Placeholder Image

字幕列表 影片播放

已審核 字幕已審核
  • Chris Anderson: Help us understand what machine learning is,

    克里斯・安德森:可以跟我們解釋一下機器學習是什麼嗎?

  • because that seems to be the key driver

    因為機器學習似乎是推動人工智慧

  • around artificial intelligence.

    的關鍵

  • How does machine learning work?

    機器學習是如何運作的呢?

  • Sebastian Thrun: So, artificial intelligence and machine learning

    賽巴斯・汀索朗:人工智慧和機器學習

  • is about 60 years old

    大約有 60 年的歷史,

  • and has not had a great day in its past until recently.

    一直到近期才達到極致。

  • And the reason is that today,

    那是因為現在,

  • we have reached a scale of computing and datasets

    我們的計算能力和資料庫規模已經達到

  • that was necessary to make machines smart.

    讓機器變聰明所必須具備的條件。

  • So here's how it works:

    它的運作方式是這樣的:

  • If you program a computer today, say, your phone,

    如果現在你要為一台電腦寫程式,比如你的手機,

  • then you hire software engineers

    你會僱用軟體工程師,

  • that write a very, very long kitchen recipe,

    他們會寫一份非常非常長的指令,像廚房食譜,

  • like, "If the water is too hot, turn down the temperature.

    像是「如果水太熱,就把溫度調低。

  • If it's too cold, turn up the temperature."

    如果水太冷,就把溫度調高。」

  • The recipes are not just 10 lines long.

    這樣的「食譜」並不是只有十行的長度。

  • They are millions of lines long.

    它們長達數百萬行

  • A modern cell phone has 12 million lines of code.

    一台現代手機有 1200 萬行的程式碼

  • A browser has five million lines of code.

    一個瀏覽器就有五百萬行的程式碼

  • And each bug in this recipe can cause your computer to crash.

    而且食譜中的每一個錯誤, 都會造成你的電腦當機

  • That's why a software engineer makes so much money.

    那就是軟體工程師能賺那麼多錢的原因

  • The new thing now is that computers can find their own rules.

    現在的新發展是,電腦能找到它們自己的規則

  • So instead of an expert deciphering, step by step,

    所以不再需要解碼專家,去針對每個情況

  • a rule for every contingency,

    一步一步地做理解辨識,

  • what you do now is you give the computer examples

    現在你的做法是,給電腦一些範例,

  • and have it infer its own rules.

    讓它推導出它自己的規則

  • A really good example is AlphaGo, which recently was won by Google.

    最近 Google 的阿爾法圍棋贏得比賽, 就是一個很好的例子

  • Normally, in game playing, you would really write down all the rules,

    通常,在玩遊戲時, 你會寫下所有的規則,

  • but in AlphaGo's case,

    但在阿爾法圍棋這個例子,

  • the system looked over a million games

    系統是去看了一百多萬場的比賽,

  • and was able to infer its own rules

    並且推導出它自己的規則,

  • and then beat the world's residing Go champion.

    然後打敗現在的世界棋王

  • That is exciting, because it relieves the software engineer

    這事件振奮人心的事, 因為軟體工程師能鬆口氣了,

  • of the need of being super smart,

    他們不需要超級聰明,

  • and pushes the burden towards the data.

    這個重任已經落到資料上頭。

  • As I said, the inflection point where this has become really possible --

    如我所言,這件事可能發生的轉折點在於──

  • very embarrassing, my thesis was about machine learning.

    真是不好意思,我的論文就是寫機器學習

  • It was completely insignificant, don't read it,

    它完全不重要,請不要去讀,

  • because it was 20 years ago

    因為那是 20 年前寫的

  • and back then, the computers were as big as a cockroach brain.

    那個時候,電腦不過和蟑螂大腦一樣大而已

  • Now they are powerful enough to really emulate

    現在,電腦已經強大到能夠真正地模擬

  • kind of specialized human thinking.

    人類的特定思想

  • And then the computers take advantage of the fact

    而且,電腦也因為可以比人類看更多的資料

  • that they can look at much more data than people can.

    而取得優勢

  • So I'd say AlphaGo looked at more than a million games.

    就如同我所說的, 阿爾法圍棋已經研究過一百多萬場的比賽

  • No human expert can ever study a million games.

    沒有任何專家能夠研究一百多萬場的比賽

  • Google has looked at over a hundred billion web pages.

    Google 已經看過了一千多億個網頁

  • No person can ever study a hundred billion web pages.

