How we'll earn money in a future without jobs | Martin Ford

1,604,689 views ・ 2017-11-16

TED


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譯者: Lilian Chiu 審譯者: Helen Chang
00:12
I'm going to begin with a scary question:
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一開始,我想先 提出一個駭人的問題:
00:15
Are we headed toward a future without jobs?
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我們是否正在邁向 一個沒有工作的未來?
00:18
The remarkable progress that we're seeing
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我們看到科技的驚人進展,
00:21
in technologies like self-driving cars
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比如自動駕駛的汽車,
00:22
has led to an explosion of interest in this question,
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讓很多人注意到我剛問的問題,
00:26
but because it's something that's been asked
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但因為在過去這個問題
已經被問過太多次了,
00:28
so many times in the past,
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00:29
maybe what we should really be asking
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也許我們真正該問的是,
00:31
is whether this time is really different.
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這次是否真的會有所不同?
00:35
The fear that automation might displace workers
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恐懼自動化會取代工人,
00:38
and potentially lead to lots of unemployment
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並可能會導致許多人失業,
00:40
goes back at a minimum 200 years to the Luddite revolts in England.
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可追溯回至少兩百年前的 盧德(勒德)份子運動。
00:44
And since then, this concern has come up again and again.
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從那之後,這種擔憂就 一而再再而三地出現。
00:47
I'm going to guess
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我猜測,
00:48
that most of you have probably never heard of the Triple Revolution report,
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在座大部份人可能從來沒有 聽過「三重革命」報告,
00:53
but this was a very prominent report.
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但它是份非常重要的報告。
00:55
It was put together by a brilliant group of people --
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它是由一群聰明人集思廣義出來的,
00:58
it actually included two Nobel laureates --
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實際上還包括兩名諾貝爾得主,
01:01
and this report was presented to the President of the United States,
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這份報告被呈交給美國總統,
01:04
and it argued that the US was on the brink of economic and social upheaval
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報告指出,美國正處在 經濟和社會動亂的邊緣,
01:09
because industrial automation was going to put millions of people
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因為工業自動化
將會讓數百萬人失去工作。
01:13
out of work.
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01:14
Now, that report was delivered to President Lyndon Johnson
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那份報告被呈交給詹森總統,
01:17
in March of 1964.
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當時是 1964 年三月。
01:19
So that's now over 50 years,
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那是至少五十年以前的事,
01:21
and, of course, that hasn't really happened.
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當然,報告說的狀況沒有發生。
01:24
And that's been the story again and again.
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那故事從此不斷重覆上演。
01:26
This alarm has been raised repeatedly,
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警報不斷重覆被發出,
01:28
but it's always been a false alarm.
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但每次都是假警報。
01:30
And because it's been a false alarm,
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因為一直都是假警報,
01:32
it's led to a very conventional way of thinking about this.
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就導致對這狀況的慣性思維。
01:35
And that says essentially that yes,
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基本上,那思維是:
01:37
technology may devastate entire industries.
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對啊,科技可能會破壞所有產業,
01:40
It may wipe out whole occupations and types of work.
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它有可能會徹底消滅 所有職業和各種工作;
01:43
But at the same time, of course,
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但同時,當然,
01:45
progress is going to lead to entirely new things.
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進步也會引來全新的事物。
01:47
So there will be new industries that will arise in the future,
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所以將來會有新的產業出現,
01:50
and those industries, of course, will have to hire people.
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而那些產業,當然,一定會僱用人。
01:53
There'll be new kinds of work that will appear,
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將來會出現新類型的工作會,
01:56
and those might be things that today we can't really even imagine.
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可能是我們現今無法想像的。
01:59
And that has been the story so far,
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目前為止,故事一直是如此,
02:01
and it's been a positive story.
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且一直是很正面的。
02:03
It turns out that the new jobs that have been created
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結果,新創造出來的工作,
02:06
have generally been a lot better than the old ones.
