you would have to program it.

你需要設計程式。

Now, programming, for those of you here that haven't done it yourself,

而現在，你們可能沒做過程式設計這件事，

requires laying out in excruciating detail

它需要規劃相當詳細的細節

every single step that you want the computer to achieve, to do

那些你想讓電腦執行的每一個步驟

in order to achieve your goal.

以達到你的目的。

Now, if you want to do something that you don't know how to do yourself,

如果你沒有概念要怎麼做的話

then this is going to be a great challenge.

那會是個很大的挑戰。

So this was the challenge faced by this man, Arthur Samuel.

亞瑟·撒姆爾也曾面對這種挑戰。

In 1956, he wanted to get this computer

他在 1956 年便想到用這台電腦

to be able to beat him at checkers.

能夠在西洋跳棋棋賽打敗他。

How can you write a program,

要如何設計這樣的程式？

lay out in excruciating detail, how to be better than you at checkers?

把細節通通寫出來，如何讓電腦比你還會下棋？

So he came up with an idea:

於是他想出了一個點子：

he had the computer play against itself thousands of times

他讓電腦與電腦本身對弈數千次

and learn how to play checkers.

以學習如何玩西洋棋。

And indeed it worked, and in fact, by 1962,

然而，在 1962 年做到了，

this computer had beaten the Connecticut state champion.

電腦打敗了康乃狄克州的冠軍。

So Arthur Samuel was the father of machine learning,

於是亞瑟·撒姆爾成為了機器學習之父，

and I have a great debt to him,

我尊敬他，

because I am a machine learning practitioner.

因為我也是個機器學習實踐者，

I was the president of Kaggle,

我曾是 Kaggle 的會長，

a community of over 200,000 machine learning practitioners.

Kaggle 是個超過 20 萬人的機器學習實踐者的社群。

Kaggle puts up competitions

Kaggle 設立了一些比賽

to try and get them to solve previously unsolved problems,

讓他們參與解決過去無法解決的問題，

and it's been successful hundreds of times.

而有上百的成功個案。

So from this vantage point, I was able to find out

從這有利的環境中，我發現

a lot about what machine learning can do in the past, can do today,

很多機器學習在過去和現在可以做到的事情，

and what it could do in the future.

還有未來可以做到的事。

Perhaps the first big success of machine learning commercially was Google.

第一個機器學習的商業成功案例是谷歌。

Google showed that it is possible to find information

谷歌展示找尋資料的方法

by using a computer algorithm,

是使用計算機演算法，

and this algorithm is based on machine learning.

而這演算法是以機器學習為基礎。

Since that time, there have been many commercial successes of machine learning.

自此，機器學習有很多的商業成功例子，

Companies like Amazon and Netflix

譬如亞馬遜和奈飛公司

use machine learning to suggest products that you might like to buy,

用機器學習會向你推薦你可能想買的商品，

movies that you might like to watch.

你可能想看的影片。

Sometimes, it's almost creepy.

有時，你可能會很訝異。

Companies like LinkedIn and Facebook

像領英和臉書等公司

sometimes will tell you about who your friends might be

有些時候會告訴你誰會是你的朋友

and you have no idea how it did it,

而你根本不知道他們是如何做到的，

and this is because it's using the power of machine learning.

因為他們用了機器學習這強大的功能。

These are algorithms that have learned how to do this from data

演算法從資料去學習這類事情

rather than being programmed by hand.

不需要動手去編寫程式。

This is also how IBM was successful

這也是 IBM 過去能成功的原因

in getting Watson to beat the two world champions at "Jeopardy,"

讓超級電腦「華生」在「危機遊戲」中打敗兩屆世界冠軍。

answering incredibly subtle and complex questions like this one.

回答一些細碎和複雜的問題，像是

["The ancient 'Lion of Nimrud' went missing from this city's national museum in 2003 (along with a lot of other stuff)"]

「2003年，古獅像在這城市的國家博物館消失了（連同其他物品）」

This is also why we are now able to see the first self-driving cars.

這也是我們現在能看到第一部自行駕駛汽車的原因。

If you want to be able to tell the difference between, say,

如果你能說出不同點，像是

a tree and a pedestrian, well, that's pretty important.

一棵樹和一條行人道，那顯得非常重要。

We don't know how to write those programs by hand,

我們不知道如何設計這樣的程式，

but with machine learning, this is now possible.

不過通過機器，這就成為可能。

And in fact, this car has driven over a million miles

事實上，這部汽車已經行駛一百萬英哩

without any accidents on regular roads.

