TED2014
Larry Page: Where's Google going next?
查理．羅斯與賴瑞．佩吉: Google 下一步將前往何方？
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在 TED2014 會議上，查理．羅斯採訪了 Google 執行長賴瑞．佩吉，話題圍繞他對公司遠景的展望。其中包括空中自行車道、網路熱氣球……隨著佩吉講述公司近期對 DeepMind 的併購，以及正在學習新奇事物的人工智慧系統，訪談變得更加生動有趣。
Larry Page - CEO of Google
Larry Page is the CEO and cofounder of Google, making him one of the ruling minds of the web. Full bio
Larry Page is the CEO and cofounder of Google, making him one of the ruling minds of the web. Full bio
Double-click the English transcript below to play the video.
00:13
Charlie Rose: So Larry sent me an email
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查理．羅斯：賴瑞發了封信給我，
00:17
and he basically said,
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基本上他就是說，
00:18
we've got to make sure that
we don't seem like we're
we don't seem like we're
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我們得確保我們看起來不能像
00:22
a couple of middle-aged boring men.
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兩個乏味的中年人。
00:27
I said, I'm flattered by that --
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我回他說，你這麼講我深感榮幸──
00:30
(Laughter) —
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（笑聲）──
00:32
because I'm a bit older,
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因為我年紀大一點，
00:36
and he has a bit more net worth than I do.
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而他的淨資產又比我多一點。
00:40
Larry Page: Well, thank you.
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賴瑞．佩吉：呵，謝謝。
00:42
CR: So we'll have a conversation about
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查理．羅斯：我們會聊聊網際網路，
00:45
the Internet, and we'll have a conversation Google,
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還會聊聊 Google，
00:48
and we'll have a conversation about search
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聊聊搜尋，
00:50
and privacy,
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和隱私，
00:51
and also about your philosophy
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還有你的處世哲學，
00:52
and a sense of how you've connected the dots
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以及你如何把這
一切聯接起來的，
一切聯接起來的，
00:55
and how this journey that began
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以及多年前開始的
00:57
some time ago
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這個旅程，
00:58
has such interesting prospects.
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具有怎樣的有趣前景。
01:00
Mainly we want to talk about the future.
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我們主要來討論一下未來。
01:03
So my first question: Where is Google
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那我的第一個問題是：Google 身在何處，
01:04
and where is it going?
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它將前往何方？
01:06
LP: Well, this is something we think about a lot,
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賴瑞．佩吉：
好的，這個問題我們思考過很多，
好的，這個問題我們思考過很多，
01:08
and our mission we defined a long time ago
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我們很早以前所定下的目標
01:11
is to organize the world's information
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就是將全世界的資訊組織起來
01:14
and make it universally accessible and useful.
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讓全世界的人們可以
獲得它並且從中受益。
獲得它並且從中受益。
01:17
And people always say,
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人們總會問，
01:19
is that really what you guys are still doing?
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你們還在做這樣的事情嗎？
01:21
And I always kind of think about that myself,
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我自己也常思考這問題，
01:23
and I'm not quite sure.
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我還不是很確定。
01:26
But actually, when I think about search,
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但事實上，說到搜尋，
01:30
it's such a deep thing for all of us,
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對所有人來說都
是個深奧的問題，
是個深奧的問題，
01:32
to really understand what you want,
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要真正理解你想要的是什麼，
01:35
to understand the world's information,
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要理解這個世界的資訊，
01:37
and we're still very much in the early stages of that,
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我們還處於非常早期的階段，
01:40
which is totally crazy.
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這真的很誇張。
01:42
We've been at it for 15 years already,
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我們在這個領域裡已有十五年，
01:45
but it's not at all done.
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卻離實現它還差得很遠。
01:48
CR: When it's done, how will it be?
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查理．羅斯：
當實現時，它會是什麼樣？
當實現時，它會是什麼樣？
01:51
LP: Well, I guess,
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賴瑞．佩吉：我猜，
01:54
in thinking about where we're going --
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想想我們的前進方向──
01:56
you know, why is it not done? --
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像是，為什麼還沒有完成？──
01:58
a lot of it is just computing's kind of a mess.
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大部分原因是
數據計算還是一團亂。
數據計算還是一團亂。
02:01
You know, your computer
doesn't know where you are,
doesn't know where you are,
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電腦不知道你在哪、
02:03
it doesn't know what you're doing,
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不知道你在做什麼，
02:05
it doesn't know what you know,
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也不知道你懂什麼。
02:06
and a lot we've been trying to do recently
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近年來我們花了很多的精力，
02:09
is just make your devices work,
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只為了讓你的設備運作起來，
02:12
make them understand your context.
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讓它理解你的大致意圖。
02:15
Google Now, you know, knows where you are,
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Google Now 知道你人在哪，
02:17
knows what you may need.
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知道你可能需要什麼。
02:19
So really having computing
work and understand you
work and understand you
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所以讓電腦真正地
運作起來、理解你
運作起來、理解你
02:23
and understand that information,
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並且理解這些資訊，
02:25
we really haven't done that yet.
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我們還沒真的做到那步。
02:27
It's still very, very clunky.
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它仍非常地不成熟。
02:29
CR: Tell me, when you look at what Google is doing,
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查理．羅斯：
對於 Google 正在做的事，
對於 Google 正在做的事，
02:31
where does Deep Mind fit?
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DeepMind 扮演什麼角色？
02:34
LP: Yeah, so Deep Mind is a company
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賴瑞．佩吉：DeepMind 這家公司，
02:36
we just acquired recently.
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我們最近才併購進來。
02:38
It's in the U.K.
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它在英國。
02:41
First, let me tell you the way we got there,
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首先，我講一下我們當時的狀況，
02:44
which was looking at search
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當時我們焦點放在搜尋，
02:46
and really understanding,
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並真正地理解，
02:48
trying to understand everything,
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試圖理解一切，
02:50
and also make the computers not clunky
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讓電腦不那麼遲鈍，
02:52
and really understand you --
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並且真正地理解你──
02:54
like, voice was really important.
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比如，語音非常重要。
02:56
So what's the state of the art
on speech recognition?
on speech recognition?
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最先進的語音辨識技術是怎樣的？
02:59
It's not very good.
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它不是很好，
03:01
It doesn't really understand you.
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它並不能真正地理解你。
03:03
So we started doing machine learning research
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於是我們研究機器學習，
03:05
to improve that.
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以改進它，
03:06
That helped a lot.
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結果成效很大。
03:08
And we started just looking at things like YouTube.
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然後我們開始轉向
YouTube 之類的東西。
YouTube 之類的東西。
03:10
Can we understand YouTube?
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我們可以理解 YouTube 嗎？
03:12
But we actually ran machine learning on YouTube
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我們實際在 YouTube 上
進行機器學習，
進行機器學習，
03:15
and it discovered cats, just by itself.
