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Growth is not dead.
经济增长并未停止。
Let's start the story 120 years ago, when American factories began to electrify their operations, igniting the Second Industrial Revolution.
让我们回到120年前，那时，美国工厂开始将生产电气化，点燃了第二次工业革命。
The amazing thing is that productivity did not increase in those factories for 30 years. Thirty years.
令人惊讶的是，三十年内，生产力并没有提升。三十年啊！
That's long enough for a generation of managers to retire.
这段时间都足够让一代经理人退休了。
You see, the first wave of managers simply replaced their steam engines with electric motors,
第一代的经理人仅仅是用电动机取代了蒸汽机，
but they didn't redesign the factories to take advantage of electricity's flexibility.
但他们并没有重新设计工厂使之充分利用电力所带来的灵活性。
It fell to the next generation to invent new work processes, and then productivity soared, often doubling or even tripling in those factories.
到第二代经理人改进运作过程后，生产力才开始飙升，达到之前的两倍甚至三倍。
Electricity is an example of a general purpose technology, like the steam engine before it.
电力是通用技术的代表之一，就像之前的蒸汽机一样。
General purpose technologies drive most economic growth,
通用技术推动了多方面的经济增长，
because they unleash cascades of complementary innovations, like lightbulbs and, yes, factory redesign.
因为它们释放了其它各级创新的潜能，例如电灯泡，还有工厂的重新设计。
Is there a general purpose technology of our era? Sure. It's the computer.
我们这个年代有没有通用技术？当然有，那就是电脑。
But technology alone is not enough. Technology is not destiny.
但是仅有技术是不够的。技术并不是终极目标。
We shape our destiny, and just as the earlier generations of managers needed to redesign their factories,
我们自己塑造我们的目标，正如早期的经理人需要重新设计工厂，
we're going to need to reinvent our organizations and even our whole economic system.
我们也需要重新改造我们的体制，甚至整个经济系统。
We're not doing as well at that job as we should be.
在这方面，我们的表现并不是很好。
As we'll see in a moment, productivity is actually doing all right,
我会在接下来给大家展现，生产效率目前发展良好，
but it has become decoupled from jobs, and the income of the typical worker is stagnating.
但是这已经和工作岗位脱节，而且普通工人的收入也正在停止增长。
These troubles are sometimes misdiagnosed as the end of innovation,
这些问题有的时候被误认为是创新的终结，
but they are actually the growing pains of what Andrew McAfee and I call the new machine age.
但实际上，它们是我和安德鲁·麦克菲称作的新机器时代的“成长的烦恼”。
Let's look at some data. So here's GDP per person in America.
让我们看一些数据。这是美国人均GDP（国内生产总值）变化图。
There's some bumps along the way, but the big story is you could practically fit a ruler to it.
中间有些颠簸起伏回落，但从整体上看我们可以用一把尺子（直线）来比量发展趋势。
This is a log scale, so what looks like steady growth is actually an acceleration in real terms.
从对数比例的角度来看，这表面上是在稳步增长，但实际上是加速度。
And here's productivity. You can see a little bit of a slowdown there in the mid-'70s,
这里显示的是生产率。大家可以看到在上世纪70年代中叶有一点停顿，
but it matches up pretty well with the Second Industrial Revolution, when factories were learning how to electrify their operations.
但这趋势与第二次工业革命的发展很像，那时工厂都在学习如何让操作电气化。
After a lag, productivity accelerated again. So maybe "history doesn't repeat itself, but sometimes it rhymes."
在一个停顿之后，生产率又加速发展了。也许“历史虽然不会简单重复，但有时却也有规律可循。”
Today, productivity is at an all-time high, and despite the Great Recession,
现在，生产率是有史以来最高的，尽管有大萧条，
it grew faster in the 2000s than it did in the 1990s, the roaring 1990s, and that was faster than the '70s or '80s.
