TED2013
Erik Brynjolfsson: The key to growth? Race with the machines
艾瑞克．布倫喬爾森 (Erik Brynjolfsson): 成長的關鍵？與電腦競爭
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Readability: 4.6
1,321,770 views
現在機器能做的工作越來越多，許多人發現自己丟了工作或是面臨加薪遙遙無期的窘境。難道我們已經走到了成長的盡頭？不，艾瑞克．布倫喬爾森 (Erik Brynjolfsson) 認為，這只是徹底重整經濟的陣痛期。他提出了一個有趣的案例來說明，如果我們願意和電腦合作，將如何創造更多的可能。一起來聽看看艾瑞克．布倫喬爾森獨特的看法。
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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成長還沒停止
00:14
(Applause)
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（掌聲）
00:16
Let's start the story 120 years ago,
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故事從 120 年前說起
00:20
when American factories began to electrify their operations,
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美國工廠開始電器化運作
00:23
igniting the Second Industrial Revolution.
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帶動了第二次工業革命
00:27
The amazing thing is
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但驚人的是
00:28
that productivity did not increase in those factories
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三十年中，那些工廠的生產力並沒有提升
00:31
for 30 years. Thirty years.
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整整三十年
00:34
That's long enough for a generation of managers to retire.
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這段時間足以讓一代的經理退休了
00:37
You see, the first wave of managers
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我們可以看到，第一批經理
00:40
simply replaced their steam engines with electric motors,
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只不過是把蒸汽機換成電動機而已
00:43
but they didn't redesign the factories to take advantage
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他們並沒有重新設計工廠
00:46
of electricity's flexibility.
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讓它利用電的多變性
00:48
It fell to the next generation to invent new work processes,
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下個世代開始發明新的工作程序
00:52
and then productivity soared,
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生產力因此大增
00:55
often doubling or even tripling in those factories.
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常常是原來工廠的兩倍，甚至是三倍
00:59
Electricity is an example of a general purpose technology,
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電力是一種通用目的技術的例子
01:03
like the steam engine before it.
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出現較早的蒸汽機也是一樣
01:06
General purpose technologies drive most economic growth,
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通用目的技術是帶動經濟發展的主力
01:09
because they unleash cascades of complementary innovations,
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因為它能帶動一連串有互補性的創新
01:13
like lightbulbs and, yes, factory redesign.
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像是燈泡，沒錯，工廠因而改頭換面
01:16
Is there a general purpose technology of our era?
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那現代有通用目的技術存在嗎？
01:20
Sure. It's the computer.
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當然有，就是電腦
01:22
But technology alone is not enough.
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但只靠科技還不夠
01:25
Technology is not destiny.
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科技不能主導命運
01:28
We shape our destiny,
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是我們掌握自己的命運
01:29
and just as the earlier generations of managers
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就像早期的經理
01:32
needed to redesign their factories,
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需要重新打造他們的工廠一樣
01:34
we're going to need to reinvent our organizations
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我們也需要重建一個組織
01:36
and even our whole economic system.
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甚至是重塑整個經濟體制
01:39
We're not doing as well at that job as we should be.
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我們並沒有達到應有的水準
01:42
As we'll see in a moment,
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我們馬上就會了解
01:44
productivity is actually doing all right,
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生產力是完全沒有問題的
01:46
but it has become decoupled from jobs,
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但生產力與工作背道而馳
01:50
and the income of the typical worker is stagnating.
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而且，一般工人的收入也減少了
01:55
These troubles are sometimes misdiagnosed
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有時候我們在創新的盡頭
01:57
as the end of innovation,
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會對這些問題有錯誤的判斷
02:01
but they are actually the growing pains
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但事實上這是一種成長必要的代價
02:03
of what Andrew McAfee and I call the new machine age.
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我和安德魯．邁克菲 (Andrew McAfee)
將其稱為「新機器時代」
將其稱為「新機器時代」
02:09
Let's look at some data.
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我們來看看一些資料
02:11
So here's GDP per person in America.
