TED2013
Erik Brynjolfsson: The key to growth? Race with the machines
埃里克·布伦乔尔森: 经济增长的要诀？和机器共同进步
Filmed:
Readability: 4.6
1,321,770 views
随着机器可以胜任越来越多的工作，很多人发现他们失了业或者根本无法加薪。经济增长停止了吗？不，埃里克·布伦乔尔森认为这只是经济重组过程中的“成长的烦恼”。一个非常引人注目的演讲阐述为什么更大的创新在等着我们……如果我们把电脑当作团队中的一员的话。别忘了去听听罗伯特·戈登的对立观点。
Erik Brynjolfsson - Innovation researcher
Erik Brynjolfsson examines the effects of information technologies on business strategy, productivity and employment. Full bio
Erik Brynjolfsson examines the effects of information technologies on business strategy, productivity and employment. Full bio
Double-click the English transcript below to play the video.
00:12
Growth is not dead.
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经济增长并未死去。
00:14
(Applause)
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（掌声）
00:16
Let's start the story 120 years ago,
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让我们回到120年前，
00:20
when American factories began to electrify their operations,
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那时，美国工厂开始将生产电气化，
00:23
igniting the Second Industrial Revolution.
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点燃了第二次工业革命。
00:27
The amazing thing is
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令人惊讶的是，
00:28
that productivity did not increase in those factories
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三十年内，生产力并没有提升。
00:31
for 30 years. Thirty years.
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三十年啊！
00:34
That's long enough for a generation of managers to retire.
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这段时间都足够让一代经理人退休了。
00:37
You see, the first wave of managers
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第一代的经理人
00:40
simply replaced their steam engines with electric motors,
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仅仅是用电动机取代了蒸汽机，
00:43
but they didn't redesign the factories to take advantage
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但他们并没有重新设计工厂使之充分利用
00:46
of electricity's flexibility.
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电力所带来的灵活性。
00:48
It fell to the next generation to invent new work processes,
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到第二代经理人改进运作过程后，
00:52
and then productivity soared,
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生产力才开始飙升，
00:55
often doubling or even tripling in those factories.
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达到之前的两倍甚至三倍。
00:59
Electricity is an example of a general purpose technology,
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电力是通用技术的代表之一，
01:03
like the steam engine before it.
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就像之前的蒸汽机一样。
01:06
General purpose technologies drive most economic growth,
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通用技术推动了多方面的经济增长，
01:09
because they unleash cascades of complementary innovations,
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因为它们释放了其它各级创新的潜能，
01:13
like lightbulbs and, yes, factory redesign.
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例如电灯泡，还有工厂的重新设计。
01:16
Is there a general purpose technology of our era?
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我们这个年代有没有通用技术？
01:20
Sure. It's the computer.
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当然有，那就是电脑。
01:22
But technology alone is not enough.
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但是仅有技术是不够的。
01:25
Technology is not destiny.
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技术并不是终极目标。
01:28
We shape our destiny,
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我们自己塑造我们的目标，
01:29
and just as the earlier generations of managers
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正如早期的经理人
01:32
needed to redesign their factories,
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需要重新设计工厂，
01:34
we're going to need to reinvent our organizations
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我们也需要重新改造我们的体制，
01:36
and even our whole economic system.
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甚至整个经济系统。
01:39
We're not doing as well at that job as we should be.
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在这方面，我们的表现有些差强人意。
01:42
As we'll see in a moment,
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我会在接下来给大家展现，
01:44
productivity is actually doing all right,
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生产效率目前发展良好，
01:46
but it has become decoupled from jobs,
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但是这已经和工作岗位脱节，
01:50
and the income of the typical worker is stagnating.
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而且普通工人的收入也正在停止增长。
01:55
These troubles are sometimes misdiagnosed
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这些问题有的时候被误认为是
01:57
as the end of innovation,
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创新的终结，
02:01
but they are actually the growing pains
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但实际上，它们是我和安德鲁·麦克菲
02:03
of what Andrew McAfee and I call the new machine age.
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称作的新机器时代的“成长的烦恼”。
02:09
Let's look at some data.
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让我们看一些数据。
02:11
So here's GDP per person in America.
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这是美国人均GDP（国内生产总值）变化图。
02:13
There's some bumps along the way, but the big story
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中间有些颠簸起伏回落，但从整体上看
02:16
is you could practically fit a ruler to it.
