TEDxBoston 2012
Andrew McAfee: Are droids taking our jobs?
安德魯 麥克菲: 機器人搶了我們的工作嗎?
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機器人和演算法做得越來越好了，像在生產汽車，寫文章，翻譯 - 這些以往需要人類從事的工作。那麼，我們人類將來有什麼樣的工作可以做呢？安德魯 邁克菲（Andrew McAfee）檢視了最近的勞動力數據，他認為: 我們沒有看到任何重大的里程碑。他回歷史上發生的重大改變的時間點，提出了一個令人驚訝的，甚至驚心動魄的對未來會發生的事情的想法。 （在TED波士頓拍攝）。
Andrew McAfee - Management theorist
Andrew McAfee studies how information technology affects businesses and society. Full bio
Andrew McAfee studies how information technology affects businesses and society. Full bio
Double-click the English transcript below to play the video.
00:16
As it turns out, when tens of millions of people
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當數以千萬計的勞工
00:19
are unemployed or underemployed,
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處於失業或是低度就業的狀況發生時
00:21
there's a fair amount of interest in what technology might be doing to the labor force.
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就會有不少人會對科技如何影響勞工這個議題有興趣
00:25
And as I look at the conversation, it strikes me
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而當我開始檢視這個議題, 赫然發現
00:28
that it's focused on exactly the right topic,
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大家關切的主題是正確的
00:31
and at the same time, it's missing the point entirely.
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但又同時全然的地忽視了關鍵要點。
00:34
The topic that it's focused on, the question is whether or not
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在這個主題上所提出的問題, 是關於
00:37
all these digital technologies are affecting people's ability
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這些數位科技是否影響了人們謀生的能力?
00:40
to earn a living, or, to say it a little bit different way,
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或者, 換個說法就是
00:43
are the droids taking our jobs?
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機器人是否正在搶走人類的工作機會?
00:46
And there's some evidence that they are.
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有一些證據顯示的確如此
00:48
The Great Recession ended when American GDP resumed
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大蕭條(2008~2012)結束時, 美國的 GDP 恢復了
00:52
its kind of slow, steady march upward, and some other
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緩慢步調的上昇, 其他的一些
00:56
economic indicators also started to rebound, and they got
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經濟指標也開始反彈，看起來
00:58
kind of healthy kind of quickly. Corporate profits
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比較健康也比較迅速了。企業的獲利
01:01
are quite high. In fact, if you include bank profits,
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是相當高的。事實上，如果把銀行業也包含進來
01:05
they're higher than they've ever been.
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這些數值比以往任何時候都來得高。
01:07
And business investment in gear, in equipment
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企業在工具與設備的投資
01:10
and hardware and software is at an all-time high.
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還有硬體和軟體方面, 都處於歷史新高。
01:13
So the businesses are getting out their checkbooks.
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所以企業都在拿出支票本花錢投資
01:16
What they're not really doing is hiring.
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但是他們並沒有真正的擴大招募員工
01:19
So this red line is the employment-to-population ratio,
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這條紅線是就業人口的比率，
01:22
in other words, the percentage of working age people
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換句話說，就是處於就業年齡的美國人
01:26
in America who have work.
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真的有工作的比例
01:28
And we see that it cratered during the Great Recession,
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我們可以看到這個比例在大蕭條時萎靡
01:31
and it hasn't started to bounce back at all.
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但是到現在都還沒有開始反彈回來
01:34
But the story is not just a recession story.
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但是這個故事並不只是關於大蕭條
01:37
The decade that we've just been through had relatively
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十年來，我們剛剛經歷了持續性的
01:40
anemic job growth all throughout, especially when we
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相對低落的就業增長，尤其是當我們
01:43
compare it to other decades, and the 2000s
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與過去的幾個十年進行比較時, 2000年這個十年
01:46
are the only time we have on record where there were
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是唯一的一次我們經歷到,
01:48
fewer people working at the end of the decade
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在十年期間的結束時的工作人口, 比十年剛開始的時候
01:52
than at the beginning. This is not what you want to see.
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還少的狀況. 這不是大家樂見的
01:55
When you graph the number of potential employees
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當你用潛在就業人口的數據
01:58
versus the number of jobs in the country, you see the gap
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來對照國內工作數量作圖，您會看到之間的差距
02:02
gets bigger and bigger over time, and then,
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隨著時間越來越大，,
02:05
during the Great Recession, it opened up in a huge way.