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這是美國每人的國內生產毛額
02:13
There's some bumps along the way, but the big story
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線上有些高低起伏，但重點是
02:16
is you could practically fit a ruler to it.
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你會發現它的路徑與直線符合
02:19
This is a log scale, so what looks like steady growth
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這是對數比例尺，所以看起來是穩定成長
02:22
is actually an acceleration in real terms.
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但事實上，它是加速進行著
02:25
And here's productivity.
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而這是生產力
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 70 年代中期，成長漸緩
02:30
but it matches up pretty well with the Second Industrial Revolution,
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但這和第二次工業革命的時間吻合
02:34
when factories were learning how to electrify their operations.
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當時工廠正在學著如何電器化運作
02:36
After a lag, productivity accelerated again.
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漸緩一段時間後，生產力再度急遽上升
02:41
So maybe "history doesn't repeat itself,
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所以或許「歷史不會自己重演
02:43
but sometimes it rhymes."
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但有時不可否認會有幾分相似。」
02:46
Today, productivity is at an all-time high,
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現在，生產力是前所未有的高
02:49
and despite the Great Recession,
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儘管是在經濟大蕭條的期間
02:51
it grew faster in the 2000s than it did in the 1990s,
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2000 年以來還是比 90 年代成長得更快
02:55
the roaring 1990s, and that was faster than the '70s or '80s.
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喧囂動盪的 90 年代還是比
70 或 80 年代增加更快
70 或 80 年代增加更快
02:59
It's growing faster than it did during the Second Industrial Revolution.
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比第二次工業革命時成長更快
03:03
And that's just the United States.
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而這只是美國而已
03:05
The global news is even better.
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全球的表現更是優秀
03:08
Worldwide incomes have grown at a faster rate
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全球所得在過去十年
03:10
in the past decade than ever in history.
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以前所未有的驚人速度成長
03:13
If anything, all these numbers actually understate our progress,
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不過，這些數據事實上低估了我們進步的程度
03:18
because the new machine age
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因為新機器時代
03:20
is more about knowledge creation
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強調的是知識的創造
03:21
than just physical production.
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而非只是實際的產量
03:24
It's mind not matter, brain not brawn,
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怎麼想比怎麼做來得重要
要動腦而不是靠蠻力
要動腦而不是靠蠻力
03:27
ideas not things.
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想法大於產物本身
03:29
That creates a problem for standard metrics,
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而這產生了測量標準的問題
03:31
because we're getting more and more stuff for free,
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因為免費的東西越來越多
03:35
like Wikipedia, Google, Skype,
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像是維基百科、谷歌、網路電話（Skype）
03:37
and if they post it on the web, even this TED Talk.
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他們把東西放到網路上
甚至是現在這篇 TED 演講
甚至是現在這篇 TED 演講
03:41
Now getting stuff for free is a good thing, right?
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有免費的東西是好事，對吧？
03:44
Sure, of course it is.
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當然是好事
03:46
But that's not how economists measure GDP.
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但經濟學家可不是這樣衡量國內生產毛額的
03:49
Zero price means zero weight in the GDP statistics.
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免費，在國內生產毛額統計上代表權重為零
03:55
According to the numbers, the music industry
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根據調查顯示，音樂產業的規模
03:57
is half the size that it was 10 years ago,
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只有十年前的二分之一
04:00
but I'm listening to more and better music than ever.
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但我現在聽到的音樂，比起以前進步很多
04:04
You know, I bet you are too.
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我想你們也有這種感覺
04:06
In total, my research estimates
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整體來說，我的研究估計
04:09
that the GDP numbers miss over 300 billion dollars per year
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國內生產毛額每年少算超過三千億美元
04:13
in free goods and services on the Internet.
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忽略了網路上提供的免費產品及服務
04:17
Now let's look to the future.
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現在我們放眼未來
04:19
There are some super smart people
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有些非常聰明的人
04:21
who are arguing that we've reached the end of growth,
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認為我們已經發展到了窮途末路
04:26
but to understand the future of growth,
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但要了解未來的發展
04:29
we need to make predictions
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我們必須對成長潛在的驅動力
04:32
about the underlying drivers of growth.