在正常路面沒有發生事故。

So we now know that computers can learn,

我們現在都知道電腦能夠學習，

and computers can learn to do things

學習做一些

that we actually sometimes don't know how to do ourselves,

有時我們自己也不知道怎麼做的事，

or maybe can do them better than us.

還可能比我們做得更好。

One of the most amazing examples I've seen of machine learning

其中一個機器學習的經典例子

happened on a project that I ran at Kaggle

是我在 Kaggle 所做的一個專案

where a team run by a guy called Geoffrey Hinton

由傑佛里·辛頓帶領的團隊

from the University of Toronto

他是多倫多大學的教授

won a competition for automatic drug discovery.

他們贏了新藥研發的比賽。

Now, what was extraordinary here is not just that they beat

他們出色地方不只打敗了

all of the algorithms developed by Merck or the international academic community,

默克藥廠或國際學術社群所研發的演算法，

but nobody on the team had any background in chemistry or biology or life sciences,

他們的團隊沒有化學生物或生命科學的背景，

and they did it in two weeks.

而且只花了兩個星期就完成。

How did they do this?

他們怎麼做到的？

They used an extraordinary algorithm called deep learning.

他們用了一個很出色的演算法叫做「深度學習」。

So important was this that in fact the success was covered

這是重要且成功的事情

in The New York Times in a front page article a few weeks later.

在數星期後被刊登在紐約時報頭版。

This is Geoffrey Hinton here on the left-hand side.

左手邊那位是傑佛里·辛頓。

Deep learning is an algorithm inspired by how the human brain works,

深度學習是一種受到人類大腦啟發的演算法，

and as a result it's an algorithm

它是一種演算法

which has no theoretical limitations on what it can do.

做法不受理論限制的演算法。

The more data you give it and the more computation time you give it,

你給它越多的資料和運算時間，

the better it gets.

會得到更好的結果。

The New York Times also showed in this article

紐約時報的文章裡

another extraordinary result of deep learning

也介紹到深度學習的非凡成就

which I'm going to show you now.

我現在要展示給你們看。

It shows that computers can listen and understand.

它顯示電腦能聽懂和理解資料的能力。

(Video) Richard Rashid: Now, the last step

（影片）理察·拉希德: 現在，最後一步是

that I want to be able to take in this process

我能夠理解這個程序

is to actually speak to you in Chinese.

我能夠跟你說中文。

Now the key thing there is,

現在關鍵的是，

we've been able to take a large amount of information from many Chinese speakers

我們從很多講中文的人士中收集大量的資訊

and produce a text-to-speech system

然後產生文字轉化語言的系統

that takes Chinese text and converts it into Chinese language,

將中文文字轉化成中文語言，

and then we've taken an hour or so of my own voice

然後錄一個小時我自己的聲音

and we've used that to modulate

我們使用它去調變

the standard text-to-speech system so that it would sound like me.

使標準文字轉化語音系統的聲音聽起來像我的聲音。

Again, the results are not perfect.

再一次，雖然結果沒有很完美，

There are in fact quite a few errors.

裡面還有一些錯誤。

(In Chinese)

（中文）

(Applause)

（掌聲）

There's much work to be done in this area.

在這個領域還有很多工作要做。

(In Chinese)

（中文）

(Applause)

（掌聲）

Jeremy Howard: Well, that was at a machine learning conference in China.

傑里米·霍華德：那是在中國舉行的機器學習研討會。

It's not often, actually, at academic conferences

那不常有，事實上，在學術會議上

that you do hear spontaneous applause,

聽到熱烈的掌聲，

although of course sometimes at TEDx conferences, feel free.

雖然有些時候 TEDx 講座不拘泥形式。

Everything you saw there was happening with deep learning.

你所看到的都是出於深度學習

(Applause) Thank you.

（掌聲）謝謝。

The transcription in English was deep learning.

英文文字翻譯由深度學習完成的。

The translation to Chinese and the text in the top right, deep learning,

翻譯成中文和右上角的文稿也是出於深度學習，

and the construction of the voice was deep learning as well.

連創建聲音也都是深度學習。

So deep learning is this extraordinary thing.

深度學習是如此的神奇。

It's a single algorithm that can seem to do almost anything,

它是個單一的演算法似乎可以完成任何事情，

and I discovered that a year earlier, it had also learned to see.

我一年前還發現它可以學會看

In this obscure competition from Germany

這個德國遊戲的比賽

called the German Traffic Sign Recognition Benchmark,

叫德國交通標誌確認基準，

deep learning had learned to recognize traffic signs like this one.

深度學習能認出這個交通標誌。

Not only could it recognize the traffic signs

它不只確認交通標誌的能力

better than any other algorithm,

比其他的演算法好，

the leaderboard actually showed it was better than people,

在排行榜上更顯示它做得比人類好，

about twice as good as people.