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它找到了貓，完全靠自己。
03:19
Now, that's an important concept.
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這是個重要的概念。
03:21
And we realized there's really something here.
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我們意識到，其中有著深義。
03:24
If we can learn what cats are,
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如果我們能學習貓是什麼，
03:26
that must be really important.
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那一定是非常重要的。
03:28
So I think Deep Mind,
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所以我認為 DeepMind，
03:31
what's really amazing about Deep Mind
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它的真正神奇之處
03:33
is that it can actually --
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在於它真的可以
03:35
they're learning things in this unsupervised way.
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自主學習，無需人的干預。
03:39
They started with video games,
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他們從遊戲開始，
03:41
and really just, maybe I can show the video,
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真的只是
──也許我可以播一下那影片──
──也許我可以播一下那影片──
03:44
just playing video games,
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只是玩遊戲，
03:46
and learning how to do that automatically.
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並且學習怎樣自動地玩。
03:48
CR: Take a look at the video games
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查理．羅斯：看一下這遊戲，
03:50
and how machines are coming to be able
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機器是如何開始有能力
03:52
to do some remarkable things.
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做一些驚人的事情。
03:55
LP: The amazing thing about this
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賴瑞．佩吉：這驚人之處在於，
03:56
is this is, I mean, obviously,
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我覺得很明顯，
03:58
these are old games,
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這些都是老遊戲，
03:59
but the system just sees what you see, the pixels,
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但是系統和你看到的
完全一樣，就是像素，
完全一樣，就是像素，
04:04
and it has the controls and it has the score,
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並且它能控制、能得分，
04:06
and it's learned to play all of these games,
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還有它學會了所有這些遊戲，
04:09
same program.
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同一個程式。
04:10
It's learned to play all of these games
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它學會了所有這些遊戲，
04:12
with superhuman performance.
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而且表現是超人級的。
在過去，電腦是做不到這些事的。
04:14
We've not been able to do things like this
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04:16
with computers before.
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04:17
And maybe I'll just narrate this one quickly.
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我要簡單說明一下，
04:20
This is boxing, and it figures out it can
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這是拳擊遊戲，系統算出
04:23
sort of pin the opponent down.
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如何制伏對手。
04:25
The computer's on the left,
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左邊的是電腦，
04:27
and it's just racking up points.
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它就是要贏得高分。
04:30
So imagine if this kind
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所以設想一下，如果這樣的
人工智慧能用在你的排程、
04:32
of intelligence were thrown at your schedule,
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04:34
or your information needs, or things like that.
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解決你的訊息需求，
或類似的事情。
或類似的事情。
04:39
We're really just at the beginning of that,
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機器學習其實還在起步階段，
04:41
and that's what I'm really excited about.
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而這讓我感到無比興奮。
04:44
CR: When you look at all that's taken place
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查理．羅斯：
當你看到 DeepMind 和拳擊遊戲
當你看到 DeepMind 和拳擊遊戲
04:46
with Deep Mind and the boxing,
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上所發生的這一切，
04:49
also a part of where we're going
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加上人工智慧
04:51
is artificial intelligence.
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也是我們前進的方向之一。
04:54
Where are we, when you look at that?
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從這些來看，我們走到哪步了？
04:57
LP: Well, I think for me,
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賴瑞．佩吉：我認為對於我來說，
04:59
this is kind of one of the most exciting things
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這是我看到的
最令人興奮的事情之一，
最令人興奮的事情之一，
05:00
I've seen in a long time.
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在很長時間以來。
05:02
The guy who started this company, Demis,
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創立這家公司的德米斯
05:05
has a neuroscience and a
computer science background.
computer science background.
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擁有神經學和電腦科學的背景。
05:07
He went back to school
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他回學校攻讀博士，
05:09
to get his Ph.D. to study the brain.
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課題是研究大腦。
05:12
And so I think we're seeing a lot of exciting work
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我們看到許多激勵人心的成果，
05:15
going on that sort of crosses computer science
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出現在跨神經學與
電腦科學的領域。
電腦科學的領域。
05:18
and neuroscience
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05:20
in terms of really understanding
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關於如何真正去理解，
05:22
what it takes to make something smart
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去打造出有智慧的機器，
05:24
and do really interesting things.
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來做一些有趣的事。
05:26
CR: But where's the level of it now?
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查理．羅斯：
我們現在處於什麼階段呢？
我們現在處於什麼階段呢？
05:28
And how fast do you think we are moving?
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你覺得我們的進展速度如何？
05:31
LP: Well, this is the state of the art right now,
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賴瑞．佩吉：
這是當前達到的最高水準，
這是當前達到的最高水準，
05:34
understanding cats on YouTube
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理解 YouTube 上的貓
05:36
and things like that,
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還有類似的事情，
05:38
improving voice recognition.
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加強語音辨識技術。
05:40
We used a lot of machine learning
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我們使用了許多機器學習
05:42
to improve things incrementally,
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來逐步改進各種問題，
05:45
but I think for me, this example's really exciting,
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我個人認為這例子非常令人興奮，
05:48
because it's one program
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因為它只是一個程式
05:50
that can do a lot of different things.
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卻可以做許多不同事情。
05:52
CR: I don't know if we can do this,
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查理．羅斯：
我不知道這樣做合不合適，
我不知道這樣做合不合適，
05:53
but we've got the image of the cat.
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我這兒有一張貓的圖片，
05:55
It would be wonderful to see this.
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這張圖意義非凡。
05:56
This is how machines looked at cats
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這就是機器看貓，
05:59
and what they came up with.
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反映出的形象。
06:00
Can we see that image?
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可以看一下圖片嗎？
06:01
LP: Yeah.
CR: There it is. Can you see the cat?
CR: There it is. Can you see the cat?
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賴瑞．佩吉：好的。
查理．羅斯：這就是了。你能看到貓嗎？
查理．羅斯：這就是了。你能看到貓嗎？
06:03
Designed by machines, seen by machines.
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機器自己設計、看到了它。
06:05
LP: That's right.
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賴瑞．佩吉：是的。
06:07
So this is learned from just watching YouTube.
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這是僅僅透過觀看 YouTube 學到的。
06:09
And there's no training,
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沒有事先訓練過，
06:11
no notion of a cat,
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沒有貓的概念，
06:12
but this concept of a cat
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但這個貓的概念挺重要的，
06:15
is something important that you would understand,
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我們都知道什麼是貓，
06:18
and now that the machines can kind of understand.
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而現在機器也有了一定理解。
06:20
Maybe just finishing
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也許它已經完成了搜尋這部分，
06:21
also on the search part,
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06:24
it started with search, really understanding
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它從搜尋開始，去理解人的意圖
06:27
people's context and their information.
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和他們的資訊。
06:29
I did have a video
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我有一個影片，
06:31
I wanted to show quickly on that
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我想快速展示一下
06:33
that we actually found.
