I'm going to talk about how AI and mankind can coexist, but first, we have to rethink about our human values.
我将会谈谈人工智能和人类如何能够共存，但首先，我们需要重新思考人类价值观。
So let me first make a confession about my errors in my values.
所以首先让我坦白我价值观中曾有的错误。
It was 11 o'clock, December 16, 1991. I was about to become a father for the first time.
那是1991年12月16日11点。我即将首次成为父亲。
My wife, Shen-Ling, lay in the hospital bed going through a very difficult 12-hour labor.
我的妻子，先铃，躺在病床上，经历着一段艰辛并为时12小时的分娩。
I sat by her bedside but looked anxiously at my watch, and I knew something that she didn't.
我坐在床边，但却焦虑地望着我的手表，而我知道一些她不知道的事。
I knew that if in one hour, our child didn't come,
我知道如果在一小时内我们的孩子还未出生，
I was going to leave her there and go back to work and make a presentation about AI to my boss, Apple's CEO.
我将要将她留在那里，赶去上班，并向我的老板，苹果的首席执行官，做一个有关人工智能的报告。
Fortunately, my daughter was born at 11:30 -- sparing me from doing the unthinkable,
幸运的是，我的女儿在11:30出生了--让我免做荒唐事，
and to this day, I am so sorry for letting my work ethic take precedence over love for my family.
而一直到今天，我非常惭愧曾经把工作摆在家庭前面。
My AI talk, however, went off brilliantly.
但是，我做的人工智能报告非常成功。
Apple loved my work and decided to announce it at TED1992, 26 years ago on this very stage.
苹果喜欢我的工作成果，并决定在TED1992会议上将其宣布，26年前就在这个舞台上。
I thought I had made one of the biggest, most important discoveries in AI,
我以为我完成了人工智能领域一个最重大的发现，
and so did the "Wall Street Journal" on the following day.
第二天《华尔街日报》也是这么认为。
But as far as discoveries went, it turned out, I didn't discover India, or America.
但随着越来越多的发现，结果是，我并没有发现印度或是美洲。
Perhaps I discovered a little island off of Portugal.
或许我发现的是葡萄牙附近的一个小岛。
But the AI era of discovery continued, and more scientists poured their souls into it.
但是人工智能的发现时代持续了下去，而越来越多的科学家全心全意投入其中。
About 10 years ago, the grand AI discovery was made by three North American scientists, and it's known as deep learning.
大约10年前，三名北美科学家做出了重大的人工智能发现，那就是深度学习。
Deep learning is a technology that can take a huge amount of data within one single domain and learn to predict or decide at superhuman accuracy.
深度学习是一个能利用海量数据，在单一领域中学会做超高精确度的预测或决策的技术。
For example, if we show the deep learning network a massive number of food photos,
例如，如果我们向深度学习网络显示海量的食物照片，
it can recognize food such as hot dog or no hot dog.
它可以辨认出食物，比如热狗或不是热狗。
Or if we show it many pictures and videos and sensor data from driving on the highway,
如果我们向它显示许多在高速公路上开车的照片、视频和传感数据，
it can actually drive a car as well as a human being on the highway.
它其实可以把车开得像人一样好，行驶在高速公路上。
And what if we showed this deep learning network all the speeches made by President Trump?
若我们向这深度学习网络显示特朗普总统发表过的所有演讲呢？
Then this artificially intelligent President Trump, actually the network -- can -- You like double oxymorons, huh?
那么这个人工智能的特朗普总统，其实是该网络--可以--你们喜欢一语双关的话，对吧？
So this network, if given the request to make a speech about AI, he, or it, might say --
所以此网络，若被要求发表一场关于人工智能的演讲，他，或它，或许会说
It's a great thing to build a better world with artificial intelligence.
运用人工智能来建造一个更完美的世界是件大好事。
And maybe in another language?
或许用另一种语言来说？
You didn't know he knew Chinese, did you?
你们不知道他会说中文吧？
So deep learning has become the core in the era of AI discovery, and that's led by the US.
所以深度学习已成为人工智能发现时代的核心，并由美国领导着。
But we're now in the era of implementation, where what really matters is execution, product quality, speed and data.
但我们现在身处实践时代，真正关键的是执行力、产品质量、速度和数据。
And that's where China comes in.
这就是中国上场的时候了。
Chinese entrepreneurs, who I fund as a venture capitalist, are incredible workers, amazing work ethic.
