TED2018
Kai-Fu Lee: How AI can save our humanity
李開復: 人工智慧如何能拯救人性
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人工智慧大大轉變我們的世界，但有一件事是它做不到的：愛。在這場很有遠見的演說中，電腦科學家李開復詳細說明美國和中國如何帶動深度學習的革命，並分享在人工智慧的時代人類要如何發揮同理心和創意來茁壯。他說：「人工智慧是種機緣。」「它來解放我們，不要再做例行性的工作；也來提醒我們，人類到底有什麼不同。」
Kai-Fu Lee - Investor, computer scientist
At Sinovation Ventures, Kai-Fu Lee invests in the next generation of Chinese high-tech companies. Full bio
At Sinovation Ventures, Kai-Fu Lee invests in the next generation of Chinese high-tech companies. Full bio
Double-click the English transcript below to play the video.
00:12
I'm going to talk about
how AI and mankind can coexist,
how AI and mankind can coexist,
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我將要談的是人類
和人工智慧要如何共存，
和人工智慧要如何共存，
00:17
but first, we have to rethink
about our human values.
about our human values.
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但首先，我們先要
重新審視人類的價值觀。
重新審視人類的價值觀。
00:21
So let me first make a confession
about my errors in my values.
about my errors in my values.
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讓我先坦誠我在
我的價值觀中所犯的錯誤。
我的價值觀中所犯的錯誤。
00:25
It was 11 o'clock, December 16, 1991.
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那是 1991 年 12 月 16 日 11 點。
00:30
I was about to become a father
for the first time.
for the first time.
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我就要成為一個新手爸爸了。
00:33
My wife, Shen-Ling,
lay in the hospital bed
lay in the hospital bed
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我的太太先玲躺在醫院的病床上，
00:36
going through a very difficult
12-hour labor.
12-hour labor.
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經歷著一段艱辛
並長達十二個小時的分娩。
並長達十二個小時的分娩。
00:40
I sat by her bedside
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我就坐在床邊，
00:42
but looked anxiously at my watch,
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但很焦慮地看著我的手錶，
00:45
and I knew something that she didn't.
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我知道一件她不知道的事。
00:47
I knew that if in one hour,
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我知道，如果一個小時內，
00:50
our child didn't come,
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我們的孩子還不出來，
00:52
I was going to leave her there
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我就得要把她丟在那裡，
00:54
and go back to work
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回去工作，
00:55
and make a presentation about AI
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去做一場關於人工智慧的簡報，
00:58
to my boss, Apple's CEO.
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對象是我的老闆，蘋果的執行長。
01:03
Fortunately, my daughter
was born at 11:30 --
was born at 11:30 --
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幸運的是，我女兒
在 11:30 出生了——
在 11:30 出生了——
01:07
(Laughter)
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（笑聲）
01:09
(Applause)
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（掌聲）
01:11
sparing me from doing the unthinkable,
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讓我不用去做我無法想像的事，
01:15
and to this day, I am so sorry
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至今，我仍然很後悔
01:17
for letting my work ethic
take precedence over love for my family.
take precedence over love for my family.
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我把工作擺在了我對家人的愛之前。
01:22
(Applause)
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（掌聲）
01:28
My AI talk, however, went off brilliantly.
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然而，我的人工智慧演說表現超好。
01:30
(Laughter)
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（笑聲）
01:33
Apple loved my work
and decided to announce it
and decided to announce it
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蘋果很愛我的作品，
01:36
at TED1992,
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決定在 TED 1992 宣佈它，
01:39
26 years ago on this very stage.
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26 年前，就在這個舞台上。
01:42
I thought I had made one of the biggest,
most important discoveries in AI,
most important discoveries in AI,
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我認為我在人工智慧上
有了最大、最重要的發現，
有了最大、最重要的發現，
01:47
and so did the "Wall Street Journal"
on the following day.
on the following day.