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我發現了什麼。
06:35
(Video) ["Soy, Kenya"]
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（影片）
「肯亞，索伊」
「肯亞，索伊」
06:40
Zack Matere: Not long ago,
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查克．馬泰爾：不久之前，
06:42
I planted a crop of potatoes.
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我種了一片馬鈴薯，
06:45
Then suddenly they started
dying one after the other.
dying one after the other.
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然後突然地，
不斷有馬鈴薯死掉。
不斷有馬鈴薯死掉。
06:48
I checked out the books and
they didn't tell me much.
they didn't tell me much.
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我查了書，但沒發現多少資訊，
06:51
So, I went and I did a search.
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所以我去搜尋了一下。
06:53
["Zack Matere, Farmer"]
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「查克．馬泰爾，農民」
06:57
Potato diseases.
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馬鈴薯、疾病。
07:00
One of the websites told me
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有一個網站告訴我
07:02
that ants could be the problem.
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問題可能是螞蟻。
07:04
It said, sprinkle wood ash over the plants.
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它說，在作物上撒一些木灰。
07:06
Then after a few days the ants disappeared.
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幾天之後螞蟻消失了。
07:08
I got excited about the Internet.
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網路讓我非常興奮。
07:11
I have this friend
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我有個朋友，
07:13
who really would like to expand his business.
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他很想擴展生意，
07:16
So I went with him to the cyber cafe
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於是我和他一起去了網咖，
07:20
and we checked out several sites.
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我們查了一些網站。
07:22
When I met him next, he was going to put a windmill
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再次見到他時，
他準備在當地學校建一座風車。
07:25
at the local school.
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07:27
I felt proud because
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我感到很驕傲，
07:29
something that wasn't there before
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因為一個以前沒有的東西，
07:31
was suddenly there.
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就這樣突然出現了。
07:33
I realized that not everybody
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我意識到，
並不是所有人都能夠用
07:35
can be able to access
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07:37
what I was able to access.
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我能用的東西。
07:39
I thought that I need to have an Internet
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我想我需要有種網路，
07:40
that my grandmother can use.
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讓我奶奶也會用它。
07:42
So I thought about a notice board.
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所以我想到了一個公告欄，
07:45
A simple wooden notice board.
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一個簡單的木製公告欄。
07:47
When I get information on my phone,
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我從手機上得到資訊的時候，
07:49
I'm able to post the information
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我就可以把它
07:51
on the notice board.
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公布在公告欄上。
07:53
So it's basically like a computer.
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所以，它有點像部電腦，
07:56
I use the Internet to help people.
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我用網際網路來幫助別人。
08:00
I think I am searching for
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我認為我是在尋找
08:03
a better life
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一個更好的生活，
08:05
for me and my neighbors.
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為我，也為我的鄰居們。
08:09
So many people have access to information,
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這樣許多人都可以得到資訊，
08:13
but there's no follow-up to that.
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但是在這之後就沒有後續了。
08:15
I think the follow-up to that is our knowledge.
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我認為「後續」就是我們的知識。
08:18
When people have the knowledge,
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人們有了知識，
08:19
they can find solutions
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他們就能找到方法，
08:21
without having to helped out.
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而不需要找人幫忙。
08:23
Information is powerful,
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資訊的力量很強大，
08:25
but it is how we use it that will define us.
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但是如何使用資訊
才決定我們的未來。
才決定我們的未來。
08:30
(Applause)
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（掌聲）
08:34
LP: Now, the amazing thing about that video,
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賴瑞．佩吉：
這段影片的精彩之處在於，
這段影片的精彩之處在於，
08:37
actually, was we just read about it in the news,
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我們是先從新聞看到，
08:38
and we found this gentlemen,
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我們才找這位先生，
08:41
and made that little clip.
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錄了這段影片。
08:43
CR: When I talk to people about you,
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查理．羅斯：當我和別人說起你，
08:44
they say to me, people who know you well, say,
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這些很了解你的人，他們對我說，
賴瑞想要改變世界，
08:47
Larry wants to change the world,
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1891
08:49
and he believes technology can show the way.
216
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4112
他相信科技可以指引方向，
08:53
And that means access to the Internet.
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而這需要有網路。
這也和語言有關。
08:55
It has to do with languages.
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08:56
It also means how people can get access
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這也意味著，
人們要如何存取網路
人們要如何存取網路
08:59
and do things that will affect their community,
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來做一些事情，
會影響到他所在的群體。
會影響到他所在的群體。
09:02
and this is an example.
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這就是一個例子。
09:04
LP: Yeah, that's right, and I think for me,
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賴瑞．佩吉：是的，對我來說，
09:08
I have been focusing on access more,
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我致力於更易用的網路，
09:10
if we're talking about the future.
224
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如果我們說的是未來的話。
09:13
We recently released this Loon Project
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我們最近推出了 Loon 專案，
09:15
which is using balloons to do it.
226
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用熱氣球來存取網路，
09:18
It sounds totally crazy.
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聽起來很瘋狂。
09:19
We can show the video here.
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2539
我們可以在這裡播一下影片。
09:22
Actually, two out of three people in the world
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世界上三分之二的人
09:23
don't have good Internet access now.
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沒好的網路可用。
09:26
We actually think this can really help people
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我們認為這個專案可以幫助人們，
09:29
sort of cost-efficiently.
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並且費用低廉。
09:31
CR: It's a balloon.
LP: Yeah, get access to the Internet.
LP: Yeah, get access to the Internet.
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查理．羅斯：這是一個氣球。
賴瑞．佩吉：是的，可以連網。
賴瑞．佩吉：是的，可以連網。
09:34
CR: And why does this balloon give you access
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查理．羅斯：為什麼可以透過這氣球連網？
09:36
to the Internet?
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09:37
Because there was some interesting things
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因為你得想出一些有趣的辦法，
09:39
you had to do to figure out how
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09:40
to make balloons possible,
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來讓氣球連網成為可能，
09:43
they didn't have to be tethered.
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1749
還不用給氣球插上線。
09:44
LP: Yeah, and this is a good example of innovation.
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賴瑞．佩吉：
是的，這是個關於創新的好例子。
是的，這是個關於創新的好例子。
09:46
Like, we've been thinking about this idea
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在我們在著手之前
09:49
for five years or more
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1772
就已經在思考這想法了，
09:51
before we started working on it,
243
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1601
有五年甚至更久，
09:52
but it was just really,
244
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1319
但問題在於，
09:54
how do we get access points up high, cheaply?
245
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如何才能便宜地
在天上設一個存取點？
在天上設一個存取點？
09:57
You normally have to use satellites
246
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傳統得用人造衛星，
09:59
and it takes a long time to launch them.
