李開復TED英文演講：AI和人類將來會如何共處

李開復TED英文演講：AI和人類將來會如何共處

李開復認為，在這場人工智能摧毀工作的浩劫中，唯有創造性工作才能從中全身而退。人類將面臨的最大考驗並非是失去工作，而是失去生活的意義。下面是小編為大家收集關於李開復TED英文演講：AI和人類將來會如何共處，歡迎借鑑參考。

李開復TED演講稿雙語版

I'm going to talk about how AI and mankind can coexist, but first, we have to rethink about our human values. So let me first make a confession about my errors in my values.

我將會談談人工智能和人類如何能夠共存， 但首先，我們需要重新思考人文價值。 所以首先讓我承認我價值觀中的錯誤。

It was 11 o'clock, December 16, 1991. I was about to become a father for the first time. My wife, Shen-Ling, lay in the hospital bed going through a very difficult 12-hour labor. I sat by her bedside but looked anxiously at my watch, and I knew something that she didn't. I knew that if in one hour, our child didn't come, I was going to leave her there and go back to work and make a presentation about AI to my boss, Apple's CEO. Fortunately, my daughter was born at 11:30 --

那時是1991年12月16日的11時。 我即將首次成為父親。 我的妻子，申玲，躺在病牀上 經歷着一段艱辛併為時12小時的分娩。 我坐在牀邊 但卻焦慮地望着我的手錶， 而我知道一些她不知道的事。 我知道如果在一小時內， 我們的孩子還未出生， 我將要將她留在那裏 並去上班 並向我的老闆，蘋果的首席執行官 呈現個有關人工智能的陳述。 幸運的是，我的女兒在11:30出生了--

sparing me from doing the unthinkable, and to this day, I am so sorry for letting my work ethic take precedence over love for my family.

為我免去了要做難以想象的事的需要， 而一直到今天，我為我優先工作倫理 於對我家人的愛之上感到抱歉。

My AI talk, however, went off brilliantly.

然而，我人工智能的呈現， 進行得非常好。

Apple loved my work and decided to announce it at TED1992, 26 years ago on this very stage. I thought I had made one of the biggest, most important discoveries in AI, and so did the "Wall Street Journal" on the following day.

蘋果喜歡我的作品，並決定在TED1992上將其宣佈， 20xx年前就在這個台上。 我以為我做了在人工智能領域內 其中一個最重大的發現， 第二天“華爾街日報”也是這麼認為。

But as far as discoveries went, it turned out, I didn't discover India, or America. Perhaps I discovered a little island off of Portugal. But the AI era of discovery continued, and more scientists poured their souls into it. About 10 years ago, the grand AI discovery was made by three North American scientists, and it's known as deep learning.

但隨着越來越多的發現， 結果是， 我並沒有發現印度或是美洲。 或許我發現的是葡萄牙附近的一個小島。 但是人工智能的發現時代持續了下去， 而越來越多的科學家全心全意地投入其中。 大約20xx年前，三名北美科學家 做出了重大的人工智能發現， 那就是深度學習。

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. For example, if we show the deep learning network a massive number of food photos, it can recognize food such as hot dog or no hot dog.

深度學習是個能在單一域名中取得大量資料 並用超人的精確度 來學習以作出預測或決定的科技。 例如，如果我們向深度學習網絡顯示 非常大量的食物照片， 它可以辨認出 例如有熱狗或沒有熱狗的食物。

Or if we show it many pictures and videos and sensor data from driving on the highway, it can actually drive a car as well as a human being on the highway. And what if we showed this deep learning network all the speeches made by President Trump? Then this artificially intelligent President Trump, actually the network --

或如果我們向它顯示許多 在高速公路上行駛的影片和傳感器數據， 它其實可以與人類媲美 在高速公路上開車。 若我們向這深度學習網絡顯示 所有特朗普總統所發表過的演説呢? 這人工智能的特朗普總統， 其實是該網絡--

can --

可以--

You like double oxymorons, huh?

你喜歡雙重矛盾修辭法，對吧?

So this network, if given the request to make a speech about AI, he, or it, might say --

所以此網絡，若被要求發表一場 關於人工智能的演説的話， 他，或它，或許會説--

(Recording) Donald Trump: It's a great thing to build a better world with artificial intelligence.

能運用人工智能來建立 一個更完美的世界是個很美好的事情。

Kai-Fu Lee: And maybe in another language?

或許用另一個語言來説?

DT: (Speaking Chinese)

人工智能正在改變世界

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

你們並不知道他會説中文吧?

