(Video) Girl: Okay, that's a cat sitting in a bed.

(影片)女孩：嗯，這是一隻貓，坐在床上。

The boy is petting the elephant.

這男孩在拍撫一隻象。

Those are people that are going on an airplane.

這些人要去搭飛機。

That's a big airplane.

好大的飛機。

Fei-Fei Li: This is a three-year-old child

主講人：這是由一位三歲的小孩

describing what she sees in a series of photos.

所描述她看到的一系列照片

She might still have a lot to learn about this world,

雖然對於這世界她還有更多要學習的地方，

but she's already an expert at one very important task:

但是她已經是其中一項重要技能的專家--

to make sense of what she sees.

為所見之聞賦予意義。

Our society is more technologically advanced than ever.

科技在我們的社會已進展到前所未有的程度：

We send people to the moon, we make phones that talk to us

我們把人送上月球、發明可以與人交談的電話，

or customize radio stations that can play only music we like.

或是客製一個電台，只播放個人喜歡的音樂。

Yet, our most advanced machines and computers

然而這台無比聰明的機器和電腦

still struggle at this task.

仍然無法發展這項技能，

So I'm here today to give you a progress report

因此今天我來到這裡向各位報告

on the latest advances in our research in computer vision,

我們在電腦視覺的最新研究進展，

one of the most frontier and potentially revolutionary

這是現階段在資訊業領域中，

technologies in computer science.

最先進、最具潛力的革命性技術。

Yes, we have prototyped cars that can drive by themselves,

是的，目前我們已經有自動駕駛的原型車，

but without smart vision, they cannot really tell the difference

但若不具備視覺辨識技術， 它將無法分辨同樣出現在馬路中，

between a crumpled paper bag on the road, which can be run over,

一團它其實輾過也無妨的破紙袋，

and a rock that size, which should be avoided.

以及一個大到它必須閃避的石塊， 兩者有何不同。

We have made fabulous megapixel cameras,

我們製造出畫素極高的相機，

but we have not delivered sight to the blind.

但我們卻無法賦予盲人視覺；

Drones can fly over massive land,

無人機可以翻山越嶺，

but don't have enough vision technology

卻沒有足夠的視覺技術可以

to help us to track the changes of the rainforests.

讓我們追蹤雨林的變化；

Security cameras are everywhere,

監視器滿佈在各個角落，

but they do not alert us when a child is drowning in a swimming pool.

卻無法在看到一個孩子將溺斃在泳池之際， 對我們發出警訊。

Photos and videos are becoming an integral part of global life.

靜態及動態影像已逐漸與全世界的生活密不可分，

They're being generated at a pace that's far beyond what any human,

它們發展的步伐已經遠遠超越人類

or teams of humans, could hope to view,

及其群體所相信的，

and you and I are contributing to that at this TED.

在座各位以及我自己 都是TED這個活動裡頭的推手。

Yet our most advanced software is still struggling at understanding

然而，目前最先進的軟體卻仍在其中苦苦掙扎，

and managing this enormous content.

無法理解與應用這龐大的資料體。

So in other words, collectively as a society,

換而言之，在這整個社會裡，

we're very much blind,

大家都有如盲人在運作，

because our smartest machines are still blind.

因為連我們最聰明的機器都還看不見。

"Why is this so hard?" you may ask.

或許有人會問：這到底有什麼困難？

Cameras can take pictures like this one

任何相機都可以產生像這樣的照片，

by converting lights into a two-dimensional array of numbers

它是藉由將有色光轉換成2D的數字陣列，

known as pixels,

也就是大家熟知的像素。

but these are just lifeless numbers.

但這些數字是死的，

They do not carry meaning in themselves.

並沒有被賦予意義。

Just like to hear is not the same as to listen,

就好像有「聽」，不代表有「到」。

to take pictures is not the same as to see,

同樣地，攝取到影像不等於看見，

and by seeing, we really mean understanding.

我們所認知的看到，應包含著了解其中的意義。

In fact, it took Mother Nature 540 million years of hard work

事實上，這樣的成果， 是大自然花了五億四千萬年的光陰

to do this task,

才得到的。

and much of that effort

這其中的努力，

went into developing the visual processing apparatus of our brains,

泰半是耗費在發展腦部的視覺處理這個區塊，

not the eyes themselves.

而不是眼睛的部分。

So vision begins with the eyes,

也就是說，視覺始於眼睛，

but it truly takes place in the brain.

但真正使它有用的，卻是大腦。

So for 15 years now, starting from my Ph.D. at Caltech

十五年來，從在加州理工學院攻讀博士開始，

and then leading Stanford's Vision Lab,

到領導史丹佛的視覺實驗室，

I've been working with my mentors, collaborators and students

我和指導教授、同事及學生們，

to teach computers to see.