247
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2939
但發射需要很長時間。
10:02
But you saw there how easy it is to launch a balloon
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2494
然後我們就想到，放個氣球到天上，
10:04
and get it up,
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是多麼簡單的事，
10:06
and actually again, it's the power of the Internet,
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2001
這再次說明網路的力量。
10:08
I did a search on it,
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1780
我確實搜尋過這件事，
10:10
and I found, 30, 40 years ago,
252
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我發現三四十年前
10:12
someone had put up a balloon
253
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就有人放出過一個氣球，
10:14
and it had gone around the Earth multiple times.
254
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而這個氣球繞著地球轉了不少圈。
10:17
And I thought, why can't we do that today?
255
605166
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然後我想，我們如今
為何不這麼做呢？
為何不這麼做呢？
10:20
And that's how this project got going.
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這個專案就這樣開始了。
10:22
CR: But are you at the mercy of the wind?
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查理．羅斯：
但是你受風的影響大嗎？
但是你受風的影響大嗎？
10:24
LP: Yeah, but it turns out,
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賴瑞．佩吉：是的，但實際上，
10:26
we did some weather simulations
259
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1493
我們做了些氣象模擬，
10:28
which probably hadn't really been done before,
260
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2547
很可能以前從來沒人做過，
10:30
and if you control the altitude of the balloons,
261
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2110
如果控制氣球的高度，
10:32
which you can do by pumping air into them
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可以通過充氣或別的方法實現，
10:35
and other ways,
263
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1822
10:37
you can actually control roughly where they go,
264
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2929
就可以大致控制氣球的動向，
10:40
and so I think we can build a worldwide mesh
265
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2205
因此，我想我們可以
建造一個世界性網路，
建造一個世界性網路，
10:42
of these balloons that can cover the whole planet.
266
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3339
用這些氣球來覆蓋全球。
10:45
CR: Before I talk about the future and transportation,
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2242
查理．羅斯：
在我們聊未來和運輸之前
在我們聊未來和運輸之前
10:47
where you've been a nerd for a while,
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1895
──這兩樣你已浸淫了一段時間。
你對運輸、自動駕駛汽車和
自行車研究很深──
自行車研究很深──
10:49
and this fascination you have with transportation
269
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2424
10:52
and automated cars and bicycles,
270
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2063
10:54
let me talk a bit about what's been the subject here
271
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1737
我先提一下有關
愛德華．史諾登的話題，
愛德華．史諾登的話題，
10:55
earlier with Edward Snowden.
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稍早前也是 TED 主題，
10:58
It is security and privacy.
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3106
事關安全與隱私。
11:01
You have to have been thinking about that.
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你一定一直有在思考這問題。
11:03
LP: Yeah, absolutely.
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賴瑞．佩吉：是的，毫無疑問。
11:05
I saw the picture of Sergey with
Edward Snowden yesterday.
Edward Snowden yesterday.
276
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2843
昨天我看到了謝爾蓋和
愛德華．史諾登的照片。
愛德華．史諾登的照片。
11:07
Some of you may have seen it.
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在座的有些人應該也看到了。
11:10
But I think, for me, I guess,
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但我個人覺得，
11:14
privacy and security are a really important thing.
279
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隱私和安全是非常重要的事情。
11:17
We think about it in terms of both things,
280
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2245
我們在這兩方面都有所思考，
11:19
and I think you can't have privacy without security,
281
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2903
我認為沒有安全就不存在隱私，
11:22
so let me just talk about security first,
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所以我先談談安全，
11:25
because you asked about Snowden and all of that,
283
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2596
因為你問到了有關史諾登的事情，
11:27
and then I'll say a little bit about privacy.
284
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2441
然後我會再講一點隱私。
11:30
I think for me, it's tremendously disappointing
285
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3800
我個人感到極度失望，
政府偷偷做了這些事
沒有告訴我們。
沒有告訴我們。
11:34
that the government
286
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11:35
secretly did all this stuff and didn't tell us.
287
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2330
11:37
I don't think we can have a democracy
288
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3303
我將不再擁有民主，
11:41
if we're having to protect you and our users
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3430
如果我們被迫由政府手中，
11:44
from the government
290
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1696
保護大家
不受未討論的事情侵害的話。
11:46
for stuff that we've never had a conversation about.
291
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2803
11:49
And I don't mean we have to know
292
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1896
我倒不是說我們必須知道
11:50
what the particular terrorist attack is they're worried
293
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1695
政府所擔心的具體
恐怖襲擊是什麼，
恐怖襲擊是什麼，
11:52
about protecting us from,
294
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1762
11:54
but we do need to know
295
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而是我們需要知道
11:56
what the parameters of it is,
296
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2410
在什麼樣的情況下，
11:58
what kind of surveillance the government's
297
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2044
政府要進行何種監控，
12:00
going to do and how and why,
298
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2168
打算怎麼做，為什麼這樣做，
12:02
and I think we haven't had that conversation.
299
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2277
我認為我們並沒有
討論過這些問題。
討論過這些問題。
12:05
So I think the government's actually done
300
713103
2567
我認為政府偷做這些事情，
這種失職造成了嚴重的傷害。
12:07
itself a tremendous disservice
301
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2168
12:09
by doing all that in secret.
302
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2161
12:11
CR: Never coming to Google
303
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1615
查理．羅斯：
絕不要找 Google 要任何東西？
絕不要找 Google 要任何東西？
12:13
to ask for anything.
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721614
1525
12:15
LP: Not Google, but the public.
305
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2030
賴瑞．佩吉：不是 Google，而是大眾。
12:17
I think we need to
have a debate about that,
have a debate about that,
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3773
我認為我們需要討論一下這個問題，
12:20
or we can't have a functioning democracy.
307
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2499
否則我們的民主就名不符實。
12:23
It's just not possible.
308
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1406
這不可能稱為民主。
12:24
So I'm sad that Google's
309
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2244
對於 Google 處在一個，
12:27
in the position of protecting you and our users
310
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2616
要防範政府偷雞摸狗的位置，
12:29
from the government
311
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1534
12:31
doing secret thing that nobody knows about.
312
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2244
我覺得很可悲。
12:33
It doesn't make any sense.
313
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1747
這毫無道理。
12:35
CR: Yeah. And then there's a privacy side of it.
314
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2990
查理．羅斯：沒錯，然後還有隱私方面的問題。
12:38
LP: Yes. The privacy side,
315
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2427
賴瑞．佩吉：是的，還有隱私面，
12:40
I think it's -- the world is changing.
316
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1969
我認為，世界在變。
12:42
You carry a phone. It knows where you are.
317
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3905
你帶著手機，它知道你在哪裡。
12:46
There's so much more information about you,
318
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3085
還有許多你的個人資訊，
12:49
and that's an important thing,
319
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2846
這是件非常重要的事情，
12:52
and it makes sense why people are asking
320
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2272
人們也合理地提出一些，
12:54
difficult questions.