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而在大蕭條的時候差距特別顯著
02:08
I did some quick calculations. I took the last 20 years of GDP growth
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我做了一些簡單的計算。我把過去的 20 年的國內生產總值增長
02:12
and the last 20 years of labor productivity growth
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和同一期間的勞動生產率的增長
02:16
and used those in a fairly straightforward way
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用相當簡單直接的方式
02:18
to try to project how many jobs the economy was going
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嘗試預測維持經濟持續成長
02:21
to need to keep growing, and this is the line that I came up with.
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所需要工作機會的數量, 而這是我算出的數據畫出的線
02:25
Is that good or bad? This is the government's projection
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這是好事還是壞事？來看看政府預測的數據
02:28
for the working age population going forward.
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關於就業人口的未來預測
02:32
So if these predictions are accurate, that gap is not going to close.
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所以如果這些預測是準確的, 這個差距不會被弭平
02:37
The problem is, I don't think these projections are accurate.
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問題是，我不認為這些預測是準確的。
02:40
In particular, I think my projection is way too optimistic,
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明白地說，我認為我的預測是太樂觀的
02:43
because when I did it, I was assuming that the future
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因為當我做預測時, 我假設了未來應該會
02:47
was kind of going to look like the past
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跟過去是相像的
02:49
with labor productivity growth, and that's actually not what I believe,
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在關於勞動生產力的成長方面，這是我不相信的會成立的假設
02:53
because when I look around, I think that we ain't seen nothing yet
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因為當我環顧四周，我認為我們並未考慮到那些
02:56
when it comes to technology's impact on the labor force.
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關於技術對勞動力市場的衝擊。
03:00
Just in the past couple years, we've seen digital tools
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只是在過去的幾年中，我們已經看到數位工具
03:04
display skills and abilities that they never, ever had before,
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顯示的技能和能力，遠超過以往
03:08
and that, kind of, eat deeply into what we human beings
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而且從某種角度來說, 已經吃進了人類的賴以為生的
03:12
do for a living. Let me give you a couple examples.
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就業領域. 讓我舉幾個例子。
03:15
Throughout all of history, if you wanted something
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在過去的所有的歷史年代，如果你想要把某個文章
03:17
translated from one language into another,
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從一種語言翻譯成另一種，
03:20
you had to involve a human being.
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必須要靠人類來做
03:22
Now we have multi-language, instantaneous,
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現在我們有了多國語言的，即時的
03:25
automatic translation services available for free
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自動翻譯服務, 還是免費的
03:29
via many of our devices all the way down to smartphones.
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經由我們使用的終端裝置, 直接在智慧手機就能用到
03:33
And if any of us have used these, we know that
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而如果有使用過這些翻譯服務，我們就會知道，
03:35
they're not perfect, but they're decent.
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做得並不是完美, 但也夠得體了。
03:38
Throughout all of history, if you wanted something written,
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在過去的所有的歷史年代，如果你想要寫下一些東西，
03:42
a report or an article, you had to involve a person.
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比如一份報告或一篇文章，你必須透過人來做
03:45
Not anymore. This is an article that appeared
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不再是這樣了。這裡有一篇文章，
03:47
in Forbes online a while back about Apple's earnings.
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不久前發表在富比世雜誌上, 是關於蘋果公司的收益的
03:51
It was written by an algorithm.
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這篇文章是用演算法寫出來的
03:53
And it's not decent, it's perfect.
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寫的不止是得體而已, 而是到了完美
03:56
A lot of people look at this and they say, "Okay,
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很多人看到這些事情會說, "那又怎樣?
03:59
but those are very specific, narrow tasks,
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這些都只是非常特定、 狹窄領域的任務，
04:02
and most knowledge workers are actually generalists,
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大多數的知識工作者實際上是通才，
04:04
and what they do is sit on top of a very large body
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他們做的是, 坐擁一個由專業技能和知識組成的
04:07
of expertise and knowledge and they use that
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龐然巨物, 這些人運用龐大的技能與知識
04:09
to react on the fly to kind of unpredictable demands,
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來隨時對無法預測的要求, 馬上做出反應
04:13
and that's very, very hard to automate."
