but first, we have to rethink about our human values.

我將要談的是人類 和人工智慧要如何共存，

So let me first make a confession about my errors in my values.

但首先，我們先要 重新審視人類的價值觀。

It was 11 o'clock, December 16, 1991.

讓我先坦誠我在 我的價值觀中所犯的錯誤。

I was about to become a father for the first time.

那是 1991 年 12 月 16 日 11 點。

My wife, Shen-Ling, lay in the hospital bed

我就要成為一個新手爸爸了。

going through a very difficult 12-hour labor.

我的太太先玲躺在醫院的病床上，

I sat by her bedside

經歷著一段艱辛 並長達十二個小時的分娩。

but looked anxiously at my watch,

我就坐在床邊，

and I knew something that she didn't.

但很焦慮地看著我的手錶，

I knew that if in one hour,

我知道一件她不知道的事。

our child didn't come,

我知道，如果一個小時內，

I was going to leave her there

我們的孩子還不出來，

and go back to work

我就得要把她丟在那裡，

and make a presentation about AI

回去工作，

to my boss, Apple's CEO.

去做一場關於人工智慧的簡報，

Fortunately, my daughter was born at 11:30 --

對象是我的老闆，蘋果的執行長。

(Laughter)

幸運的是，我女兒 在 11:30 出生了——

(Applause)

（笑聲）

sparing me from doing the unthinkable,

（掌聲）

and to this day, I am so sorry

讓我不用去做我無法想像的事，

for letting my work ethic take precedence over love for my family.

至今，我仍然很後悔

(Applause)

我把工作擺在了我對家人的愛之前。

My AI talk, however, went off brilliantly.

（掌聲）

(Laughter)

然而，我的人工智慧演說表現超好。

Apple loved my work and decided to announce it

（笑聲）

at TED1992,

蘋果很愛我的作品，

26 years ago on this very stage.

決定在 TED 1992 宣佈它，

I thought I had made one of the biggest, most important discoveries in AI,

26 年前，就在這個舞台上。

and so did the "Wall Street Journal" on the following day.

我認為我在人工智慧上 有了最大、最重要的發現，

But as far as discoveries went,

隔天的《華爾街日報》也這麼認為。

it turned out,

但隨著越來越多的「發現」，

I didn't discover India, or America.

結果是，

Perhaps I discovered a little island off of Portugal.

我沒有發現印度，或美洲。

But the AI era of discovery continued,

也許我發現的是 葡萄牙外海的一個小島。

and more scientists poured their souls into it.

但人工智慧發現的時代 仍然繼續走下去了，

About 10 years ago, the grand AI discovery

更多的科學家 全心全意地投入其中。

was made by three North American scientists,

大約十年前，三位北美科學家

and it's known as deep learning.

有了偉大的人工智慧發現，

Deep learning is a technology that can take a huge amount of data

即大家所知的深度學習。

within one single domain

深度學習這項技術， 是一個能利用海量數據

and learn to predict or decide at superhuman accuracy.

在單一領域中

For example, if we show the deep learning network

學會做超高精度的預測或判斷的技術。

a massive number of food photos,

比如，如果我們給深度學習網路

it can recognize food

提供海量的食物照片，

such as hot dog or no hot dog.

它就能辨識出食物，

(Applause)

比如熱狗，或不是熱狗。

Or if we show it many pictures and videos and sensor data

（掌聲）

from driving on the highway,

或者，如果我讓它看很多 在公路上開車的照片、

it can actually drive a car as well as a human being

影片，以及感測器資料，

on the highway.

它就能夠在高速公路上 嫺熟地駕駛一台車，

And what if we showed this deep learning network

絲毫不輸給人類。

all the speeches made by President Trump?

如果我們向這個深度學習網路展示

Then this artificially intelligent President Trump,

川普總統做過的所有演說呢？

actually the network --

那麼，這位人工智慧的川普總統，

(Laughter)

其實就是這個網路——

can --

（笑聲）

(Applause)

能夠——

You like double oxymorons, huh?

（掌聲）

(Laughter)

你們喜歡一語雙關，是吧？

(Applause)

（笑聲） （註：artificial 也是「虛假造作的」）

So this network, if given the request to make a speech about AI,

（掌聲）

he, or it, might say --

所以，如果要求這個網路 做出一段關於人工智慧的演說，

(Recording) Donald Trump: It's a great thing

他，或它，可能會說——

to build a better world with artificial intelligence.

（錄音）川普：這是很棒的事，

Kai-Fu Lee: And maybe in another language?

