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  • It used to be that if you wanted to get a computer to do something new,

    過去如果想用電腦來作點新東西,

  • 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,

  • but was built entirely with deep learning using no medical expertise

    不過沒有藥物專家來建構整套深度學習系統

  • by people who have no background in the field.

    系統是由一些沒有專業背景的人完成。

  • Similarly, here, this neuron segmentation.

    同樣地,從是細胞分裂。

  • We can now segment neurons about as accurately as humans can,

    我們的系統可以像人類般精確地分裂神經細胞,

  • but this system was developed with deep learning

    不過開發這套深度學習系統

  • using people with no previous background in medicine.

    沒有一個人來自醫學背景。