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一般來說,比舊的工作好很多。
02:08
They have, for example, been more engaging.
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比如,新的工作比較吸引人。
02:11
They've been in safer, more comfortable work environments,
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工作環境比較安全、比較舒適,
02:15
and, of course, they've paid more.
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當然,薪水也比較高。
02:16
So it has been a positive story.
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所以這個故事一直很正面。
02:18
That's the way things have played out so far.
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目前為止的發展也的確是這樣。
02:21
But there is one particular class of worker
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但特別有一類的工作者,
02:24
for whom the story has been quite different.
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對他們來說,故事相當不同。
02:27
For these workers,
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對這些工作者而言,
02:29
technology has completely decimated their work,
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科技可說是大舉毀滅了他們的工作,
02:32
and it really hasn't created any new opportunities at all.
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且完全沒有再創造出 新的機會給他們。
02:35
And these workers, of course,
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當然,這些工作者
02:37
are horses.
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是馬。
02:38
(Laughter)
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(笑聲)
02:40
So I can ask a very provocative question:
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我問一個會引發爭議的問題:
02:43
Is it possible that at some point in the future,
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有沒有可能,在未來的某個時點,
02:46
a significant fraction of the human workforce is going to be made redundant
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將有一大部份的人類勞動力過剩,
02:51
in the way that horses were?
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就像馬所遭遇的情況。
02:53
Now, you might have a very visceral, reflexive reaction to that.
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對那個問題,你可能會有 很本能、反射性的反應。
02:56
You might say, "That's absurd.
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你也許會說:「太荒唐了。
02:58
How can you possibly compare human beings to horses?"
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你怎麼能把人類拿來和馬做比較?」
03:02
Horses, of course, are very limited,
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當然,馬非常受限,
03:04
and when cars and trucks and tractors came along,
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當汽車、卡車、牽引機 (拖拉機)出現,
03:07
horses really had nowhere else to turn.
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馬就無處可去了。
03:09
People, on the other hand, are intelligent;
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另一方面,人有智慧;
03:12
we can learn, we can adapt.
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我們能學習,我們能適應。
03:14
And in theory,
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理論上,
03:15
that ought to mean that we can always find something new to do,
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那應該意味著 我們總能找到新的事情來做,
03:18
and that we can always remain relevant to the future economy.
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我們總能與未來的經濟持續相關。
03:21
But here's the really critical thing to understand.
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但要了解非常重要的一點。
03:24
The machines that will threaten workers in the future
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在未來會威脅到工作者的機器,
03:27
are really nothing like those cars and trucks and tractors
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完全不像取代了馬的汽車、
03:30
that displaced horses.
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卡車、牽引機。
03:32
The future is going to be full of thinking, learning, adapting machines.
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未來將會滿是會思考、 學習、適應的機器。
03:37
And what that really means
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那意味著,
03:38
is that technology is finally beginning to encroach
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科技最終將會開始侵犯到
03:41
on that fundamental human capability --
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基礎的人類能力──
03:44
the thing that makes us so different from horses,
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讓我們和馬大不相同的能力,
03:47
and the very thing that, so far,
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也是這能力,讓我們目前為止
03:49
has allowed us to stay ahead of the march of progress
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能走在這進步發展的前端
03:52
and remain relevant,
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並保有相關性,
03:53
and, in fact, indispensable to the economy.
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事實上,也讓經濟少不了我們。
03:58
So what is it that is really so different
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所以,相對於我們過去所看到的,
04:00
about today's information technology
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現今的資訊科技到底
04:02
relative to what we've seen in the past?
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有什麼如此不同的地方?
04:04
I would point to three fundamental things.
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我要指出根本的三樣。
04:07
The first thing is that we have seen this ongoing process
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第一,我們已見到這正在進行的過程
04:12
of exponential acceleration.