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隔天的《華爾街日報》也這麼認為。
01:51
But as far as discoveries went,
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但隨著越來越多的「發現」，
01:54
it turned out,
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結果是，
01:55
I didn't discover India, or America.
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我沒有發現印度，或美洲。
01:58
Perhaps I discovered
a little island off of Portugal.
a little island off of Portugal.
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也許我發現的是
葡萄牙外海的一個小島。
葡萄牙外海的一個小島。
02:02
But the AI era of discovery continued,
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但人工智慧發現的時代
仍然繼續走下去了，
仍然繼續走下去了，
02:06
and more scientists
poured their souls into it.
poured their souls into it.
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更多的科學家
全心全意地投入其中。
全心全意地投入其中。
02:09
About 10 years ago, the grand AI discovery
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大約十年前，三位北美科學家
02:11
was made by three
North American scientists,
North American scientists,
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有了偉大的人工智慧發現，
02:15
and it's known as deep learning.
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即大家所知的深度學習。
02:17
Deep learning is a technology
that can take a huge amount of data
that can take a huge amount of data
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深度學習這項技術，
是一個能利用海量數據
是一個能利用海量數據
02:21
within one single domain
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在單一領域中
02:22
and learn to predict or decide
at superhuman accuracy.
at superhuman accuracy.
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學會做超高精度的預測或判斷的技術。
02:27
For example, if we show
the deep learning network
the deep learning network
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比如，如果我們給深度學習網路
02:30
a massive number of food photos,
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提供海量的食物照片，
02:33
it can recognize food
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它就能辨識出食物，
02:35
such as hot dog or no hot dog.
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比如熱狗，或不是熱狗。
02:38
(Applause)
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（掌聲）
02:41
Or if we show it many pictures
and videos and sensor data
and videos and sensor data
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或者，如果我讓它看很多
在公路上開車的照片、
在公路上開車的照片、
02:46
from driving on the highway,
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影片，以及感測器資料，
02:49
it can actually drive a car
as well as a human being
as well as a human being
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它就能夠在高速公路上
嫺熟地駕駛一台車，
嫺熟地駕駛一台車，
絲毫不輸給人類。
02:53
on the highway.
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02:55
And what if we showed
this deep learning network
this deep learning network
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如果我們向這個深度學習網路展示
02:57
all the speeches made by President Trump?
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川普總統做過的所有演說呢？
03:01
Then this artificially
intelligent President Trump,
intelligent President Trump,
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那麼，這位人工智慧的川普總統，
03:05
actually the network --
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其實就是這個網路——
03:07
(Laughter)
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（笑聲）
03:09
can --
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能夠——
03:10
(Applause)
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（掌聲）
03:14
You like double oxymorons, huh?
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你們喜歡一語雙關，是吧？
03:17
(Laughter)
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（笑聲）
（註：artificial 也是「虛假造作的」）
（註：artificial 也是「虛假造作的」）
03:21
(Applause)
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（掌聲）
03:27
So this network, if given the request
to make a speech about AI,
to make a speech about AI,
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所以，如果要求這個網路
做出一段關於人工智慧的演說，
做出一段關於人工智慧的演說，
03:32
he, or it, might say --
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他，或它，可能會說——
03:36
(Recording) Donald Trump:
It's a great thing
It's a great thing
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（錄音）川普：這是很棒的事，
03:38
to build a better world
with artificial intelligence.
with artificial intelligence.
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用人工智慧來建造一個更好的世界。
03:41
Kai-Fu Lee: And maybe in another language?
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講者：換個語言呢？
03:43
DT: (Speaking Chinese)
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川普：（說中文）
人工智能正在改變世界。
人工智能正在改變世界。
03:45
(Laughter)
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（笑聲）
03:46
KFL: You didn't know
he knew Chinese, did you?
he knew Chinese, did you?
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講者：你們不知道川普會說中文吧？
03:50
So deep learning has become the core
in the era of AI discovery,
in the era of AI discovery,
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所以，深度學習已經成為
人工智慧發現時代的核心，
人工智慧發現時代的核心，
03:55
and that's led by the US.
