What AI is -- and isn't | Sebastian Thrun and Chris Anderson

翻译人员: Yan Gao 校对人员: Yolanda Zhang

Chris Anderson: Help us understand what machine learning is,

克里斯·安德森（CA）： 给我们讲讲机器学习是什么，

because that seems to be the key driver

它似乎是一个关键动力，

of so much of the excitement and also of the concern

驱动着很多让人兴奋的事， 还有围绕着人工智能的

around artificial intelligence.

那么多关注。

机器学习到底是怎么工作的？

How does machine learning work?

Sebastian Thrun: So, artificial intelligence and machine learning

塞巴斯蒂安·斯伦（ST）： 人工智能和机器学习

已经有60年的历史了，

is about 60 years old

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

直到最近才初露锋芒。

And the reason is that today,

原因在于，当今，

我们的计算和数据集的规模

we have reached a scale of computing and datasets

已经达到了机器智能化 所必需的水平。

that was necessary to make machines smart.

So here's how it works.

它的工作原理是这样的。

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

假设今天你想编写一个计算机程序， 给自己打造一部智能手机，

then you hire software engineers

那么你会聘请软件工程师

that write a very, very long kitchen recipe,

编写很长很长的（类似）烹饪食谱，

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

比如“如果水太热，请调低温度，

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

如果太凉，调高温度。”

The recipes are not just 10 lines long.

但我们的食谱不只是10行。

They are millions of lines long.

它们长达数百万行。

A modern cell phone has 12 million lines of code.

现代手机拥有1200万行代码。

A browser has five million lines of code.

一个浏览器有500万行代码。

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

而这个食谱中的每个错误 都可能导致你的电脑崩溃。

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

这就是为什么软件工程师 赚那么多钱。

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

现在的新现象是 电脑可以找到自己的规则。

So instead of an expert deciphering, step by step,

所以不再是专家一步一步地

为每一个偶然事件破译出规则，

a rule for every contingency,

what you do now is you give the computer examples

而现在的做法是 给计算机提供实例，

and have it infer its own rules.

让计算机推断出自己的规则。

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

一个很好的例子是谷歌 刚获胜的阿尔法围棋。

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

通常，在比赛中， 你会真的写下全部规则，

but in AlphaGo's case,

而对于阿尔法围棋，

the system looked over a million games

它的系统观摩了 超过一百万次的比赛，

and was able to infer its own rules

并且能够推断出自己的规则，

and then beat the world's residing Go champion.

然后击败了当下的世界围棋冠军。

That is exciting, because it relieves the software engineer

这很令人兴奋，因为它不再需要

软件工程师必须超级聪明，

of the need of being super smart,

and pushes the burden towards the data.

而是把负担推到数据上。

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

如我所说，这个转折点 已经真正成为可能——

非常尴尬，我的论文 是关于机器学习的。

very embarrassing, my thesis was about machine learning.

It was completely insignificant, don't read it,

它的内容完全不重要，千万别读，

因为那是20年前的，

because it was 20 years ago

那时候，计算机只有蟑螂脑袋的容量。

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

Now they are powerful enough to really emulate

现在，计算机足够强大，

kind of specialized human thinking.

可以真正模仿特定的人类思维。

And then the computers take advantage of the fact

然后计算机借助一个事实，

它们能读取的数据比人类多得多。

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

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

所以我会说阿尔法围棋 看了上百万次比赛。

No human expert can ever study a million games.

没有人类专家可以 研究一百万次比赛。

Google has looked at over a hundred billion web pages.

谷歌已经浏览了超过千亿的网页。

No person can ever study a hundred billion web pages.

也没有人类可以做到这一点。

So as a result, the computer can find rules

因此，电脑能找到

that even people can't find.

连人类也找不到的规则。

CA：所以它思考的不是 “如果他那么走，我要那么走，”

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

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

而更像是“这应该是获胜模式，

here is what looks like a winning pattern."

这也应该是获胜模式。”

ST：对，想想如何抚养孩子。

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

你不会把前18年用来 给孩子创建每个细节的规则，

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

再放他们出去， 那他们就麻烦大了。

and set them free and they have this big program.

孩子会摸爬滚打，跌倒再站起来， 他们失败、受挫，

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

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

他们获得正面的经验， 在学校里取得好成绩，

and they figure it out on their own.