    從來沒有人有能力研究一千多億個網頁

  • So as a result, the computer can find rules

    因此,電腦能夠找出

  • that even people can't find.

    人類找不到的規則

  • CA: So instead of looking ahead to, "If he does that, I will do that,"

    安德森:那麼,電腦應該不是: 「如果他那樣下,我就這樣下。」

  • it's more saying, "Here is what looks like a winning pattern,

    應該比較像是:「下在這裡比較像是獲勝的模式,

  • here is what looks like a winning pattern."

    下在那裡比較像是獲勝的模式。」

  • ST: Yeah. I mean, think about how you raise children.

    索朗:沒錯, 想想看你如何養育你的孩子

  • You don't spend the first 18 years giving kids a rule for every contingency

    你並不會花前 18 年的時間, 針對每種狀況給孩子一條規則,

  • and set them free and they have this big program.

    然後放他們自由, 他們就會做出這個大程式

  • They stumble, fall, get up, they get slapped or spanked,

    他們會摔跤,會爬起來, 他們會被賞巴掌或打屁股,

  • and they have a positive experience, a good grade in school,

    他們會有正向的經驗, 在學校有好的成績,

  • and they figure it out on their own.

    他們會靠自己去了解這些

  • That's happening with computers now,

    現在的電腦也是這樣,

  • which makes computer programming so much easier all of a sudden.

    突然間電腦寫程式變得簡單多了

  • Now we don't have to think anymore. We just give them lots of data.

    我們不用再花腦筋思考,只要給它們大量資料即可

  • CA: And so, this has been key to the spectacular improvement

    安德森:所以,這是車輛自動駕駛能力

  • in power of self-driving cars.

    能夠有重大改善的關鍵

  • I think you gave me an example.

    我想你給了我一個例子

  • Can you explain what's happening here?

    你可以解釋一下這裡發生了什麼事嗎?

  • ST: This is a drive of a self-driving car

    索朗:這是自動駕駛車輛,

  • that we happened to have at Udacity

    我們優達學城(Udacity)剛好有,

  • and recently made into a spin-off called Voyage.

    最近成為 Voyage 的副產品

  • We have used this thing called deep learning

    我們用所謂的「深度學習」

  • to train a car to drive itself,

    來訓練汽車自動駕駛

  • and this is driving from Mountain View, California,

    這趟行程從加州的山景城出發,

  • to San Francisco

    前往舊金山,

  • on El Camino Real on a rainy day,

    在雨天行駛在 El Camino Real 路上,

  • with bicyclists and pedestrians and 133 traffic lights.

    路上有腳踏車騎士及行人, 並且經過了 133 個紅綠燈

  • And the novel thing here is,

    新奇的事情是,

  • many, many moons ago, I started the Google self-driving car team.

    許多個月前,我成立了 Google 自動駕駛汽車團隊

  • And back in the day, I hired the world's best software engineers

    那時,我僱用世界上最厲害的軟體工程師,

  • to find the world's best rules.

    來找出世界上最好的規則。

  • This is just trained.

    這是訓練出來的

  • We drive this road 20 times,

    這條路我們開了 20 次

  • we put all this data into the computer brain,

    我們把所有資料放到電腦中,

  • and after a few hours of processing,

    經過幾個小時的處理,

  • it comes up with behavior that often surpasses human agility.

    電腦所找出的行為,通常都超越人類的機敏。

  • So it's become really easy to program it.

    所以電腦很容易為它寫程式

  • This is 100 percent autonomous, about 33 miles, an hour and a half.

    這是 100% 自動化的,大約 33 英哩,一個半小時

  • CA: So, explain it -- on the big part of this program on the left,

    安德生:來解釋一下──這程式左邊大部分,

  • you're seeing basically what the computer sees as trucks and cars

    我們可以看到電腦所看到的卡車和汽車,

  • and those dots overtaking it and so forth.

    還有那些超越它的點。

  • ST: On the right side, you see the camera image, which is the main input here,

    索朗:右邊可以看到攝影機的影像,也就是主要輸入,

  • and it's used to find lanes, other cars, traffic lights.

    這個用來找車道、其它車輛和紅綠燈

  • The vehicle has a radar to do distance estimation.

    這個車用雷達用來估算距離

  • This is very commonly used in these kind of systems.

    這是這類系統常用的方式

  • On the left side you see a laser diagram,

    左邊的是雷射圖,

  • where you see obstacles like trees and so on depicted by the laser.