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且是由美國來領導的。
03:57
But we're now in the era
of implementation,
of implementation,
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但現在，我們已經進入實踐的時代，
04:00
where what really matters is execution,
product quality, speed and data.
product quality, speed and data.
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在這個時代，重要的是執行、
產品品質、速度，和資料。
產品品質、速度，和資料。
04:05
And that's where China comes in.
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這就是中國出場的時候了。
04:07
Chinese entrepreneurs,
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身為創業投資家，
04:09
who I fund as a venture capitalist,
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我投資的中國企業家
04:11
are incredible workers,
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是很棒的實幹家，
04:13
amazing work ethic.
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有很了不起的職業道德。
04:15
My example in the delivery room is nothing
compared to how hard people work in China.
compared to how hard people work in China.
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我在產房的那個例子，
完全不及中國人們賣力工作的程度。
完全不及中國人們賣力工作的程度。
04:20
As an example, one startup
tried to claim work-life balance:
tried to claim work-life balance:
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舉個例子，有間新興公司
提出「工作—生活」的平衡：
提出「工作—生活」的平衡：
04:24
"Come work for us because we are 996."
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「來為我們工作，因為我們是 996。」
04:27
And what does that mean?
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那是什麼意思？
04:29
It means the work hours
of 9am to 9pm, six days a week.
of 9am to 9pm, six days a week.
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意思是，工作時間從早上 9 點
到晚上 9 點，一週工作 6 天。
到晚上 9 點，一週工作 6 天。
04:34
That's contrasted
with other startups that do 997.
with other startups that do 997.
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相對的，其他新興公司都是 997。
04:39
And the Chinese product quality
has consistently gone up
has consistently gone up
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而中國的產品品質
在過去十年間持續不斷地提升，
04:42
in the past decade,
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04:44
and that's because of
a fiercely competitive environment.
a fiercely competitive environment.
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那是因為這個環境中的
競爭太激烈了。
競爭太激烈了。
04:48
In Silicon Valley, entrepreneurs
compete in a very gentlemanly fashion,
compete in a very gentlemanly fashion,
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在矽谷，企業家會
以很紳仕的方式競爭，
以很紳仕的方式競爭，
04:54
sort of like in old wars
in which each side took turns
in which each side took turns
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有點像是古老的戰爭，雙方會輪流
04:58
to fire at each other.
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向對方開火。
04:59
(Laughter)
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（笑聲）
05:00
But in the Chinese environment,
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但在中國的環境中，
05:03
it's truly a gladiatorial
fight to the death.
fight to the death.
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就像是羅馬競技場，
不死不休的戰鬥。
不死不休的戰鬥。
05:06
In such a brutal environment,
entrepreneurs learn to grow very rapidly,
entrepreneurs learn to grow very rapidly,
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在這麼殘酷的環境中，
企業家學會要非常快速地成長，
企業家學會要非常快速地成長，
05:13
they learn to make their products
better at lightning speed,
better at lightning speed,
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他們學會要雷厲風行地
讓自己的產品變更好，
讓自己的產品變更好，
05:17
and they learn
to hone their business models
to hone their business models
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他們學會要一直鍛鍊
他們的商業模型，
他們的商業模型，
05:19
until they're impregnable.
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直到堅不可摧為止。
05:21
As a result, great Chinese products
like WeChat and Weibo
like WeChat and Weibo
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結果就是，優秀的中國產品，
如微信和微博，
如微信和微博，
05:25
are arguably better
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可以說比美國
05:26
than the equivalent American products
from Facebook and Twitter.
from Facebook and Twitter.
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相對應產品，
如臉書和推特，還要好。
如臉書和推特，還要好。
05:31
And the Chinese market
embraces this change
embraces this change
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且中國市場擁抱這樣的改變、
05:35
and accelerated change
and paradigm shifts.
and paradigm shifts.