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以指數級的速率加速。
04:14
I know you all know about Moore's law,
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我知道你們都明白摩爾定律,
04:16
but in fact, it's more broad-based than that;
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但事實上,它的根基還要更廣; (註:不止適用於積體電路)
04:18
it extends in many cases, for example, to software,
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在許多情況下,它會延伸, 比如,延伸到軟體,
04:22
it extends to communications, bandwidth and so forth.
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它也會延伸到通訊、頻寬、等等。
04:25
But the really key thing to understand
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但,需要了解的關鍵點是,
04:27
is that this acceleration has now been going on for a really long time.
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這種加速現象已經 發生很長一段時間了。
04:30
In fact, it's been going on for decades.
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事實上,已經有數十年了。
04:32
If you measure from the late 1950s,
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如果從 1950 年代末期開始算,
04:35
when the first integrated circuits were fabricated,
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當第一個積體電路被製造出來,
04:38
we've seen something on the order of 30 doublings in computational power
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從那時起,
我們目睹電腦運算的效能 倍增了大約三十次。
04:42
since then.
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04:44
That's just an extraordinary number of times to double any quantity,
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不論起初的量是多少, 倍增了那麼多次都是很可觀的。
04:47
and what it really means
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它真正的意涵是,
04:49
is that we're now at a point where we're going to see
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我們正處在一個時點,
即將要看到很大量的絕對進展,
04:51
just an extraordinary amount of absolute progress,
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04:54
and, of course, things are going to continue to also accelerate
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當然,這個時間點之後的加速
還是會持續下去。
04:57
from this point.
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04:58
So as we look forward to the coming years and decades,
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所以當我們期待未來的 幾年及幾十年,
05:00
I think that means that we're going to see things
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我們將會看到
我們完全沒準備會看到的事物,
05:03
that we're really not prepared for.
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05:04
We're going to see things that astonish us.
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我們將會看到讓我們吃驚的事物。
05:06
The second key thing
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第二個要點是
05:08
is that the machines are, in a limited sense, beginning to think.
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機器開始有限的思考。
05:12
And by this, I don't mean human-level AI,
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我並不是指人類水平級的人工智慧,
05:14
or science fiction artificial intelligence;
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或科幻小說中的人工智慧;
05:17
I simply mean that machines and algorithms are making decisions.
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我指的只是會決策的機器和演算法。
05:22
They're solving problems, and most importantly, they're learning.
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它們會解決問題, 更重要的是,它們會學習。
05:26
In fact, if there's one technology that is truly central to this
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事實上,有項技術扮演著中心角色,
05:29
and has really become the driving force behind this,
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同時也是背後的推動力,
05:32
it's machine learning,
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就是機器學習,
05:33
which is just becoming this incredibly powerful,
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它開始變得非常強大、
05:36
disruptive, scalable technology.
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具顛覆性,是可擴展的技術。
05:39
One of the best examples I've seen of that recently
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近期我看過最好的例子之一,
05:42
was what Google's DeepMind division was able to do
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是 Google 的 DeepMind 團隊
05:44
with its AlphaGo system.
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用他們開發的 AlphaGo 系統 能夠做到什麼。
05:46
Now, this is the system that was able to beat the best player in the world
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這個系統能在古老的圍棋賽中
打敗世界最強的棋手。
05:50
at the ancient game of Go.
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05:52
Now, at least to me,
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至少對我而言,
05:53
there are two things that really stand out about the game of Go.
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圍棋比賽有兩點特別突出。
05:57
One is that as you're playing the game,
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第一,當你在下圍棋時,
05:59
the number of configurations that the board can be in
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棋盤上有可能發生的 棋子配置組合數,
06:02
is essentially infinite.
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基本上是無限多。
06:03
There are actually more possibilities than there are atoms in the universe.
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可能的組合數, 比宇宙中的原子數還要多。
06:07
So what that means is,
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那意味著,
06:09
you're never going to be able to build a computer to win at the game of Go
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你永遠不能建造一台 贏得圍棋比賽的電腦,
06:12
the way chess was approached, for example,
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採用以前建造下西洋棋的 電腦那類的方式,
06:15
which is basically to throw brute-force computational power at it.