試圖讓電腦擁有智能之眼，

Our research field is called computer vision and machine learning.

我們研究的領域稱之為電腦視覺與機器學習，

It's part of the general field of artificial intelligence.

這是人工智慧其中一環。

So ultimately, we want to teach the machines to see just like we do:

我們的終極目標就是教導機器能夠像人一樣理解所見之物，

naming objects, identifying people, inferring 3D geometry of things,

像是識別物品、辨認人臉、 推論物體的幾何形態，

understanding relations, emotions, actions and intentions.

進而理解其中的關聯、情緒、動作及意圖。

You and I weave together entire stories of people, places and things

在座每一位和我，都可以在匆匆一瞥的瞬間，

the moment we lay our gaze on them.

理解到人事、地、物所交織而成的網絡，

The first step towards this goal is to teach a computer to see objects,

要電腦達成這個目標的第一步，就是教導它辨別物品，

the building block of the visual world.

這是視覺的基石。

In its simplest terms, imagine this teaching process

簡單來說，我們教導的方法就是

as showing the computers some training images

給電腦看一些特定物體的影像，

of a particular object, let's say cats,

例如貓咪。

and designing a model that learns from these training images.

我們設計了一個程式讓電腦利用這些影像來學習

How hard can this be?

這有啥困難？

After all, a cat is just a collection of shapes and colors,

貓咪不就是由一些幾何圖形和顏色所組成的嘛，

and this is what we did in the early days of object modeling.

這就是我們初期所做的物體模型。

We'd tell the computer algorithm in a mathematical language

我們用數學語言來告知電腦演繹方法，

that a cat has a round face, a chubby body,

貓就是有圓圓的臉、胖胖的身體，

two pointy ears, and a long tail,

兩個尖尖的耳朵和一條長尾巴。

and that looked all fine.

看起來很好啊，

But what about this cat?

但如果貓咪長這樣呢？

(Laughter)

(觀眾笑)

It's all curled up.

全身都捲起來了。

Now you have to add another shape and viewpoint to the object model.

這下子我們又得在原來的模型 加上新的形狀和不同的視野角度。

But what if cats are hidden?

又，如果貓咪是躲著的呢？

What about these silly cats?

像這群傻貓？

Now you get my point.

這樣各位了解我的意思嗎？

Even something as simple as a household pet

即使簡單如貓這樣的家庭寵物，

can present an infinite number of variations to the object model,

也會有相對於原型以外，無數的其他形態表徵，

and that's just one object.

而這只是其中一樣。

So about eight years ago,

因此八年前，

a very simple and profound observation changed my thinking.

一項極其簡單和深刻的觀察，改變了我的想法，

No one tells a child how to see,

沒有人教導孩子如何去「看」，

especially in the early years.

特別是在早期發育階段，

They learn this through real-world experiences and examples.

他們是從真實世界的經驗中學習。

If you consider a child's eyes

如果你把孩童的眼睛

as a pair of biological cameras,

當成生物相機的概念，

they take one picture about every 200 milliseconds,

就如同每200毫秒就拍一張照片一樣，

the average time an eye movement is made.

這是眼球移動的平均時間。

So by age three, a child would have seen hundreds of millions of pictures

年紀到了三歲時， 孩子們已經看過了真實世界中

of the real world.

數以百萬計的照片，

That's a lot of training examples.

這樣的訓練範例是很大量的。

So instead of focusing solely on better and better algorithms,

因此，我的直覺告訴我 應該以孩童的學習經驗法則，

my insight was to give the algorithms the kind of training data

並兼以質與量，

that a child was given through experiences

提供訓練的資料給電腦，

in both quantity and quality.

而非一昧追求更好的程式演算。

Once we know this,

有了上述的洞見，

we knew we needed to collect a data set

我們接下來必須要收集

that has far more images than we have ever had before,

前所未有的大量資料群，

perhaps thousands of times more,

甚至於是千倍以上的。

and together with Professor Kai Li at Princeton University,

於是我與普林斯頓大學的李凱教授

we launched the ImageNet project in 2007.

共同於2007年開始了 我們稱之為 ImageNet 的專案。

Luckily, we didn't have to mount a camera on our head

很幸運地，我們不必在頭上綁一個相機，

and wait for many years.

然後花費數年收集影像，

We went to the Internet,

而是轉而由網際網路，

the biggest treasure trove of pictures that humans have ever created.

這個由人類所創造出來 龐大的影像寶窟，

We downloaded nearly a billion images

我們下載了數以百萬計的影像，

and used crowdsourcing technology like the Amazon Mechanical Turk platform

並且使用如Amazon Mechanical Turk 這樣的群眾外包平台，

to help us to label these images.

