Audience: Apple. Kenneth Cukier: Apple. Of course it is.

觀眾：蘋果派。

How do we know it?

講者：蘋果派，當然啦！

Because of data.

我們怎麼知道？

You look at supermarket sales.

因為有數據。

You look at supermarket sales of 30-centimeter pies

我們分析超市銷售數據，

that are frozen, and apple wins, no contest.

分析直徑 30 公分冷凍蘋果派的超市銷售數據，

The majority of the sales are apple.

蘋果派最夯，銷量一面倒。

But then supermarkets started selling

顧客幾乎都是買蘋果派。

smaller, 11-centimeter pies,

但是後來，超市開始賣小派，

and suddenly, apple fell to fourth or fifth place.

直徑 11 公分的派，

Why? What happened?

突然，蘋果派銷量掉到第四、五名，

Okay, think about it.

為什麼？發生了什麼事？

When you buy a 30-centimeter pie,

好，你想想：

the whole family has to agree,

如果是買 30 公分的大派，

and apple is everyone's second favorite.

全家人都得同意，

(Laughter)

而蘋果是全家每個人的第二選擇，

But when you buy an individual 11-centimeter pie,

（觀眾笑聲）

you can buy the one that you want.

但是當你分開買 11 公分的小派，

You can get your first choice.

就可以買你自己想吃的，

You have more data.

每個人都可以選自己最愛的口味。

You can see something

這就會產生更多的數據。

that you couldn't see

你會有新發現，

when you only had smaller amounts of it.

看出數據少的時候，無法發現的現象。

Now, the point here is that more data

現在，這個例子的重點是，

doesn't just let us see more,

數據增加，不只是讓我們看見更「多」，

more of the same thing we were looking at.

更多我們本來就已經知道的；

More data allows us to see new.

數據增加，讓我們看見「新」資訊，

It allows us to see better.

看得更「準確」，

It allows us to see different.

看見「不同」。

In this case, it allows us to see

在這個例子，它使我們看到

what America's favorite pie is:

美國人真正最喜歡的派是什麼：

not apple.

不是蘋果派。

Now, you probably all have heard the term big data.

你們可能都聽過「大數據」這個詞，

In fact, you're probably sick of hearing the term

其實，你們可能已經聽膩了。

big data.

的確有很多大肆宣傳，

It is true that there is a lot of hype around the term,

非常遺憾。

and that is very unfortunate,

因為大數據是極為重要的工具，

because big data is an extremely important tool

將會推動社會進步。

by which society is going to advance.

過去，我們依賴少量數據，

In the past, we used to look at small data

研究其含義，

and think about what it would mean

試圖了解我們的世界。

to try to understand the world,

現在我們有了更多數據，

and now we have a lot more of it,

遠超過以往能力所及。

more than we ever could before.

我們發現，

What we find is that when we have

當我們擁有龐大的數據，

a large body of data, we can fundamentally do things

就可以做過去數據較少時做不到的事。

that we couldn't do when we only had smaller amounts.

大數據很重要，

Big data is important, and big data is new,

大數據也很新。

and when you think about it,

你想一想，

the only way this planet is going to deal

唯一能幫助地球因應全球的挑戰：

with its global challenges —

解決饑荒、

to feed people, supply them with medical care,

提供醫療、

supply them with energy, electricity,

提供能源和電力、

and to make sure they're not burnt to a crisp

確保我們不被全球暖化烤焦，

because of global warming —

唯一的方法，就是靠善用數據。

is because of the effective use of data.

所以大數據有什麼稀奇？

So what is new about big data? What is the big deal?

有什麼好「大」驚小怪？

Well, to answer that question, let's think about

要回答這個問題，

what information looked like,

讓我們先來看資訊以前長什麼樣子。

physically looked like in the past.

好，

In 1908, on the island of Crete,

1908 年，在克里特島，

archaeologists discovered a clay disc.

