How AI can bring on a second Industrial Revolution | Kevin Kelly

340,981 views ・ 2017-01-12

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00:00
Translator: Leslie Gauthier Reviewer: Camille Martínez
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譯者: 易帆 余 審譯者: Xueting Wang
00:14
I'm going to talk a little bit about where technology's going.
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我準備來談談未來科技的走勢。
00:19
And often technology comes to us,
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每當新的科技發明,
00:22
we're surprised by what it brings.
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我們總是驚嘆它所帶給我們的驚喜。
00:24
But there's actually a large aspect of technology
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但是實際上科技有一大方面
00:28
that's much more predictable,
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是很容易預測的,
00:29
and that's because technological systems of all sorts have leanings,
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因為所有的科技系統 都有一定的脈絡可循,
00:34
they have urgencies,
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它們有迫切性,
00:35
they have tendencies.
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有一定的趨勢,
00:36
And those tendencies are derived from the very nature of the physics,
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而這些趨勢都是來自於
電線、開關、電子的 物理本質與化學原理,
00:41
chemistry of wires and switches and electrons,
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00:45
and they will make reoccurring patterns again and again.
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而這些模式會周而復始地發生。
00:49
And so those patterns produce these tendencies, these leanings.
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所以是這些模式造就了 科技的趨勢及走向。
00:54
You can almost think of it as sort of like gravity.
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你幾乎可以把它 看做是一種「萬有引力」。
00:57
Imagine raindrops falling into a valley.
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想像一下,就像雨滴落到山谷中,
00:59
The actual path of a raindrop as it goes down the valley
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雨滴流到山谷中的實際路徑
01:02
is unpredictable.
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是無法預測的。
01:04
We cannot see where it's going,
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我們看不到雨滴會怎麼流,
01:06
but the general direction is very inevitable:
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但大致上的方向是一定的:
01:08
it's downward.
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這個方向是向下的。
01:10
And so these baked-in tendencies and urgencies
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而這些深根在科技系統裡的
01:14
in technological systems
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趨勢及迫切性,
01:17
give us a sense of where things are going at the large form.
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告訴了我們科技的大方向。
01:21
So in a large sense,
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具體說,
01:22
I would say that telephones were inevitable,
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我認為電話的發明是必然的,
01:27
but the iPhone was not.
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但 iPhone 就不是了。
01:29
The Internet was inevitable,
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網際網路的發明是必然的,
01:31
but Twitter was not.
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但推特就不是了。
01:33
So we have many ongoing tendencies right now,
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所以我們現在有很多趨勢正在進行,
01:36
and I think one of the chief among them
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而我認為它們其中一個 主要的趨勢就是,
01:39
is this tendency to make things smarter and smarter.
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東西越來越聰明了。
01:44
I call it cognifying -- cognification --
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我稱這個過程為 「認知化 」──認知──
01:46
also known as artificial intelligence, or AI.
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也就是大家知道的 人工智慧,或者「AI」
01:50
And I think that's going to be one of the most influential developments
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我認為未來 20 年,
AI 將成為我們社會其中一個 最有影響力的發展、趨勢及驅動力。
01:53
and trends and directions and drives in our society in the next 20 years.
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02:00
So, of course, it's already here.
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當然,AI 已經出現了,
02:02
We already have AI,
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我們已經有 AI 了,
02:04
and often it works in the background,
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而且它經常在幕後幫助我們,
02:06
in the back offices of hospitals,
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它出現在醫院後面的辦公室,
02:08
where it's used to diagnose X-rays better than a human doctor.
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用 AI 來診斷 X 光片的能力 比人類醫生還精準。
02:13
It's in legal offices,
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它會出現在律師事務所,
02:14
where it's used to go through legal evidence
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用 AI 審閱法律文件,
02:17
better than a human paralawyer.
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速度比人類的律師還要快。
02:19
It's used to fly the plane that you came here with.
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各位今天坐的飛機也有人工智慧,
02:24
Human pilots only flew it seven to eight minutes,
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人工駕駛只有 7~8 分鐘,
02:26
the rest of the time the AI was driving.