然后他们自己摸索出人生。

That's happening with computers now,

现在这也发生在计算机上，

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

所以突然间计算机编程容易多了。

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

现在不用我们思考了。 我们只要给计算机大量的数据。

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

CA：所以，这才是 自动驾驶汽车的影响

大幅提升的关键。

in power of self-driving cars.

I think you gave me an example.

我记得你给我举例了。

能解释下这是个什么场景吗？

Can you explain what's happening here?

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

ST：这是一个 自动驾驶汽车的行驶过程，

that we happened to have at Udacity

这刚好是我们优达学城的车，

and recently made into a spin-off called Voyage.

最近做成名叫Voyage的改装车。

We have used this thing called deep learning

我们一直用“深度学习”

to train a car to drive itself,

来训练一辆汽车自行驾驶，

and this is driving from Mountain View, California,

这是在一个雨天从加州的

山景城出发开到旧金山，

to San Francisco

on El Camino Real on a rainy day,

行驶在El Camino Real路上，

with bicyclists and pedestrians and 133 traffic lights.

路上有人骑车，有人步行， 有133个交通信号灯。

And the novel thing here is,

这里的创新点是，

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

很久以前我组建了 谷歌自动驾驶团队。

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

那时，我聘请了 世界上最好的软件工程师

to find the world's best rules.

来寻找世界上最好的规则。

This is just trained.

而这只是训练出来的。

We drive this road 20 times,

我们在这条路上跑上个20次，

we put all this data into the computer brain,

把所有数据放到电脑里，

and after a few hours of processing,

经过几个小时的处理，

it comes up with behavior that often surpasses human agility.

它创造出的行为 常常超越人类的操作能力。

So it's become really easy to program it.

所以对它进行编程变得非常简单。

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

这是百分之百自主操作， 大约33英里，一个半小时。

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

CA：那么，详细说说—— 这个程序的左边这一大块，

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

我们看到的基本上就是 电脑看到的卡车和轿车，

and those dots overtaking it and so forth.

各种超车的亮点，等等。

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

ST：右侧是摄像机图像， 在这里是主要输入，

用来找车道、其他车辆， 交通信号灯。

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

The vehicle has a radar to do distance estimation.

这辆车有雷达来做测距。

This is very commonly used in these kind of systems.

这在类似系统里很常见。

On the left side you see a laser diagram,

左侧的是激光图，

可以看到激光绘制的 树木等障碍物。

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

但是现在几乎所有有趣的工作 都集中在相机图像上。

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

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

我们正在从雷达和激光等 精密传感器

转向非常便宜、商品化的传感器。

into very cheap, commoditized sensors.

成本低于8美元的相机。

A camera costs less than eight dollars.

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

CA：左边那个绿色的圆点是什么？

Is that anything meaningful?

它有什么意义吗？

ST：这是自适应巡航控制的先行点，

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

它可以帮助我们了解

so it helps us understand how to regulate velocity

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

如何根据车前方的距离来调节速度。

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

CA：那么，我想你还有一个例子

of how the actual learning part takes place.

是说明实际的学习部分 是如何发生的。

也许我们可以看那个例子， 来谈谈这个话题。

Maybe we can see that. Talk about this.

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

ST：这个示例是我们 向优达学城的学生们发起的挑战，

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

用于获得我们的 自动驾驶“纳米”学位。

We gave them this dataset

我们给他们提供这个数据集，

说：“嘿，你们能不能 找到这辆车的驾驶方法？”

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

And if you look at the images,

如果你观看图像，

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

即使对于人类，也不大可能完美转向。

我们办了一场竞赛，说： “这是深度学习竞赛，

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

AI competition,"

人工智能竞赛，”

and we gave the students 48 hours.

我们给了学生48小时。

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

如果你是像谷歌或脸书 那样的软件公司，

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

那么这样的工作至少要花费 六个月的时间。

So we figured 48 hours is great.

所以我们认为48小时 就能解决问题简直太赞了。

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

在48小时内，我们收到了 大约100份学生交稿，

and the top four got it perfectly right.

前四名的答案完全正确。

It drives better than I could drive on this imagery,

它比我在这个情景中驾驶得更好，

using deep learning.

它用的是深度学习。

And again, it's the same methodology.

重申，方法是一样的。

It's this magical thing.

就是这个神奇的东西。

When you give enough data to a computer now,

现在如果给计算机 提供足够的数据，

and give enough time to comprehend the data,

并且给它足够的时间来理解数据，

it finds its own rules.