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我们可以用一把尺子（直线）来比量发展趋势。
02:19
This is a log scale, so what looks like steady growth
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从对数比例的角度来看，这表面上是在稳步增长
02:22
is actually an acceleration in real terms.
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但实际上是加速度。
02:25
And here's productivity.
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这里显示的是生产率。
02:27
You can see a little bit of a slowdown there in the mid-'70s,
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大家可以看到在上世纪70年代中叶有一点停顿，
02:30
but it matches up pretty well with the Second Industrial Revolution,
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但这趋势与第二次工业革命的发展很像，
02:34
when factories were learning how to electrify their operations.
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那时工厂都在学习如何让操作电气化。
02:36
After a lag, productivity accelerated again.
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在一个停顿之后，生产率又加速发展了。
02:41
So maybe "history doesn't repeat itself,
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也许“历史虽然不会简单重复，
02:43
but sometimes it rhymes."
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但有时却也有规律可循。”
02:46
Today, productivity is at an all-time high,
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现在，生产率是有史以来最高的，
02:49
and despite the Great Recession,
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尽管有大萧条，
02:51
it grew faster in the 2000s than it did in the 1990s,
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2000年代的生产率还是要比上世纪90年代的发展得要快，
02:55
the roaring 1990s, and that was faster than the '70s or '80s.
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繁荣的90年代的生产率又比70或者80年代的发展快。
02:59
It's growing faster than it did during the Second Industrial Revolution.
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它比第二次工业革命的生产率发展的要快。
03:03
And that's just the United States.
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而这仅仅是美国的数据。
03:05
The global news is even better.
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全球的情况更好。
03:08
Worldwide incomes have grown at a faster rate
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全球收入增长比之前
03:10
in the past decade than ever in history.
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任意一个时代的发展都要快。
03:13
If anything, all these numbers actually understate our progress,
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这些数字实际上低估了我们所取得的进步，
03:18
because the new machine age
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因为新机器时代
03:20
is more about knowledge creation
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更多的是知识创造
03:21
than just physical production.
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而不是具体的物质生产。
03:24
It's mind not matter, brain not brawn,
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它是思想不是事实，是头脑不是体力，
03:27
ideas not things.
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是想法而不是具体事物。
03:29
That creates a problem for standard metrics,
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这为那些标准化的测量指标提出了挑战，
03:31
because we're getting more and more stuff for free,
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因为我们正在免费的获得越来越多的信息，
03:35
like Wikipedia, Google, Skype,
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比如维基大百科、谷歌、Skype,
03:37
and if they post it on the web, even this TED Talk.
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以及发布在网上的内容，比如这个TED演讲。
03:41
Now getting stuff for free is a good thing, right?
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免费获得东西是好事，对吧？
03:44
Sure, of course it is.
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当然，那还用说。
03:46
But that's not how economists measure GDP.
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但那不是经济学家如何测算GDP的。
03:49
Zero price means zero weight in the GDP statistics.
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免费的东西意味着在GDP统计里没有任何权重。
03:55
According to the numbers, the music industry
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根据这些数据来看，音乐工业
03:57
is half the size that it was 10 years ago,
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只是过去十年的一半的规模，
04:00
but I'm listening to more and better music than ever.
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但我正在听比过去更多和更好的音乐。
04:04
You know, I bet you are too.
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我相信大家也有同感。
04:06
In total, my research estimates
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我的研究预测
04:09
that the GDP numbers miss over 300 billion dollars per year
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我们每年总共少计算三千亿美元的GDP，
04:13
in free goods and services on the Internet.
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也就是免费在互联网上获得的商品和服务。
04:17
Now let's look to the future.
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让我们展望未来。
04:19
There are some super smart people
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有些非常聪明的人们
04:21
who are arguing that we've reached the end of growth,
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认为我们的经济增长已经停滞，
04:26
but to understand the future of growth,
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但是，为了理解未来发展的走势，
04:29
we need to make predictions
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我们要预测经济发展的
04:32
about the underlying drivers of growth.
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深层动力是什么。
04:35
I'm optimistic, because the new machine age
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我是乐观的，因为新机器时代是
04:39
is digital, exponential and combinatorial.