中国企业家，我作为风险投资人提供他们资本，他们是非凡的实干者，工作拼命。
My example in the delivery room is nothing compared to how hard people work in China.
我在产房的例子与中国人的工作卖力程度相比根本不算什么。
As an example, one startup tried to claim work-life balance: "Come work for us because we are 996."
例如，有个创业公司声称能工作生活平衡：“加入我们吧，因为我们是996。”
And what does that mean? It means the work hours of 9am to 9pm, six days a week.
那是什么意思？那是指工作时间从上午9点至晚上9点、每周六天。
That's contrasted with other startups that do 997.
这与其他实施997的创业公司形成对比。
And the Chinese product quality has consistently gone up in the past decade,
中国产品的质量在过去十年中持续提升，
and that's because of a fiercely competitive environment.
这归功于极其激烈的竞争环境。
In Silicon Valley, entrepreneurs compete in a very gentlemanly fashion,
在硅谷，企业家用非常绅士的方式来竞争，
sort of like in old wars in which each side took turns to fire at each other.
有点像是旧时的战争双方轮流向对方开火。
But in the Chinese environment, it's truly a gladiatorial fight to the death.
但在中国的环境里，它真的是一场不死不休的角斗士之战。
In such a brutal environment, entrepreneurs learn to grow very rapidly,
在如此残酷的环境里，企业家学会如何迅速成长，
they learn to make their products better at lightning speed,
他们学会如何雷厉风行地改进产品，
and they learn to hone their business models until they're impregnable.
他们学会如何完善其商业模式直至坚不可摧。
As a result, great Chinese products like WeChat and Weibo
结果是，优秀的中国产品，如微信和微博，
are arguably better than the equivalent American products from Facebook and Twitter.
可以说比脸书和推特等同类美国产品更好。
And the Chinese market embraces this change and accelerated change and paradigm shifts.
中国市场欢迎这种变化，并进一步加速变化和转型。
As an example, if any of you go to China, you will see it's almost cashless and credit card-less,
比如说，如果你们中任何人去中国，你将会看到它几乎是无现金和无信用卡社会，
because that thing that we all talk about, mobile payment, has become the reality in China.
因为我们都在讨论的那件事，移动支付，在中国已成为现实。
In the last year, 18.8 trillion US dollars were transacted on mobile internet,
在过去一年，18.8万亿美金通过移动网络完成交易，
and that's because of very robust technologies built behind it.
而这归功于支撑其项背的强劲科技。
It's even bigger than the China GDP. And this technology, you can say, how can it be bigger than the GDP?
它甚至大过中国的国内生产总值。而此技术，你可以说，它怎么会大过国内生产总值？
Because it includes all transactions: wholesale, channels, retail, online, offline,
这是因为它包括了所有交易：批发、渠道、零售、网上、离线，
going into a shopping mall or going into a farmers market like this.
去大商场或去这样的农贸市场。
The technology is used by 700 million people to pay each other, not just merchants,
这项技术被7亿人用来互相支付，不仅局限于商家，
so it's peer to peer, and it's almost transaction-fee-free.
所以它是点对点的，而它几乎是无手续费的。
And it's instantaneous, and it's used everywhere. And finally, the China market is enormous.
它是即时的，被到处使用。最后一点，中国市场十分巨大。
This market is large, which helps give entrepreneurs more users, more revenue, more investment, but most importantly,
市场庞大，帮助了企业家获得更多用户、更高收入、更多投资，但最重要的，
it gives the entrepreneurs a chance to collect a huge amount of data which becomes rocket fuel for the AI engine.
它给了企业家一个收集海量数据的机会，这成为了人工智能引擎的燃料。
So as a result, the Chinese AI companies have leaped ahead so that today, the most valuable companies in computer vision,
结果，中国人工智能公司已往前飞跃，所以如今，在电脑视觉、
speech recognition, speech synthesis, machine translation and drones are all Chinese companies.
语音识别、语音合成、机器翻译和无人机领域中，最具价值的公司都是中国公司。

So with the US leading the era of discovery and China leading the era of implementation,
所以，随着美国引领发现时代和中国引领实践时代，
we are now in an amazing age where the dual engine of the two superpowers are working together
我们正处于一个伟大时代，两个超级大国的双联引擎正合作共进，
to drive the fastest revolution in technology that we have ever seen as humans.