So deep learning has become the core in the era of AI discovery, and that's led by the US. But we're now in the era of implementation, where what really matters is execution, product quality, speed and data. And that's where China comes in. Chinese entrepreneurs, who I fund as a venture capitalist, are incredible workers, amazing work ethic. My example in the delivery room is nothing compared to how hard people work in China. As an example, one startup tried to claim work-life balance: "Come work for us because we are 996." And what does that mean? It means the work hours of 9am to 9pm, six days a week. That's contrasted with other startups that do 997.

所以深度學習成為了人工智能發現時代的核心， 並由美國領導着。 但我們現在身處於實踐時代， 被看重的是實行、產品質量、速度和數據。 這就是中國被牽涉其中的時候了。 中國企業家， 我為這些斗膽的資本家提供資金， 他們是非凡的員工， 有非常棒的工作倫理。 我在產房的例子與中國人工作用功的程度 相比之下不算什麼。 例如，有個新公司聲稱工作與生活的平衡： “加入我們吧，因我們是996。” 那是什麼意思呢? 那表示的是上午9時至晚上9時、 每週六天的工作時間。 這與其他實施997的新公司形成對比。

And the Chinese product quality has consistently gone up in the past decade, and that's because of a fiercely competitive environment. In Silicon Valley, entrepreneurs compete in a very gentlemanly fashion, sort of like in old wars in which each side took turns to fire at each other.

而在過去的十年中， 中國製的產品質量在持續地提升， 這歸功於具有極其競爭力的環境。 在硅谷，企業家用非常紳士的方式來競爭， 有點像是舊時的戰爭 雙方輪流向對方發火。

But in the Chinese environment, it's truly a gladiatorial fight to the death. In such a brutal environment, entrepreneurs learn to grow very rapidly, they learn to make their products better at lightning speed, and they learn to hone their business models until they're impregnable. As a result, great Chinese products like WeChat and Weibo are arguably better than the equivalent American products from Facebook and Twitter.

但在中國的環境內， 它就像是角鬥士般往死裏鬥。 在這個極其殘酷的環境內， 企業家學習如何迅速成長， 他們學習如何光速地將產品變得更好， 他們而學習將他們的企業模型 修飾至堅不可摧。 結果是，像是微信和微博的傑出中國產品 可説是比像是面子書和推特的相等美國產品更好。 而中國市場欣然接受這項變化和 加速變化以及範式轉變。

And the Chinese market embraces this change and accelerated change and paradigm shifts. As an example, if any of you go to China, you will see it's almost cashless and credit card-less, because that thing that we all talk about, mobile payment, has become the reality in China. In the last year, 18.8 trillion US dollars were transacted on mobile internet, and that's because of very robust technologies built behind it. It's even bigger than the China GDP. And this technology, you can say, how can it be bigger than the GDP? Because it includes all transactions: wholesale, channels, retail, online, offline, going into a shopping mall or going into a farmers market like this. The technology is used by 700 million people to pay each other, not just merchants, so it's peer to peer, and it's almost transaction-fee-free. And it's instantaneous, and it's used everywhere. And finally, the China market is enormous. This market is large, which helps give entrepreneurs more users, more revenue, more investment, but most importantly, it gives the entrepreneurs a chance to collect a huge amount of data which becomes rocket fuel for the AI engine. So as a result, the Chinese AI companies have leaped ahead so that today, the most valuable companies in computer vision, speech recognition, speech synthesis, machine translation and drones are all Chinese companies.

比如説，如果你們其中幾個去到中國， 你將會看到它幾乎是無現金及無信用卡， 因為我們常常討論的事物，移動支付， 在中國已成為了現實。 在過去的一年， 18.8萬億美金是通過移動網絡來交易， 而這歸功於在其身後被建設的強勁科技。 它比中國的國內生產總值還更高。 而此技術，你可以説， 它如何能比國內生產總值還更高? 這是因為它包括了所有的交易： 批發、頻道、零售、網上、離線， 如此般進入購物商場或是農貿市場。 這項技術被7億人用來互相支付， 不僅僅侷限於商家， 所以它是點對點的， 而它幾乎是無手續費的。 它是即時的， 並在每個地點被採用。 而最終，中國市場十分巨大。 此市場巨大， 這給了企業家更多用户、更高的收入、更多投資， 但最重要的， 它給了企業家一個收集大量數據的機會 這成為了人工智能引擎的燃料。 結果，中國人工智能公司已往前飛躍， 所以如今，在機械視覺、語言識別、 語言合成、機械翻譯和無人機領域中 最具價值的公司都是中國公司。 所以有着由美國帶領的發現時代 以及由中國帶領的實踐時代， 我們目前身處於的時代是 兩個超級大國的雙聯引擎正一同合作 來驅動我們人類從未見識過 最迅速的科技革命。