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加速的改變，以及範式轉移。
05:38
As an example, if any of you go to China,
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舉個例，如果你去中國，
05:40
you will see it's almost cashless
and credit card-less,
and credit card-less,
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你會發現幾乎沒有
在用現金或是信用卡，
在用現金或是信用卡，
05:44
because that thing that we all
talk about, mobile payment,
talk about, mobile payment,
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原因就是我們大家都在談的
那樣東西，移動支付，
那樣東西，移動支付，
05:47
has become the reality in China.
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在中國已經實現。
05:49
In the last year,
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去年，
05:50
18.8 trillion US dollars
were transacted on mobile internet,
were transacted on mobile internet,
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行動網路上的交易金額
達到 18.8 兆美元，
達到 18.8 兆美元，
05:56
and that's because
of very robust technologies
of very robust technologies
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那是因為在行動支付背後的技術
05:59
built behind it.
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非常穩定健全。
06:00
It's even bigger than the China GDP.
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這個數字比中國的國內生產總值還高。
06:04
And this technology, you can say,
how can it be bigger than the GDP?
how can it be bigger than the GDP?
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你會問，這項技術怎麼可能
超越國內生產總值？
超越國內生產總值？
06:07
Because it includes all transactions:
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因為它包含所有的交易：
06:09
wholesale, channels,
retail, online, offline,
retail, online, offline,
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批發、電視台、零售、線上、線下、
06:13
going into a shopping mall
or going into a farmers market like this.
or going into a farmers market like this.
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到購物中心去買，
或到像這樣的農民市場去買。
或到像這樣的農民市場去買。
06:18
The technology is used
by 700 million people
by 700 million people
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有 7 億人使用移動支付
06:22
to pay each other, not just merchants,
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來付錢給彼此，不只付錢給商人，
06:24
so it's peer to peer,
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所以，它是點對點的，
06:25
and it's almost transaction-fee-free.
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且幾乎不需要交易手續費。
06:29
And it's instantaneous,
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且是立即生效，
06:32
and it's used everywhere.
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到處都在使用。
06:34
And finally, the China market is enormous.
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最後一點，中國的市場很巨大。
06:37
This market is large,
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這個市場很大，
06:39
which helps give entrepreneurs
more users, more revenue,
more users, more revenue,
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讓企業家能有更多的
使用者、更多的收益、
使用者、更多的收益、
06:44
more investment, but most importantly,
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更多的投資，最重要的是，
06:46
it gives the entrepreneurs a chance
to collect a huge amount of data
to collect a huge amount of data
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它讓企業家有機會
能收集到大量的數據資料，
能收集到大量的數據資料，
06:51
which becomes rocket fuel
for the AI engine.
for the AI engine.
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這些資料就成了
人工智慧引擎的火箭燃料。
人工智慧引擎的火箭燃料。
06:54
So as a result, the Chinese AI companies
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結果就是，中國的人工智慧公司
06:57
have leaped ahead
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已經大躍進，跳到前頭，
06:59
so that today, the most valuable companies
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所以，現今，最有價值的公司，
07:02
in computer vision, speech recognition,
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不論是在電腦視覺、語音識別、
07:05
speech synthesis,
machine translation and drones
machine translation and drones
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語音合成、機器翻譯，
及無人機的領域，
及無人機的領域，
07:08
are all Chinese companies.
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都是中國公司稱霸。
07:11
So with the US leading
the era of discovery
the era of discovery
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美國領導的是發現的時代，
07:14
and China leading
the era of implementation,
the era of implementation,
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而中國領導的是導入的時代，
07:17
we are now in an amazing age
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我們現在身處一個很了不起的時代，
07:20
where the dual engine
of the two superpowers
of the two superpowers
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這兩種超能力的雙重引擎
07:23
are working together
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在同心協力，
07:25
to drive the fastest
revolution in technology
revolution in technology
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在科技領域中，
推動我們人類所見過
推動我們人類所見過
07:29
that we have ever seen as humans.