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剩下的都是 AI 在駕駛。
02:28
And of course, in Netflix and Amazon,
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當然, Netflix 和 Amazon 也有,
02:30
it's in the background, making those recommendations.
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它在幕後給出做出推薦和建議。
這是我們目前已經實現的。
02:33
That's what we have today.
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02:34
And we have an example, of course, in a more front-facing aspect of it,
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當然,還有一個更先進的案例,
02:39
with the win of the AlphaGo, who beat the world's greatest Go champion.
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就是打敗世界圍棋冠軍的 AlphaGo。
02:46
But it's more than that.
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但人工智慧不僅於此。
02:50
If you play a video game, you're playing against an AI.
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如果你在玩電動,你對抗的是 AI,
02:53
But recently, Google taught their AI
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但最近,Google 開始教他們的 AI
02:57
to actually learn how to play video games.
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實際意義上的學習如何打電動。
03:00
Again, teaching video games was already done,
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重申一下,教 AI 「打電動」 是一種層次,
03:03
but learning how to play a video game is another step.
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但教 AI 「學習如何打電動」 又是另一種層次。
03:07
That's artificial smartness.
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這是人造的智能產品。
03:10
What we're doing is taking this artificial smartness
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而我們正在做的就是將 這種人造的智能產品
03:15
and we're making it smarter and smarter.
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變得越來越聰明。
03:18
There are three aspects to this general trend
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這個趨勢大致上有三個面向,
03:22
that I think are underappreciated;
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我認為尚未被充分認知;
03:24
I think we would understand AI a lot better
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我想如果搞懂這三個面向,
03:26
if we understood these three things.
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我們對 AI 的了解,會更深入一些。
03:28
I think these things also would help us embrace AI,
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我認為了解這些事, 也可以幫助我們擁抱 AI,
03:32
because it's only by embracing it that we actually can steer it.
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唯有擁抱 AI 才能駕馭 AI。
03:35
We can actually steer the specifics by embracing the larger trend.
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藉由懷抱更大趨勢來駕馭細節。
03:39
So let me talk about those three different aspects.
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所以容我來談談 AI 的三個不同面向。
03:42
The first one is: our own intelligence has a very poor understanding
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第一:以人類目前對智慧的了解,
我們對智慧的認知仍相當貧乏。
03:46
of what intelligence is.
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03:48
We tend to think of intelligence as a single dimension,
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我們似乎把智能看的太單一面向了,
03:51
that it's kind of like a note that gets louder and louder.
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它有點像是個音符,會越來越大聲。
03:54
It starts like with IQ measurement.
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剛開始像個 IQ 測量儀。
03:57
It starts with maybe a simple low IQ in a rat or mouse,
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一開始的智商也許跟老鼠一樣低,
04:01
and maybe there's more in a chimpanzee,
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有的像猩猩,稍微多一點,
04:03
and then maybe there's more in a stupid person,
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之後開始像個低智商的人類,
04:06
and then maybe an average person like myself,
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然後進化到像我一樣的普通人,
04:08
and then maybe a genius.
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然後變成一個天才。
04:09
And this single IQ intelligence is getting greater and greater.
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IQ 智能分數越來越高。
04:14
That's completely wrong.
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這種看法完全是錯誤的。
04:15
That's not what intelligence is -- not what human intelligence is, anyway.
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這不是智慧該有的樣子── 人類的智慧不僅於此。
04:19
It's much more like a symphony of different notes,
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它像是一首交響樂, 由不同的音符組成,
04:24
and each of these notes is played on a different instrument of cognition.
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而每一個音符, 由不同的認知樂器所伴奏。
04:27
There are many types of intelligences in our own minds.
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人類腦中有很多不同種類的智慧,
04:31
We have deductive reasoning,
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我們有演繹推理的能力,
04:34
we have emotional intelligence,
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我們有情感的智慧,
04:36
we have spatial intelligence;
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我們有空間概念的智慧,
04:38
we have maybe 100 different types that are all grouped together,
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我們可能有 100 種 不同的智能聚合在一起,
04:42
and they vary in different strengths with different people.