驱动我们人类从未见过的最迅速的科技革命。
And this will bring tremendous wealth, unprecedented wealth:
这将会带来极大的财富、前所未有的财富：
16 trillion dollars, according to PwC, in terms of added GDP to the worldwide GDP by 2030.
据普华永道估计，到2030年，人工智能将带来16万亿美元的全球GDP增长。
It will also bring immense challenges in terms of potential job replacements.
它也将在可能出现的失业再就业问题上带来巨大挑战。
Whereas in the Industrial Age it created more jobs
在工业革命时代，它创造了更多工作，
because craftsman jobs were being decomposed into jobs in the assembly line, so more jobs were created.
因为手工工匠的工作被分解成生产线上的各种工作，所以创造了更多工作。
But AI completely replaces the individual jobs in the assembly line with robots.
但是人工智能让流水线上的个体工作完全被机器人取代。
And it's not just in factories,
这不仅发生在工厂里，
but truckers, drivers and even jobs like telesales, customer service and hematologists as well as radiologists
而且货车司机、驾驶员甚至于像是电话销售、客服、血液科和放射科医生的工作，
over the next 15 years are going to be gradually replaced by artificial intelligence.
在未来的15年内都将慢慢被人工智能取代。
And only the creative jobs -- I have to make myself safe, right?
而只有创造性工作--我必须保护我自己，对吧？
Really, the creative jobs are the ones that are protected, because AI can optimize but not create.
真的，创造性工作是有保障的工作，因为人工智能可以优化但不能创造。
But what's more serious than the loss of jobs is the loss of meaning,
但比失去工作更严重的是失去意义，
because the work ethic in the Industrial Age has brainwashed us into thinking that work is the reason we exist,
因为工业革命时代的工作伦理已给我们洗脑，相信工作赋予我们存在的理由，
that work defined the meaning of our lives. And I was a prime and willing victim to that type of workaholic thinking.
工作赋予我们生活的意义。而我就是个典型并自愿接受那种工作狂思想的受害者。
I worked incredibly hard. That's why I almost left my wife in the delivery room,
我工作异常努力。那就是为什么我几乎将我的妻子独自留在产房内，
that's why I worked 996 alongside my entrepreneurs.
那就是为什么我996地与企业家们工作。
And that obsession that I had with work ended abruptly a few years ago when I was diagnosed with fourth stage lymphoma.
我对工作的痴迷在几年前戛然而止，因为我被确诊患上第四期淋巴瘤。
The PET scan here shows over 20 malignant tumors jumping out like fireballs, melting away my ambition.
这个PET扫描显示二十多个恶性肿瘤，如火球般喷涌而出，把我的壮志雄心付之一炬。
But more importantly, it helped me reexamine my life.
但更重要的是，它帮我重新审视我的人生。
Knowing that I may only have a few months to live
知道我可能只剩下几个月的生命，
caused me to see how foolish it was for me to base my entire self-worth on how hard I worked and the accomplishments from hard work.
令我看清，把自我价值完全建立在工作强度和工作成就上是多么愚蠢。
My priorities were completely out of order. I neglected my family.
我生活中的优先级完全本末倒置。我疏于关心家庭。
My father had passed away, and I never had a chance to tell him I loved him.
我的父亲过世了，我从没机会告诉他我爱他。
My mother had dementia and no longer recognized me, and my children had grown up.
我的母亲失智了，再也认不出我，我的孩子们都已长大成人。
During my chemotherapy, I read a book by Bronnie Ware who talked about dying wishes and regrets of the people in the deathbed.
在我化疗期间，我读了邦妮·韦尔的一本书，写的是人们濒死时的心愿和懊悔。
She found that facing death, nobody regretted that they didn't work hard enough in this life.
她发现面对死亡时，没人遗憾自己工作得不够努力。
They only regretted that they didn't spend enough time with their loved ones and that they didn't spread their love.
他们只后悔自己没花更多时间与所爱之人相伴相守，后悔没有传递自己的爱。
So I am fortunately today in remission.
值得庆幸的是，我的病情现在有所缓解。
So I can be back at TED again to share with you that I have changed my ways.
所以我可以重回TED舞台，和你们分享我的改变。
I now only work 965 -- occasionally 996, but usually 965.
我如今只工作965--偶尔996，但通常965。
I moved closer to my mother, my wife usually travels with me,
我搬到离母亲更近的住所，我妻子通常与我相伴旅行，
and when my kids have vacation, if they don't come home, I go to them.