用人工智慧來建造一個更好的世界。

DT: (Speaking Chinese)

講者：換個語言呢？

(Laughter)

川普：（說中文） 人工智能正在改變世界。

KFL: You didn't know he knew Chinese, did you?

（笑聲）

So deep learning has become the core in the era of AI discovery,

講者：你們不知道川普會說中文吧？

and that's led by the US.

所以，深度學習已經成為 人工智慧發現時代的核心，

But we're now in the era of implementation,

且是由美國來領導的。

where what really matters is execution, product quality, speed and data.

但現在，我們已經進入實踐的時代，

And that's where China comes in.

在這個時代，重要的是執行、 產品品質、速度，和資料。

Chinese entrepreneurs,

這就是中國出場的時候了。

who I fund as a venture capitalist,

身為創業投資家，

are incredible workers,

我投資的中國企業家

amazing work ethic.

是很棒的實幹家，

My example in the delivery room is nothing compared to how hard people work in China.

有很了不起的職業道德。

As an example, one startup tried to claim work-life balance:

我在產房的那個例子， 完全不及中國人們賣力工作的程度。

"Come work for us because we are 996."

舉個例子，有間新興公司 提出「工作—生活」的平衡：

And what does that mean?

「來為我們工作，因為我們是 996。」

It means the work hours of 9am to 9pm, six days a week.

那是什麼意思？

That's contrasted with other startups that do 997.

意思是，工作時間從早上 9 點 到晚上 9 點，一週工作 6 天。

And the Chinese product quality has consistently gone up

相對的，其他新興公司都是 997。

in the past decade,

而中國的產品品質

and that's because of a fiercely competitive environment.

在過去十年間持續不斷地提升，

In Silicon Valley, entrepreneurs compete in a very gentlemanly fashion,

那是因為這個環境中的 競爭太激烈了。

sort of like in old wars in which each side took turns

在矽谷，企業家會 以很紳仕的方式競爭，

to fire at each other.

有點像是古老的戰爭，雙方會輪流

(Laughter)

向對方開火。

But in the Chinese environment,

（笑聲）

it's truly a gladiatorial fight to the death.

但在中國的環境中，

In such a brutal environment, entrepreneurs learn to grow very rapidly,

就像是羅馬競技場， 不死不休的戰鬥。

they learn to make their products better at lightning speed,

在這麼殘酷的環境中， 企業家學會要非常快速地成長，

and they learn to hone their business models

他們學會要雷厲風行地 讓自己的產品變更好，

until they're impregnable.

他們學會要一直鍛鍊 他們的商業模型，

As a result, great Chinese products like WeChat and Weibo

直到堅不可摧為止。

are arguably better

結果就是，優秀的中國產品， 如微信和微博，

than the equivalent American products from Facebook and Twitter.

可以說比美國

And the Chinese market embraces this change

相對應產品， 如臉書和推特，還要好。

and accelerated change and paradigm shifts.

且中國市場擁抱這樣的改變、

As an example, if any of you go to China,

加速的改變，以及範式轉移。

you will see it's almost cashless and credit card-less,

舉個例，如果你去中國，

because that thing that we all talk about, mobile payment,

你會發現幾乎沒有 在用現金或是信用卡，

has become the reality in China.

原因就是我們大家都在談的 那樣東西，移動支付，

In the last year,

在中國已經實現。

18.8 trillion US dollars were transacted on mobile internet,

去年，

and that's because of very robust technologies

行動網路上的交易金額 達到 18.8 兆美元，

built behind it.

那是因為在行動支付背後的技術

It's even bigger than the China GDP.

非常穩定健全。

And this technology, you can say, how can it be bigger than the GDP?

這個數字比中國的國內生產總值還高。

Because it includes all transactions:

你會問，這項技術怎麼可能 超越國內生產總值？

wholesale, channels, retail, online, offline,

因為它包含所有的交易：

going into a shopping mall or going into a farmers market like this.

批發、電視台、零售、線上、線下、

The technology is used by 700 million people

到購物中心去買， 或到像這樣的農民市場去買。

to pay each other, not just merchants,

有 7 億人使用移動支付

so it's peer to peer,

來付錢給彼此，不只付錢給商人，

and it's almost transaction-fee-free.

所以，它是點對點的，

And it's instantaneous,

且幾乎不需要交易手續費。

and it's used everywhere.

且是立即生效，

And finally, the China market is enormous.