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而且每個人各有各的強項。
04:46
And of course, if we go to animals, they also have another basket --
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當然,以動物而言, 牠們可能是另一套體系──
04:50
another symphony of different kinds of intelligences,
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另一種不同的智能交響樂,
04:53
and sometimes those same instruments are the same that we have.
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有時候跟我們人類的一樣。
04:56
They can think in the same way, but they may have a different arrangement,
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牠們可能思考方式相同, 但著重點不同,
05:00
and maybe they're higher in some cases than humans,
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也許在某些方面超過人類,
05:03
like long-term memory in a squirrel is actually phenomenal,
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像是松鼠的長期記憶力,相當出色,
05:05
so it can remember where it buried its nuts.
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能清楚記得堅果的埋藏之處。
05:08
But in other cases they may be lower.
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但其它方面,牠們也許就比較弱了。
05:10
When we go to make machines,
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當我們要製造機器時,
05:12
we're going to engineer them in the same way,
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我們會用同樣的方式來設計機器,
05:15
where we'll make some of those types of smartness much greater than ours,
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有些智慧型裝置 做得比人類聰明得多,
05:20
and many of them won't be anywhere near ours,
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但其它方面則遠遠不如我們,
05:22
because they're not needed.
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因為根本不需要。
05:24
So we're going to take these things,
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我們會將這些產品
05:26
these artificial clusters,
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這些人工產品,
05:28
and we'll be adding more varieties of artificial cognition to our AIs.
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在不同的 AI 上, 裝置不同的人工認知功能,
05:34
We're going to make them very, very specific.
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我們可以把它們的特定功能 做得相當、相當出色。
05:38
So your calculator is smarter than you are in arithmetic already;
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所以你的計算機在計算方面 比你聰明許多;
05:45
your GPS is smarter than you are in spatial navigation;
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你的 GPS 在空間導航上 比你聰明得多;
05:49
Google, Bing, are smarter than you are in long-term memory.
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Google, Bing 的長期記憶比你強。
05:54
And we're going to take, again, these kinds of different types of thinking
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然後我們再把這些不同種類的智能,
05:58
and we'll put them into, like, a car.
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放在,像是,車子裡。
06:00
The reason why we want to put them in a car so the car drives,
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我們之所以這麼做的原因,
06:03
is because it's not driving like a human.
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是因為它們不會像人類那樣開車,
06:06
It's not thinking like us.
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它們不會像人類那樣思考。
06:07
That's the whole feature of it.
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這是它唯一的特色。
06:09
It's not being distracted,
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它不會分心,
06:11
it's not worrying about whether it left the stove on,
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它不用擔心瓦斯爐沒關,
06:13
or whether it should have majored in finance.
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它不用考慮要不要主修財經。
06:16
It's just driving.
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它只會開車。
06:17
(Laughter)
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(笑聲)
06:18
Just driving, OK?
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只會開車,好嗎?
06:20
And we actually might even come to advertise these
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而我們最終可能會拿它來廣告
06:23
as "consciousness-free."
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「無意識」。
06:24
They're without consciousness,
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它們沒有意識,
06:26
they're not concerned about those things,
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它們不會關心這些瑣事,
06:28
they're not distracted.
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它們不會分心。
06:29
So in general, what we're trying to do
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所以,我們應該盡我們所能
06:32
is make as many different types of thinking as we can.
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去嘗試做出一些不同的想法。
06:37
We're going to populate the space
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我們將會天馬行空,
06:39
of all the different possible types, or species, of thinking.
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去嘗試所有可能的思考方式。
06:44
And there actually may be some problems
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也許還有一些
06:46
that are so difficult in business and science
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相當不好解決的商業及科學問題,
06:49
that our own type of human thinking may not be able to solve them alone.
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單憑人類自身的想法可能無法解決。
06:53
We may need a two-step program,
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我們可能需要分兩步走,
06:55
which is to invent new kinds of thinking
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先發明出新的思考方式,
06:59
that we can work alongside of to solve these really large problems,
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再來解決這些真正的難題,
07:03
say, like dark energy or quantum gravity.