考古學家發現一個泥土圓盤，

They dated it from 2000 B.C., so it's 4,000 years old.

鑑定大約是公元前 2 千年製成的，

Now, there's inscriptions on this disc,

所以已經有 4 千年之久。

but we actually don't know what it means.

圓盤上刻有古文字，

It's a complete mystery, but the point is that

但無法解讀，

this is what information used to look like

是個謎團。

4,000 years ago.

但重點是，4 千年前資訊是這個樣貌，

This is how society stored

古人是用這種方式儲存、傳遞資訊。

and transmitted information.

到現在，社會並沒有進步那麼多，

Now, society hasn't advanced all that much.

我們還是把資訊存在碟片上，

We still store information on discs,

只是現在可以儲存更多資訊，

but now we can store a lot more information,

空前的多。

more than ever before.

搜尋更容易，複製更容易，

Searching it is easier. Copying it easier.

分享更容易，處理更容易。

Sharing it is easier. Processing it is easier.

我們可以重複使用這些資訊，

And what we can do is we can reuse this information

用途之廣，超乎想像，

for uses that we never even imagined

超乎我們蒐集資訊時的預期。

when we first collected the data.

這樣看來，資訊已經

In this respect, the data has gone

從「存料」 變成「流動」；

from a stock to a flow,

從靜止、靜態的，

from something that is stationary and static

變成流體、動態的。

to something that is fluid and dynamic.

資訊可說是，有流動性。

There is, if you will, a liquidity to information.

那個 4 千年之久的克里特圓盤，

The disc that was discovered off of Crete

它很重，

that's 4,000 years old, is heavy,

儲存的資訊量不多，

it doesn't store a lot of information,

內容也不能更改。

and that information is unchangeable.

相較之下，

By contrast, all of the files

愛德華．史諾登盜走的所有檔案，

that Edward Snowden took

就是他從美國國安局竊走的資料，

from the National Security Agency in the United States

可以全部存在一個記憶卡，

fits on a memory stick

體積只有指甲般的大小。

the size of a fingernail,

並且可以用光速來傳輸分享。

and it can be shared at the speed of light.

更多的數據！

More data. More.

更多。

Now, one reason why we have so much data in the world today

今天之所以有這麼多的數據，

is we are collecting things

原因之一是我們正在蒐集過去

that we've always collected information on,

儲存資訊的物體；

but another reason why is we're taking things

原因之二是，

that have always been informational

我們把一些經常很資訊性的東西——

but have never been rendered into a data format

從未數據化的資訊，

and we are putting it into data.

把它們變成數據，

Think, for example, the question of location.

例如，地理位置。

Take, for example, Martin Luther.

舉馬丁．路德為例，

If we wanted to know in the 1500s

如果我們想知道十六世紀時，

where Martin Luther was,

馬丁．路德去過哪些地方，

we would have to follow him at all times,

我們必須隨時跟著他到處跑，

maybe with a feathery quill and an inkwell,

可能還要帶著羽毛筆和墨水瓶，

and record it,

隨時記錄。

but now think about what it looks like today.

但是看看現在的做法，

You know that somewhere,

你知道世界上某處，

probably in a telecommunications carrier's database,

可能是電信商的資料庫裡面，

there is a spreadsheet or at least a database entry

有一個試算表或至少有一筆記錄，

that records your information

存著關於你的資訊，

of where you've been at all times.

記錄你去過的所有地方。

If you have a cell phone,

如果你有一支手機，

and that cell phone has GPS, but even if it doesn't have GPS,

手機有 GPS，但就算沒有 GPS，

it can record your information.

還是可以記錄你的資訊。

In this respect, location has been datafied.

就這個角度來說，位置已經被數據化。

Now think, for example, of the issue of posture,

現在再想想這個例子：姿勢，

the way that you are all sitting right now,

就是你們現在的坐姿，

the way that you sit,

你的坐姿、

the way that you sit, the way that you sit.