它总会找到自己的规则。

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

CA：那么它已经引起了

开发各种领域的强大应用程序。

in all sorts of areas.

You were talking to me the other day about cancer.

前几天你跟我说过癌症。

Can I show this video?

我可以展示这个视频吗？

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

ST：当然可以，请便。 CA：这个很酷。

ST：这是在一个完全不同的领域

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

in a completely different domain.

洞察所发生的事。

This is augmenting, or competing --

这是增强或竞争——

it's in the eye of the beholder --

在旁观者的眼中——

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

与每年拿40万美元的人、

皮肤科医生、

dermatologists,

highly trained specialists.

训练有素的专家的竞争。

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

需要十多年的培训才能 成为一名优秀的皮肤科医生。

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

你在这里看到的是 它的机器学习版本。

It's called a neural network.

它叫做神经网络。

“神经网络”是这些 机器学习算法的技术术语。

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

They've been around since the 1980s.

20世纪80年代就有了。

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

而这个是1988年由脸书研究员 扬·勒丘恩发明的，

and it propagates data stages

它传送数据的方式

through what you could think of as the human brain.

跟人脑分段的工作方式很相似。

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

不完全一样，但它模仿人脑。

It goes stage after stage.

一个阶段一个阶段地运行。

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

在第一阶段，它获取视觉输入

and rods and dots.

并提取边、线、点。

下一阶段变成更复杂的边

And the next one becomes more complicated edges

and shapes like little half-moons.

以及小半月之类的形状。

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

最终，它能够构建 非常复杂的概念。

Andrew Ng has been able to show

吴恩达已经能证明

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

它能够在大量的图像中

in vast amounts of images.

找出猫脸和狗脸。

What my student team at Stanford has shown is that

我在斯坦福大学的学生团队展示了

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

如果用12.9万张展示皮肤状况的 图片对它进行训练，

including melanoma and carcinomas,

包括黑色素瘤和癌症，

you can do as good a job

那么你就可以像最好的

as the best human dermatologists.

人类皮肤科医生一样工作。

为了证明这是真的，

And to convince ourselves that this is the case,

we captured an independent dataset that we presented to our network

我们找到一个独立的数据组，

展示给我们的网络以及 25位认证的斯坦福级别皮肤医生，

and to 25 board-certified Stanford-level dermatologists,

and compared those.

然后比较结果。

And in most cases,

多数情况下，

they were either on par or above the performance classification accuracy

它们表现出的分类准确率

等同或高于人类皮肤科医生。

of human dermatologists.

CA: You were telling me an anecdote.

CA：你给我讲过一个故事。

I think about this image right here.

我正在这里想这个画面。

What happened here?

这背后的故事是什么？

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

ST：这是上个星期四的事儿。 挺让人激动的。

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

我们之前展示过，并且今年早些时候 在“自然”杂志上发表了的

was this idea that we show dermatologists images

想法是，我们同时给皮肤科医生

和计算机程序看图片，

and our computer program images,

and count how often they're right.

然后统计正确率。

But all these images are past images.

但所有图片都是用过的。

那些图片都做过活检， 以确保我们分类正确。

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

但这一个不是。

This one wasn't.

这个实际上是我们在斯坦福 一个合作人得到的照片。

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

The story goes that our collaborator,

故事是，我们的这位合作人

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

是世界著名的皮肤科医生， 显然是最好的三位之一，

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

他看着这个痣，说： “这不是皮肤癌。”

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

然后他犹豫了一下，他说：

“等等，让我用应用程序查查。”

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

于是，他拿出自己的iPhone， 打开我们的软件，

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

our "pocket dermatologist," so to speak,

就是我们的“口袋皮肤医生”，

and the iPhone said: cancer.

iPhone说：癌症。

It said melanoma.

它说是黑色素瘤。

And then he was confused.

然后他就纠结了。

他决定，“好吧，也许我 信iPhone比信自己多一点，”

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

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

于是把样品送到实验室进行活检。

And it came up as an aggressive melanoma.

结果它是侵略性的黑色素瘤。

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

所以我觉得这可能是 我们第一次真正发现，

in the practice of using deep learning,

在使用深度学习的实践中，

an actual person whose melanoma would have gone unclassified,

如果没有深度学习， 真会有人长了黑色素瘤

had it not been for deep learning.