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最快速的革命。
07:32
And this will bring tremendous wealth,
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這會帶來驚人的財富，
07:35
unprecedented wealth:
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前所未有的財富：
07:36
16 trillion dollars, according to PwC,
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根據普華永道會計師事務所的資料，
07:40
in terms of added GDP
to the worldwide GDP by 2030.
to the worldwide GDP by 2030.
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到 2030 年，全世界的國內生產總值
會增加 16 兆美元。
會增加 16 兆美元。
07:46
It will also bring immense challenges
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這也會帶來極大的挑戰：
07:48
in terms of potential job replacements.
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潛在的工作取代性問題。
07:52
Whereas in the Industrial Age
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在工業時代，
07:55
it created more jobs
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它創造了更多的工作，
07:57
because craftsman jobs were being
decomposed into jobs in the assembly line,
decomposed into jobs in the assembly line,
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因為技工的工作被重新
分解成為生產線的工作，
分解成為生產線的工作，
08:03
so more jobs were created.
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所以創造了更多的工作。
08:05
But AI completely replaces
the individual jobs
the individual jobs
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但人工智慧完全取代了個人的工作，
08:10
in the assembly line with robots.
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在生產線上直接用機器人。
08:12
And it's not just in factories,
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且不只是在工廠，
08:14
but truckers, drivers
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還有貨車司機、一般司機，
08:16
and even jobs like telesales,
customer service
customer service
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甚至像電話銷售、客戶服務、
08:20
and hematologists as well as radiologists
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血液學學者，以及放射線研究者
08:23
over the next 15 years
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在未來十五年
08:25
are going to be gradually replaced
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都可能漸漸被
08:28
by artificial intelligence.
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人工智慧取代。
08:30
And only the creative jobs --
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只有有創意的工作——
08:32
(Laughter)
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（笑聲）
（表中講者被列在「安全」）
（表中講者被列在「安全」）
08:34
I have to make myself safe, right?
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我得讓我自己很安全，對吧？
08:38
Really, the creative jobs
are the ones that are protected,
are the ones that are protected,
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說真的，有創意的工作
是受到保護的工作，
是受到保護的工作，
08:41
because AI can optimize but not create.
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因為人工智慧能夠
做到最佳化，但不能創造。
做到最佳化，但不能創造。
08:45
But what's more serious
than the loss of jobs
than the loss of jobs
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但比失去工作更嚴重的，
08:49
is the loss of meaning,
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是失去意義，
08:51
because the work ethic
in the Industrial Age
in the Industrial Age
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因為在工業時代的工作倫理
08:54
has brainwashed us into thinking
that work is the reason we exist,
that work is the reason we exist,
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已經將我們洗腦，
認為工作是我們存在的理由，
認為工作是我們存在的理由，
09:00
that work defined
the meaning of our lives.
the meaning of our lives.
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認為工作定義了我們人生的意義。
09:03
And I was a prime and willing victim
to that type of workaholic thinking.
to that type of workaholic thinking.
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我就曾是那種工作狂思想的
主要受害者，也是自願受害者。
主要受害者，也是自願受害者。
09:09
I worked incredibly hard.
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我工作非常努力。
09:11
That's why I almost left
my wife in the delivery room,
my wife in the delivery room,
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那就是為什麼在產房時
我差一點就要丟下我太太，
我差一點就要丟下我太太，
09:14
that's why I worked 996
alongside my entrepreneurs.
alongside my entrepreneurs.
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那就是為什麼我和我的
企業家們的工作時間是 996。
企業家們的工作時間是 996。
09:18
And that obsession that I had with work
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我先前對於工作的這種迷戀，
09:23
ended abruptly a few years ago
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在幾年前突然終止了，
09:26
when I was diagnosed
with fourth stage lymphoma.
with fourth stage lymphoma.