你的坐姿，和你的坐姿，

It's all different, and it's a function of your leg length

都不一樣，取決於你的腿長、

and your back and the contours of your back,

你的背和背部輪廓。

and if I were to put sensors, maybe 100 sensors

要是我現在裝 1 百個感應器，

into all of your chairs right now,

到你們每個人的椅子上，

I could create an index that's fairly unique to you,

我可以建出你個人獨特的索引資料，

sort of like a fingerprint, but it's not your finger.

有點像指紋，但不是你的手指。

So what could we do with this?

這有什麼用？

Researchers in Tokyo are using it

東京的研究員用這種數據

as a potential anti-theft device in cars.

來研發汽車防盜裝置。

The idea is that the carjacker sits behind the wheel,

概念是，偷車賊坐在駕駛座，

tries to stream off, but the car recognizes

急著開車逃逸，

that a non-approved driver is behind the wheel,

但是車子辨識出開車的人未經授權，

and maybe the engine just stops, unless you

引擎就自動熄火，

type in a password into the dashboard

除非你輸入密碼到儀表板，

to say, "Hey, I have authorization to drive." Great.

告訴系統：「嘿，我可是有經授權喔！」

What if every single car in Europe

很好。

had this technology in it?

若歐洲每輛汽車都有這個裝置呢？

What could we do then?

那又能做什麼？

Maybe, if we aggregated the data,

或許，我們可以聚集所有的數據，

maybe we could identify telltale signs

或許能提早偵測到警訊，

that best predict that a car accident

預測車禍

is going to take place in the next five seconds.

即將在 5 秒鐘內發生。

And then what we will have datafied

然後我們還可以數據化

is driver fatigue,

駕駛員的疲勞狀態，

and the service would be when the car senses

汽車系統可以偵測到

that the person slumps into that position,

駕駛癱坐成某個姿勢，

automatically knows, hey, set an internal alarm

自動感知，發出指令啟動響鈴，

that would vibrate the steering wheel, honk inside

導致方向盤震動，

to say, "Hey, wake up,

車內喇叭作響，大喊：「嘿，快醒來！

pay more attention to the road."

注意路況！」

These are the sorts of things we can do

這一類的事都可以做到，

when we datafy more aspects of our lives.

當我們把更多的生活層面數據化。

So what is the value of big data?

那麼，大數據究竟有什麼價值？

Well, think about it.

想想看，

You have more information.

現在有更多資訊，

You can do things that you couldn't do before.

可以做過去不能做的事。

One of the most impressive areas

這概念的應用當中，最驚人的領域之一，

where this concept is taking place

就是「機器學習」。

is in the area of machine learning.

機器學習是人工智慧的一個分支，

Machine learning is a branch of artificial intelligence,

人工智慧又是電腦科學的分支。

which itself is a branch of computer science.

基本概念是：

The general idea is that instead of

不必告訴電腦要做什麼，

instructing a computer what to do,

只要把數據輸入到問題裡，

we are going to simply throw data at the problem

然後叫電腦自己想辦法。

and tell the computer to figure it out for itself.

我們回顧一下源頭，就會比較容易了解。

And it will help you understand it

1950 年代，IBM 有位電腦科學家

by seeing its origins.

名叫亞瑟．山姆爾，很愛下跳棋，

In the 1950s, a computer scientist

所以他寫了一個電腦程式，

at IBM named Arthur Samuel liked to play checkers,

叫電腦跟他對打。

so he wrote a computer program

他開始下棋，結果他贏了。

so he could play against the computer.

他再開始下棋，結果他又贏了。

He played. He won.

他再下，還是他贏。

He played. He won.

因為電腦只會

He played. He won,

棋步的規則。

because the computer only knew

而亞瑟．山姆爾會得更多，

what a legal move was.

他懂得策略。

Arthur Samuel knew something else.

所以他又寫了一個副程式，

Arthur Samuel knew strategy.