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数字化的、指数化（增长）的和组合性的。
04:44
When goods are digital, they can be replicated
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当商品是数字化的时候，它们可以
04:47
with perfect quality at nearly zero cost,
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被近乎无附加值的完美复制，
04:51
and they can be delivered almost instantaneously.
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而且它们几乎可以在瞬间传送。
04:55
Welcome to the economics of abundance.
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欢迎来到丰饶经济学。
04:58
But there's a subtler benefit to the digitization of the world.
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但是还有一个全球电子化带来的微妙好处。
05:02
Measurement is the lifeblood of science and progress.
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测量是科学与进步的生命线。
05:06
In the age of big data,
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在大数据时代，
05:08
we can measure the world in ways we never could before.
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我们可以用从未有过的方式来测量世界。
05:13
Secondly, the new machine age is exponential.
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其次，新机器时代是指数化（发展）的。
05:17
Computers get better faster than anything else ever.
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电脑正比任何事物都发展得更快更好。
05:23
A child's Playstation today is more powerful
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今天一个孩子的Playstation比
05:26
than a military supercomputer from 1996.
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1996年的军事超级计算机还要强大。
05:30
But our brains are wired for a linear world.
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但是我们习惯了一个线性发展的世界。
05:33
As a result, exponential trends take us by surprise.
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因此，我们都惊讶于指数形式的发展趋势。
05:37
I used to teach my students that there are some things,
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我以前告诉我的学生，
05:40
you know, computers just aren't good at,
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有些事情是电脑做不好的，
05:42
like driving a car through traffic.
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比如说开车。
05:44
(Laughter)
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（笑声）
05:46
That's right, here's Andy and me grinning like madmen
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对，这是我和安迪笑得像个傻子，
05:50
because we just rode down Route 101
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因为我们刚在一辆无人驾驶的汽车里
05:52
in, yes, a driverless car.
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穿过了101大道。
05:56
Thirdly, the new machine age is combinatorial.
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第三，新机器时代是组合性的。
05:58
The stagnationist view is that ideas get used up,
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停滞的观点认为所有的创新都用完了，
06:02
like low-hanging fruit,
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比如那些显而易见的，
06:04
but the reality is that each innovation
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但事实是每个创新
06:07
creates building blocks for even more innovations.
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都为更多的创新奠定了基石。
06:11
Here's an example. In just a matter of a few weeks,
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举个例子。在几周内，
06:14
an undergraduate student of mine
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我的一个学生
06:16
built an app that ultimately reached 1.3 million users.
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开发了一个吸引了大概一百三十万用户的应用。
06:20
He was able to do that so easily
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他可以这么轻松的完成
06:22
because he built it on top of Facebook,
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是因为这个应用是在脸书上搭建起来的，
06:24
and Facebook was built on top of the web,
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而脸书又依托于网络，
06:26
and that was built on top of the Internet,
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而网络又是在互联网上建造起来的，
06:27
and so on and so forth.
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等等等等。
06:30
Now individually, digital, exponential and combinatorial
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电子化、指数化（发展）和组合化，
06:35
would each be game-changers.
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任何一个都会带来翻天覆地的变化。
06:37
Put them together, and we're seeing a wave
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把它们结合起来，我们就会看到
06:39
of astonishing breakthroughs,
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新一轮的惊人突破，
06:41
like robots that do factory work or run as fast as a cheetah
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比如机器人来做工厂的工作或者跑得像猎豹一样快
06:44
or leap tall buildings in a single bound.
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或者一个飞跃就跃过高楼大厦。
06:46
You know, robots are even revolutionizing
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机器人甚至正在变革
06:49
cat transportation.
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对猫的运输方式。
06:50
(Laughter)
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（笑声）
06:53
But perhaps the most important invention,
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但也许最重要的发明，
06:55
the most important invention is machine learning.
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就是机器学习。
07:00
Consider one project: IBM's Watson.
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看看IBM的沃森项目。
07:04
These little dots here,
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这些小圆点们，
07:05
those are all the champions on the quiz show "Jeopardy."
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这些是益智游戏“杰帕迪”的冠军们。
07:10
At first, Watson wasn't very good,
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最初，沃森变现得并不出色，
07:13
but it improved at a rate faster than any human could,
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但是它比任何人类改进得都快，
07:18
and shortly after Dave Ferrucci showed this chart
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很快，在大卫·费鲁奇（沃森项目负责人）给我在MIT
07:21
to my class at MIT,
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的学生看这张图之后不久，
07:23
Watson beat the world "Jeopardy" champion.