到處都在使用。

This market is large,

最後一點，中國的市場很巨大。

which helps give entrepreneurs more users, more revenue,

這個市場很大，

more investment, but most importantly,

讓企業家能有更多的 使用者、更多的收益、

it gives the entrepreneurs a chance to collect a huge amount of data

更多的投資，最重要的是，

which becomes rocket fuel for the AI engine.

它讓企業家有機會 能收集到大量的數據資料，

So as a result, the Chinese AI companies

這些資料就成了 人工智慧引擎的火箭燃料。

have leaped ahead

結果就是，中國的人工智慧公司

so that today, the most valuable companies

已經大躍進，跳到前頭，

in computer vision, speech recognition,

所以，現今，最有價值的公司，

speech synthesis, machine translation and drones

不論是在電腦視覺、語音識別、

are all Chinese companies.

語音合成、機器翻譯， 及無人機的領域，

So with the US leading the era of discovery

都是中國公司稱霸。

and China leading the era of implementation,

美國領導的是發現的時代，

we are now in an amazing age

而中國領導的是導入的時代，

where the dual engine of the two superpowers

我們現在身處一個很了不起的時代，

are working together

這兩種超能力的雙重引擎

to drive the fastest revolution in technology

在同心協力，

that we have ever seen as humans.

在科技領域中， 推動我們人類所見過

And this will bring tremendous wealth,

最快速的革命。

unprecedented wealth:

這會帶來驚人的財富，

16 trillion dollars, according to PwC,

前所未有的財富：

in terms of added GDP to the worldwide GDP by 2030.

根據普華永道會計師事務所的資料，

It will also bring immense challenges

到 2030 年，全世界的國內生產總值 會增加 16 兆美元。

in terms of potential job replacements.

這也會帶來極大的挑戰：

Whereas in the Industrial Age

潛在的工作取代性問題。

it created more jobs

在工業時代，

because craftsman jobs were being decomposed into jobs in the assembly line,

它創造了更多的工作，

so more jobs were created.

因為技工的工作被重新 分解成為生產線的工作，

But AI completely replaces the individual jobs

所以創造了更多的工作。

in the assembly line with robots.

但人工智慧完全取代了個人的工作，

And it's not just in factories,

在生產線上直接用機器人。

but truckers, drivers

且不只是在工廠，

and even jobs like telesales, customer service

還有貨車司機、一般司機，

and hematologists as well as radiologists

甚至像電話銷售、客戶服務、

over the next 15 years

血液學學者，以及放射線研究者

are going to be gradually replaced

在未來十五年

by artificial intelligence.

都可能漸漸被

And only the creative jobs --

人工智慧取代。

(Laughter)

只有有創意的工作——

I have to make myself safe, right?

（笑聲） （表中講者被列在「安全」）

Really, the creative jobs are the ones that are protected,

我得讓我自己很安全，對吧？

because AI can optimize but not create.

說真的，有創意的工作 是受到保護的工作，

But what's more serious than the loss of jobs

因為人工智慧能夠 做到最佳化，但不能創造。

is the loss of meaning,

但比失去工作更嚴重的，

because the work ethic in the Industrial Age

是失去意義，

has brainwashed us into thinking that work is the reason we exist,

因為在工業時代的工作倫理

that work defined the meaning of our lives.

已經將我們洗腦， 認為工作是我們存在的理由，

And I was a prime and willing victim to that type of workaholic thinking.

認為工作定義了我們人生的意義。

I worked incredibly hard.

我就曾是那種工作狂思想的 主要受害者，也是自願受害者。

That's why I almost left my wife in the delivery room,

我工作非常努力。

that's why I worked 996 alongside my entrepreneurs.

那就是為什麼在產房時 我差一點就要丟下我太太，

And that obsession that I had with work

那就是為什麼我和我的 企業家們的工作時間是 996。

ended abruptly a few years ago

我先前對於工作的這種迷戀，

when I was diagnosed with fourth stage lymphoma.

在幾年前突然終止了，

The PET scan here shows over 20 malignant tumors

因為我被診斷出第四期的淋巴癌。

jumping out like fireballs,

這裡的正子掃描結果顯示 有超過 20 個惡性腫瘤

melting away my ambition.

像火球一樣跳出來，

But more importantly,

融化了我的野心。

it helped me reexamine my life.

但，更重要的是，

Knowing that I may only have a few months to live

它讓我重新檢視我的人生。

caused me to see how foolish it was

知道我可能只剩下幾個月能活，

for me to base my entire self-worth

讓我看清了我有多愚蠢，

on how hard I worked and the accomplishments from hard work.

因為我把我的整個自我價值

My priorities were completely out of order.