    我們可以看到雷射槍描繪出來的障礙物,如樹木等等

  • But almost all the interesting work is centering on the camera image now.

    但幾乎所有有趣的部份,都是著重在攝影機影像上

  • We're really shifting over from precision sensors like radars and lasers

    我們其實從精準的感測器,像是雷達和雷射,

  • into very cheap, commoditized sensors.

    轉換到更便宜的一般感測器

  • A camera costs less than eight dollars.

    一台攝影機的成本不到 $8

  • CA: And that green dot on the left thing, what is that?

    安德森:左邊的綠點是什麼?

  • Is that anything meaningful?

    是有意義的嗎?

  • ST: This is a look-ahead point for your adaptive cruise control,

    索朗:這是「向前看」的點,提供自動調整航程控制用,

  • so it helps us understand how to regulate velocity

    它會根據前車的距離,

  • based on how far the cars in front of you are.

    幫助我們調整速度

  • CA: And so, you've also got an example, I think,

    安德森:這樣的話,我認為,你也有個例子

  • of how the actual learning part takes place.

    說明真正的學習是如何進行的

  • Maybe we can see that. Talk about this.

    也許我們可以邊看那個畫面,邊談這個。

  • ST: This is an example where we posed a challenge to Udacity students

    索朗:這是我們挑戰 Udacity 學生的一個例子,

  • to take what we call a self-driving car Nanodegree.

    是取得「自駕車奈米學位」的挑戰

  • We gave them this dataset

    我們提供他們這個資料庫,

  • and said "Hey, can you guys figure out how to steer this car?"

    並且告訴他們:「你們能不能想出要如何駕駛這台車?」

  • And if you look at the images,

    如果從影像來看,

  • it's, even for humans, quite impossible to get the steering right.

    即使是人類也很難駕駛正確。

  • And we ran a competition and said, "It's a deep learning competition,

    我們進行了一項競賽,並說: 「這是場深度學習競賽,

  • AI competition,"

    這是人工智慧競賽。」

  • and we gave the students 48 hours.

    我們給學生 48 小時

  • So if you are a software house like Google or Facebook,

    如果你是間軟體公司,如 Google 或臉書,

  • something like this costs you at least six months of work.

    像這樣的東西會花你至少六個月的時間

  • So we figured 48 hours is great.

    所以我們認為 48 小時是很棒的

  • And within 48 hours, we got about 100 submissions from students,

    在 48 小時內,我們得到了約 100 件學生提交的結果,

  • and the top four got it perfectly right.

    前四名完全正確。

  • It drives better than I could drive on this imagery,

    和我在這影像上用深度學習相比,

  • using deep learning.

    它駕駛得更好

  • And again, it's the same methodology.

    再一次,同樣的方法,

  • It's this magical thing.

    這是件很神奇的事

  • When you give enough data to a computer now,

    當你提供電腦足夠的資料,

  • and give enough time to comprehend the data,

    並給它足夠時間來理解這些資料,

  • it finds its own rules.

    它就會自己找到規則

  • CA: And so that has led to the development of powerful applications

    安德森:所以,那就導致了在各種領域

  • in all sorts of areas.

    應用程式的強大發展

  • You were talking to me the other day about cancer.

    之前你有和我談過癌症的事

  • Can I show this video?

    我能播那段影片嗎?

  • ST: Yeah, absolutely, please. CA: This is cool.

    索朗:當然,請放。安德森:這很酷

  • ST: This is kind of an insight into what's happening

    索朗:這有點像是對完全不同的領域

  • in a completely different domain.

    洞察所發生的事

  • This is augmenting, or competing --

    在旁觀者眼裡,

  • it's in the eye of the beholder --

    這可以說是

  • with people who are being paid 400,000 dollars a year,

    和那些年薪 $40 萬的

  • dermatologists,

    皮膚科醫生的擴增或競爭,

  • highly trained specialists.

    他們是訓練良好的專家,

  • It takes more than a decade of training to be a good dermatologist.

    要受十年以上的訓練才可能成為好的皮膚科醫生

  • What you see here is the machine learning version of it.

    這裡所看到的是它的機器學習版本,

  • It's called a neural network.

    稱為「神經網路」

  • "Neural networks" is the technical term for these machine learning algorithms.

    「神經網路」是機器學習演算法的專有名詞,

  • They've been around since the 1980s.

    大約出自 1980 年代

  • This one was invented in 1988 by a Facebook Fellow called Yann LeCun,

    這個是在 1988 年由臉書的研究專員揚・勒丘恩所發明的

  • and it propagates data stages

    它透過一種你可視為是人腦的方式

  • through what you could think of as the human brain.