正確性是人類的兩倍。

So by 2011, we had the first example

2011 以前，我們有了第一個例子

of computers that can see better than people.

視力高於人類的電腦。

Since that time, a lot has happened.

從那時開始，許多電腦也可以做到。

In 2012, Google announced that they had a deep learning algorithm

2012 年谷歌宣佈使用深度學習演算法

to watch YouTube videos

來監看 Youtube 影片

and crunched the data on 16,000 computers for a month,

收集一個月 1,600 台電電腦的資料，

and the computer independently learned about concepts such as people and cats

電腦獨立識別人或貓的概念

just by watching the videos.

僅透過觀看影片。

This is much like the way that humans learn.

這樣更像人類的學習方式。

Humans don't learn by being told what they see,

人類並非通過別人的指示來學習，

but by learning for themselves what these things are.

而是從自己搞懂事情來學習。

Also in 2012, Geoffrey Hinton, who we saw earlier,

在 2012 年傑佛里·辛頓我們之前看到的人，

won the very popular ImageNet competition,

贏了很有名的映像網路比賽，

looking to try to figure out from one and a half million images

嘗試從 150 萬的圖像中找出

what they're pictures of.

想要的圖像。

As of 2014, we're now down to a six percent error rate

2014 年, 我們現在圖像辨識的錯誤率

in image recognition.

降到 6％ 以下。

This is better than people, again.

這再次證明它比人類優秀。

So machines really are doing an extraordinarily good job of this,

可見機器真可以做到如此非凡的成就，

and it is now being used in industry.

它現在已經用在產業上了。

For example, Google announced last year

比如說，谷歌去年宣佈

that they had mapped every single location in France in two hours,

他們可以在兩小時内把法國每一個位置繪成地圖，

and the way they did it was that they fed street view images

他們用的方式是把街景圖像

into a deep learning algorithm to recognize and read street numbers.

輸入深度學習演算法來辨認和讀取街道號碼。

Imagine how long it would have taken before:

想想我們以前需要花多少時間？

dozens of people, many years.

至少好幾十人加上好幾年呢。

This is also happening in China.

同樣的情況也發生在中國。

Baidu is kind of the Chinese Google, I guess,

我想「百度」類似中國的谷歌，

and what you see here in the top left

在左上角你會看見

is an example of a picture that I uploaded to Baidu's deep learning system,

一張我上傳到百度深度學習系統的圖片，

and underneath you can see that the system has understood what that picture is

下方你可以看到系統可以理解這張圖片

and found similar images.

而且能找到相似的圖像。

The similar images actually have similar backgrounds,

類似的圖像也就是有相似的背景，

similar directions of the faces,

相似面孔的角度，

even some with their tongue out.

有的圖像甚至有伸出舌頭。

This is not clearly looking at the text of a web page.

這個網頁的文字看不大清楚，

All I uploaded was an image.

因為我上傳的都是圖像。

So we now have computers which really understand what they see

這顯示了電腦能明白他們所看到的

and can therefore search databases

電腦能夠搜尋資料庫

of hundreds of millions of images in real time.

以即時的方式從億萬張圖片中搜尋。

So what does it mean now that computers can see?

現在的電腦能夠去看是表示什麼意思呢？

Well, it's not just that computers can see.

其實電腦不只能看見。

In fact, deep learning has done more than that.

事實上深度學習可以做得更多。

Complex, nuanced sentences like this one

像這個樣複雜，僅有小小差別的句子

are now understandable with deep learning algorithms.

現在的深度學習演算法能夠理解。

As you can see here,

你可以看到，

this Stanford-based system showing the red dot at the top

這以史丹福為基礎的系統顯示上面的紅點

has figured out that this sentence is expressing negative sentiment.

指這句子是在表達負面的情緒。

Deep learning now in fact is near human performance

深度學習現在已經接近人類的行為

at understanding what sentences are about and what it is saying about those things.

能理解句子是要表達什麼。

Also, deep learning has been used to read Chinese,

同時，深度學習也能用以閱讀中文，

again at about native Chinese speaker level.

程度相當於以中文為母語的水平。

This algorithm developed out of Switzerland

這演算法發展於瑞士

by people, none of whom speak or understand any Chinese.

沒有一個會說中文的團隊。

As I say, using deep learning

像我說的，深度學習

is about the best system in the world for this,

是一個最好的系統對完成這任務來說，

even compared to native human understanding.

甚至比人類還要好。

This is a system that we put together at my company

這個系統是我公司建立的

which shows putting all this stuff together.