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比如說,像是暗能量或量子引力。
07:08
What we're doing is making alien intelligences.
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我們所做的實際上 就是在創造「異形智能」。
07:11
You might even think of this as, sort of, artificial aliens
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在某種程度上,
這概念有點像是,人造異形。
07:15
in some senses.
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07:16
And they're going to help us think different,
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它們將幫助我們從不同的角度思考,
07:18
because thinking different is the engine of creation
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因為不同的想法是創新、
07:22
and wealth and new economy.
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財富和新經濟的引擎。
07:25
The second aspect of this is that we are going to use AI
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第二方面:我們將用 AI
07:30
to basically make a second Industrial Revolution.
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進行第二次的工業革命。
07:34
The first Industrial Revolution was based on the fact
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在第一次工業革命中,
07:36
that we invented something I would call artificial power.
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是以我稱之為「人工力量」 為基礎的革命。
07:40
Previous to that,
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在此之前,
07:42
during the Agricultural Revolution,
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在農業革命時期,
07:44
everything that was made had to be made with human muscle
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每樣東西都需要用人力
07:47
or animal power.
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或畜力完成。
07:49
That was the only way to get anything done.
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除此之外別無它法。
07:51
The great innovation during the Industrial Revolution was,
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在工業革命期間最偉大的發明就是
07:54
we harnessed steam power, fossil fuels,
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我們利用水蒸氣、石化燃料
07:57
to make this artificial power that we could use
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產生人工力量,
來做任何我們想做的事情。
08:01
to do anything we wanted to do.
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08:03
So today when you drive down the highway,
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今日,當你開車行駛在高速公路上,
08:06
you are, with a flick of the switch, commanding 250 horses --
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只要輕輕撥弄開關, 就相當於在駕馭 250 匹馬,
08:11
250 horsepower --
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或者說,250 馬力。
08:12
which we can use to build skyscrapers, to build cities, to build roads,
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它可以讓我們蓋大樓、 建造城市、修建道路,
08:17
to make factories that would churn out lines of chairs or refrigerators
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開辦能夠源源不斷 生產椅子或冰箱的工廠,
這都遠遠超出人力所為。
08:23
way beyond our own power.
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08:24
And that artificial power can also be distributed on wires on a grid
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而且這樣的人工電力 可以透過電線、電網
08:31
to every home, factory, farmstead,
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輸送到每一個家庭、工廠、農場,
08:34
and anybody could buy that artificial power,
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讓每個人都可以買到 這樣的人工電力,
08:38
just by plugging something in.
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只要插上插頭就可以使用。
08:39
So this was a source of innovation as well,
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所以,這也是創新的來源之一,
08:42
because a farmer could take a manual hand pump,
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因為農民可以為手工幫浦通上電,
08:45
and they could add this artificial power, this electricity,
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有了這種人工力量,
08:48
and he'd have an electric pump.
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就變成了電動幫浦。
08:50
And you multiply that by thousands or tens of thousands of times,
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你將這種力量擴大成千上萬倍,
08:53
and that formula was what brought us the Industrial Revolution.
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而這個公式為我們帶來了工業革命。
08:56
All the things that we see, all this progress that we now enjoy,
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而我們所看到的一切、 那些我們現今享受的過程,
09:00
has come from the fact that we've done that.
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幾乎都來源於此。
09:02
We're going to do the same thing now with AI.
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現在我們也要在 AI 上做同樣的事。
09:04
We're going to distribute that on a grid,
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我們將用網路傳送 AI,
09:07
and now you can take that electric pump.
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現在好比你有一個「電泵」,
09:09
You can add some artificial intelligence,
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你把「電泵」加上人工智能,
09:12
and now you have a smart pump.
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你就會得到聰明的「電泵」,
09:13
And that, multiplied by a million times,
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類似的改造做上幾百萬次,
09:15
is going to be this second Industrial Revolution.
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就會引爆第二次的工業革命。
09:18
So now the car is going down the highway,
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將來汽車行駛在高速公路上,
09:20
it's 250 horsepower, but in addition, it's 250 minds.