321
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2036
難以回答的問題。
12:56
We spend a lot of time thinking about this
322
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3367
我們花了很多時間去思考這一點，
13:00
and what the issues are.
323
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2711
以及問題所在。
13:02
I'm a little bit --
324
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1729
我有一點……
13:04
I think the main thing that we need to do
325
772569
1260
我認為我們需要做的事情裡最主要的一點，
13:05
is just provide people choice,
326
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2362
就是讓人們可以選擇，
13:08
show them what data's being collected --
327
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2512
告訴他們什麼數據會被收集──
13:10
search history, location data.
328
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4751
搜尋記錄、位置資訊。
13:15
We're excited about incognito mode in Chrome,
329
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2772
我們對於 Chrome 瀏覽器的
無痕模式感到很興奮，
無痕模式感到很興奮，
13:18
and doing that in more ways,
330
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2249
將它應用到更多的方面，
13:20
just giving people more choice
331
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1396
也就是給予人們更多選擇，
13:21
and more awareness of what's going on.
332
789871
3293
讓他們更完整地
認識到發生了什麼事。
認識到發生了什麼事。
13:25
I also think it's very easy.
333
793164
2393
我也認為這非常簡單。
13:27
What I'm worried is that we throw out
334
795557
1277
我所擔心的是，
13:28
the baby with the bathwater.
335
796834
2090
我們會因噎廢食。
13:30
And I look at, on your show, actually,
336
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2914
我看到，在你的節目上，
13:33
I kind of lost my voice,
337
801838
1719
我嗓子有點啞了，
13:35
and I haven't gotten it back.
338
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1331
我還沒有恢復。
13:36
I'm hoping that by talking to you
339
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1644
我希望和你聊聊
13:38
I'm going to get it back.
340
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1653
能恢復得快一點。
13:40
CR: If I could do anything, I would do that.
341
808185
1732
查理．羅斯：
如果我能幫上什麼忙，我一定會幫。
如果我能幫上什麼忙，我一定會幫。
13:41
LP: All right. So get out your voodoo doll
342
809917
2180
賴瑞．佩吉：那好，拿出你的巫毒娃娃，
13:44
and whatever you need to do.
343
812097
2419
該做什麼儘管做。
13:46
But I think, you know what, I look at that,
344
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2328
但是我認為，我看著這件事，
13:48
I made that public,
345
816844
1830
我把它公開化了，
13:50
and I got all this information.
346
818674
1217
我得到很多資訊。
13:51
We got a survey done on medical conditions
347
819891
2729
我做了個關於身體狀況的調查，
13:54
with people who have similar issues,
348
822620
3371
調查對象都有些類似的問題。
13:57
and I look at medical records, and I say,
349
825991
4741
我一邊看著醫療記錄，一邊說，
14:02
wouldn't it be amazing
350
830732
1405
如果每個人的醫療記錄
14:04
if everyone's medical records were available
351
832137
2050
都可以匿名地提供給
14:06
anonymously
352
834187
1683
做研究的醫生，
14:07
to research doctors?
353
835870
2636
豈不是很好？
14:10
And when someone accesses your medical record,
354
838506
3041
當有人查看你的醫療記錄時，
14:13
a research doctor,
355
841547
1609
一個做研究的醫生，
14:15
they could see, you could see which doctor
356
843156
2634
他們可以看到，你也可以看到
14:17
accessed it and why,
357
845790
1860
是哪位醫生看了，為什麼，
14:19
and you could maybe learn about
358
847650
1580
然後你也許可以了解到
14:21
what conditions you have.
359
849230
1630
你的狀況如何。
14:22
I think if we just did that,
360
850860
1502
我想我們若做到這點，
14:24
we'd save 100,000 lives this year.
361
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2165
一年就可以多救十萬人。
14:26
CR: Absolutely. Let me go — (Applause)
362
854527
2948
查理．羅斯：毫無疑問。讓我……
（掌聲）
（掌聲）
14:29
LP: So I guess I'm just very worried that
363
857475
2762
賴瑞．佩吉：我想我就是非常擔心
14:32
with Internet privacy,
364
860237
1806
網路隱私的問題。
14:34
we're doing the same thing we're
doing with medical records,
doing with medical records,
365
862043
2300
我們的問題和醫療記錄一樣，
14:36
is we're throwing out the baby with the bathwater,
366
864347
2529
就是我們因噎廢食了，
14:38
and we're not really thinking
367
866876
1828
我們沒有真正地思考過
14:40
about the tremendous good that can come
368
868704
2210
資訊共享帶來的巨大益處，
14:42
from people sharing information
369
870914
2191
人們分享資訊，
14:45
with the right people in the right ways.
370
873105
2577
與正確的人分享，用正確的方式。
14:47
CR: And the necessary condition
371
875682
2237
查理．羅斯：還有一個必要條件，
14:49
that people have to have confidence
372
877919
1702
就是人們得有信心，
14:51
that their information will not be abused.
373
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2455
相信他們的資訊不會被濫用。
14:54
LP: Yeah, and I had this problem with my voice stuff.
374
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1777
賴瑞．佩吉：是的，
我在嗓音上有同樣的問題，
我在嗓音上有同樣的問題，
14:55
I was scared to share it.
375
883853
1508
我害怕分享出來。
14:57
Sergey encouraged me to do that,
376
885361
1890
謝爾蓋鼓勵我這麼做，
14:59
and it was a great thing to do.
377
887251
1827
這件事非常值得做。
15:01
CR: And the response has been overwhelming.
378
889078
1734
查理．羅斯：
而且大家的反應出奇地好。
而且大家的反應出奇地好。
15:02
LP: Yeah, and people are super positive.
379
890812
1660
賴瑞．佩吉：
是的，而且人們的反應極為正面。
是的，而且人們的反應極為正面。
15:04
We got thousands and thousands of people
380
892472
2833
我們調查了成千上萬的人，
15:07
with similar conditions,
381
895305
1288
都有類似狀況，
15:08
which there's no data on today.
382
896593
3028
而這些數據至今都是沒有的。
15:11
So it was a really good thing.
383
899621
1356
所以這是件非常好的事情。
15:12
CR: So talking about the future, what is it about you
384
900977
3019
查理．羅斯：
說到未來，你是怎麼
說到未來，你是怎麼
15:15
and transportation systems?
385
903996
3758
注意到運輸系統的？
15:19
LP: Yeah. I guess I was just frustrated
386
907754
2177
賴瑞．佩吉：
我在密西根州讀大學的時候，
我在密西根州讀大學的時候，
15:21
with this when I was at college in Michigan.
387
909931
2539
我是感到非常沮喪的。
15:24
I had to get on the bus and take it
388
912470
1450
我必須坐公共汽車，
15:25
and wait for it.
389
913920
1642
還要等它。
15:27
And it was cold and snowing.