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這是非常、 非常難以自動化的工作"
04:15
One of the most impressive knowledge workers
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就以一個最令人印象深刻的知識工作者
04:17
in recent memory is a guy named Ken Jennings.
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大家可能記得最近有一個人, 名叫肯恩 詹寧斯。
04:19
He won the quiz show "Jeopardy!" 74 times in a row,
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他在益智問答節目 "Jeopardy!" 連續贏了74次
04:24
took home three million dollars.
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把 300 萬美金的獎金帶回家。
04:27
That's Ken on the right getting beat three to one by
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在右邊的就是 肯恩, 比數是 三比一,
04:31
Watson, the "Jeopardy!"-playing supercomputer from IBM.
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在與 IBM 的超級電腦 華生(Watson) 進行的
"Jeopardy!" 遊戲中被打敗了
"Jeopardy!" 遊戲中被打敗了
04:36
So when we look at what technology can do
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所以當我們在看技術會怎樣影響到
04:38
to general knowledge workers, I start to think
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一般知識工作者的時候，我開始思考
04:40
there might not be something so special about this idea
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也許所謂的通才的特殊之處並不存在
04:43
of a generalist, particularly when we start doing things
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尤其是當我們開始能夠做到例如
04:46
like hooking Siri up to Watson and having technologies
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把 Siri (蘋果手機的語音助理) 連結到 華生 (IBM的超級電腦)
04:50
that can understand what we're saying
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並且逐漸發展一些技術, 能了解人類說話內容
04:52
and repeat speech back to us.
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並且用人類語音回答我們
04:54
Now, Siri is far from perfect, and we can make fun
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現在，Siri 還撐不上完美, 我們也常拿它的一些差錯
04:57
of her flaws, but we should also keep in mind that
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來開玩笑，但是我們仍應該記住，
05:00
if technologies like Siri and Watson improve
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如果像 Siri 和 華生 這樣的技術的改進
05:02
along a Moore's Law trajectory, which they will,
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是沿著 摩爾法則 的預測軌跡，他們將
05:06
in six years, they're not going to be two times better
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在六年中，這些技術將不只是進步兩倍
05:09
or four times better, they'll be 16 times better than they are right now.
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或進步四倍，他們會比現在進步 16 倍。
05:14
So I start to think that a lot of knowledge work is going to be affected by this.
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所以我開始覺得, 很多知識工作都將會受到技術的影響
05:17
And digital technologies are not just impacting knowledge work.
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而且 數位技術不只影響知識工作而已
05:21
They're starting to flex their muscles in the physical world as well.
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它們也開始在實體世界大展身手了
05:25
I had the chance a little while back to ride in the Google
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前一陣子我有機會坐上了 Google 的自動駕駛汽車
05:27
autonomous car, which is as cool as it sounds. (Laughter)
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它坐起來跟聽起來一樣的酷
05:33
And I will vouch that it handled the stop-and-go traffic
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我可以做證, 它能夠處理走走停停的路況
05:36
on U.S. 101 very smoothly.
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在101號公路上面, 開得非常平穩
05:39
There are about three and a half million people
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總共大概有 350萬的人
05:41
who drive trucks for a living in the United States.
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在美國這裡, 以開卡車為職業謀生
05:43
I think some of them are going to be affected by this
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我想這些人中, 有一部份會受到這項科技的影響
05:45
technology. And right now, humanoid robots are still
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在目前, 人形機器人仍然還
05:49
incredibly primitive. They can't do very much.
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非常的原始。它們會做的事情不多
05:52
But they're getting better quite quickly, and DARPA,
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但是它們發展得很快, 而且 DARPA,
05:54
which is the investment arm of the Defense Department,
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就是國防部的投資部門,
05:58
is trying to accelerate their trajectory.
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一直試著讓他們的發展更加速。
05:59
So, in short, yeah, the droids are coming for our jobs.
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所以，簡單地說，對啦，機器人就要來搶我們的工作了。
06:04
In the short term, we can stimulate job growth
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在短期內，我們可以刺激就業增長
06:08
by encouraging entrepreneurship and by investing
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透過鼓勵創業, 還有投資在基礎建設上
06:11
in infrastructure, because the robots today still aren't
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因為機器人目前仍然不是
06:14
very good at fixing bridges.