却识别不出。

CA: I mean, that's incredible.

CA：真不可思议。

(Applause)

（掌声）

感觉现在就对这样的应用程序 有即时需求了，

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

that you might freak out a lot of people.

但你可能吓到了很多人。

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

你在考虑做这种 能自我检查的应用程序吗？

ST：我的收件箱充斥着 关于癌症应用程序的邮件，

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

with heartbreaking stories of people.

还有人们让人心碎的故事。

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

有些人已经切除了 10、15、20个黑色素瘤，

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

害怕可能会漏掉一个，就像这个，

and also, about, I don't know,

还有些是关于，

flying cars and speaker inquiries these days, I guess.

我猜是飞行汽车和演讲咨询吧。

My take is, we need more testing.

我认为，我们需要更多的测试。

I want to be very careful.

我想要非常谨慎。

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

给TED观众一个华丽的， 让人印象深刻的答案很容易。

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

但真正做出符合伦理道德的 事情就难得多。

And if people were to use the app

如果人们要用这个应用程序，

并选择不去寻求医生的帮助，

and choose not to consult the assistance of a doctor

because we get it wrong,

但实际上是我们搞错了，

I would feel really bad about it.

我会感觉非常糟糕。

所以我们现在正在进行临床试验，

So we're currently doing clinical tests,

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

如果这些临床试验开始后， 我们的数据还能保持正确，

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

那么在某一时刻 我们或许可以采用这种技术，

and take it out of the Stanford clinic

把它从斯坦福大学的诊所

and bring it to the entire world,

带到全世界，

带到斯坦福的医生 从未踏足过的地方。

places where Stanford doctors never, ever set foot.

CA: And do I hear this right,

CA：如果我听的没错，

that it seemed like what you were saying,

好像你说过，

因为你跟优达学城的 学生军团打交道

because you are working with this army of Udacity students,

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

你使用了与工业界不同形式的

than might take place in a company,

机器学习方式，

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

也就是将机器学习与 群体智慧相结合。

你是否在说，有时候你认为

Are you saying that sometimes you think that could actually outperform

这能超越公司能做的事情， 甚至是一个巨型公司？

what a company can do, even a vast company?

ST：我相信现在有一些事情 完全超乎我想象，

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

and I'm still trying to understand.

我还在试着去理解。

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

克里斯指的是 我们举办的这些比赛。

We turn them around in 48 hours,

我们在48小时内完成，

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

我们已经能够造出自动驾驶车，

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

它能在大街上从山景城开到旧金山。

这与谷歌的七年努力还不太能比，

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

but it's getting there.

但是也快要实现了。

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

而且我们只用了两个工程师， 三个月就完成了这个任务。

And the reason is, we have an army of students

原因是，我们有一批

who participate in competitions.

参加比赛的学生军团。

We're not the only ones who use crowdsourcing.

我们不是唯一使用众包的人。

Uber and Didi use crowdsource for driving.

优步和滴滴也使用众包进行驾驶。

Airbnb uses crowdsourcing for hotels.

Airbnb使用众包做酒店。

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

现在有很多例子， 人们用众包找程序漏洞，

or protein folding, of all things, in crowdsourcing.

或蛋白质折叠，各种众包。

But we've been able to build this car in three months,

但是我们已经做到 在三个月内造出这辆车，

so I am actually rethinking

所以我实际上正在重新思考

how we organize corporations.

应该如何管理企业。

We have a staff of 9,000 people who are never hired,

我们有从未雇用的9000员工，

that I never fire.

我也从不解雇任何人。

They show up to work and I don't even know.

他们来上班，我甚至不知道。

Then they submit to me maybe 9,000 answers.

然后他们向我提交了 大概9000个答案。

I'm not obliged to use any of those.

我并不必须使用任何一个答案。

I end up -- I pay only the winners,

最后，我只付钱给赢家，

so I'm actually very cheapskate here, which is maybe not the best thing to do.

所以这方面我很吝啬， 这可能不太好。

But they consider it part of their education, too, which is nice.

但他们也认为这是 教育的一部分，这很好。

But these students have been able to produce amazing deep learning results.

但是这些学生已经能够做出 惊人的深度学习成果。

So yeah, the synthesis of great people and great machine learning is amazing.