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基本上是以蠻力狂加運算的效能。
06:19
So clearly, a much more sophisticated, thinking-like approach is needed.
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很顯然,需要有 更精密的類思考方式。
06:24
The second thing that really stands out is that,
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第二個特點是,
06:27
if you talk to one of the championship Go players,
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如果你和圍棋冠軍賽的棋手交談,
06:30
this person cannot necessarily even really articulate what exactly it is
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這個人不見得能明確表達出
06:34
they're thinking about as they play the game.
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他們在比賽時腦中想的是什麼。
06:37
It's often something that's very intuitive,
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通常他們就是非常直覺地在下棋,
06:39
it's almost just like a feeling about which move they should make.
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就像是他們能夠感覺到 下一步棋要怎麼下。
06:42
So given those two qualities,
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在這兩種特色的前提下,
06:44
I would say that playing Go at a world champion level
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我會說能用世界冠軍的水平來下圍棋
06:48
really ought to be something that's safe from automation,
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應該是自動化做不到的事,
06:51
and the fact that it isn't should really raise a cautionary flag for us.
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但事實卻不是如此, 這應該要讓我們有所警覺。
06:55
And the reason is that we tend to draw a very distinct line,
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原因是,我們都傾向於 畫一條很清楚的線,
06:59
and on one side of that line are all the jobs and tasks
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線一邊的所有工作和任務
07:03
that we perceive as being on some level fundamentally routine and repetitive
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被我們歸類於具有某種程度的 基本例行性、可重覆性、
07:08
and predictable.
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並且是可被預測的。
07:09
And we know that these jobs might be in different industries,
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我們知道這些工作 可能分屬不同的產業,
07:12
they might be in different occupations and at different skill levels,
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可能是不同的職業, 對技巧的需求也不同;
07:15
but because they are innately predictable,
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但由於它們先天的可預測性,
07:17
we know they're probably at some point going to be susceptible
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我們知道,可能在某個時間點,
它們會受機器學習影響,
07:21
to machine learning,
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07:22
and therefore, to automation.
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而被自動化取代掉。
07:23
And make no mistake -- that's a lot of jobs.
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別誤會,很多工作都是如此。
07:25
That's probably something on the order of roughly half
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可能在經濟體中有大約一半的工作
07:28
the jobs in the economy.
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都屬這一類。
07:30
But then on the other side of that line,
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但在線的另一邊,
07:32
we have all the jobs that require some capability
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是需要某些能力的所有工作,
07:36
that we perceive as being uniquely human,
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我們認為是人類獨有的能力,
07:38
and these are the jobs that we think are safe.
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我們認為這些工作是安全的。
07:41
Now, based on what I know about the game of Go,
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根據我對圍棋的所知,
07:43
I would've guessed that it really ought to be on the safe side of that line.
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我會猜測它應該屬於 線的這一邊,安全的這一邊。
07:47
But the fact that it isn't, and that Google solved this problem,
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但事實是它不在這一邊, Google 破解了這個問題,
07:50
suggests that that line is going to be very dynamic.
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意味著那條線是非常動態的。
07:52
It's going to shift,
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它會移動,
07:53
and it's going to shift in a way that consumes more and more jobs and tasks
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它移動和取代掉 越來越多的工作和任務,
07:58
that we currently perceive as being safe from automation.
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那些我們目前認為是安全、 不會被自動化的。
08:01
The other key thing to understand
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還要了解另一件重要的事,
08:03
is that this is by no means just about low-wage jobs or blue-collar jobs,
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這現象絕對不會只發生在 低薪或藍領工作上、
08:08
or jobs and tasks done by people
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或由相對比較低教育程度的人
08:10
that have relatively low levels of education.
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所做的工作上。
08:12
There's lots of evidence to show
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有很多證據顯示,
08:14
that these technologies are rapidly climbing the skills ladder.