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很擅長修復橋樑。
06:16
But in the not-too-long-term, I think within the lifetimes
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但在不用太久，我想在場的各位
06:19
of most of the people in this room, we're going to transition
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在有生之年，我們將會經歷到
06:22
into an economy that is very productive but that
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經濟型態的轉變, 一種非常具有生產力
06:25
just doesn't need a lot of human workers,
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但是不需要許多的人類工作者的狀況
06:28
and managing that transition is going to be
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而如何管理這個轉變的發生, 將會是
06:30
the greatest challenge that our society faces.
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我們的社會所面臨的最大挑戰。
06:33
Voltaire summarized why. He said, "Work saves us
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伏爾泰總結了其中的原因。他說，"工作讓我們避開了
06:35
from three great evils: boredom, vice and need."
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三個魔鬼： 無聊、 墮落, 和需要。"
06:41
But despite this challenge, I'm personally,
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縱使有這樣的挑戰，至少就我個人來說，
06:43
I'm still a huge digital optimist, and I am
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我仍然是個超級的數位樂觀主義者，我也同時
06:46
supremely confident that the digital technologies that we're
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十分自信地認為，我們現在發展的數位技術
06:49
developing now are going to take us into a utopian future,
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將會帶領我們進入一個烏托邦的未來，
06:53
not a dystopian future. And to explain why,
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而不是一個 反烏托邦式的未來。要解釋為什麼，
06:56
I want to pose kind of a ridiculously broad question.
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我想要丟出一個有些過度誇張大的問題。
06:58
I want to ask what have been the most important
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我想問的是, 在人類歷史上
07:01
developments in human history?
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最重要的發展是什麼?
07:03
Now, I want to share some of the answers that I've gotten
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現在，我想分享一些我所找到的答案
07:06
in response to this question. It's a wonderful question
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來回答這個問題。這是一個很棒的問題
07:08
to ask and to start an endless debate about,
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一問了就會展開無窮無盡的爭論
07:10
because some people are going to bring up
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因為有些人會搬出
07:13
systems of philosophy in both the West and the East that
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西方和東方的哲學的系統，
07:16
have changed how a lot of people think about the world.
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這些的確改變了很多人看待世界的方式
07:19
And then other people will say, "No, actually, the big stories,
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然後其他人會說："才不是這樣，真正重大的
07:22
the big developments are the founding of the world's
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關鍵的發展, 是世界上主要宗教的建立
07:24
major religions, which have changed civilizations
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宗教改變了各地的文明
07:28
and have changed and influenced how countless people
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也改變並影響了無數人的一生如何度過
07:30
are living their lives." And then some other folk will say,
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然後一些其他人會說，
07:33
"Actually, what changes civilizations, what modifies them
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"其實，改變文明的，改變人們觀點的,
07:37
and what changes people's lives
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改變人們生活的
07:39
are empires, so the great developments in human history
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其實是帝國，在人類歷史上的重大發展
07:43
are stories of conquest and of war."
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主要是關於征服與戰爭的故事"
07:46
And then some cheery soul usually always pipes up
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然後一些愛開玩笑的人就會跟著提出說
07:48
and says, "Hey, don't forget about plagues." (Laughter)
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"嘿，別忘了還有那些瘟疫。"（笑聲）
07:54
There are some optimistic answers to this question,
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對這個問題，有一些樂觀的答案
07:57
so some people will bring up the Age of Exploration
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比如有些人會提出的是 探索的年代(十五世紀)
07:59
and the opening up of the world.
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對整個世界的開拓
08:01
Others will talk about intellectual achievements
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其他人則將提出: 智慧方面的成就
08:03
in disciplines like math that have helped us get
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在一些學科, 例如 數學, 就幫助人類對於
08:05
a better handle on the world, and other folk will talk about
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世界有更好的理解, 還有一些人會提出
08:08
periods when there was a deep flourishing
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那個 藝術與科學 深度繁榮發展
08:10
of the arts and sciences. So this debate will go on and on.