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沃森就击败了“杰帕迪”的世界冠军。
07:26
At age seven, Watson is still kind of in its childhood.
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那时沃森只有7岁，还是个孩子。
07:30
Recently, its teachers let it surf the Internet unsupervised.
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最近，它的老师们让它自行上网。
07:36
The next day, it started answering questions with profanities.
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第二天，它就开始用脏话来回答问题了。
07:42
Damn. (Laughter)
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糟糕。（笑声）
07:44
But you know, Watson is growing up fast.
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但是，沃森正在快速的成长。
07:46
It's being tested for jobs in call centers, and it's getting them.
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它应聘了客服类的工作，而且它很胜任。
07:50
It's applying for legal, banking and medical jobs,
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它正在应聘法律、银行和医药类的工作，
07:54
and getting some of them.
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而且也拿到了一些工作。
07:56
Isn't it ironic that at the very moment
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是不是很讽刺，我们在这个非常时期
07:58
we are building intelligent machines,
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正在建造可能是
08:00
perhaps the most important invention in human history,
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人类历史上最重要的发明－－智能机器，
08:04
some people are arguing that innovation is stagnating?
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而一些人还在说创新停滞不前了？
08:08
Like the first two industrial revolutions,
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就像之前的两次工业革命，
08:10
the full implications of the new machine age
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新机器时代的全面影响
08:13
are going to take at least a century to fully play out,
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至少会用一个世纪才能完全发挥出来，
08:16
but they are staggering.
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但这将会是惊人的。
08:19
So does that mean we have nothing to worry about?
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这是不是说我们没有什么可担心的了？
08:22
No. Technology is not destiny.
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不！技术不是目的。
08:26
Productivity is at an all time high,
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生产率是史上最高的，
08:28
but fewer people now have jobs.
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但是更少的人现在还有工作。
08:31
We have created more wealth in the past decade than ever,
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我们在过去十年创造了比过去更多的财富，
08:35
but for a majority of Americans, their income has fallen.
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但是大部分的美国家庭，他们的收入却降低了。
08:38
This is the great decoupling
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这是生产率和就业率，
08:41
of productivity from employment,
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财富和工作的
08:44
of wealth from work.
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严重脱节，
08:47
You know, it's not surprising that millions of people
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要知道，有数百万人受到
08:49
have become disillusioned by the great decoupling,
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被这种严重脱节的现象所迷惑，这并不让人惊讶，
08:52
but like too many others,
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但是像很多其他的人一样，
08:54
they misunderstand its basic causes.
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人们误解了这种现象的根本原因。
08:57
Technology is racing ahead,
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科技正在领跑，
09:00
but it's leaving more and more people behind.
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但它把越来越多的人甩在了后面。
09:03
Today, we can take a routine job,
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今天，我们可以把一个日常工作
09:07
codify it in a set of machine-readable instructions,
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编译成一组机器可读的指令，
09:10
and then replicate it a million times.
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然后就可以把它复制百万次。
09:12
You know, I recently overheard a conversation
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我最近就听到了一段
09:15
that epitomizes these new economics.
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反映这些新经济现象的对话。
09:17
This guy says, "Nah, I don't use H&R Block anymore.
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有个人说，“我不再用布洛克税务公司的专人服务了。
09:21
TurboTax does everything that my tax preparer did,
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波税务软件可以我的报税员做的任何工作，
09:23
but it's faster, cheaper and more accurate."
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但它更快、更便宜也更准确。“
09:28
How can a skilled worker
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一个专业人士
09:30
compete with a $39 piece of software?
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怎么能和一个售价只有39美元的软件相比？
09:33
She can't.
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不可能的。
09:35
Today, millions of Americans do have faster,
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今天，数百万的美国人有了更快、
09:37
cheaper, more accurate tax preparation,
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更便宜和更准确的税款准备，
09:40
and the founders of Intuit
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而且Intuit公司(创造TurboTax软件的公司）创始人
09:41
have done very well for themselves.
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也为自己收获颇丰。
09:44
But 17 percent of tax preparers no longer have jobs.