    依階段傳播數據

  • It's not quite the same thing, but it emulates the same thing.

    它不是人腦,但它模仿人腦

  • It goes stage after stage.

    一個階段接著一個階段,

  • In the very first stage, it takes the visual input and extracts edges

    在第一個階段取得視覺輸入,粹取出邊界、

  • and rods and dots.

    線和點

  • And the next one becomes more complicated edges

    下個階段就變成更複雜的邊界

  • and shapes like little half-moons.

    以及像是半月的形狀。

  • And eventually, it's able to build really complicated concepts.

    最後,它能建立出非常複雜的概念。

  • Andrew Ng has been able to show

    Andrew Ng 就展示過,

  • that it's able to find cat faces and dog faces

    它能夠在非常大量的影像中

  • in vast amounts of images.

    找出貓和狗的臉。

  • What my student team at Stanford has shown is that

    我在史丹佛的學生團隊也展示過,

  • if you train it on 129,000 images of skin conditions,

    如果你用十二萬九千張皮膚症狀的影像來訓練它,

  • including melanoma and carcinomas,

    包括黑色素瘤和癌,

  • you can do as good a job

    你就能和最好的人類皮膚科醫生

  • as the best human dermatologists.

    做得一樣好。

  • And to convince ourselves that this is the case,

    為了說服我們自己事實確實是如此,

  • we captured an independent dataset that we presented to our network

    我們取得了一個獨立的資料集,拿給我們的網路看,

  • and to 25 board-certified Stanford-level dermatologists,

    也拿給 25 位認證過的史丹佛水準的皮膚科醫生看,

  • and compared those.

    來做比較

  • And in most cases,

    在大部份狀況,

  • they were either on par or above the performance classification accuracy

    在分類正確性上,網路的表現都和人類皮膚科醫生

  • of human dermatologists.

    並駕齊驅或者更好

  • CA: You were telling me an anecdote.

    安德森:你跟我說過一則軼事

  • I think about this image right here.

    我想應該是這張影像的這個地方

  • What happened here?

    這裡發生了什麼事?

  • ST: This was last Thursday. That's a moving piece.

    索朗:時間是上星期四,是個正在進行的故事。

  • What we've shown before and we published in "Nature" earlier this year

    我們之前展示過,今年稍早也刊在「Nature」期刊中,

  • was this idea that we show dermatologists images

    我們的想法是,我們讓皮膚科醫生看影像,

  • and our computer program images,

    也讓我們的電腦程式看,

  • and count how often they're right.

    計算它們判斷正確的頻率

  • But all these images are past images.

    但所有影像都是過去的影像

  • They've all been biopsied to make sure we had the correct classification.

    都已經做過切片檢查,確保分類正確

  • This one wasn't.

    但是這一張沒有

  • This one was actually done at Stanford by one of our collaborators.

    這張其實是史丹佛的一位合作者做的

  • The story goes that our collaborator,

    這個故事跟我們的合作者有關,

  • who is a world-famous dermatologist, one of the three best, apparently,

    他是世界知名的皮膚科醫生,很顯然是三位最好的皮膚科醫生之一,

  • looked at this mole and said, "This is not skin cancer."

    他看著這個痣說:「這不是皮膚癌。」

  • And then he had a second moment, where he said,

    他想了一下,接著說:

  • "Well, let me just check with the app."

    「讓我用應用程式確認一下。」

  • So he took out his iPhone and ran our piece of software,

    他拿出他的 iPhone,執行我們的軟體,

  • our "pocket dermatologist," so to speak,

    iPhone 可說是我們的「口袋皮膚科醫生」,

  • and the iPhone said: cancer.

    而 iPhone 說是癌症,

  • It said melanoma.

    是黑色素瘤

  • And then he was confused.

    他很困惑,

  • And he decided, "OK, maybe I trust the iPhone a little bit more than myself,"

    他決定:「好吧,也許我應該相信 iPhone 比相信我自己多一點。」

  • and he sent it out to the lab to get it biopsied.

    他把它送去實驗室做切片檢查,

  • And it came up as an aggressive melanoma.

    結果是惡性黑色素瘤

  • So I think this might be the first time that we actually found,

    我想,這可能是我們第一次

  • in the practice of using deep learning,

    在深度學習上實際遇到,

  • an actual person whose melanoma would have gone unclassified,

    如果沒有這個深度學習的機會,

  • had it not been for deep learning.