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這些科技所需要的技術 正在快速攀升。
08:17
So we already see an impact on professional jobs --
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我們已經看到影響力 開始觸及專業工作──
08:21
tasks done by people like accountants,
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由類似像會計、
財務分析師、
08:25
financial analysts,
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08:26
journalists,
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記者、
律師、放射學家這類人 所做的工作任務。
08:28
lawyers, radiologists and so forth.
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08:30
So a lot of the assumptions that we make
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我們對於這類職業、
08:32
about the kind of occupations and tasks and jobs
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任務、工作,所做的許多假設,
08:35
that are going to be threatened by automation in the future
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在未來將會被自動化給威脅,
08:38
are very likely to be challenged going forward.
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往前也將會受到挑戰。
08:40
So as we put these trends together,
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當我們整合這些趨勢,
08:42
I think what it shows is that we could very well end up in a future
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就會顯示
我們未來可能面臨嚴重的失業。
08:45
with significant unemployment.
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08:48
Or at a minimum,
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或至少,
08:49
we could face lots of underemployment or stagnant wages,
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我們可能會面臨許多大材小用 或者是薪水停滯不前,
08:53
maybe even declining wages.
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甚至可能薪水下降。
08:56
And, of course, soaring levels of inequality.
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當然,不平等的情況也會加劇。
08:58
All of that, of course, is going to put a terrific amount of stress
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當然,這一切將會對於社會的結構
09:03
on the fabric of society.
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造成很大的壓力。
09:04
But beyond that, there's also a fundamental economic problem,
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但在那之外,還有個 根本的經濟問題,
09:08
and that arises because jobs are currently the primary mechanism
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問題出現的原因 是目前主要靠著「工作」這機制
09:13
that distributes income, and therefore purchasing power,
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來分配收入、和它帶來的購買力,
09:16
to all the consumers that buy the products and services we're producing.
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給那些向我們購買 產品與服務的消費者。
09:22
In order to have a vibrant market economy,
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為了要有活躍的市場經濟,
09:25
you've got to have lots and lots of consumers
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你得要有很多有能力購買
09:27
that are really capable of buying the products and services
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那些被製造出來之產品和服務
09:30
that are being produced.
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的消費者。
09:31
If you don't have that, then you run the risk
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如果沒有,你要冒的風險就是
09:34
of economic stagnation,
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經濟停滯、
09:35
or maybe even a declining economic spiral,
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或甚至下降的經濟螺旋,
09:39
as there simply aren't enough customers out there
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因為就是沒有足夠的客人
09:41
to buy the products and services being produced.
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來購買製出的產品和服務。
09:44
It's really important to realize
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非常重要的是要了解到,
09:46
that all of us as individuals rely on access to that market economy
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我們每個人都仰賴市場經濟,
09:52
in order to be successful.
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才有可能成功。
09:53
You can visualize that by thinking in terms of one really exceptional person.
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視覺化的方式是,你可以 想像一個非常特殊的人。
09:58
Imagine for a moment you take, say, Steve Jobs,
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想像一下,比如你可以選賈伯斯,
10:01
and you drop him on an island all by himself.
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你把他丟在一個無人島上。
10:03
On that island, he's going to be running around,
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在島上,他會到處跑來跑去,
10:06
gathering coconuts just like anyone else.
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收集椰子,就和所有其他人一樣。
10:08
He's really not going to be anything special,
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他不會有什麼特別的地方,
10:11
and the reason, of course, is that there is no market
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而原因當然是因為,那裡沒有市場
10:14
for him to scale his incredible talents across.
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來讓他發揮他出色的才華。
10:17
So access to this market is really critical to us as individuals,
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所以對於個人來說,能進入 這個市場是很重要的,
10:20
and also to the entire system in terms of it being sustainable.
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此外,進入這個體制, 在永續面也是很重要的。
10:25
So the question then becomes: What exactly could we do about this?