所以，优秀的人和优秀的的机器学习 结合起来简直太棒了。

CA：加里·卡斯帕罗夫 在TED2017的第一天就说，

CA: I mean, Gary Kasparov said on the first day [of TED2017]

that the winners of chess, surprisingly, turned out to be two amateur chess players

国际象棋的胜利者 竟然是两个业余棋手，

with three mediocre-ish, mediocre-to-good, computer programs,

用三个很一般，或者 中等偏上的计算机程序，

that could outperform one grand master with one great chess player,

赢了一个大师，一个很牛的棋手，

like it was all part of the process.

就像一切都是程序的一部分。

And it almost seems like you're talking about a much richer version

看起来好像你正在 说的是同一想法的

of that same idea.

更丰富的版本。

ST：是的，你也关注了昨天上午 那些很棒的小组讨论，

ST: Yeah, I mean, as you followed the fantastic panels yesterday morning,

two sessions about AI,

两个关于人工智能、

robotic overlords and the human response,

机器人霸主和人类反应的会议，

many, many great things were said.

说了很多很多很棒的东西。

But one of the concerns is that we sometimes confuse

但是其中一个问题是， 我们有时候

会把人工智能真正做的事 与这种霸主威胁混淆，

what's actually been done with AI with this kind of overlord threat,

where your AI develops consciousness, right?

威胁说人工智能 发展出意识了，对吧？

The last thing I want is for my AI to have consciousness.

我最不想看到的 就是我的人工智能有意识了。

I don't want to come into my kitchen

我不想走进自己的厨房

and have the refrigerator fall in love with the dishwasher

突然发现冰箱爱上了洗碗机，

and tell me, because I wasn't nice enough,

还告诉我，因为我表现不错，

my food is now warm.

所以把我的饭热好了。

I wouldn't buy these products, and I don't want them.

我不会买这些产品的， 我也不想要。

But the truth is, for me,

但事实是，对于我来说，

AI has always been an augmentation of people.

人工智能一直是对人的增强。

It's been an augmentation of us,

它是对我们的增强，

to make us stronger.

使我们更强大。

And I think Kasparov was exactly correct.

我认为卡斯帕罗夫是完全正确的。

It's been the combination of human smarts and machine smarts

是人类智慧和机器智慧的结合

that make us stronger.

使我们变得更加强大。

The theme of machines making us stronger is as old as machines are.

机器使我们更强大的想法 与机器一样古老。

The agricultural revolution took place because it made steam engines

农业革命发生的原因是

它制造的蒸汽机和 农具不能自己种植，

and farming equipment that couldn't farm by itself,

机器从来没有取代我们； 只是让我们变得更强大。

that never replaced us; it made us stronger.

我相信这个人工智能新浪潮

And I believe this new wave of AI will make us much, much stronger

会让我们作为人类更加强大。

as a human race.

CA: We'll come on to that a bit more,

CA：我们待会儿 再继续探讨这个问题，

but just to continue with the scary part of this for some people,

先说说对一些人来说可怕的部分，

like, what feels like it gets scary for people is when you have

比如，有点让人担心的是 你有一台计算机，

a computer that can, one, rewrite its own code,

它能改写它自己的代码，

so, it can create multiple copies of itself,

所以，它能自己复制很多个自己，

还试验好多不同的代码版本，

try a bunch of different code versions,

甚至可能是随机的版本，

possibly even at random,

and then check them out and see if a goal is achieved and improved.

然后自己检验，看看 目标有没有实现或得到改进。

So, say the goal is to do better on an intelligence test.

比如说，目标是 在智力测验上表现更好。

You know, a computer that's moderately good at that,

你知道，计算机很擅长这个，

you could try a million versions of that.

可以尝试一百万个版本。

You might find one that was better,

可能会发现一个更好的，

and then, you know, repeat.

然后，自己重复。

And so the concern is that you get some sort of runaway effect

所以让人担心的是， 会发生类似失控效应，

where everything is fine on Thursday evening,

比如周四晚上一切正常，

and you come back into the lab on Friday morning,

周五早晨到实验室，

由于计算机的速度等等，

and because of the speed of computers and so forth,

things have gone crazy, and suddenly --

一切都开始失控，突然——

ST: I would say this is a possibility,

ST：我只能说这是一种可能性，

but it's a very remote possibility.

但是这个可能性非常遥远。

So let me just translate what I heard you say.