当我的孩子们休假时，若他们不回家，我会去看他们。
So it's a new form of life that helped me recognize how important it is that love is for me,
这种新生活方式帮我认清爱对我来说是多么重要，
and facing death helped me change my life,
濒死经历改变了我的生活，
but it also helped me see a new way of how AI should impact mankind and work and coexist with mankind,
而且让我重新审视人工智能应如何影响人类影响工作，与人共存，
that really, AI is taking away a lot of routine jobs, but routine jobs are not what we're about.
确实，人工智能正带走很多重复性工作，但我们并非因为擅长重复性工作而为人。
Why we exist is love. When we hold our newborn baby, love at first sight,
我们存在的理由是爱。当我们怀抱新生儿，当我们一见钟情，
or when we help someone in need, humans are uniquely able to give and receive love, and that's what differentiates us from AI.
当我们助人于难，唯独人类才能爱与被爱，爱使我们有别于人工智能。
Despite what science fiction may portray, I can responsibly tell you that AI has no love.
无论科幻电影如何描述，我可以负责任地告诉你，人工智能程序没有爱的能力。
When AlphaGo defeated the world champion Ke Jie, while Ke Jie was crying and loving the game of go,
当阿法狗围棋打败世界冠军柯洁时，柯洁哭着并爱着围棋，
AlphaGo felt no happiness from winning and certainly no desire to hug a loved one.
但阿法狗无法从胜利中感受到喜悦，也不会渴望拥抱一个心爱的人。
So how do we differentiate ourselves as humans in the age of AI?
那我们如何在人工智能时代中将自己作为人类区分出来？
We talked about the axis of creativity, and certainly that is one possibility,
我们谈到过创造性维度，那当然是一个可能性，
and now we introduce a new axis that we can call compassion, love, or empathy.
现在我们要介绍一个新维度，称之为同情心、爱或同理心。
Those are things that AI cannot do. So as AI takes away the routine jobs,
那些都是人工智能做不到的事。当人工智能带走重复性工作时，
I like to think we can, we should and we must create jobs of compassion.
我想我们可以、应该而且必须创造关爱型工作。
You might ask how many of those there are, but I would ask you:
你或许会问那种工作到底有多少？但我想问问你：
Do you not think that we are going to need a lot of social workers to help us make this transition?
你不认为我们将需要许多社工来帮助我们平稳过渡吗？
Do you not think we need a lot of compassionate caregivers to give more medical care to more people?
你不认为我们需要许多富有同情心的看护，来为更多人提供更多医疗护理吗？
Do you not think we're going to need 10 times more teachers
你不认为我们将需要多10倍的老师，
to help our children find their way to survive and thrive in this brave new world?
来帮助孩子们寻找在这个勇敢新世界里的生存和成长之道吗？
And with all the newfound wealth, should we not also make labors of love into careers
有了新获得的财富，我们不应该创造以人性关爱为本的工作，
and let elderly accompaniment or homeschooling become careers also?
把老人护工或在家教育变成工作种类吗？
This graph is surely not perfect, but it points at four ways that we can work with AI.
这个图表不甚完美，但展示了四种我们与人工智能共事的方式。
AI will come and take away the routine jobs and in due time, we will be thankful.
人工智能将代替我们承担重复性工作，到时候我们将甚感欣慰。
AI will become great tools for the creatives so that scientists, artists, musicians and writers can be even more creative.
人工智能将成为创造者的好工具，所以科学家、艺术家、音乐家和作家能变得更有创造力。
AI will work with humans as analytical tools that humans can wrap their warmth around for the high-compassion jobs.
人工智能将作为分析工具与人共事，人类将温暖倾注于高同情性工作。
And we can always differentiate ourselves with the uniquely capable jobs that are both compassionate and creative,
我们可以区分自己，通过独特擅长的工作兼具同情心和创造性，
using and leveraging our irreplaceable brains and hearts.
充分利用我们独一无二的头脑和内心。
So there you have it: a blueprint of coexistence for humans and AI.
这就是：人类与人工智能共存的蓝图。
AI is serendipity. It is here to liberate us from routine jobs, and it is here to remind us what it is that makes us human.
人工智能的发展是机缘巧合。它的到来将把我们从常规工作中解放出来，它的到来也提醒我们人因何为人。
So let us choose to embrace AI and to love one another. Thank you.
所以让我们选择拥抱人工智能并互相关爱。谢谢。