在背景執行，只做一件事：

So he wrote a small sub-program alongside it

就是計算機率，

operating in the background, and all it did

評估目前的棋局，

was score the probability

比較贏棋和輸棋的機率，

that a given board configuration would likely lead

每下一步棋，就重算一次。

to a winning board versus a losing board

然後他又跟電腦對打，結果他贏。

after every move.

再對打，還是他贏。

He plays the computer. He wins.

再對打，還是他贏。

He plays the computer. He wins.

然後亞瑟．山姆爾讓電腦自己對打。

He plays the computer. He wins.

它就自己下棋，一邊收集數據。

And then Arthur Samuel leaves the computer

越收集越多，它的預測準確度就提高。

to play itself.

然後亞瑟．山姆爾再回來跟電腦對打。

It plays itself. It collects more data.

他開始下棋，結果他輸了。

It collects more data. It increases the accuracy of its prediction.

他又下，又輸了。

And then Arthur Samuel goes back to the computer

再下，還是輸。

and he plays it, and he loses,

亞瑟．山姆爾創造了一台機器，

and he plays it, and he loses,

它的能力青出於藍，更甚於藍。

and he plays it, and he loses,

而這種機器學習的概念，

and Arthur Samuel has created a machine

現在到處可見。

that surpasses his ability in a task that he taught it.

你想我們怎麼會有自動駕駛汽車？

And this idea of machine learning

把全部交通規則都輸入到軟體，可以改善社會嗎？

is going everywhere.

不是。

How do you think we have self-driving cars?

因為記憶體更便宜嗎？不是。

Are we any better off as a society

演算法變快了？不。

enshrining all the rules of the road into software?

有更好的處理器？不。

No. Memory is cheaper. No.

這些都很重要，但不是真正的原因。

Algorithms are faster. No. Processors are better. No.

真正的原因是我們改變了問題的本質。

All of those things matter, but that's not why.

我們把問題從

It's because we changed the nature of the problem.

明確指示電腦如何開車，

We changed the nature of the problem from one

改成對電腦說：

in which we tried to overtly and explicitly

「我給你大量的開車數據，

explain to the computer how to drive

你自個兒看著辦吧！」

to one in which we say,

你自己判斷出那是紅綠燈，

"Here's a lot of data around the vehicle.

而且現在亮紅燈，不是綠燈，

You figure it out.

表示你要停車，

You figure it out that that is a traffic light,

不能繼續開。」

that that traffic light is red and not green,

機器學習也是

that that means that you need to stop

我們許多網路活動的基礎：

and not go forward."

搜尋引擎、

Machine learning is at the basis

亞馬遜的個人化演算法、

of many of the things that we do online:

電腦翻譯、

search engines,

語音辨識系統。

Amazon's personalization algorithm,

研究專家近來研究

computer translation,

活組織切片檢查，

voice recognition systems.

癌組織切片，

Researchers recently have looked at

他們叫電腦自己判別，

the question of biopsies,

電腦分析數據和存活率，

cancerous biopsies,

判斷是否為癌症細胞。

and they've asked the computer to identify

果然，當你把數據丟給電腦，

by looking at the data and survival rates

透過一個機器學習的演算法，

to determine whether cells are actually

電腦真的能找出

cancerous or not,

12 大危險徵兆，

and sure enough, when you throw the data at it,

預測這個乳房癌細胞的切片

through a machine-learning algorithm,

真的就是癌腫瘤。

the machine was able to identify

問題來了：醫學文獻只知道

the 12 telltale signs that best predict

其中 9 項。

that this biopsy of the breast cancer cells

另外 3 項特性

are indeed cancerous.

是我們以前不需檢查的，

The problem: The medical literature

卻被電腦找出來了。

only knew nine of them.

好。

Three of the traits were ones

不過，大數據也有不好的一面。

that people didn't need to look for,

它會改善我們的生活，

but that the machine spotted.

但是也有我們必須注意的問題。