390
915562
2179
當時很冷，又在下雪。
15:29
I did some research on how much it cost,
391
917741
2655
我做了點成本研究，
15:32
and I just became a bit obsessed
with transportation systems.
with transportation systems.
392
920396
6425
然後我就有點迷上了運輸系統。
15:38
CR: And that began the idea of an automated car.
393
926821
2370
查理．羅斯：
於是就有了自動駕駛汽車的想法。
於是就有了自動駕駛汽車的想法。
15:41
LP: Yeah, about 18 years ago I learned about
394
929191
1694
賴瑞．佩吉：
是的，大約 18 年前我發現
是的，大約 18 年前我發現
15:42
people working on automated cars,
395
930885
3182
有人在研究自動駕駛，
15:46
and I became fascinated by that,
396
934067
1623
我被深深吸引，
15:47
and it takes a while to
get these projects going,
get these projects going,
397
935690
2777
讓這些專案有所進展得花點時間，
15:50
but I'm super excited about the possibilities of that
398
938467
5097
但是想到有可能讓世界變得更好，
我感到無比興奮。
15:55
improving the world.
399
943564
1668
15:57
There's 20 million people or more injured per year.
400
945232
4526
每年有超過兩千萬人受傷。
16:01
It's the leading cause of death
401
949758
1986
這是美國 34 歲以下群體
16:03
for people under 34 in the U.S.
402
951744
2130
的主要死因。
16:05
CR: So you're talking about saving lives.
403
953874
1551
查理．羅斯：這就是拯救生命了。
16:07
LP: Yeah, and also saving space
404
955425
2355
賴瑞．佩吉：是的，也是節省空間
16:09
and making life better.
405
957780
3915
和讓生活更美好。
16:13
Los Angeles is half parking lots and roads,
406
961695
4245
在洛杉磯一半的土地
都是停車場和道路，
都是停車場和道路，
16:17
half of the area,
407
965940
1733
一半的土地，
16:19
and most cities are not far behind, actually.
408
967673
2827
而且大部分城市其實也差不多了。
16:22
It's just crazy
409
970500
1564
這實在是太瘋狂了，
16:24
that that's what we use our space for.
410
972064
1593
我們居然這樣利用空間。
16:25
CR: And how soon will we be there?
411
973657
2343
查理．羅斯：我們什麼時候可以實現？
16:28
LP: I think we can be there very, very soon.
412
976000
1926
賴瑞．佩吉：
我想非常、非常快就可以實現了。
我想非常、非常快就可以實現了。
16:29
We've driven well over 100,000 miles
413
977926
3501
我們已正常行駛超過十萬英里，
16:33
now totally automated.
414
981427
4093
現在完全是自動行駛。
16:37
I'm super excited about getting that out quickly.
415
985520
3652
能夠這麼快地實現它，讓我無比興奮。
16:41
CR: But it's not only you're
talking about automated cars.
talking about automated cars.
416
989172
2405
查理．羅斯：但你考慮的
不只是自動駕駛汽車，
不只是自動駕駛汽車，
16:43
You also have this idea for bicycles.
417
991577
2386
你對自行車也有這樣的想法。
16:45
LP: Well at Google, we got this idea
418
993963
2246
賴瑞．佩吉：
在 Google，我們有個想法，
在 Google，我們有個想法，
16:48
that we should just provide free bikes to everyone,
419
996209
3451
我們應該向每一個人
提供免費自行車，
提供免費自行車，
16:51
and that's been amazing, most of the trips.
420
999660
2768
這非常棒，對大多數旅行都是。
16:54
You see bikes going everywhere,
421
1002428
1586
自行車哪都能去，
16:56
and the bikes wear out.
422
1004014
1566
而自行車會磨損，
16:57
They're getting used 24 hours a day.
423
1005580
1454
一天 24 小時都在用。
16:59
CR: But you want to put them above the street, too.
424
1007034
2160
查理．羅斯：但你也想把自行車放到街道上。
17:01
LP: Well I said, how do we get people
425
1009194
1575
賴瑞．佩吉：我就說，怎樣才能
17:02
using bikes more?
426
1010769
1527
讓人們多騎自行車呢？
17:04
CR: We may have a video here.
427
1012296
1625
查理．羅斯：我們這有一段影片。
17:05
LP: Yeah, let's show the video.
428
1013921
1278
賴瑞．佩吉：
好，我們來播一下影片，
好，我們來播一下影片，
17:07
I just got excited about this.
429
1015199
3092
這個讓我非常興奮。
17:10
(Music)
430
1018291
4042
（音樂）
其實這就是把自行車與
汽車分離的最經濟方法，
汽車分離的最經濟方法，
17:16
So this is actually how you might separate
431
1024213
2425
17:18
bikes from cars with minimal cost.
432
1026638
3629
17:26
Anyway, it looks totally crazy,
433
1034711
1755
這看起來很瘋狂，
但實際上我考慮的是我們的校園，
17:28
but I was actually thinking about our campus,
434
1036466
2327
和許多城市等等一起合作，
17:30
working with the Zippies and stuff,
435
1038793
2060
17:32
and just trying to get a lot more bike usage,
436
1040853
2298
就是想大大提高自行車使用率，
17:35
and I was thinking about,
437
1043151
1548
我還在想，
17:36
how do you cost-effectively separate
438
1044699
2831
我們怎樣才能有效並且廉價地
把自行車從車流中分離？
17:39
the bikes from traffic?
439
1047530
1414
17:40
And I went and searched,
440
1048944
1150
我做了研究，
17:42
and this is what I found.
441
1050094
1371
這就是我所得到的。
17:43
And we're not actually working on this,
442
1051465
1845
我們其實沒有研究這個，
17:45
that particular thing,
443
1053310
1292
我是說這個具體方案，
17:46
but it gets your imagination going.
444
1054602
2054
但它擴展了想像力。
17:48
CR: Let me close with this.
445
1056656
1764
查理．羅斯：
我們把這個話題先告一段落，
我們把這個話題先告一段落，
17:50
Give me a sense of the philosophy
of your own mind.
of your own mind.
446
1058420
2345
說一下你內心的哲學。
17:52
You have this idea of [Google X].
447
1060765
2488
你有了 Google X 這個想法，
17:55
You don't simply want
448
1063253
2996
你想要的不只是一些
17:58
to go in some small, measurable arena of progress.
449
1066249
5596
小的，規模有限的舞臺。
18:03
LP: Yeah, I think
450
1071845
1713
賴瑞．佩吉：是的，我認為
18:05
many of the things we just
talked about are like that,
talked about are like that,
451
1073558
2131
我們剛討論過的許多事情就是這樣，
18:07
where they're really --
452
1075689
2952
它們真是……
18:10
I almost use the economic concept of additionality,
453
1078641
3630
我差點要用經濟學
概念上的額外性了，
概念上的額外性了，
18:14
which means that you're doing something
454
1082271
2190
就是說，你要做的事情
本來並不會發生，
本來並不會發生，
18:16
that wouldn't happen unless
you were actually doing it.
you were actually doing it.