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做些預測
04:35
I'm optimistic, because the new machine age
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我抱持樂觀的態度，因為新機器時代
04:39
is digital, exponential and combinatorial.
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是數位化、指數化及組合化的時代
04:44
When goods are digital, they can be replicated
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當產品數位化，就能夠複製
04:47
with perfect quality at nearly zero cost,
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幾乎不用花半毛錢，就能有很好的品質
04:51
and they can be delivered almost instantaneously.
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而且可以立即傳送
04:55
Welcome to the economics of abundance.
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歡迎來到經濟蓬勃的時代
04:58
But there's a subtler benefit to the digitization of the world.
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世界數位化有個比較其次的好處
05:02
Measurement is the lifeblood of science and progress.
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測量是科學及進步的重要指標
05:06
In the age of big data,
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在充斥大量資料的時代
05:08
we can measure the world in ways we never could before.
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我們可以用過去辦不到的方法
來衡量現在的世界
來衡量現在的世界
05:13
Secondly, the new machine age is exponential.
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第二，新機器時代是指數化的時代
05:17
Computers get better faster than anything else ever.
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電腦比任何東西跑得更快
05:23
A child's Playstation today is more powerful
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現在小朋友的遊戲機（Playstation）
05:26
than a military supercomputer from 1996.
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比 1996 年軍隊的超級電腦更進步
05:30
But our brains are wired for a linear world.
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但我們的大腦是習慣線性世界的
05:33
As a result, exponential trends take us by surprise.
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因此，指數化的趨勢讓我們大吃 一驚
05:37
I used to teach my students that there are some things,
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過去我都教學生說，有些事
05:40
you know, computers just aren't good at,
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你知道嗎？電腦根本做不來
05:42
like driving a car through traffic.
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像開車通過擁擠的車潮
05:44
(Laughter)
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（笑聲）
05:46
That's right, here's Andy and me grinning like madmen
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沒錯，這張照片是我和安迪，像瘋子一樣在大笑
05:50
because we just rode down Route 101
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因為我們剛下國道 101
05:52
in, yes, a driverless car.
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沒錯，就在一台無人駕駛的車子裡
05:56
Thirdly, the new machine age is combinatorial.
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第三，新機器時代是組合化的時代
05:58
The stagnationist view is that ideas get used up,
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想法停滯就是想法用完了
06:02
like low-hanging fruit,
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輕而易舉
06:04
but the reality is that each innovation
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但事實上，每一種創新
06:07
creates building blocks for even more innovations.
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都是激盪出更多創新的墊腳石
06:11
Here's an example. In just a matter of a few weeks,
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舉例來說，大約幾個禮拜前
06:14
an undergraduate student of mine
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我的一位大學生
06:16
built an app that ultimately reached 1.3 million users.
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開發了一個應用程式，最後使用者高達 130 萬
06:20
He was able to do that so easily
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他輕而易舉就能辦到
06:22
because he built it on top of Facebook,
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因為他是在臉書上建立的
06:24
and Facebook was built on top of the web,
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而臉書是個網站
06:26
and that was built on top of the Internet,
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網站又建立在網路之上
06:27
and so on and so forth.
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等等的關聯
06:30
Now individually, digital, exponential and combinatorial
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現在個人數位化、指數化及組合化
06:35
would each be game-changers.
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分別都能改變這場遊戲
06:37
Put them together, and we're seeing a wave
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把這些通通集結起來，我們會看到
06:39
of astonishing breakthroughs,
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一連串驚人的突破
06:41
like robots that do factory work or run as fast as a cheetah
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像是機器人，能在工廠工作
跑得跟印度豹一樣快
跑得跟印度豹一樣快
06:44
or leap tall buildings in a single bound.
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或是一躍就能上高樓
06:46
You know, robots are even revolutionizing
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其實，機器人甚至改變了
06:49
cat transportation.
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貓的運輸方式
06:50
(Laughter)
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（笑聲）
06:53
But perhaps the most important invention,
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但或許最重要的發明
06:55
the most important invention is machine learning.