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於是,問題變成了: 對此,我們到底能做什麼?
10:29
And I think you can view this through a very utopian framework.
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我想,可以透過一個 非常理想化的框架來看此事。
10:32
You can imagine a future where we all have to work less,
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你可以想像在未來, 我們工作量減少,
10:35
we have more time for leisure,
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有比較多休閒時間,
10:38
more time to spend with our families,
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比較多家庭時間,
10:40
more time to do things that we find genuinely rewarding
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比較多時間去做我們 真正認為有價值的事,
10:43
and so forth.
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諸如此類。
10:44
And I think that's a terrific vision.
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我認為那是很棒的遠景。
10:46
That's something that we should absolutely strive to move toward.
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我們絕對應該朝那方向努力。
10:50
But at the same time, I think we have to be realistic,
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但同時,我認為我們得要實際一點,
10:52
and we have to realize
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我們得要了解,
10:54
that we're very likely to face a significant income distribution problem.
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我們非常有可能會要面臨 一個嚴重的收入分配問題。
10:59
A lot of people are likely to be left behind.
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很多人可能會被扔在後頭。
11:03
And I think that in order to solve that problem,
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我認為,要解決那個問題,
11:05
we're ultimately going to have to find a way
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我們最終得要找到一個方式,
11:07
to decouple incomes from traditional work.
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將收入和傳統工作給分離開。
11:10
And the best, more straightforward way I know to do that
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如果要這樣做,我所知道 最好、最直接的方法
11:13
is some kind of a guaranteed income or universal basic income.
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就是某種保障收入 或是全體基本收入。
11:16
Now, basic income is becoming a very important idea.
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基本收入正變成一個很重要的想法。
11:19
It's getting a lot of traction and attention,
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它得到許多的注意力和關注,
11:21
there are a lot of important pilot projects
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有許多重要的前導計畫
11:23
and experiments going on throughout the world.
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及實驗在全世界進行。
11:26
My own view is that a basic income is not a panacea;
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我自己的看法是, 基本收入並非萬靈丹;
11:29
it's not necessarily a plug-and-play solution,
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它未必是插電就可以解決的方案,
11:32
but rather, it's a place to start.
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但總是個起始點,
11:34
It's an idea that we can build on and refine.
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我們可以從這想法開始,再改善它。
11:36
For example, one thing that I have written quite a lot about
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比如,我寫了很多的一個題材,
11:39
is the possibility of incorporating explicit incentives into a basic income.
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是明確地將獎勵 納入基本收入當中的可行性。
11:44
To illustrate that,
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讓我解釋一下,
11:46
imagine that you are a struggling high school student.
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想像你是個讀得很辛苦的高中生。
11:48
Imagine that you are at risk of dropping out of school.
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想像你有可能會被退學。
11:52
And yet, suppose you know that at some point in the future,
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但假設你知道在未來某個時間點,
11:55
no matter what,
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不論如何,
11:56
you're going to get the same basic income as everyone else.
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你和別人得到的基本收入是一樣的。
12:00
Now, to my mind, that creates a very perverse incentive
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我認為那會在你腦中 產生橫下心來的動機,
12:03
for you to simply give up and drop out of school.
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使你直接放棄並退學。
12:06
So I would say, let's not structure things that way.
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我會說,咱們 不要設計成那樣的結構。
12:08
Instead, let's pay people who graduate from high school somewhat more
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而是支付高中畢業生較高的薪水,
12:14
than those who simply drop out.
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比中綴生要高。
12:16
And we can take that idea of building incentives into a basic income,
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我們可以把這個將獎勵 納入基本收入中的想法,
12:19
and maybe extend it to other areas.
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也許再延伸至其他的領域。
12:21
For example, we might create an incentive to work in the community
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比如,我們可以針對 在社區中助人的行為,
12:25
to help others,
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創造一種獎勵;
12:26
or perhaps to do positive things for the environment,
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或是去獎勵人們 為環境做出正面的貢獻,
12:29
and so forth.