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因為我被診斷出第四期的淋巴癌。
09:31
The PET scan here shows
over 20 malignant tumors
over 20 malignant tumors
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這裡的正子掃描結果顯示
有超過 20 個惡性腫瘤
有超過 20 個惡性腫瘤
09:35
jumping out like fireballs,
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像火球一樣跳出來，
09:37
melting away my ambition.
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融化了我的野心。
09:40
But more importantly,
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但，更重要的是，
09:41
it helped me reexamine my life.
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它讓我重新檢視我的人生。
09:44
Knowing that I may only have
a few months to live
a few months to live
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知道我可能只剩下幾個月能活，
09:47
caused me to see how foolish it was
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讓我看清了我有多愚蠢，
09:50
for me to base my entire self-worth
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因為我把我的整個自我價值
09:54
on how hard I worked
and the accomplishments from hard work.
and the accomplishments from hard work.
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建立在工作努力程度上，
以及努力帶來的成就上。
以及努力帶來的成就上。
09:59
My priorities were
completely out of order.
completely out of order.
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我的優先順序完全錯亂。
10:02
I neglected my family.
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我忽視了我的家庭。
10:05
My father had passed away,
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我父親已經過世，
10:06
and I never had a chance
to tell him I loved him.
to tell him I loved him.
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我從來沒有機會告訴他我愛他。
10:10
My mother had dementia
and no longer recognized me,
and no longer recognized me,
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我母親有失智症，已經不認得我，
10:13
and my children had grown up.
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我的孩子已經長大。
10:16
During my chemotherapy,
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在我做化療期間，
10:18
I read a book by Bronnie Ware
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我讀了一本布羅妮韋爾寫的書，
10:20
who talked about dying wishes and regrets
of the people in the deathbed.
of the people in the deathbed.
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談到臨終的人的死前願望和悔恨。
10:26
She found that facing death,
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她發現，面對死亡時，
10:28
nobody regretted that they didn't
work hard enough in this life.
work hard enough in this life.
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沒有人後悔這一生的
工作不夠努力。
工作不夠努力。
10:32
They only regretted that they didn't
spend enough time with their loved ones
spend enough time with their loved ones
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他們只會後悔沒有花
足夠時間陪他們愛的人，
足夠時間陪他們愛的人，
10:38
and that they didn't spread their love.
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沒有把他們的愛散播出去。
10:42
So I am fortunately today in remission.
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現在，我很幸運處於緩解期。
10:46
(Applause)
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（掌聲）
10:53
So I can be back at TED again
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所以我能再次回到 TED，
10:55
to share with you
that I have changed my ways.
that I have changed my ways.
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和各位分享，
我已經改變了我的方式。
我已經改變了我的方式。
10:59
I now only work 965 --
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我現在的工作方式是 965 ——
11:03
occasionally 996, but usually 965.
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偶爾是 996，但通常是 965。
11:07
I moved closer to my mother,
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我搬到離我母親比較近的地方，
11:09
my wife usually travels with me,
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我太太通常與我一同旅行，
11:11
and when my kids have vacation,
if they don't come home, I go to them.
if they don't come home, I go to them.
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當我的孩子有假期時，
若他們不回家，我會去找他們。
若他們不回家，我會去找他們。
11:15
So it's a new form of life
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這是一種新的生活形式，
11:17
that helped me recognize
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協助我了解到
11:19
how important it is that love is for me,
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愛對我而言有多重要，
11:22
and facing death
helped me change my life,
helped me change my life,
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面對死亡，協助我改變了我的生活，
11:26
but it also helped me see a new way
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也協助我找到了一種新的方式，
11:28
of how AI should impact mankind
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看待人工智慧對於人類的衝擊，
11:32
and work and coexist with mankind,
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以及如何和人類合作與共存，
11:36
that really, AI is taking away
a lot of routine jobs,
a lot of routine jobs,
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確實，人工智慧會奪走
很多例行性的工作，
很多例行性的工作，
11:41
but routine jobs are not what we're about.