先让我翻译一下你所说的话。

In the AlphaGo case, we had exactly this thing:

在阿尔法围棋中， 我们确实有这样的情况：

the computer would play the game against itself

计算机跟自己比赛，

然后学到新规则。

and then learn new rules.

And what machine learning is is a rewriting of the rules.

而机器学习就是改写规则。

It's the rewriting of code.

改写代码。

But I think there was absolutely no concern

但我认为绝对不用担心

that AlphaGo would take over the world.

阿尔法围棋会占领世界。

It can't even play chess.

它连国际象棋也不会玩。

CA：没错没错，但现在 这些都是非常单一领域的东西。

CA: No, no, no, but now, these are all very single-domain things.

But it's possible to imagine.

但能够想象。

我是说，我们刚刚看到一个计算机

I mean, we just saw a computer that seemed nearly capable

好像几乎能够通过大学入学考试了，

of passing a university entrance test,

不过——它不像我们一样阅读和理解，

that can kind of -- it can't read and understand in the sense that we can,

but it can certainly absorb all the text

却能吸收所有文字，

and maybe see increased patterns of meaning.

还能看见更多的意义模式。

Isn't there a chance that, as this broadens out,

会不会有可能， 随着这个继续发展壮大，

there could be a different kind of runaway effect?

会出现另一种失控效应？

ST: That's where I draw the line, honestly.

ST：老实说，这就是我 划分界限的地方。

And the chance exists -- I don't want to downplay it --

可能性是存在的—— 我不想轻描淡写——

but I think it's remote, and it's not the thing that's on my mind these days,

但我认为它很遥远， 目前我脑子里不会想这个，

因为我认为 大改革是指另一回事。

because I think the big revolution is something else.

Everything successful in AI to the present date

到今天，人工智能所有的成功

has been extremely specialized,

都是极度专业化的，

and it's been thriving on a single idea,

并且它的繁荣一直 基于单一的理念，

which is massive amounts of data.

就是大量的数据。

The reason AlphaGo works so well is because of massive numbers of Go plays,

阿尔法围棋这么成功的原因 是大量的围棋比赛数据，

and AlphaGo can't drive a car or fly a plane.

阿尔法围棋不能开车 也不能开飞机。

The Google self-driving car or the Udacity self-driving car

谷歌自动驾驶车或 优达学城自动驾驶车

thrives on massive amounts of data, and it can't do anything else.

在海量数据上建成， 但做不了其他事。

甚至控制不了摩托车。

It can't even control a motorcycle.

It's a very specific, domain-specific function,

这是一个非常具体的、 特定领域的功能，

and the same is true for our cancer app.

我们的癌症应用程序也是如此。

There has been almost no progress on this thing called "general AI,"

而所谓“通用人工智能”， 几乎没有进展，

where you go to an AI and say, "Hey, invent for me special relativity

“通用”就是你去对人工智能说： “嘿，为我发明个狭义相对论

or string theory."

或弦理论。”

It's totally in the infancy.

那完全是在婴儿期。

The reason I want to emphasize this,

我想强调这一点的原因是，

I see the concerns, and I want to acknowledge them.

我明白大家的担忧， 我想告诉大家我了解。

But if I were to think about one thing,

但是如果我只能考虑一件事情，

I would ask myself the question, "What if we can take anything repetitive

我会问自己： “如果我们 把所有重复性的事情解决掉，

让自己的效率提高100倍，会怎样？”

and make ourselves 100 times as efficient?"

事实证明，三百年前，我们都务农，

It so turns out, 300 years ago, we all worked in agriculture

耕种，做重复的事。

and did farming and did repetitive things.

Today, 75 percent of us work in offices

今天，我们75％的人 在办公室里工作，

and do repetitive things.

仍然做重复的事。

We've become spreadsheet monkeys.

我们已经变成专做表格的猴子。

And not just low-end labor.

不只是低端劳动力，

We've become dermatologists doing repetitive things,

我们已经变成了 皮肤科医生在做重复的工作，

lawyers doing repetitive things.

律师也在做重复的工作。

I think we are at the brink of being able to take an AI,

我想我们处于一个边缘， 能够利用人工智能

look over our shoulders,

替我们仔细查看，

and they make us maybe 10 or 50 times as effective in these repetitive things.

帮我们在这些重复的事情上 把效率提高10倍或50倍。

That's what is on my mind.