建立在工作努力程度上， 以及努力帶來的成就上。

I neglected my family.

我的優先順序完全錯亂。

My father had passed away,

我忽視了我的家庭。

and I never had a chance to tell him I loved him.

我父親已經過世，

My mother had dementia and no longer recognized me,

我從來沒有機會告訴他我愛他。

and my children had grown up.

我母親有失智症，已經不認得我，

During my chemotherapy,

我的孩子已經長大。

I read a book by Bronnie Ware

在我做化療期間，

who talked about dying wishes and regrets of the people in the deathbed.

我讀了一本布羅妮韋爾寫的書，

She found that facing death,

談到臨終的人的死前願望和悔恨。

nobody regretted that they didn't work hard enough in this life.

她發現，面對死亡時，

They only regretted that they didn't spend enough time with their loved ones

沒有人後悔這一生的 工作不夠努力。

and that they didn't spread their love.

他們只會後悔沒有花 足夠時間陪他們愛的人，

So I am fortunately today in remission.

沒有把他們的愛散播出去。

(Applause)

現在，我很幸運處於緩解期。

So I can be back at TED again

（掌聲）

to share with you that I have changed my ways.

所以我能再次回到 TED，

I now only work 965 --

和各位分享， 我已經改變了我的方式。

occasionally 996, but usually 965.

我現在的工作方式是 965 ——

I moved closer to my mother,

偶爾是 996，但通常是 965。

my wife usually travels with me,

我搬到離我母親比較近的地方，

and when my kids have vacation, if they don't come home, I go to them.

我太太通常與我一同旅行，

So it's a new form of life

當我的孩子有假期時， 若他們不回家，我會去找他們。

that helped me recognize

這是一種新的生活形式，

how important it is that love is for me,

協助我了解到

and facing death helped me change my life,

愛對我而言有多重要，

but it also helped me see a new way

面對死亡，協助我改變了我的生活，

of how AI should impact mankind

也協助我找到了一種新的方式，

and work and coexist with mankind,

看待人工智慧對於人類的衝擊，

that really, AI is taking away a lot of routine jobs,

以及如何和人類合作與共存，

but routine jobs are not what we're about.

確實，人工智慧會奪走 很多例行性的工作，

Why we exist is love.

但我們並不只會做例行性的工作。

When we hold our newborn baby,

我們存在的目的是愛。

love at first sight,

當我們抱著新生的嬰兒，

or when we help someone in need,

第一眼就產生了愛，

humans are uniquely able to give and receive love,

當我們協助了需要幫忙的人，

and that's what differentiates us from AI.

人類能給予愛和接收愛， 這是獨一無二的，

Despite what science fiction may portray,

這就是我們和人工智慧的差別。

I can responsibly tell you that AI has no love.

不論科幻小說怎麼寫，

When AlphaGo defeated the world champion Ke Jie,

我可以很確定地告訴各位， 人工智慧沒有愛。

while Ke Jie was crying and loving the game of go,

當 AlphaGo 打敗世界冠軍柯潔，

AlphaGo felt no happiness from winning

當柯潔在哭泣，他很愛圍棋比賽，

and certainly no desire to hug a loved one.

AlphaGo 卻不會感到勝利的喜悅，

So how do we differentiate ourselves

肯定也不會想要去擁抱愛人。

as humans in the age of AI?

所以，在人工智慧的時代，

We talked about the axis of creativity,

我們要如何讓人類有所不同？

and certainly that is one possibility,

我們剛有談到「創意」這個軸，

and now we introduce a new axis

那肯定是其中一種可能性，

that we can call compassion, love, or empathy.

現在，我們要再介紹一個新的軸，

Those are things that AI cannot do.

我們可以稱之為 同情心、愛，或是同理心。

So as AI takes away the routine jobs,

那些是人工智慧做不到的。

I like to think we can, we should and we must create jobs of compassion.

所以，當人工智慧奪走 例行性的工作時，

You might ask how many of those there are,

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。

but I would ask you:

你可能會問，這類工作有多少個？

Do you not think that we are going to need a lot of social workers

但我會反問你：

to help us make this transition?

你不認為我們會需要很多社工

Do you not think we need a lot of compassionate caregivers

來協助我們做這項轉變嗎？

to give more medical care to more people?

你不認為我們會需要 很多有同情心的照護者

Do you not think we're going to need 10 times more teachers

來給予更多人醫療照護嗎？

to help our children find their way

你不認為我們會需要十倍的老師

to survive and thrive in this brave new world?

來協助我們的孩子找到他們的路，