來協助我們處理及分類這些照片。

At its peak, ImageNet was one of the biggest employers

在高峰期，ImageNet 甚至是整個亞馬遜平台

of the Amazon Mechanical Turk workers:

最大的雇主之一，

together, almost 50,000 workers

我們一共聘請了來自167個國家，

from 167 countries around the world

約5萬個工作者，

helped us to clean, sort and label

來協助我們分類處理並標示

nearly a billion candidate images.

將近10億幅影像，

That was how much effort it took

花費了這麼多的資源，

to capture even a fraction of the imagery

就是為了捕捉那一絲絲

a child's mind takes in in the early developmental years.

孩童在早期心智發展的浮光掠影。

In hindsight, this idea of using big data

用現在眼光看來，使用大量的資料

to train computer algorithms may seem obvious now,

來訓練電腦演算是明顯合理的，

but back in 2007, it was not so obvious.

然而在2007年的世界卻非如此。

We were fairly alone on this journey for quite a while.

有好長一段時間， 我們在這個旅途中孤獨地踽踽而行，

Some very friendly colleagues advised me to do something more useful for my tenure,

有些同事好心地建議我， 與其苦苦掙扎於研究經費的募集，

and we were constantly struggling for research funding.

還不如轉而先做些比較好拿到終身聘的研究，

Once, I even joked to my graduate students

我還曾跟我的研究生開玩笑說

that I would just reopen my dry cleaner's shop to fund ImageNet.

我乾脆再開一間乾洗店來資助ImageNet 好了，

After all, that's how I funded my college years.

畢竟那就是我用以支付大學學費的方法。

So we carried on.

就這樣我們還是繼續往前走，

In 2009, the ImageNet project delivered

2009年起，ImageNet 已經是個擁有

a database of 15 million images

涵蓋了兩萬兩千種不同類別，

across 22,000 classes of objects and things

多達150億幅圖像的資料庫，

organized by everyday English words.

並組織以英語日常生活用字為主，

In both quantity and quality,

這樣的規模，不論是「質」或「量」

this was an unprecedented scale.

都是史無前例的。

As an example, in the case of cats,

用貓來舉個例子說明，

we have more than 62,000 cats

我們有超過六萬兩千種

of all kinds of looks and poses

不同外觀和姿勢的貓咪，

and across all species of domestic and wild cats.

橫跨不同的種類，有家貓，也有野貓。

We were thrilled to have put together ImageNet,

ImageNet 的成果讓我們非常激動，

and we wanted the whole research world to benefit from it,

我們希望它有助於全世界的研究，

so in the TED fashion, we opened up the entire data set

就如同 TED 的貢獻，我們免費提供整個資料庫

to the worldwide research community for free.

給全世界的研究單位。

(Applause)

(觀眾鼓掌)

Now that we have the data to nourish our computer brain,

有了這些資料，我們可以教育我們的電腦，

we're ready to come back to the algorithms themselves.

下一步就是回到程式演算的部分了。

As it turned out, the wealth of information provided by ImageNet

結果我們發現，ImageNet 所提供的豐富資訊

was a perfect match to a particular class of machine learning algorithms

恰巧與機器學習演算的其中一門特定領域 不謀而合，

called convolutional neural network,

我們稱它為「卷積神經網絡」，

pioneered by Kunihiko Fukushima, Geoff Hinton, and Yann LeCun

在七零及八零年代，福島邦彥、Geoff Hinton

back in the 1970s and '80s.

和 Yann LeCun 等學者為該領域的先驅。

Just like the brain consists of billions of highly connected neurons,

正如同大腦是由無數個緊密連結的神經元所組成，

a basic operating unit in a neural network

神經網絡的基本運作單位

is a neuron-like node.

也是一個類神經元的節點。

It takes input from other nodes

它的運作方式是從別的節點得到資料，

and sends output to others.

然後再傳給其他的節點。

Moreover, these hundreds of thousands or even millions of nodes

而且這些數不清的節點

are organized in hierarchical layers,

擁有層層的組織架構，

also similar to the brain.

就好像我們的大腦一樣。

In a typical neural network we use to train our object recognition model,

在一般的神經網絡中， 我們用作訓練的物品辨識模型

it has 24 million nodes,

就有兩千四百萬個節點、

140 million parameters,

一億四千萬個參數，

and 15 billion connections.

以及一百五十億個連結。

That's an enormous model.

這是一個大的不得了的模型。

Powered by the massive data from ImageNet

由ImageNet 提供巨大的資料群、

and the modern CPUs and GPUs to train such a humongous model,

並使用先進的核心處理器及圖型處理器來訓練 這個龐然大物，

the convolutional neural network

卷積神經網絡就在眾人的意料外

blossomed in a way that no one expected.