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它不僅有 250 匹馬力, 還有 250 種腦力。
09:25
That's the auto-driven car.
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這就是自動駕駛車。
09:26
It's like a new commodity;
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它是一種新的商品;
09:28
it's a new utility.
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它是一種新的基礎設施。
09:29
The AI is going to flow across the grid -- the cloud --
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AI 將會在網路、雲端上傳輸
09:32
in the same way electricity did.
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就跟電一樣。
09:34
So everything that we had electrified,
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所以之前我們把每樣東西都電力化,
09:36
we're now going to cognify.
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現在,我們要把它們認知化。
09:38
And I would suggest, then,
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所以,我會推測,
09:40
that the formula for the next 10,000 start-ups
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接下來的一萬家初創公司的公式,
09:43
is very, very simple,
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相當, 相當簡單,
09:45
which is to take x and add AI.
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就是拿某樣東西 X,加上 AI。
09:49
That is the formula, that's what we're going to be doing.
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這個公式就是我們將來要做的。
09:51
And that is the way in which we're going to make
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我們將以這種方式
09:55
this second Industrial Revolution.
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創造第二次的工業革命。
09:57
And by the way -- right now, this minute,
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順帶一提,目前,此時此刻,
09:59
you can log on to Google
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你可以登入Google
10:00
and you can purchase AI for six cents, 100 hits.
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用六美分購買 AI 來提交一百個圖像識別請求。
10:04
That's available right now.
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目前已經有這項服務了。
10:06
So the third aspect of this
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第三個形勢:
10:09
is that when we take this AI and embody it,
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如果我們將 AI 編組起來,
10:12
we get robots.
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我們會得到機械人。
10:13
And robots are going to be bots,
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而機械人就是一些小型的任務執行器,
10:14
they're going to be doing many of the tasks that we have already done.
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它們將會取代我們現在已經在做的事。
10:20
A job is just a bunch of tasks,
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工作只是一堆任務,
10:21
so they're going to redefine our jobs
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所以人類的工作會被重新定義,
10:23
because they're going to do some of those tasks.
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因為它們會幫我們執行這些任務。
10:25
But they're also going to create whole new categories,
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但它們也會創造出全新的分類
10:29
a whole new slew of tasks
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很多全新種類的任務,
10:31
that we didn't know we wanted to do before.
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一些我們從未聽過的工作。
10:33
They're going to actually engender new kinds of jobs,
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它們實際上會催生出新的職業,
10:37
new kinds of tasks that we want done,
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一些我們願意從事的新工作,
10:39
just as automation made up a whole bunch of new things
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就像自動化所引發的許多新事物,
10:43
that we didn't know we needed before,
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我們之前不知道會需要它們,
10:45
and now we can't live without them.
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但時至今日, 我們已經離不開它們了。
10:47
So they're going to produce even more jobs than they take away,
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機器人產生的新工作 比我們被取代的工作還要多,
10:51
but it's important that a lot of the tasks that we're going to give them
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更重要的是, 我們交給它們的那些任務
10:54
are tasks that can be defined in terms of efficiency or productivity.
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都需要效率或生產率。
10:59
If you can specify a task,
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如果一個任務, 不管是體力的還是腦力的,
11:01
either manual or conceptual,
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11:03
that can be specified in terms of efficiency or productivity,
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可以用效率或生產率來衡量的話,
11:08
that goes to the bots.
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那麽就應該交給機器人來完成。
11:10
Productivity is for robots.
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機器人擅長的就是生產率。
11:12
What we're really good at is basically wasting time.
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我們真正擅長的是浪費時間。
11:16
(Laughter)
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(笑聲)
11:17
We're really good at things that are inefficient.
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我們最擅長做那些沒有效率的事情。
11:19
Science is inherently inefficient.
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科學從本質上來說是低效的。
11:22
It runs on that fact that you have one failure after another.
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它的運作方式實際上是 一次又一次的失敗,
11:25
It runs on the fact that you make tests and experiments that don't work,
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很多試驗和嘗試都徒勞無功,
11:29
otherwise you're not learning.