455
1084461
2948
除非你真的動手做。
18:19
And I think the more you can do things like that,
456
1087409
3140
我認為這樣的事情你做得越多，
18:22
the bigger impact you have,
457
1090549
2071
你的影響力就越大，
18:24
and that's about doing things
458
1092620
2990
重點在於
18:27
that people might not think are possible.
459
1095610
3607
去做人們認為不可能的事。
18:31
And I've been amazed,
460
1099217
1829
我驚訝地發現，
18:33
the more I learn about technology,
461
1101046
2229
我懂的技術越多，
18:35
the more I realize I don't know,
462
1103275
2196
就越意識到自己的不足。
18:37
and that's because this technological horizon,
463
1105471
3337
這是因為技術的眼界提高了，
18:40
the thing that you can see to do next,
464
1108808
2897
也就是預見下一步
該怎麼做的能力。
該怎麼做的能力。
18:43
the more you learn about technology,
465
1111705
1840
你懂的技術越多，
18:45
the more you learn what's possible.
466
1113545
2602
你就越知道什麼是可能的。
18:48
You learn that the balloons are possible
467
1116147
2246
你知道氣球專案是可能的，
18:50
because there's some material
that will work for them.
that will work for them.
468
1118393
2337
因為有合適的材料可用。
18:52
CR: What's interesting about
you too, though, for me,
you too, though, for me,
469
1120730
2379
查理．羅斯：不過在我看來，
你的有趣之處在於，
你的有趣之處在於，
18:55
is that, we have lots of people
470
1123109
1711
有很多的人在思考未來，
18:56
who are thinking about the future,
471
1124820
2142
有很多的人在思考未來，
18:58
and they are going and looking
and they're coming back,
and they're coming back,
472
1126962
3268
他們去看了看，又回來了，
19:02
but we never see the implementation.
473
1130230
2127
而我們卻沒有看到最終實現。
19:04
I think of somebody you knew
474
1132357
1605
我想到了一個人，你一定知道，
19:05
and read about, Tesla.
475
1133962
2907
特斯拉。
19:08
The principle of that for you is what?
476
1136869
3804
你在這方面的原則是怎樣的？
19:12
LP: Well, I think invention is not enough.
477
1140673
1785
賴瑞．佩吉：
我認為僅僅有發明是不夠的。
我認為僅僅有發明是不夠的。
19:14
If you invent something,
478
1142458
1221
如果你發明一樣東西，
19:15
Tesla invented electric power that we use,
479
1143679
3195
特斯拉發明了
我們用的電力系統，
我們用的電力系統，
19:18
but he struggled to get it out to people.
480
1146874
2661
但是他推廣起來就非常困難，
19:21
That had to be done by other people.
481
1149535
1684
普及是由別人實現的，
19:23
It took a long time.
482
1151219
1626
花費了很長時間。
19:24
And I think if we can actually combine both things,
483
1152845
3867
我認為，如果我們能將
二者真正結合起來，
二者真正結合起來，
19:28
where we have an innovation and invention focus,
484
1156712
3531
同時著眼於創新與發明，
19:32
plus the ability to really -- a company
485
1160243
2972
再加上一家公司，
19:35
that can really commercialize things
486
1163215
1998
可以使成果真正商業化，
19:37
and get them to people
487
1165213
1630
讓人們接觸到它，
19:38
in a way that's positive for the world
488
1166843
2075
讓它對世界有積極的影響，
19:40
and to give people hope.
489
1168918
2056
並給人們帶來希望。
19:42
You know, I'm amazed with the Loon Project
490
1170974
2774
你知道，大家對氣球專案的關注程度
19:45
just how excited people were about that,
491
1173748
2786
讓我很是吃驚，
19:48
because it gave them hope
492
1176534
1814
因為它帶來了希望，
19:50
for the two thirds of the world
493
1178348
1621
尤其是對世界上無法
上網的三分之二來說，
上網的三分之二來說，
19:51
that doesn't have Internet right now that's any good.
494
1179969
2726
19:54
CR: Which is a second thing about corporations.
495
1182695
2122
查理．羅斯：
這就是關於公司的第二件事。
這就是關於公司的第二件事。
19:56
You are one of those people who believe
496
1184817
2476
有些人，包括你，認為，
19:59
that corporations are an agent of change
497
1187293
2317
公司可以成為帶來改變的媒介，
20:01
if they are run well.
498
1189610
1471
如果好好經營的話。
20:03
LP: Yeah. I'm really dismayed
499
1191081
1821
賴瑞．佩吉：是的，
多數人認為企業是邪惡的，
多數人認為企業是邪惡的，
20:04
most people think companies are basically evil.
500
1192902
3294
這讓我很是沮喪，
20:08
They get a bad rap.
501
1196196
1766
這麼說並不公正，
20:09
And I think that's somewhat correct.
502
1197962
2241
但我認為在某程度上又是正確的。
20:12
Companies are doing the same incremental thing
503
1200203
2870
公司做的事情就是漸進發展，
20:15
that they did 50 years ago
504
1203073
1763
五十年前的公司就這樣做，
20:16
or 20 years ago.
505
1204836
1631
或者說二十年前，
20:18
That's not really what we need.
506
1206467
1370
這也並非是我們真正需要的。
20:19
We need, especially in technology,
507
1207837
2218
我們需要的是，特別是在科技上，
20:22
we need revolutionary change,
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2117
是革命性改變，
20:24
not incremental change.
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而不是漸進式改變。
20:25
CR: You once said, actually,
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查理．羅斯：你曾說過，
20:26
as I think I've got this about right,
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我希望我的理解是對的，
20:28
that you might consider,
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就是，你可能考慮，
20:30
rather than giving your money,
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相較於直接捐出你的錢，
20:31
if you were leaving it to some cause,
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3320
你更願意用於某些事業，
20:35
just simply giving it to Elon Musk,
515
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2006
給伊隆．馬斯克就好了，
20:37
because you had confidence
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1163
因為你相信
20:38
that he would change the future,
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他會改變未來，
20:40
and that you would therefore —
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1777
因此你就會……
20:42
LP: Yeah, if you want to go Mars,
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1584
賴瑞．佩吉：是的，如果你想去火星，
20:43
he wants to go to Mars,
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1721
他想去火星，
20:45
to back up humanity,
521
1233383
1971
來為人類尋找後備方案，
這目標很有價值，
但對公司來說是慈善事業。
但對公司來說是慈善事業。
20:47
that's a worthy goal, but it's a company,
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20:49
and it's philanthropical.
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2555
20:51
So I think we aim to do kind of similar things.