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諸如此類。
12:30
So by incorporating incentives into a basic income,
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把獎勵納入到基本收入當中,
12:33
we might actually improve it,
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我們可能可以改善它,
12:35
and also, perhaps, take at least a couple of steps
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另外,也許也可以更接近
12:37
towards solving another problem
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解決另一個我認為
12:40
that I think we're quite possibly going to face in the future,
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在未來也很可能要面臨的問題,
12:43
and that is, how do we all find meaning and fulfillment,
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就是:我們要如何 找到意義和實現人生、
12:47
and how do we occupy our time
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以及我們要如何把時間
12:49
in a world where perhaps there's less demand for traditional work?
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花在一個也許比較不需求 傳統工作的世界裡?
12:54
So by extending and refining a basic income,
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透過延伸和改善基本收入,
12:57
I think we can make it look better,
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我想我們可以讓它看起來更好,
12:59
and we can also, perhaps, make it more politically and socially acceptable
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我們也能讓它在政治面 和社會面更容易被接受,
13:04
and feasible --
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也更可行──
13:05
and, of course, by doing that,
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當然,透過那樣做,
13:07
we increase the odds that it will actually come to be.
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我們就會增加實現它的可能性。
13:11
I think one of the most fundamental,
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我想,對於基本收入這個想法,
13:14
almost instinctive objections
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或是擴展安全網,
13:16
that many of us have to the idea of a basic income,
284
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我們所有人最主要、
13:19
or really to any significant expansion of the safety net,
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3732
也最直覺的反對意見,
13:23
is this fear that we're going to end up with too many people
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就是害怕最後會有太多人
13:27
riding in the economic cart,
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爬上這經濟車箱,
13:28
and not enough people pulling that cart.
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而沒有足夠人去拉這車廂。
13:31
And yet, really, the whole point I'm making here, of course,
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但,其實,我在這裡要說的重點是,
13:33
is that in the future,
290
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1361
在未來,
13:35
machines are increasingly going to be capable of pulling that cart for us.
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3826
機器將會有能力為我們拉車。
13:39
That should give us more options
292
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1990
那就會讓我們有更多選項,
13:41
for the way we structure our society and our economy,
293
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可用以不同的方式 架構我們的社會和經濟,
13:45
And I think eventually, it's going to go beyond simply being an option,
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我認為,最終它將不只是個選項,
13:48
and it's going to become an imperative.
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而將變成勢在必行。
13:50
The reason, of course, is that all of this is going to put
296
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當然,因為這一切
將會帶給社會一定程度的壓力,
13:53
such a degree of stress on our society,
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2014
13:55
and also because jobs are that mechanism
298
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2514
也因為要靠「工作」這個機制,
13:57
that gets purchasing power to consumers
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1965
將購買力分配給消費者,
13:59
so they can then drive the economy.
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2516
他們接著才能夠帶動經濟。
14:02
If, in fact, that mechanism begins to erode in the future,
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事實上,如果未來那機制開始腐蝕了,
14:05
then we're going to need to replace it with something else
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我們就得要用其他東西來取代它,
14:08
or we're going to face the risk
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不然我們就要面臨
14:10
that our whole system simply may not be sustainable.
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整個體制不夠永續的風險。
14:12
But the bottom line here is that I really think
305
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但這裡的關鍵是,我真的認為
14:15
that solving these problems,
306
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2436
解決這些問題,
14:17
and especially finding a way to build a future economy
307
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特別是找出方法來建立一種對社會
14:21
that works for everyone,
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2013
每個層級的每個人都
14:23
at every level of our society,
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行得通的未來經濟,
14:25
is going to be one of the most important challenges that we all face
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將會是未來幾年和幾十年間,
我們所有人要面臨 的最重大挑戰之一。
14:28
in the coming years and decades.
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2043
14:30
Thank you very much.
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非常謝謝。
14:32
(Applause)
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(掌聲)
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