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不這樣做,你學不到東西。
11:30
It runs on the fact
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事實就是,
11:31
that there is not a lot of efficiency in it.
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科學研究沒有效率可言。
11:33
Innovation by definition is inefficient,
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創新從定義上來說就是低效的。
11:36
because you make prototypes,
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因為我們需要製作原型,
11:38
because you try stuff that fails, that doesn't work.
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需要做各種嘗試,經歷各種失敗。
11:40
Exploration is inherently inefficiency.
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探索本質上是低效的。
11:44
Art is not efficient.
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藝術是低效的。
11:45
Human relationships are not efficient.
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人際關係也是低效的。
11:47
These are all the kinds of things we're going to gravitate to,
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這些都是我們喜歡做的事情,
11:50
because they're not efficient.
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因為它們是低效的。
11:52
Efficiency is for robots.
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要效率找機器人才對。
11:55
We're also going to learn that we're going to work with these AIs
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我們要知道, 我們將和 AI 一起工作,
11:59
because they think differently than us.
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因為它們的思維與我們不同。
12:02
When Deep Blue beat the world's best chess champion,
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當深藍打敗西洋棋的世界冠軍後,
12:06
people thought it was the end of chess.
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人們認為西洋棋玩完了。
12:08
But actually, it turns out that today, the best chess champion in the world
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但事實上,如今全世界 最厲害的西洋棋冠軍
12:12
is not an AI.
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並不是 AI,
12:14
And it's not a human.
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也不是人類,
12:16
It's the team of a human and an AI.
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而是由人類和 AI 組成的團隊。
12:18
The best medical diagnostician is not a doctor, it's not an AI,
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最棒的醫學診療師 不是醫生,也不是 AI,
12:22
it's the team.
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而是他們組成的團隊。
12:24
We're going to be working with these AIs,
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我們將和 AI 一起工作,
12:26
and I think you'll be paid in the future
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你將來的薪資,
12:28
by how well you work with these bots.
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很可能取決於 你跟機器人合作得如何。
12:31
So that's the third thing, is that they're different,
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這就是我想說的第三點: AI 是不同於我們的,
12:35
they're utility
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它們是基礎設施,
12:36
and they are going to be something we work with rather than against.
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我們將與它們一起工作,
12:40
We're working with these rather than against them.
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而非競爭。
12:42
So, the future:
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所以,未來:
12:44
Where does that take us?
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AI 將帶我們到哪裡?
12:45
I think that 25 years from now, they'll look back
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我想,二十五年後,
12:49
and look at our understanding of AI and say,
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人們回頭看今日 我們對 AI 的理解,會說:
12:52
"You didn't have AI. In fact, you didn't even have the Internet yet,
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「你們那都不叫 AI,實際上,
你們甚至都還沒有真正的 網際網路呢!」
和 25 年後相比較的話,
12:56
compared to what we're going to have 25 years from now."
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12:59
There are no AI experts right now.
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我們還沒有真正的 AI 專家。
13:02
There's a lot of money going to it,
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目前有大量的資本投資在這個領域, 已經花了數十億美金;
13:04
there are billions of dollars being spent on it;
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13:06
it's a huge business,
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這是一個巨大的產業。
13:09
but there are no experts, compared to what we'll know 20 years from now.
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和 20 年後相比較, 我們尚未有真正的 AI 專家。
13:14
So we are just at the beginning of the beginning,
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我們還處在剛開始的開始,
13:16
we're in the first hour of all this.
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所有這一切才剛開始。
13:19
We're in the first hour of the Internet.
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我們處在網際網路的 第一個小時裡。
13:21
We're in the first hour of what's coming.
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我們正處在未來發展的 第一個小時裡。
13:23
The most popular AI product in 20 years from now,
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二十年後最受人們喜愛的 AI 產品,
13:27
that everybody uses,
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人人都會用的 AI 產品,
13:29
has not been invented yet.
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還沒有被發明出來。
13:32
That means that you're not late.
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也就是說,你還為時未晚。
13:35
Thank you.
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謝謝!
13:36
(Laughter)
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(笑聲)
13:37
(Applause)
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(掌聲)
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