20世纪初的生产率还是要比上世纪90年代的发展得要快，繁荣的90年代的生产率又比70或者80年代的发展快。
It's growing faster than it did during the Second Industrial Revolution.
它比第二次工业革命的生产率发展的要快。
And that's just the United States. The global news is even better.
而这仅仅是美国的数据。全球的情况更好。
Worldwide incomes have grown at a faster rate in the past decade than ever in history.
全球收入增长比之前任意一个时代的发展都要快。
If anything, all these numbers actually understate our progress,
这些数字实际上低估了我们所取得的进步，
because the new machine age is more about knowledge creation than just physical production.
因为新机器时代更多的是知识创造，而不是具体的物质生产。
It's mind not matter, brain not brawn, ideas not things.
它是思想不是事实，是头脑不是体力，是想法而不是具体事物。
That creates a problem for standard metrics, because we're getting more and more stuff for free,
这为那些标准化的测量指标提出了挑战，因为我们正在免费的获得越来越多的信息，
like Wikipedia, Google, Skype, and if they post it on the web, even this TED Talk.
比如维基大百科、谷歌、Skype，以及发布在网上的内容，比如这个TED演讲。
Now getting stuff for free is a good thing, right? Sure, of course it is.
免费获得东西是好事，对吧？当然，那还用说。
But that's not how economists measure GDP. Zero price means zero weight in the GDP statistics.
但那不是经济学家如何测算GDP的。免费的东西意味着在GDP统计里没有任何权重。
According to the numbers, the music industry is half the size that it was 10 years ago,
根据这些数据来看，音乐工业只是过去十年的一半的规模，
but I'm listening to more and better music than ever. You know, I bet you are too.
但我正在听比过去更多和更好的音乐。我相信大家也有同感。
In total, my research estimates that the GDP numbers miss over 300 billion dollars per year in free goods and services on the Internet.
我的研究预测我们每年总共少计算三千亿美元的GDP，也就是免费在互联网上获得的商品和服务。
Now let's look to the future. There are some super smart people who are arguing that we've reached the end of growth,
让我们展望未来。有些非常聪明的人们认为我们的经济增长已经停滞，
but to understand the future of growth, we need to make predictions about the underlying drivers of growth.
但是，为了理解未来发展的走势，我们要预测经济发展的深层动力是什么。
I'm optimistic, because the new machine age is digital, exponential and combinatorial.
我是乐观的，因为新机器时代是数字化的、指数化（增长）的和组合性的。
When goods are digital, they can be replicated with perfect quality at nearly zero cost, and they can be delivered almost instantaneously.
当商品是数字化的时候，它们可以被近乎无附加值的完美复制，而且它们几乎可以在瞬间传送。
Welcome to the economics of abundance. But there's a subtler benefit to the digitization of the world.
欢迎来到丰饶经济学。但是还有一个全球电子化带来的微妙好处。
Measurement is the lifeblood of science and progress. In the age of big data, we can measure the world in ways we never could before.
测量是科学与进步的生命线。在大数据时代，我们可以用从未有过的方式来测量世界。
Secondly, the new machine age is exponential. Computers get better faster than anything else ever.
其次，新机器时代是指数化（发展）的。电脑正比任何事物都发展得更快更好。
A child's Playstation today is more powerful than a military supercomputer from 1996.
今天一个孩子的Playstation比1996年的军事超级计算机还要强大。
But our brains are wired for a linear world. As a result, exponential trends take us by surprise.
但是我们习惯了一个线性发展的世界。因此，我们都惊讶于指数形式的发展趋势。
I used to teach my students that there are some things, you know, computers just aren't good at, like driving a car through traffic.
我以前告诉我的学生，有些事情是电脑做不好的，比如说开车。
That's right, here's Andy and me grinning like madmen because we just rode down Route 101 in, yes, a driverless car.