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但17％的报税员却失去了工作。
09:48
That is a microcosm of what's happening,
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这只是正在发生着的改变的一个缩影。
09:50
not just in software and services, but in media and music,
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不仅是在软件和服务领域，也在媒体和音乐界，
09:55
in finance and manufacturing, in retailing and trade --
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在金融、制造业、零售和外贸 －
09:59
in short, in every industry.
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总而言之，在每个行业中都在发生着。
10:02
People are racing against the machine,
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人类在和机器较量，
10:05
and many of them are losing that race.
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很多人都在失去这场较量。
10:09
What can we do to create shared prosperity?
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我们怎样才能达到共同繁荣？
10:12
The answer is not to try to slow down technology.
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答案绝对不是试图减缓科技发展。
10:15
Instead of racing against the machine,
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与其和机器赛跑，
10:18
we need to learn to race with the machine.
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我们应该学着如何与机器一同进步。
10:22
That is our grand challenge.
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这是我们最大的挑战。
10:25
The new machine age
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新机器时代
10:27
can be dated to a day 15 years ago
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可以从15年前的一天开始算起，
10:30
when Garry Kasparov, the world chess champion,
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当世界国际象棋冠军加里·卡斯帕罗夫
10:33
played Deep Blue, a supercomputer.
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和一台叫做深蓝的超级计算机下棋的时候。
10:37
The machine won that day,
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当时机器赢了，
10:39
and today, a chess program running on a cell phone
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而现在，一个在手机上的国际象棋程序
10:42
can beat a human grandmaster.
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也可以打败一个人类大师。
10:44
It got so bad that, when he was asked
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事情糟糕到，当被问到如果和一台电脑
10:48
what strategy he would use against a computer,
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下棋他会使用什么样的战术时，
10:50
Jan Donner, the Dutch grandmaster, replied,
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约翰·唐纳，荷兰象棋大师，回应道，
10:54
"I'd bring a hammer."
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“我会带个锤子。”
10:56
(Laughter)
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（笑声）
11:00
But today a computer is no longer the world chess champion.
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但今天电脑不再是世界国际象棋大赛冠军。
11:04
Neither is a human,
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也不是一个人，
11:07
because Kasparov organized a freestyle tournament
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因为卡斯帕罗夫组织了一个自由式比赛
11:10
where teams of humans and computers
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人类和电脑可以组团
11:12
could work together,
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一起合作，
11:14
and the winning team had no grandmaster,
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最终的获胜者团队里既没有大师，
11:17
and it had no supercomputer.
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也没有超级电脑。
11:20
What they had was better teamwork,
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他们有的是更好的团队合作，
11:24
and they showed that a team of humans and computers,
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这证明了一个由人和电脑共同协作的团队，
11:29
working together, could beat any computer
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可以打败任何一个单一作战的电脑
11:32
or any human working alone.
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或者个人。
11:36
Racing with the machine
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和机器一同前进
11:37
beats racing against the machine.
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要远远好过和机器竞赛。
11:40
Technology is not destiny.
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技术不是终极目标。
11:42
We shape our destiny.
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我们塑造自己的目标。
11:44
Thank you.
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谢谢大家。
11:45
(Applause)
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（掌声）
ABOUT THE SPEAKER
Erik Brynjolfsson - Innovation researcherErik Brynjolfsson examines the effects of information technologies on business strategy, productivity and employment.
Why you should listen
The director of the MIT Center for Digital Business and a research associate at the National Bureau of Economic Research, Erik Brynjolfsson asks how IT affects organizations, markets and the economy. His recent work studies data-driven decision-making, management practices that drive productivity, the pricing implications of Internet commerce and the role of intangible assets.
Brynjolfsson was among the first researchers to measure the productivity contributions of information and community technology (ICT) and the complementary role of organizational capital and other intangibles. His research also provided the first quantification of the value of online product variety, often known as the “Long Tail,” and developed pricing and bundling models for information goods.
His books include Wired for Innovation: How IT Is Reshaping the Economy and Race Against the Machine: How the Digital Revolution Is Accelerating Innovation, Driving Productivity and Irreversibly Transforming Employment and the Economy (with Andrew McAfee); and the recent article "Big Data: The Management Revolution" (with Andrew McAfee).
Erik Brynjolfsson | Speaker | TED.com