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的時期。所以像這樣的辯論可以一直談下去
08:14
It's an endless debate, and there's no conclusive,
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這個辯論談不完, 也不會有結論
08:17
no single answer to it. But if you're a geek like me,
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也沒有唯一的答案。但如果你像我一樣，是個阿宅工程師
08:20
you say, "Well, what do the data say?"
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你會問，"嗯，有沒有實際的資料, 資料怎麼說？"
08:23
And you start to do things like graph things that we might
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那你就會開始做一些我們有興趣的事情, 像是畫圖表
08:26
be interested in, the total worldwide population, for example,
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比方全世界的人口總數，
08:30
or some measure of social development,
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或是某些社會發展的數據，
08:33
or the state of advancement of a society,
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或是社會進步的狀態
08:35
and you start to plot the data, because, by this approach,
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然後你開始繪製這些資料，因為，通過這樣的方式，
08:39
the big stories, the big developments in human history,
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整個故事的全貌，在人類歷史上的大發展
08:41
are the ones that will bend these curves a lot.
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應該會是那些造成這些圖表曲線變彎很多的
08:44
So when you do this, and when you plot the data,
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所以當你這樣做了，把資料畫出圖表了
08:46
you pretty quickly come to some weird conclusions.
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你很快就會得到一些奇怪的結論
08:49
You conclude, actually, that none of these things
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你做出的結論是，事實上，前面講的這些答案
08:52
have mattered very much. (Laughter)
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沒有一個是真正重要的。（笑聲）
08:57
They haven't done a darn thing to the curves. (Laughter)
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這些答案根本對這些圖表曲線沒有影響。（笑聲）
09:01
There has been one story, one development
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事實上只有一個故事, 一項發展
09:05
in human history that bent the curve, bent it just about
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在人類的歷史上, 真正折彎了那些曲線, 而且彎了
09:07
90 degrees, and it is a technology story.
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將近90 度，這個故事, 就是 技術。
09:11
The steam engine, and the other associated technologies
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像是蒸汽引擎, 還有其它的相關技術
09:14
of the Industrial Revolution changed the world
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帶動了工業革命, 改變了整個世界
09:17
and influenced human history so much,
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對人類歷史產生的重大的影響
09:20
that in the words of the historian Ian Morris,
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套用 歷史學家 伊恩 · 莫里斯 (Ian Morris) 的話說，
09:22
they made mockery out of all that had come before.
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這項發展讓先前發生的其它事情都變得微不足道了
09:26
And they did this by infinitely multiplying the power
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這項發展, 把我們的肌肉力量 放大了無窮倍
09:29
of our muscles, overcoming the limitations of our muscles.
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克服了人類身體肌肉的限制
09:32
Now, what we're in the middle of now
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而現在, 我們正經歷著
09:34
is overcoming the limitations of our individual brains
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超越人類個別大腦的限制的時機
09:37
and infinitely multiplying our mental power.
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將我們的心智能力放大無窮多倍的時候
09:40
How can this not be as big a deal as overcoming
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這必然也是一個至少 跟克服人類的肌肉力量限制
09:44
the limitations of our muscles?
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一樣重大的發展吧?
09:46
So at the risk of repeating myself a little bit, when I look
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所以請原諒我又再重覆了，當我觀察到
09:50
at what's going on with digital technology these days,
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這段期間內數位科技的發展
09:53
we are not anywhere near through with this journey,
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我們離這段期間的終點還很遠
09:55
and when I look at what is happening to our economies
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而當我看到所發生的事情, 對我們經濟
09:58
and our societies, my single conclusion is that
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還有社會所發生的影響, 我的唯一結論是
10:01
we ain't seen nothing yet. The best days are really ahead.
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我們還沒看到重大的里程碑, 最好的日子還在未來。
10:04
Let me give you a couple examples.
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讓我舉幾個例子。
10:06
Economies don't run on energy. They don't run on capital,
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經濟體並不是靠能源運作的, 也不是靠資本
10:10
they don't run on labor. Economies run on ideas.
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也不是靠勞力。經濟體的運行靠的是想法。
10:14
So the work of innovation, the work of coming up with
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所以創新的工作, 產生新的想法的工作
10:17
new ideas, is some of the most powerful,
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是人類所能做的 多種 最強大的
10:19
some of the most fundamental work that we can do
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最基本的 工作之一，這些工作是人類在經濟體裡
10:21
in an economy. And this is kind of how we used to do innovation.