对，这是我和安迪，笑得像个傻子，因为我们刚在一辆无人驾驶的汽车里穿过了101大道。
Thirdly, the new machine age is combinatorial. The stagnationist view is that ideas get used up, like low-hanging fruit,
第三，新机器时代是组合性的。停滞的观点认为所有的创新都用完了，比如那些显而易见的，
but the reality is that each innovation creates building blocks for even more innovations. Here's an example.
但事实是每个创新都为更多的创新奠定了基石。举个例子。
In just a matter of a few weeks, an undergraduate student of mine built an app that ultimately reached 1.3 million users.
在几周内，我的一个学生开发了一个吸引了大概一百三十万用户的应用。
He was able to do that so easily because he built it on top of Facebook,
他可以这么轻松的完成是因为这个应用是在脸书上搭建起来的，
and Facebook was built on top of the web, and that was built on top of the Internet, and so on and so forth.
而脸书又依托于网络，而网络又是在互联网上建造起来的，等等等等。
Now individually, digital, exponential and combinatorial would each be game-changers.
电子化、指数化（发展）和组合化，任何一个都会带来翻天覆地的变化。
Put them together, and we're seeing a wave of astonishing breakthroughs,
把它们结合起来，我们就会看到新一轮的惊人突破，
like robots that do factory work or run as fast as a cheetah or leap tall buildings in a single bound.
比如机器人来做工厂的工作或者跑得像猎豹一样快，或者一个飞跃就跃过高楼大厦。
You know, robots are even revolutionizing cat transportation.
机器人甚至正在变革对猫的运输方式。
But perhaps the most important invention, the most important invention is machine learning.
但也许最重要的发明，就是机器学习。
Consider one project: IBM's Watson. These little dots here, those are all the champions on the quiz show "Jeopardy."
看看IBM的沃森项目。这些小圆点们，这些是益智游戏“杰帕迪”的冠军们。
At first, Watson wasn't very good, but it improved at a rate faster than any human could,
最初，沃森变现得并不出色，但是它比任何人类改进得都快，很快，
and shortly after Dave Ferrucci showed this chart to my class at MIT, Watson beat the world "Jeopardy" champion.
在大卫·费鲁奇（沃森项目负责人）给我在MIT的学生看这张图之后不久，沃森就击败了“杰帕迪”的世界冠军。
At age seven, Watson is still kind of in its childhood.
那时沃森只有7岁，还是个孩子。
Recently, its teachers let it surf the Internet unsupervised.
最近，它的老师们让它自行上网。
The next day, it started answering questions with profanities. Damn.
第二天，它就开始用脏话来回答问题了。糟糕。
But you know, Watson is growing up fast.
但是，沃森正在快速的成长。
It's being tested for jobs in call centers, and it's getting them.
它应聘了客服类的工作，而且它很胜任。
It's applying for legal, banking and medical jobs, and getting some of them.
它正在应聘法律、银行和医药类的工作，而且也拿到了一些工作。
Isn't it ironic that at the very moment we are building intelligent machines, perhaps the most important invention in human history,
是不是很讽刺，我们在这个非常时期正在建造可能是人类历史上最重要的发明--智能机器，
some people are arguing that innovation is stagnating?
而一些人还在说创新停滞不前？
Like the first two industrial revolutions,
就像之前的两次工业革命，
the full implications of the new machine age are going to take at least a century to fully play out, but they are staggering.
新机器时代的全面影响至少会用一个世纪才能完全发挥出来，但这将会是惊人的。
So does that mean we have nothing to worry about? No. Technology is not destiny.
这是不是说我们没有什么可担心的了？不！技术不是目的。
Productivity is at an all time high, but fewer people now have jobs.
生产率是史上最高的，但是更少的人现在还有工作。
We have created more wealth in the past decade than ever, but for a majority of Americans, their income has fallen.
我们在过去十年创造了比过去更多的财富，但是大部分的美国家庭，他们的收入却降低了。
This is the great decoupling of productivity from employment, of wealth from work.