这才是我在考虑的事。

CA：听起来很刺激。

CA: That sounds super exciting.

The process of getting there seems a little terrifying to some people,

实现这些的过程会让 一些人内心多少有些抵触，

因为一旦电脑可以比皮肤科医生，

because once a computer can do this repetitive thing

much better than the dermatologist

尤其是比司机

or than the driver, especially, is the thing that's talked about

更能胜任重复劳动，

so much now,

现在这是热门话题，

suddenly millions of jobs go,

突然上百万工作消失了，

and, you know, the country's in revolution

并且，你知道，国家变得速度很快，

我们根本来不及实现更耀眼的成就。

before we ever get to the more glorious aspects of what's possible.

ST: Yeah, and that's an issue, and it's a big issue,

ST：是的，这是个问题， 是个大问题，

and it was pointed out yesterday morning by several guest speakers.

昨天上午也有几位演讲嘉宾提到了。

在我上台之前，

Now, prior to me showing up onstage,

I confessed I'm a positive, optimistic person,

我承认我是一个积极乐观的人，

so let me give you an optimistic pitch,

所以让我给你一个乐观的意见，

which is, think of yourself back 300 years ago.

假想你在300年前。

Europe just survived 140 years of continuous war,

欧洲刚刚经历了140年的连续战争，

none of you could read or write,

没有人会读书写字，

there were no jobs that you hold today,

没有现代社会的工作，

like investment banker or software engineer or TV anchor.

比如投资银行家、 软件工程师或电视主播。

We would all be in the fields and farming.

我们都要在田野里种地。

Now here comes little Sebastian with a little steam engine in his pocket,

现在小塞巴斯蒂安来了， 口袋里装着一个小蒸汽机，

saying, "Hey guys, look at this.

他说：“嘿，伙计们，看看这个，

它会让你强壮100倍， 然后你就可以做点别的了。”

It's going to make you 100 times as strong, so you can do something else."

And then back in the day, there was no real stage,

那时候，没有真正的舞台，

but Chris and I hang out with the cows in the stable,

我和克里斯在牛棚里跟牛闲晃，

他说，“我真的很担心，

and he says, "I'm really concerned about it,

因为我每天挤牛奶，如果机器 也能干这活儿了，我可怎么办呐？”

because I milk my cow every day, and what if the machine does this for me?"

The reason why I mention this is,

我之所以提到这个，

是因为我们总是擅长 承认过去的进步和好处，

we're always good in acknowledging past progress and the benefit of it,

like our iPhones or our planes or electricity or medical supply.

比如iPhone或飞机， 电力或者医疗供应。

We all love to live to 80, which was impossible 300 years ago.

我们都喜欢活到80年， 这在300年前是不可能的。

But we kind of don't apply the same rules to the future.

但是我们对未来的态度 却并不基于相同的规则。

So if I look at my own job as a CEO,

如果我审视自己的 首席执行官工作，

I would say 90 percent of my work is repetitive,

我认为我的工作中 有90％是重复性的，

我不喜欢，

I don't enjoy it,

I spend about four hours per day on stupid, repetitive email.

我每天花四个小时在 愚蠢、重复的电子邮件上。

And I'm burning to have something that helps me get rid of this.

我正心急如焚想要 找谁帮我摆脱这一点。

为什么？

Why?

因为我相信每个人都有无限创造力。

Because I believe all of us are insanely creative;

I think the TED community more than anybody else.

我认为TED社区更是如此。

But even blue-collar workers; I think you can go to your hotel maid

但即使是蓝领工人， 你可以找酒店清洁工

and have a drink with him or her,

跟他或她喝一杯，

and an hour later, you find a creative idea.

一小时后，你就会发现 有创意的想法。

What this will empower is to turn this creativity into action.

人工智能将赋予我们的力量是 将这种创造力转化为行动。

Like, what if you could build Google in a day?

比如，如果你能 在一天内造出谷歌会怎样？

如果你坐这儿喝着啤酒， 就发明出下一个Snapchat会怎样？

What if you could sit over beer and invent the next Snapchat,

whatever it is,

不管发明的是什么吧，

and tomorrow morning it's up and running?

第二天早上它就完工、 投入运行会怎样？

And that is not science fiction.

那不是科幻小说。

What's going to happen is,

可以预见的是，

我们已经处于历史当中。

we are already in history.