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但我們並不只會做例行性的工作。
11:44
Why we exist is love.
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我們存在的目的是愛。
11:47
When we hold our newborn baby,
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當我們抱著新生的嬰兒，
11:49
love at first sight,
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第一眼就產生了愛，
11:50
or when we help someone in need,
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當我們協助了需要幫忙的人，
11:52
humans are uniquely able
to give and receive love,
to give and receive love,
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人類能給予愛和接收愛，
這是獨一無二的，
這是獨一無二的，
11:56
and that's what differentiates us from AI.
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這就是我們和人工智慧的差別。
12:00
Despite what science fiction may portray,
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不論科幻小說怎麼寫，
12:03
I can responsibly tell you
that AI has no love.
that AI has no love.
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我可以很確定地告訴各位，
人工智慧沒有愛。
人工智慧沒有愛。
12:07
When AlphaGo defeated
the world champion Ke Jie,
the world champion Ke Jie,
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當 AlphaGo 打敗世界冠軍柯潔，
12:10
while Ke Jie was crying
and loving the game of go,
and loving the game of go,
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當柯潔在哭泣，他很愛圍棋比賽，
12:13
AlphaGo felt no happiness from winning
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AlphaGo 卻不會感到勝利的喜悅，
12:17
and certainly no desire
to hug a loved one.
to hug a loved one.
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肯定也不會想要去擁抱愛人。
12:23
So how do we differentiate ourselves
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所以，在人工智慧的時代，
12:26
as humans in the age of AI?
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我們要如何讓人類有所不同？
12:28
We talked about the axis of creativity,
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我們剛有談到「創意」這個軸，
12:31
and certainly that is one possibility,
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那肯定是其中一種可能性，
12:34
and now we introduce a new axis
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現在，我們要再介紹一個新的軸，
12:37
that we can call compassion,
love, or empathy.
love, or empathy.
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我們可以稱之為
同情心、愛，或是同理心。
同情心、愛，或是同理心。
12:40
Those are things that AI cannot do.
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那些是人工智慧做不到的。
12:43
So as AI takes away the routine jobs,
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所以，當人工智慧奪走
例行性的工作時，
例行性的工作時，
12:46
I like to think we can, we should
and we must create jobs of compassion.
and we must create jobs of compassion.
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我會認為 ，我們能夠、應該而且必須
創造出同情心相關的工作。
創造出同情心相關的工作。
12:51
You might ask how many of those there are,
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你可能會問，這類工作有多少個？
12:54
but I would ask you:
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但我會反問你：
12:55
Do you not think that we are going
to need a lot of social workers
to need a lot of social workers
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你不認為我們會需要很多社工
12:59
to help us make this transition?
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來協助我們做這項轉變嗎？
13:01
Do you not think we need
a lot of compassionate caregivers
a lot of compassionate caregivers
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你不認為我們會需要
很多有同情心的照護者
很多有同情心的照護者
13:05
to give more medical care to more people?
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來給予更多人醫療照護嗎？
13:07
Do you not think we're going to need
10 times more teachers
10 times more teachers
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你不認為我們會需要十倍的老師
13:11
to help our children find their way
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來協助我們的孩子找到他們的路，
13:14
to survive and thrive
in this brave new world?
in this brave new world?
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在這個勇敢的新世界中
存活並茁壯嗎？
存活並茁壯嗎？
13:17
And with all the newfound wealth,
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且當有這麼多新的財富產生時，
13:19
should we not also make
labors of love into careers
labors of love into careers
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我們不應該創造以人性為本的職業，
13:24
and let elderly accompaniment
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把陪伴老人，
13:27
or homeschooling become careers also?
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或是居家教育也變成職業嗎？
13:30
(Applause)
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（掌聲）
13:36
This graph is surely not perfect,
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這張圖的確不完美，
13:39
but it points at four ways
that we can work with AI.
that we can work with AI.