这是生产率和就业率，财富和工作的严重脱节。
You know, it's not surprising that millions of people have become disillusioned by the great decoupling,
要知道，有数百万人被这种严重脱节的现象所迷惑，这并不让人惊讶，
but like too many others, they misunderstand its basic causes.
但是像很多其他的人一样，人们误解了这种现象的根本原因。
Technology is racing ahead, but it's leaving more and more people behind.
科技正在领跑，但它把越来越多的人甩在了后面。
Today, we can take a routine job, codify it in a set of machine-readable instructions, and then replicate it a million times.
今天，我们可以把一个日常工作编译成一组机器可读的指令，然后就可以把它复制百万次。
You know, I recently overheard a conversation that epitomizes these new economics.
我最近就听到了一段反映这些新经济现象的对话。
This guy says, "Nah, I don't use H&R Block anymore.
有个人说，“我不再用布洛克税务公司的专人服务了。
TurboTax does everything that my tax preparer did, but it's faster, cheaper and more accurate."
波税务软件可以做我的报税员的任何工作，但它更快、更便宜也更准确。”
How can a skilled worker compete with a $39 piece of software? She can't.
一个专业人士怎么能和一个售价只有39美元的软件相比？不可能的。
Today, millions of Americans do have faster, cheaper, more accurate tax preparation,
今天，数百万的美国人有了更快、更便宜和更准确的税款准备，
and the founders of Intuit have done very well for themselves.
而且Intuit公司创始人自己也做得很好。
But 17 percent of tax preparers no longer have jobs.
但17%的报税员却失去了工作。
That is a microcosm of what's happening, not just in software and services,
这只是正在发生着的改变的一个缩影，不仅是在软件和服务领域，
but in media and music, in finance and manufacturing, in retailing and trade -- in short, in every industry.
也在媒体和音乐界，在金融、制造业、零售和外贸--总而言之，在每个行业中都在发生着。
People are racing against the machine, and many of them are losing that race.
人类在和机器较量，很多人都输了。
What can we do to create shared prosperity? The answer is not to try to slow down technology.
我们怎样才能达到共同繁荣？答案绝对不是试图减缓科技发展。
Instead of racing against the machine, we need to learn to race with the machine. That is our grand challenge.
与其和机器赛跑，我们应该学着如何与机器一同进步。这是我们最大的挑战。
The new machine age can be dated to a day 15 years ago when Garry Kasparov, the world chess champion, played Deep Blue, a supercomputer.
新机器时代，可以从15年前的一天开始算起，当世界国际象棋冠军加里·卡斯帕罗夫和一台叫做深蓝的超级计算机下棋的时候。
The machine won that day, and today, a chess program running on a cell phone can beat a human grandmaster.
当时机器赢了，而现在，一个在手机上的国际象棋程序也可以打败一个人类大师。
It got so bad that, when he was asked what strategy he would use against a computer, Jan Donner, the Dutch grandmaster, replied, "I'd bring a hammer."
事情糟糕到，当被问到如果和一台电脑下棋他会使用什么样的战术时，荷兰象棋大师约翰·唐纳回应道，“我会带个锤子。”
But today a computer is no longer the world chess champion.
但今天电脑不再是世界国际象棋大赛冠军。
Neither is a human, because Kasparov organized a freestyle tournament where teams of humans and computers could work together,
也不是一个人，因为卡斯帕罗夫组织了一个自由式比赛，人类和电脑可以组团一起合作，
and the winning team had no grandmaster, and it had no supercomputer.
最终的获胜者团队里既没有大师，也没有超级电脑。
What they had was better teamwork, and they showed that a team of humans and computers, working together,
他们有的是更好的团队合作，这证明了一个由人和电脑共同协作的团队，
could beat any computer or any human working alone.
可以打败任何一个单一作战的电脑或者个人。
Racing with the machine beats racing against the machine.
和机器一同前进要远远好过和机器竞赛。
Technology is not destiny. We shape our destiny. Thank you.
科技不能主导我们的命运，是我们主导自己的命运。谢谢大家。