要把這些東西都集中在一起。

These are pictures which have no text attached,

這是一些沒有文字描述的圖片，

and as I'm typing in here sentences,

我在這裡輸入句子，

in real time it's understanding these pictures

它在同步理解這些照片

and figuring out what they're about

找出它們是有關什麼的照片

and finding pictures that are similar to the text that I'm writing.

也找出跟我句子相關類似的圖片。

So you can see, it's actually understanding my sentences

所以你看，它真的能理解我的句子。

and actually understanding these pictures.

也完全的理解這些圖片。

I know that you've seen something like this on Google,

你在谷歌上也看過類似的，

where you can type in things and it will show you pictures,

你可以輸入文字而它會顯示圖片，

but actually what it's doing is it's searching the webpage for the text.

但事實上，它在尋索網頁上的文字。

This is very different from actually understanding the images.

這跟理解圖片有很大的不同。

This is something that computers have only been able to do

理解圖片只有電腦可以做

for the first time in the last few months.

電腦在過去幾個月才會做的事。

So we can see now that computers can not only see but they can also read,

電腦不單能看見也能閱讀，

and, of course, we've shown that they can understand what they hear.

而且我們顯示了電腦能理解所聽到的。

Perhaps not surprising now that I'm going to tell you they can write.

或許不意外地，我要告訴你們電腦也能書寫。

Here is some text that I generated using a deep learning algorithm yesterday.

這是我昨天用深度學習演算法所產生的文字。

And here is some text that an algorithm out of Stanford generated.

這裡有一些非史丹佛演算法所產生的文字。

Each of these sentences was generated

這些句子的產生

by a deep learning algorithm to describe each of those pictures.

是透過深度學習演算法對圖片進行描述。

This algorithm before has never seen a man in a black shirt playing a guitar.

這演算法是電腦從來沒有看見過一個穿黑襯衫的男子彈吉他。

It's seen a man before, it's seen black before,

電腦見過男人，看過黑色，

it's seen a guitar before,

見過吉他，

but it has independently generated this novel description of this picture.

它自己便對圖片做出描述。

We're still not quite at human performance here, but we're close.

雖然還沒有超越人類，不過很接近了。

In tests, humans prefer the computer-generated caption

依據統計，人們較喜歡電腦的圖片說明

one out of four times.

有四分之一的人會做這樣的選擇。

Now this system is now only two weeks old,

這系統在兩個星期前開發完成，

so probably within the next year,

估計在明年，

the computer algorithm will be well past human performance

電腦演算法將會超越人類

at the rate things are going.

如果依照這樣的速度發展下的話。

So computers can also write.

到時候電腦也會書寫了。

So we put all this together and it leads to very exciting opportunities.

我們把這些都放在一起，讓它來引導到一個令人振奮的時機。

For example, in medicine,

像在藥物方面，

a team in Boston announced that they had discovered

一個波士頓的團隊宣佈他們發現了

dozens of new clinically relevant features

數十種腫瘤的臨床特徵

of tumors which help doctors make a prognosis of a cancer.

幫助醫生預測癌症。

Very similarly, in Stanford,

同樣的，在史丹佛，

a group there announced that, looking at tissues under magnification,

一個組織宣佈在放大鏡下觀察組織，

they've developed a machine learning-based system

他們開發一個以機器學習為基礎的系統

which in fact is better than human pathologists

比人類病理學家更有效地

at predicting survival rates for cancer sufferers.

預測癌症病患的生存率。

In both of these cases, not only were the predictions more accurate,

這些例子，不但能更準確地預測，

but they generated new insightful science.

而且也能帶來更多科技上的洞見。

In the radiology case,

在放射學的個案中，

they were new clinical indicators that humans can understand.

他們是人類所能理解的新臨床指標。

In this pathology case,

在這病理學個案，

the computer system actually discovered that the cells around the cancer

電腦系統發現癌症周圍的細胞

are as important as the cancer cells themselves

在診斷的時候

in making a diagnosis.

是跟癌細胞一樣重要。

This is the opposite of what pathologists had been taught for decades.

這跟病理學家10 年來的說法相反。

In each of those two cases, they were systems developed

在這兩個個案，系統的開發人員

by a combination of medical experts and machine learning experts,

是由醫學專家和機器學習專家所組成，

but as of last year, we're now beyond that too.

但自去年開始，我們也超越了這些。

This is an example of identifying cancerous areas

這是確認癌症範圍的例子

of human tissue under a microscope.

是在顯微鏡下的人類組織。

The system being shown here can identify those areas more accurately,

系統顯示可以更準確地確認範圍，

or about as accurately, as human pathologists,

如病理學家般準確，