So with the US leading the era of discovery and China leading the era of implementation, we are now in an amazing age where the dual engine of the two superpowers are working together to drive the fastest revolution in technology that we have ever seen as humans. And this will bring tremendous wealth, unprecedented wealth: 16 trillion dollars, according to PwC, in terms of added GDP to the worldwide GDP by 2030. It will also bring immense challenges in terms of potential job replacements. Whereas in the Industrial Age it created more jobs because craftsman jobs were being decomposed into jobs in the assembly line, so more jobs were created. But AI completely replaces the individual jobs in the assembly line with robots. And it's not just in factories, but truckers, drivers and even jobs like telesales, customer service and hematologists as well as radiologists over the next 15 years are going to be gradually replaced by artificial intelligence. And only the creative jobs --

這將會帶來極大的財富、 空前的財富： 據普華永道稱，16萬億美金， 在2030年，為附加至全球國內生產總值的 國內生產總值。 它也將帶來巨大的挑戰， 在潛在工作更替方面。 而在工業時期， 它創造了更多工作 因為工匠的工作被分解成 生產線中的各式工作， 所以創造了更多工作。 但是人工智能用機械人 將生產線中的獨立工作給替代了。 而這不只是在工廠內， 而貨車司機、駕駛員 甚至於像是電話銷售、客服、 血液學家和放射學家的工作， 在未來的20xx年內 都將會慢慢地被人工智能所替代。 而只有具創造力的工作--

I have to make myself safe, right? Really, the creative jobs are the ones that are protected, because AI can optimize but not create.

我必須保護我自己，對吧? 真的，具創造力的工作是被保護的那羣， 因為人工智能可以優化但不能創造。

But what's more serious than the loss of jobs is the loss of meaning, because the work ethic in the Industrial Age has brainwashed us into thinking that work is the reason we exist, that work defined the meaning of our lives. And I was a prime and willing victim to that type of workaholic thinking. I worked incredibly hard. That's why I almost left my wife in the delivery room, that's why I worked 996 alongside my entrepreneurs. And that obsession that I had with work ended abruptly a few years ago when I was diagnosed with fourth stage lymphoma. The PET scan here shows over 20 malignant tumors jumping out like fireballs, melting away my ambition. But more importantly, it helped me reexamine my life. Knowing that I may only have a few months to live 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. My priorities were completely out of order. I neglected my family. My father had passed away, and I never had a chance to tell him I loved him. My mother had dementia and no longer recognized me, and my children had grown up.

但比失去工作更嚴重的是失去意義， 因為在工業時期的工作倫理 將我們洗腦並灌輸工作是我們存在的原因， 工作定義了我們生活的意義。 而我就是個典型並自願接受 那種工作狂思想的受害者。 我非常努力地工作。 那就是為什麼我幾乎將我的妻子獨自留在產房內， 那就是為什麼我996地與企業家們工作。 而我對工作的痴迷在幾年前 當我被診斷患上了第四期淋巴瘤時 突然地結束了。 這個正子斷層掃描顯示超過20個惡性腫瘤 向火球那樣地跳了出來， 令我的夙願逐漸地消失。 但更重要的是， 它幫助我重新審視我的生活。 知道我可能只剩下幾個月的生命 令我看清將自己所有的自尊 建立在工作艱辛程度以及努力工作的成就上 是有多麼的愚蠢。 我的優先事項亂套了。 我忽略了我的家庭。 我父親過世了， 而我從來沒有機會告訴他我愛他。 我母親患上了痴呆症並從此認不出我了， 而我的孩子們已經長大了。

During my chemotherapy, I read a book by Bronnie Ware who talked about dying wishes and regrets of the people in the deathbed. She found that facing death, nobody regretted that they didn't work hard enough in this life. They only regretted that they didn't spend enough time with their loved ones and that they didn't spread their love.

在我化療的過程中， 我讀了布朗妮·維爾的一本書 述説了人們臨終前的各種臨終心願以及遺憾。 她發現面對死亡時， 沒有人為自己在生命中 工作得不夠努力而感到惋惜。 他們只後悔自己沒有花足夠的時間與愛的人相處 並且沒有傳遞自己的愛。

So I am fortunately today in remission.

我今天很幸運地處於緩解期中。

So I can be back at TED again to share with you that I have changed my ways. I now only work 965 -- occasionally 996, but usually 965. I moved closer to my mother, my wife usually travels with me, and when my kids have vacation, if they don't come home, I go to them. So it's a new form of life that helped me recognize how important it is that love is for me, and facing death helped me change my life, but it also helped me see a new way of how AI should impact mankind and work and coexist with mankind, that really, AI is taking away a lot of routine jobs, but routine jobs are not what we're about.