開花結果了。

It became the winning architecture

在物品辨識領域中，這樣的架構

to generate exciting new results in object recognition.

以令人興奮的嶄新成果，傲視群雄。

This is a computer telling us

電腦告訴我們

this picture contains a cat

這張圖中有隻貓，

and where the cat is.

還告訴我們貓在哪裡。

Of course there are more things than cats,

當然，這世界不會只有貓，

so here's a computer algorithm telling us

電腦的演算告訴我們

the picture contains a boy and a teddy bear;

這張圖中有一個男孩和一隻泰迪熊；

a dog, a person, and a small kite in the background;

有狗，一個人，以及背景中的一支小風箏；

or a picture of very busy things

或這一張令人眼花撩亂的圖，

like a man, a skateboard, railings, a lampost, and so on.

有人、滑板、欄杆、路燈，等等。

Sometimes, when the computer is not so confident about what it sees,

有時候，如果電腦不確定自己所見到的東西時，

we have taught it to be smart enough

我們已經將它教到可以聰明地

to give us a safe answer instead of committing too much,

給一個安全的答案，而非莽撞地回答，

just like we would do,

就像一般人會做的。

but other times our computer algorithm is remarkable at telling us

更有些時候，電腦的運算竟能夠

what exactly the objects are,

精準地辨別物體品項

like the make, model, year of the cars.

例如製造商、型號、車子的年份。

We applied this algorithm to millions of Google Street View images

Google 將這個演算程式廣泛地運用在

across hundreds of American cities,

數百個美國城市的街景裡，

and we have learned something really interesting:

也因此我們從中得到了一些有趣的概念。

first, it confirmed our common wisdom

首先，它證實了一項廣為人知的說法，

that car prices correlate very well

也就是汽車價格和家庭收入

with household incomes.

是息息相關的。

But surprisingly, car prices also correlate well

然而令人驚訝的是，汽車價格也和

with crime rates in cities,

城市中的犯罪率

or voting patterns by zip codes.

以及區域選舉模式，有相當的關係。

So wait a minute. Is that it?

等等，難道說我今天

Has the computer already matched or even surpassed human capabilities?

就是來告訴各位電腦已經趕上 甚至超越人類了嗎？

Not so fast.

還早得很呢。

So far, we have just taught the computer to see objects.

到目前為止，我們只是教導電腦識別物品，

This is like a small child learning to utter a few nouns.

就像小孩子牙牙學語一樣，

It's an incredible accomplishment,

雖然這是個傲人的進展，

but it's only the first step.

但它不過是第一步而已，

Soon, another developmental milestone will be hit,

很快地，下一波具指標性的後浪就會打上來了，

and children begin to communicate in sentences.

小孩子開始進展到用句子來溝通。

So instead of saying this is a cat in the picture,

因此，他已經不會用「這是貓」 來描述圖片，

you already heard the little girl telling us this is a cat lying on a bed.

而是會聽到這個小女孩說「這是躺在床上的貓」。

So to teach a computer to see a picture and generate sentences,

因此，要教導電腦看到圖並說出句子，

the marriage between big data and machine learning algorithm

必須進一步地仰賴龐大資料群

has to take another step.

以及機器的學習演算。

Now, the computer has to learn from both pictures

現在，電腦不僅要學習圖片識別，

as well as natural language sentences

還要學習人類自然的

generated by humans.

說話方式。

Just like the brain integrates vision and language,

就如同大腦要結合視覺和語言一樣，

we developed a model that connects parts of visual things

我們做出了一個模型， 它可以連結不同的可視物體，

like visual snippets

就像視覺片段一樣，

with words and phrases in sentences.

並附上句子用的字詞和片語。

About four months ago,

約四個月前，

we finally tied all this together

我們終於把所有的元素全部兜起來了，

and produced one of the first computer vision models

做出了第一個電腦版的模型，

that is capable of generating a human-like sentence

它有辦法在初次看到照片時

when it sees a picture for the first time.

說出像人類般自然的句子，

Now, I'm ready to show you what the computer says

好，現在我要給各位看看電腦

when it sees the picture

對於演講一開頭

that the little girl saw at the beginning of this talk.

那位小女孩所看到的影像， 它又是如何理解的。

(Video) Computer: A man is standing next to an elephant.

(電腦) 有個人站在大象旁邊。

A large airplane sitting on top of an airport runway.

一架大飛機停在機場跑道上。

FFL: Of course, we're still working hard to improve our algorithms,

(主講人) 當然，我們仍戮力於改善這電腦程式，

and it still has a lot to learn.

它還有很多要學。

(Applause)

(觀眾鼓掌)

And the computer still makes mistakes.

電腦還是會犯錯。