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最重要的發明是讓機器學習
07:00
Consider one project: IBM's Watson.
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想想這個計畫：IBM 的沃森（Watson）
07:04
These little dots here,
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這些點顯示的是
07:05
those are all the champions on the quiz show "Jeopardy."
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智力節目《危險邊緣》裡所有的冠軍選手
07:10
At first, Watson wasn't very good,
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一開始，沃森表現不佳
07:13
but it improved at a rate faster than any human could,
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但它進步的速度超乎常人
07:18
and shortly after Dave Ferrucci showed this chart
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就在戴維．費魯奇 （Dave Ferrucci）
給我在麻省理工學院的學生
給我在麻省理工學院的學生
07:21
to my class at MIT,
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看這張圖的不久後
07:23
Watson beat the world "Jeopardy" champion.
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沃森打敗了《危險邊緣》的世界冠軍
07:26
At age seven, Watson is still kind of in its childhood.
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七歲，沃森差不多還在童年時期
07:30
Recently, its teachers let it surf the Internet unsupervised.
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最近，沃森的老師讓它在
無人指導的情況下上網
無人指導的情況下上網
07:36
The next day, it started answering questions with profanities.
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隔天，它開始以髒話回答問題
07:42
Damn. (Laughter)
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該死！（笑聲）
07:44
But you know, Watson is growing up fast.
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但你們知道嗎？沃森長得很快
07:46
It's being tested for jobs in call centers, and it's getting them.
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它參加客服中心工作的考試，全數通過
07:50
It's applying for legal, banking and medical jobs,
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它申請法律、銀行及醫療方面的工作
07:54
and getting some of them.
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有一些通過了
07:56
Isn't it ironic that at the very moment
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這種情況下
07:58
we are building intelligent machines,
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我們發明了智慧型機器
08:00
perhaps the most important invention in human history,
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或許還是人類史上最重要的發明
08:04
some people are arguing that innovation is stagnating?
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卻有人說創新停滯了，這不是很諷刺嗎？
08:08
Like the first two industrial revolutions,
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像第一及第二次工業革命
08:10
the full implications of the new machine age
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新機器時代涵蓋的所有層面
08:13
are going to take at least a century to fully play out,
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至少要一個世紀才會完全落幕
08:16
but they are staggering.
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但這樣的革命是很驚人的
08:19
So does that mean we have nothing to worry about?
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所以這代表我們沒有後顧之憂了嗎？
08:22
No. Technology is not destiny.
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不，科技不能主導命運
08:26
Productivity is at an all time high,
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生產力是前所未有的高
08:28
but fewer people now have jobs.
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但有工作的人變少了
08:31
We have created more wealth in the past decade than ever,
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過去十年來，我們創造了史無前例的財富
08:35
but for a majority of Americans, their income has fallen.
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但多數的美國人，所得卻下降了
08:38
This is the great decoupling
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這是很嚴重的排擠效應
08:41
of productivity from employment,
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生產力排擠就業率
08:44
of wealth from work.
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財富排擠了工作
08:47
You know, it's not surprising that millions of people
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其實，這種情況不意外，幾百萬人
08:49
have become disillusioned by the great decoupling,
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對於這樣的排擠效應感到失望
08:52
but like too many others,
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但就像大多數人一樣
08:54
they misunderstand its basic causes.
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他們誤解了基本的原因
08:57
Technology is racing ahead,
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科技發展神速
09:00
but it's leaving more and more people behind.
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把越來越多人拋諸腦後
09:03
Today, we can take a routine job,
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現在的例行公事，我們都可以
09:07
codify it in a set of machine-readable instructions,
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將其改編成一組機器可讀的指令
09:10
and then replicate it a million times.
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然後複製一百萬遍
09:12
You know, I recently overheard a conversation
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最近我偶然聽到一則對話
09:15
that epitomizes these new economics.
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可以象徵這些經濟狀況
09:17
This guy says, "Nah, I don't use H&R Block anymore.
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有個男的說：「不，我不要再請稅務公司了
09:21
TurboTax does everything that my tax preparer did,
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報稅軟體能完成所有報稅員該做的事
09:23
but it's faster, cheaper and more accurate."