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但它能展示出我們
和人工智慧合作的四種方式。
和人工智慧合作的四種方式。
13:42
AI will come and take away
the routine jobs
the routine jobs
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人工智慧會到來，
也會奪走例行性的工作，
也會奪走例行性的工作，
13:46
and in due time, we will be thankful.
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到時，我們會很感激。
13:49
AI will become great tools
for the creatives
for the creatives
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對有創意的人，
人工智慧會變成很棒的工具，
人工智慧會變成很棒的工具，
13:52
so that scientists, artists,
musicians and writers
musicians and writers
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讓科學家、藝術家、音樂家及作家
13:55
can be even more creative.
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都更有創意。
13:58
AI will work with humans
as analytical tools
as analytical tools
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人工智慧也會扮演
分析工具的角色來和人類合作，
分析工具的角色來和人類合作，
14:03
that humans can wrap their warmth around
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對於高同情心的工作，
14:06
for the high-compassion jobs.
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人類可以將溫暖傾注其中。
14:08
And we can always differentiate ourselves
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我們還有方式讓我們自己不同，
14:10
with the uniquely capable jobs
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就是通過參與需要獨特能力的工作，
14:12
that are both compassionate and creative,
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要有同情心又要有創意，
14:16
using and leveraging
our irreplaceable brains and hearts.
our irreplaceable brains and hearts.
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充分利用和發揮
我們無可替代的頭腦和内心，
我們無可替代的頭腦和内心，
14:21
So there you have it:
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所以，這就是：
14:22
a blueprint of coexistence
for humans and AI.
for humans and AI.
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人類和人工智慧共存的藍圖。
14:27
AI is serendipity.
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人工智慧的發展是種機緣。
14:29
It is here to liberate us
from routine jobs,
from routine jobs,
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它的出現將我們
從例行性的工作中解放出來，
從例行性的工作中解放出來，
14:32
and it is here to remind us
what it is that makes us human.
what it is that makes us human.
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它也是來提醒我們，人因何為人。
14:36
So let us choose to embrace AI
and to love one another.
and to love one another.
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所以，讓我們選擇
擁抱人工智慧，並去關愛彼此吧。
擁抱人工智慧，並去關愛彼此吧。
14:40
Thank you.
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謝謝。
14:41
(Applause)
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（掌聲）
ABOUT THE SPEAKER
Kai-Fu Lee - Investor, computer scientistAt Sinovation Ventures, Kai-Fu Lee invests in the next generation of Chinese high-tech companies.
Why you should listen
Dr. Kai-Fu Lee has a unique perspective on the global technology industry, having worked extensively between the US and China, and has been in artificial intelligence research, development and investment for more than 30 years. He is one of China's top technology investors, undertaking early pioneering work in the field of artificial intelligence and working with multiple US technology giants.
Lee is the Chairman and CEO of Sinovation Ventures, managing a $1.7 billion dual-currency investment fund. Sinovation is a leading technology-savvy investment firm focusing on developing Chinese high-tech companies. Lee also serves as president of the Sinovation Ventures Artificial Intelligence Institute.
Prior to founding Sinovation in 2009, Lee was the President of Google China. Previously, he held executive positions at Microsoft, SGI and Apple. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), and he was Vice Chairman of the Committee of 100 and named one of the 100 most influential people in the world by TIME Magazine. He is the author of seven best-selling books in Chinese, launching his new book AI Superpowers internationally in fall 2018, and has over 50 million followers on social media.
In the field of artificial intelligence, Lee founded Microsoft Research China, which was named as the hottest research lab by MIT Technology Review. Later renamed Microsoft Research Asia, this institute has trained the great majority of AI leaders in China, including CTOs or AI heads at Baidu, Tencent, Alibaba, Lenovo, Huawei and Haier. While with Apple, Lee led AI projects in speech and natural language, which appeared on Good Morning America on ABC Television and the front page of The Wall Street Journal. He has authored 10 US patents, and more than 100 journal and conference papers.
Kai-Fu Lee | Speaker | TED.com