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所以我覺得我們的目標
是做些類似的事情。
是做些類似的事情。
20:54
And I think, you ask, we have a lot of employees
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2987
你問過，我們在 Google 有許多員工，
20:57
at Google who have become pretty wealthy.
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3315
他們非常富有，
21:00
People make a lot of money in technology.
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2520
通過技術賺了很多錢，
21:03
A lot of people in the room are pretty wealthy.
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2156
很多人都非常富有。
21:05
You're working because you
want to change the world.
want to change the world.
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2314
你工作的目的是改變世界，
21:07
You want to make it better.
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1762
你想讓世界變得更好。
21:09
Why isn't the company that you work for
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為什麼你工作的這家公司，
21:13
worthy not just of your time
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1943
值得你投入時間，
21:14
but your money as well?
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2151
卻不值得你投入金錢呢？
21:17
I mean, but we don't have a concept of that.
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1722
我的意思是，我們並不這樣認為，
21:18
That's not how we think about companies,
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2304
我們也不是這樣看待公司的。
21:21
and I think it's sad,
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1467
我也覺得很傷感，
21:22
because companies are most of our effort.
537
1270619
3767
因為我們所付出的努力
絕大部分都花在了公司上。
絕大部分都花在了公司上。
21:26
They're where most of people's time is,
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2515
人們在這裡付出了最多的時間，
21:28
where a lot of the money is,
539
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1854
也花費了許多金錢，
21:30
and so I think I'd like for us to help out
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2352
所以我想我要幫助大家，
21:33
more than we are.
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1126
而非只顧自己。
21:34
CR: When I close conversations with lots of people,
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查理．羅斯：
我跟許多人的談話結束時，
我跟許多人的談話結束時，
21:35
I always ask this question:
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1779
我總是問這樣的一個問題：
21:37
What state of mind,
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1515
怎樣的心態，
21:39
what quality of mind is it
545
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1809
怎樣的心靈特質，
21:41
that has served you best?
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1767
讓你最有收穫？
21:42
People like Rupert Murdoch have said curiosity,
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2521
像魯柏．梅鐸這樣的人
說是好奇心，
說是好奇心，
21:45
and other people in the media have said that.
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2628
別的媒體人士也這樣說。
21:47
Bill Gates and Warren Buffett have said focus.
549
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3024
比爾．蓋茲和華倫．巴菲特
說是專注，
說是專注，
21:50
What quality of mind,
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1298997
1427
什麼樣的心靈特質
21:52
as I leave this audience,
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1374
──在與觀眾說再見前──
21:53
has enabled you to think about the future
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3530
使得你能夠思考未來，
21:57
and at the same time
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1647
而且與此同時，
21:58
change the present?
554
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2205
改變現在？
22:01
LP: You know, I think the most important thing --
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賴瑞．佩吉：
我認為最重要的事情，
我認為最重要的事情，
22:02
I looked at lots of companies
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1612
我見過很多公司，
22:04
and why I thought they don't succeed over time.
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3303
為什麼我認為它們
沒能經受時間的考驗。
沒能經受時間的考驗。
22:07
We've had a more rapid turnover of companies.
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2833
如今公司的人員流動更快，
22:10
And I said, what did they fundamentally do wrong?
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2769
我問，他們出錯的根源是什麼？
22:13
What did those companies all do wrong?
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2167
這些公司都錯在了哪裡？
22:15
And usually it's just that they missed the future.
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3272
通常就是因為他們錯失了未來。
22:18
And so I think, for me,
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2444
所以在我看來，
22:21
I just try to focus on that and say,
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2424
我就是專注於這一點，並且在想，
22:23
what is that future really going to be
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2184
未來將真正走向何方，
22:25
and how do we create it,
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1787
我們要如何創造未來，
22:27
and how do we cause our organization,
566
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4667
我們怎樣才能讓我們的組織
22:32
to really focus on that
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2440
真正專注於它，
22:34
and drive that at a really high rate?
568
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3325
並且帶領組織快速行動呢？
22:38
And so that's been curiosity,
569
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1360
所以那就是好奇心，
22:39
it's been looking at things
570
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1733
去尋找人們
22:41
people might not think about,
571
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1718
可能沒有想過的東西，
22:42
working on things that no one else is working on,
572
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3105
研究別人所沒有研究過的東西，
22:45
because that's where the additionality really is,
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3306
因為那才是真正的額外性，
22:49
and be willing to do that,
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1551
同時樂於去做，
22:50
to take that risk.
575
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1382
樂於承擔風險。
22:52
Look at Android.
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1065
看看 Android，
22:53
I felt guilty about working on Android
577
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2785
為 Android 花心力曾讓我感到內疚，
22:56
when it was starting.
578
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1316
在它剛起步時，
22:57
It was a little startup we bought.
579
1365398
1958
我們併購它時，
它只是個小公司。
它只是個小公司。
22:59
It wasn't really what we were really working on.
580
1367356
2670
它當時也不是我們
真正努力的方向。
真正努力的方向。
23:02
And I felt guilty about spending time on that.
581
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2495
為它花時間讓我感到內疚，
23:04
That was stupid.
582
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1454
那真是非常傻。
23:05
That was the future, right?
583
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1051
但那就是未來，對吧？
23:07
That was a good thing to be working on.
584
1375026
2285
那是個很棒的東西，
值得為之努力。
值得為之努力。
23:09
CR: It is great to see you here.
585
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1417
查理．羅斯：
很高興在這裡見到你，
很高興在這裡見到你，
23:10
It's great to hear from you,
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1460
很高興聽到你的講述，
23:12
and a pleasure to sit at this table with you.
587
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2297
和你一起坐在這也是我的榮幸。
23:14
Thanks, Larry.
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928
謝謝賴瑞。
23:15
LP: Thank you.
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2103
賴瑞．佩吉：謝謝你。
23:17
(Applause)
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3932
（掌聲）
23:21
CR: Larry Page.
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3311
查理．羅斯：賴瑞．佩吉。
ABOUT THE SPEAKER
Larry Page - CEO of GoogleLarry Page is the CEO and cofounder of Google, making him one of the ruling minds of the web.
Why you should listen
Larry Page and Sergey Brin met in grad school at Stanford in the mid-'90s, and in 1996 started working on a search technology based on a new idea: that relevant results come from context. Their technology analyzed the number of times a given website was linked to by other sites — assuming that the more links, the more relevant the site — and ranked sites accordingly. In 1998, they opened Google in a garage-office in Menlo Park. In 1999 their software left beta and started its steady rise to web domination.
Beyond the company's ubiquitous search, including AdSense/AdWords, Google Maps, Google Earth and the mighty Gmail. In 2011, Page stepped back into his original role of chief executive officer. He now leads Google with high aims and big thinking, and finds time to devote to his projects like Google X, the idea lab for the out-there experiments that keep Google pushing the limits.
Larry Page | Speaker | TED.com