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能做的。而這也是我們過去如何創新的方式
10:25
We'd find a bunch of fairly similar-looking people
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我們會發現一大群看起來相當類似的人
10:29
— (Laughter) —
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— — （笑聲） — —
10:32
we'd take them out of elite institutions, we'd put them into
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我們帶他們離開原本的精英的機構，把他們放到
10:35
other elite institutions, and we'd wait for the innovation.
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另一個精英的機構，然後等著創新的發生
10:38
Now — (Laughter) —
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現在 — — （笑聲） — —
10:42
as a white guy who spent his whole career at MIT
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作為一個在麻省理工學院還有哈佛度過整個職涯的白種人
10:44
and Harvard, I got no problem with this. (Laughter)
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我對這沒有什麼問題。（笑聲）
10:51
But some other people do, and they've kind of crashed
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但一些其他人遇到了問題，他們有點像是
10:53
the party and loosened up the dress code of innovation.
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搞砸了派對, 而且放鬆了創新應有的規範
10:56
(Laughter)
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（笑聲）
10:57
So here are the winners of a Top Coder programming challenge,
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這裡是一些 頂尖程式員寫程式大賽的優勝者
11:00
and I assure you that nobody cares
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我向你保證沒有人在意
11:03
where these kids grew up, where they went to school,
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這些孩子是在哪裡長大, 在哪裡念書,
11:07
or what they look like. All anyone cares about
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或是他們的長相。所有人只會在意
11:09
is the quality of the work, the quality of the ideas.
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他們工作產出的品質, 他們的點子的品質。
11:12
And over and over again, we see this happening
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一次又一次的，我們看到這種情況發生
11:14
in the technology-facilitated world.
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在這個科技推動的世界
11:17
The work of innovation is becoming more open,
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創新的工作越來越開放，
11:19
more inclusive, more transparent, and more merit-based,
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更具包容性、 更透明、 和更以志業為基礎，
11:23
and that's going to continue no matter what MIT and Harvard
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這會繼續下去, 不管 麻省理工學院和哈佛大學
11:26
think of it, and I couldn't be happier about that development.
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的觀點，而我對這樣感到非常的快樂。
11:29
I hear once in a while, "Okay, I'll grant you that,
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我偶爾會聽到，"好吧，我同意你的這個說法，
11:32
but technology is still a tool for the rich world,
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但技術仍是富裕世界的工具
11:35
and what's not happening, these digital tools are not
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有些事情仍不會發生，這些數位工具也不會
11:38
improving the lives of people at the bottom of the pyramid."
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改善金字塔底部的人民的生活"。
11:41
And I want to say to that very clearly: nonsense.
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我對這樣的說法有個清楚的回應： 一派胡言。
11:44
The bottom of the pyramid is benefiting hugely from technology.
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金字塔的底部的人民, 正大大受益於技術的發展。
11:47
The economist Robert Jensen did this wonderful study
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經濟學家 羅伯特 · 詹森 (Robert Jensen) 做了這項很棒的研究
11:50
a while back where he watched, in great detail,
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在前一陣子，他詳細的研究了
11:53
what happened to the fishing villages of Kerala, India,
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在 印度喀拉拉邦的漁村發生的事情
11:57
when they got mobile phones for the very first time,
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當行動電話第一次交到當地人手上的時候
12:00
and when you write for the Quarterly Journal of Economics,
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若你寫的文章是要刊在 經濟學季刊雜誌 的時候
12:02
you have to use very dry and very circumspect language,
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您必須使用非常乏味和非常周到的語言，
12:05
but when I read his paper, I kind of feel Jensen is trying
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但當我讀他的論文的時候，我覺得詹森試圖
12:08
to scream at us, and say, look, this was a big deal.
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對我們尖叫，說，你看，這是一個大題目啊。
12:11
Prices stabilized, so people could plan their economic lives.
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價格變穩定了，因此人們可以計畫他們的經濟生活。
12:15
Waste was not reduced; it was eliminated.
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廢棄物不僅是減少而已；根本就是沒有廢棄物。
12:19
And the lives of both the buyers and the sellers
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這些村莊裡的買家和賣家的生活
12:21
in these villages measurably improved.
