever they want to be.
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그듀이 μ›ν•˜λŠ” λŒ€λ‘œ ν•  수 μžˆλ„λ‘ 격렀해 μ£Όμ‹­μ‹œμ˜€.
09:34
(Applause)
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(λ°•μˆ˜)
09:41
The jobs will be there.
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그곳에 μΌμžλ¦¬κ°€ μžˆμ„ κ²ƒμž…λ‹ˆλ‹€.
09:45
Those tech CEOs
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기술 CEO듀은
09:48
that are clamoring for STEM grads,
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STEM μ‘Έμ—…μž₯을 μš”κ΅¬ν•©λ‹ˆλ‹€.
09:51
you know what they're hiring for?
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그듀이 λˆ„κ΅¬λ₯Ό μ±„μš©ν•˜λŠ”μ§€ μ•„μ„Έμš”?
09:54
Google, Apple, Facebook.
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ꡬ글, μ• ν”Œ, 페이슀뢁.
09:57
Sixty-five percent of their open job opportunities
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이듀 νšŒμ‚¬μ˜ 일자리 쀑 65%λŠ”
10:01
are non-technical:
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기술직이 μ•„λ‹™λ‹ˆλ‹€.
10:03
marketers, designers, project managers, program managers,
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판맀직, λ””μžμ΄λ„ˆ, ν”„λ‘œμ νŠΈ λ§€λ‹ˆμ €, ν”„λ‘œκ·Έλž¨ λ§€λ‹ˆμ €
10:08
product managers, lawyers, HR specialists,
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μ œν’ˆ λ§€λ‹ˆμ €, λ³€ν˜Έμ‚¬, 인사 관리 μ „λ¬Έκ°€
10:12
trainers, coaches, sellers, buyers, on and on.
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강사, μ½”μΉ˜, μ˜μ—…, κ΅¬λ§€λ‹΄λ‹Ήμž λ“±μž…λ‹ˆλ‹€.
10:15
These are the jobs they're hiring for.
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이런 일을 ν•  μ‚¬λžŒλ“€μ„ μ°Ύκ³  μžˆμŠ΅λ‹ˆλ‹€.
10:20
And if there's one thing that our future workforce needs --
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미래 인λ ₯μ—κ²Œ ν•„μš”ν•œ ν•œκ°€μ§€κ°€ μžˆμŠ΅λ‹ˆλ‹€.
10:26
and I think we can all agree on this --
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λ‹€λ“€ λ™μ˜ν•˜μ‹€ 거라 μƒκ°ν•΄μš”.
10:29
it's diversity.
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λ°”λ‘œ λ‹€μ–‘μ„±μž…λ‹ˆλ‹€.
10:31
But that diversity shouldn't end with gender or race.
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ν•˜μ§€λ§Œ 그런 닀양성이 μ„±λ³„μ΄λ‚˜ μΈμ’…μœΌλ‘œ λλ‚˜μ„  μ•ˆλ©λ‹ˆλ‹€.
10:35
We need a diversity of backgrounds
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μš°λ¦¬μ—κ²Œ ν•„μš”ν•œ 건 배경의 λ‹€μ–‘μ„±μž…λ‹ˆλ‹€.
10:39
and skills,
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그리고 λŠ₯λ ₯의 닀양성이죠.
10:42
with introverts and extroverts
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내성적인 μ‚¬λžŒκ³Ό μ™Έν–₯적인 μ‚¬λžŒ
10:45
and leaders and followers.
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μ•žμ„œλŠ” μ‚¬λžŒκ³Ό λ’€λ”°λ₯΄λŠ” μ‚¬λžŒμ΄ μžˆμŠ΅λ‹ˆλ‹€.
10:48
That is our future workforce.
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그것이 우리 미래의 인λ ₯μž…λ‹ˆλ‹€.
10:51
And the fact that the technology is getting easier and more accessible
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기술이 점점 쉽고 더 κ°„νŽΈν•΄μ§€κ³  μžˆμœΌλ‹ˆ
10:57
frees that workforce up
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그런 인λ ₯듀이 자유둭게
10:59
to study whatever they damn well please.
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μžμ‹ μ΄ μž˜ν•˜λŠ” 것을 λ°°μš°λ„λ‘ ν•΄μ£Όμ„Έμš”.
11:03
Thank you.
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κ°μ‚¬ν•©λ‹ˆλ‹€.
11:04
(Applause)
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(λ°•μˆ˜)
이 μ›Ήμ‚¬μ΄νŠΈ 정보

이 μ‚¬μ΄νŠΈλŠ” μ˜μ–΄ ν•™μŠ΅μ— μœ μš©ν•œ YouTube λ™μ˜μƒμ„ μ†Œκ°œν•©λ‹ˆλ‹€. μ „ 세계 졜고의 μ„ μƒλ‹˜λ“€μ΄ κ°€λ₯΄μΉ˜λŠ” μ˜μ–΄ μˆ˜μ—…μ„ 보게 될 κ²ƒμž…λ‹ˆλ‹€. 각 λ™μ˜μƒ νŽ˜μ΄μ§€μ— ν‘œμ‹œλ˜λŠ” μ˜μ–΄ μžλ§‰μ„ 더블 ν΄λ¦­ν•˜λ©΄ κ·Έκ³³μ—μ„œ λ™μ˜μƒμ΄ μž¬μƒλ©λ‹ˆλ‹€. λΉ„λ””μ˜€ μž¬μƒμ— 맞좰 μžλ§‰μ΄ μŠ€ν¬λ‘€λ©λ‹ˆλ‹€. μ˜κ²¬μ΄λ‚˜ μš”μ²­μ΄ μžˆλŠ” 경우 이 문의 양식을 μ‚¬μš©ν•˜μ—¬ λ¬Έμ˜ν•˜μ‹­μ‹œμ˜€.

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