    這個人的黑色素瘤就不會被發現

  • CA: I mean, that's incredible.

    安德森:那真的很了不起。

  • It feels like there'd be an instant demand for an app like this right now,

    像這樣的應用程式,現在可能已經有很迫切的需求,

  • that you might freak out a lot of people.

    這可能會嚇壞很多人

  • Are you thinking of doing this, making an app that allows self-checking?

    你有想過要這麼做嗎?做個自我檢測的應用程式?

  • ST: So my in-box is flooded about cancer apps,

    索朗:我的收件匣被關於癌症應用程式的信件給淹沒了,

  • with heartbreaking stories of people.

    那些信都是令人心碎的故事

  • I mean, some people have had 10, 15, 20 melanomas removed,

    有些人已經移除了 10、15、20 個黑色素瘤,

  • and are scared that one might be overlooked, like this one,

    很害怕會漏掉任何一個,就像這個例子一樣,

  • and also, about, I don't know,

    還有些內容是,我不知道,

  • flying cars and speaker inquiries these days, I guess.

    飛天車、這幾天的演說邀請,我猜是吧

  • My take is, we need more testing.

    我的重點是,我們需要更多測試

  • I want to be very careful.

    我必須非常小心,

  • It's very easy to give a flashy result and impress a TED audience.

    畢竟 TED 的觀眾很容易會對一些出色的演說結果感到印象深刻

  • It's much harder to put something out that's ethical.

    相對地,要端出合乎道德的東西就難很多

  • And if people were to use the app

    如果人們要用這個應用程式,

  • and choose not to consult the assistance of a doctor

    而選擇不去尋求醫生的協助,

  • because we get it wrong,

    如果程式判斷錯誤的話,

  • I would feel really bad about it.

    我就會感覺非常難過

  • So we're currently doing clinical tests,

    所以我們目前在做臨床實驗,

  • and if these clinical tests commence and our data holds up,

    如果這些實驗開始之後, 我們的資料站得住腳,

  • we might be able at some point to take this kind of technology

    在某個時間點,我們或許可以把這技術

  • and take it out of the Stanford clinic

    應用到史丹佛的臨床課程,

  • and bring it to the entire world,

    甚至把它帶到全世界,

  • places where Stanford doctors never, ever set foot.

    帶到史丹佛的醫生從來不會去的地方

  • CA: And do I hear this right,

    安德森:我沒聽錯吧,

  • that it seemed like what you were saying,

    你的意思聽起來像是

  • because you are working with this army of Udacity students,

    因為你在和這支 Udacity 學生大軍合作,

  • that in a way, you're applying a different form of machine learning

    以某種方式,你們在應用 一種不同形式的機器學習,

  • than might take place in a company,

    和一般公司運作的形式不同,

  • which is you're combining machine learning with a form of crowd wisdom.

    也就是你們將機器學習與一種群眾智慧的形式相互結合

  • Are you saying that sometimes you think that could actually outperform

    你說的是, 有時你認為這個能力可以超越一般公司,

  • what a company can do, even a vast company?

    甚至是大型公司?

  • ST: I believe there's now instances that blow my mind,

    索朗:我相信現在有一些讓我很驚艷的例子,

  • and I'm still trying to understand.

    我還在試著了解

  • What Chris is referring to is these competitions that we run.

    克里斯指的是,我們舉辦的這些

  • We turn them around in 48 hours,

    進行大約 48 小時的競賽,

  • and we've been able to build a self-driving car

    而且我們有能力建立自駕車,

  • that can drive from Mountain View to San Francisco on surface streets.

    它能從山景城開上馬路直抵舊金山

  • It's not quite on par with Google after seven years of Google work,

    雖然它尚未趕上 Google 投入七年心血的成果,

  • but it's getting there.

    但是就快追上了

  • And it took us only two engineers and three months to do this.

    我們的研發只用了兩個工程師和三個月的時間

  • And the reason is, we have an army of students

    原因是,我們有一支學生大軍,

  • who participate in competitions.

    也就是參與競賽的那些學生

  • We're not the only ones who use crowdsourcing.

    我們並非唯一使用「群眾外包」的人,

  • Uber and Didi use crowdsource for driving.

    Uber 和 Didi 也是用群眾外包招募駕駛,

  • Airbnb uses crowdsourcing for hotels.

    Airbnb 用群眾外包做飯店

  • There's now many examples where people do bug-finding crowdsourcing

    現在有許多群眾外包的例子,比如除錯工作