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都被明顯地改善了
12:24
Now, what I don't think is that Jensen got extremely lucky
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現在，我不認為 詹森 只是很幸運的
12:28
and happened to land in the one set of villages
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剛好遇上了一群的村莊
12:30
where technology made things better.
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碰巧在這些村莊裡 科技讓生活變得更好了
12:32
What happened instead is he very carefully documented
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實際上發生的狀況, 是他詳細地記錄了
12:35
what happens over and over again when technology
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這些一再重複發生的現像, 當技術
12:38
comes for the first time to an environment and a community.
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第一次進到一個環境和社會。
12:41
The lives of people, the welfares of people, improve dramatically.
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人民的生活, 人民的幸福, 都顯著地提高了。
12:45
So as I look around at all the evidence, and I think about
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所以，當我看到這些證據, 我想到
12:47
the room that we have ahead of us, I become a huge
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未來我們可以有的發展空間, 我當然會變成一個
12:50
digital optimist, and I start to think that this wonderful
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超級的數位樂觀主義者, 我開始覺得,
12:53
statement from the physicist Freeman Dyson
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物理學家 福利曼 戴森 說的這句話很棒
12:56
is actually not hyperbole. This is an accurate assessment of what's going on.
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他說的話並不誇張, 而是對於目前正在發生的現象的一個精準的描述。
13:00
Our digital -- our technologies are great gifts,
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我們面臨的數位化 還有科技, 都是偉大的恩賜
13:03
and we, right now, have the great good fortune
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處於這個時代的我們, 是非常幸運的
13:06
to be living at a time when digital technology is flourishing,
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能夠活在這個數位技術蓬勃發展的時期
13:09
when it is broadening and deepening and
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這些技術的影響越來越廣, 也越來越深
13:11
becoming more profound all around the world.
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深刻地影響了整個世界
13:14
So, yeah, the droids are taking our jobs,
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所以，是啊，機器人正在搶走我們的工作，
13:18
but focusing on that fact misses the point entirely.
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但若只著重這件事情, 就會漏掉了整件事情的重點了
13:21
The point is that then we are freed up to do other things,
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真正的重點是, 人類可以被解放出來, 做其他的事情
13:25
and what we are going to do, I am very confident,
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而我們可以做的事情, 我非常確定的說
13:27
what we're going to do is reduce poverty and drudgery
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我們會去做的是減少貧困和苦差事
13:30
and misery around the world. I'm very confident
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減少世界各地的苦難。我很有信心
13:33
we're going to learn to live more lightly on the planet,
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我們會學習如何在這個星球上更輕鬆的過活
13:36
and I am extremely confident that what we're going to do
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我也非常的確信, 我們將會運用
13:40
with our new digital tools is going to be so profound
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我們的全新的數位化工具, 非常深切的
13:43
and so beneficial that it's going to make a mockery
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並且非常良善的用它, 讓先前發生過的每個改變
13:45
out of everything that came before.
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相較之下都變得微不足道了。
13:47
I'm going to leave the last word to a guy who had
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我最後有一句話, 要留給一個人
13:50
a front row seat for digital progress,
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這個人在數位時代的演進, 是先驅者的地位
13:52
our old friend Ken Jennings. I'm with him.
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就是我們的老朋友, 肯恩 詹寧斯, 我同意他的看法
13:54
I'm going to echo his words:
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我打算這樣回應他的話：
13:56
"I, for one, welcome our new computer overlords." (Laughter)
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"我，代表我自己，歡迎我們的新電腦領主"。（笑聲）
14:00
Thanks very much. (Applause)
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非常感謝。（掌聲）
ABOUT THE SPEAKER
Andrew McAfee - Management theoristAndrew McAfee studies how information technology affects businesses and society.
Why you should listen
Andrew McAfee studies the ways that information technology (IT) affects businesses, business as a whole, and the larger society. His research investigates how IT changes the way companies perform, organize themselves and compete. At a higher level, his work also investigates how computerization affects competition, society, the economy and the workforce.
He's a principal research scientist at the Center for Digital Business at the MIT Sloan School of Management. His books include Enterprise 2.0 and Race Against the Machine (with Erik Brynjolfsson). Read more on his blog.
Andrew McAfee | Speaker | TED.com