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而且更快、更便宜還更精確。」
09:28
How can a skilled worker
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一個專業的工作人員
09:30
compete with a $39 piece of software?
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要怎麼跟一個 39 塊美金的軟體競爭呢？
09:33
She can't.
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她沒辦法比
09:35
Today, millions of Americans do have faster,
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現在，的確有幾百萬美國人
09:37
cheaper, more accurate tax preparation,
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能更快、更便宜又更精確的報稅
09:40
and the founders of Intuit
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這報稅軟體的創辦人
09:41
have done very well for themselves.
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他們自己也做得很好
09:44
But 17 percent of tax preparers no longer have jobs.
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但是 17% 的報稅員丟了工作
09:48
That is a microcosm of what's happening,
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這只是一部分的縮影
09:50
not just in software and services, but in media and music,
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不只是軟體和服務方面
還包括媒體及音樂
還包括媒體及音樂
09:55
in finance and manufacturing, in retailing and trade --
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財務及製造業，零售及貿易
09:59
in short, in every industry.
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簡單來說，是所有產業
10:02
People are racing against the machine,
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人類在跟機器比速度
10:05
and many of them are losing that race.
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大部分都輸了
10:09
What can we do to create shared prosperity?
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該怎麼做才能共同創造繁榮的社會？
10:12
The answer is not to try to slow down technology.
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答案不會是放慢科技發展的速度
10:15
Instead of racing against the machine,
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我們不要去對抗機器
10:18
we need to learn to race with the machine.
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而是應該學會去跟機器一起競爭
10:22
That is our grand challenge.
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這是很大的挑戰
10:25
The new machine age
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新機器時代
10:27
can be dated to a day 15 years ago
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可以回朔到 15 年前的某一天
10:30
when Garry Kasparov, the world chess champion,
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國際西洋棋世界冠軍
加里．卡斯帕羅夫（Gary Kasparov）
加里．卡斯帕羅夫（Gary Kasparov）
10:33
played Deep Blue, a supercomputer.
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跟一台超級電腦：深藍（Deep Blue），一起比賽
10:37
The machine won that day,
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那天電腦贏了
10:39
and today, a chess program running on a cell phone
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而現在，一支手機裡的西洋棋遊戲
10:42
can beat a human grandmaster.
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都可以打敗一位西洋棋大師
10:44
It got so bad that, when he was asked
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這種情況真慘，當被問到
10:48
what strategy he would use against a computer,
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他會用什麼方法來對抗電腦
10:50
Jan Donner, the Dutch grandmaster, replied,
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荷蘭西洋棋大師
約翰．唐納（Jan Donner）回答：
約翰．唐納（Jan Donner）回答：
10:54
"I'd bring a hammer."
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「我會帶鐵鎚去。」
10:56
(Laughter)
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（笑聲）
11:00
But today a computer is no longer the world chess champion.
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但現在電腦已經不是西洋棋世界冠軍了
11:04
Neither is a human,
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冠軍也不是人
11:07
because Kasparov organized a freestyle tournament
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因為卡斯帕羅夫舉辦了一種自由式比賽
11:10
where teams of humans and computers
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這種比賽讓人類和電腦
11:12
could work together,
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可以一起合作
11:14
and the winning team had no grandmaster,
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贏家不是大師
11:17
and it had no supercomputer.
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也不是超級電腦
11:20
What they had was better teamwork,
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冠軍有的是團隊合作
11:24
and they showed that a team of humans and computers,
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他們展現了人類和電腦
11:29
working together, could beat any computer
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是如何並肩作戰，打敗任何一台電腦
11:32
or any human working alone.
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或是任何一個人孤軍奮戰
11:36
Racing with the machine
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和電腦一起競爭
11:37
beats racing against the machine.
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比對抗電腦來得有效
11:40
Technology is not destiny.
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科技不能主導我們的命運
11:42
We shape our destiny.
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是我們主導自己的命運
11:44
Thank you.
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謝謝大家
11:45
(Applause)
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（掌聲）
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