所以我可以回到TED 與你們分享我已經改變了我的方法。 我如今965地工作-- 偶爾996，但通常965。 我搬遷至母親附近， 我妻子通常與我一同旅行， 當我的孩子們有假期時， 若他們不回家，我將到他們那兒去。 這新的生活方式幫助我認清 愛對我來説是多麼的重要， 而面對死亡幫助我改變自己的生活， 但它同時也幫助我用新的方式來看待 人工智能該如何影響人類 並工作以及與人類並存， 確實，人工智能帶走了很多規律性工作， 但這些規律性工作不代表着我們。

Why we exist is love. When we hold our newborn baby, love at first sight, or when we help someone in need, humans are uniquely able to give and receive love, and that's what differentiates us from AI.

我們存在的原因是愛。 當我們抱着我們的新生兒時， 一見鍾情， 或當我們幫助有需要的人時， 人類很獨特地能夠給予並接收愛， 這就是將我們與人工智能區分開來的事情。

Despite what science fiction may portray, I can responsibly tell you that AI has no love. When AlphaGo defeated the world champion Ke Jie, while Ke Jie was crying and loving the game of go, AlphaGo felt no happiness from winning and certainly no desire to hug a loved one.

不管任何科幻有可能描述的東西， 我能很負責任地告訴你人工智能沒有愛。 當阿法圍棋打敗了世界冠軍柯潔時， 當柯潔哭泣並愛着圍棋時， 阿法圍棋沒有從勝利中感受到開心的滋味 當然沒有擁抱愛的人的渴望。

So how do we differentiate ourselves as humans in the age of AI? We talked about the axis of creativity, and certainly that is one possibility, and now we introduce a new axis that we can call compassion, love, or empathy. Those are things that AI cannot do. So as AI takes away the routine jobs, I like to think we can, we should and we must create jobs of compassion. You might ask how many of those there are, but I would ask you: Do you not think that we are going to need a lot of social workers to help us make this transition? Do you not think we need a lot of compassionate caregivers to give more medical care to more people? Do you not think we're going to need 10 times more teachers to help our children find their way to survive and thrive in this brave new world? And with all the newfound wealth, should we not also make labors of love into careers and let elderly accompaniment or homeschooling become careers also?

那我們該如何在人工智能時代中 將我們為人們與其區分開來? 我們説到了創造性的軸， 當然那是其中一個可能性， 而如今我們介紹一個稱為 同情、愛或同感的新軸。 那些都是人工智能不能做的事情。 當人工智能帶走規律性工作的同時， 我想我們可以、應該以及必須創造同情性工作。 你或許會問那種工作到底有多少? 但我想問問你： 你不認為我們將需要許多社會福利工作者 來幫助我們完成這段過渡期嗎? 你不認為我們需要許多富有同情心的看護 來為更多人提供更多醫療看護嗎? 你不認為我們將需要多10倍的老師 來幫助我們的孩子們 來幫助他們找尋自己在這個新世界中 生存和成長的方法嗎? 有着這些新獲得的財富， 我們不應該將愛的勞工變成工作的一種 以及將老人伴隨或在家教育變成工作的一種嗎?

This graph is surely not perfect, but it points at four ways that we can work with AI. AI will come and take away the routine jobs and in due time, we will be thankful. AI will become great tools for the creatives so that scientists, artists, musicians and writers can be even more creative. AI will work with humans as analytical tools that humans can wrap their warmth around for the high-compassion jobs. And we can always differentiate ourselves with the uniquely capable jobs that are both compassionate and creative, using and leveraging our irreplaceable brains and hearts. So there you have it: a blueprint of coexistence for humans and AI.

這個圖表當然不是完美的， 但它指出了四種我們能與人工智能一同合作的方法。 人工智能將帶來並帶走規律性工作， 同時，我們將感到欣慰。 人工智能將成為創造者很好的工具 所以科學家、藝術家、音樂家和作家 能夠變得更有創造力。 人工智能將以分析工具的方式與人們工作， 所以人們可以將他們的温暖傾注於高同情性的工作。 我們可以用具獨特能力 並同時是具同情心和創造力的工作 將自己區分開來， 運用並影響我們不可取代的頭腦和內心。 所以你可以看到： 人類與人工智能共存的藍圖。 人工智能是湊巧的。 它的到來是將我們從規律性工作中解放出來， 它的到來也是提醒我們是什麼使我們成為人們。 所以讓我們選擇欣然接受人工智能並彼此相愛。 謝謝。