We've unleashed this amazing creativity

我们已经释放出惊人的创造力，

by de-slaving us from farming

先从农耕解放出来，

and later, of course, from factory work

又从工厂劳动解放出来，

and have invented so many things.

我们发明了这么多东西。

It's going to be even better, in my opinion.

我认为，将来会更好的。

And there's going to be great side effects.

当然也会有更大的副作用。

One of the side effects will be

其中一个副作用就是

比如食物、医疗、教育、庇护

that things like food and medical supply and education and shelter

交通等这些东西，

and transportation

will all become much more affordable to all of us,

将会让所有人都承受得起，

not just the rich people.

而不只是富人。

CA：嗯。

CA: Hmm.

So when Martin Ford argued, you know, that this time it's different

所以，之前马丁·福特提出的， 与这一次有所不同，

because the intelligence that we've used in the past

说因为我们以前的

用来寻找新方法的智慧

to find new ways to be

will be matched at the same pace

将被计算机接管，

by computers taking over those things,

以相同的步调继续下去，

而我听你的意思，那不完全对，

what I hear you saying is that, not completely,

because of human creativity.

原因是人的创造力。

Do you think that that's fundamentally different from the kind of creativity

你是否认为人的创造力 与计算机的那种创造力

that computers can do?

有着根本的区别？

ST: So, that's my firm belief as an AI person --

ST：那是我作为 一个AI人的坚定信念——

that I haven't seen any real progress on creativity

在创造力和创新思维方面， 我并没有看到

and out-of-the-box thinking.

任何真正的进展。

What I see right now -- and this is really important for people to realize,

我现在所看到的—— 大家也一定要意识到，

由于“人工智能”一词 如此有威胁性，

because the word "artificial intelligence" is so threatening,

而且史蒂夫·斯皮尔伯格 又加进一部电影，

and then we have Steve Spielberg tossing a movie in,

电影里突然之间 计算机变成我们的霸主——

where all of a sudden the computer is our overlord,

but it's really a technology.

但人工智能真的只是一种技术。

是帮我们做重复工作的技术。

It's a technology that helps us do repetitive things.

And the progress has been entirely on the repetitive end.

而且进展完全发生在 重复性事件上。

It's been in legal document discovery.

比如法律文件探索、

It's been contract drafting.

合同起草、

It's been screening X-rays of your chest.

胸部X光片筛查，

And these things are so specialized,

这些都是非常专业的，

I don't see the big threat of humanity.

我不觉得对人类有什么大威胁。

In fact, we as people --

事实上，我们作为人类——

I mean, let's face it: we've become superhuman.

让我们面对事实： 我们已经变成了超人。

We've made us superhuman.

我们把自己变成了超人。

We can swim across the Atlantic in 11 hours.

我们能用11个小时游过大西洋。

We can take a device out of our pocket

我们能从口袋里掏出设备

and shout all the way to Australia,

喊到澳大利亚去，

and in real time, have that person shouting back to us.

并且同时，那人可以喊回来。

That's physically not possible. We're breaking the rules of physics.

这在物理学上是不可能的。 我们正在打破物理规则。

When this is said and done, we're going to remember everything

当这样说了，这样做了，我们会记住

we've ever said and seen,

我们曾说过和见过的一切，

you'll remember every person,

你会记得每一个人，

which is good for me in my early stages of Alzheimer's.

这对我的早期老年痴呆有好处。

对不起，我在说什么？我忘了。

Sorry, what was I saying? I forgot.

CA: (Laughs)

CA：（笑）

ST: We will probably have an IQ of 1,000 or more.

ST：我们的智商可能超过1000。

There will be no more spelling classes for our kids,

我们的孩子将不再有拼写课，

because there's no spelling issue anymore.

因为不存在拼写问题了。

There's no math issue anymore.

也不存在数学问题了。

And I think what really will happen is that we can be super creative.

我认为真正会发生的是， 我们将变得充满创意。

And we are. We are creative.

是的，我们很有创意。

That's our secret weapon.

这是我们的秘密武器。

CA: So the jobs that are getting lost,

CA：所以那些将要消失的工作，

in a way, even though it's going to be painful,

某种程度上，即使痛苦，

humans are capable of more than those jobs.

人类能够做的远不止那些工作。

This is the dream.

这才是（人工智能的最终）梦想。

梦想人类可以上升到能量与探索的

The dream is that humans can rise to just a new level of empowerment

and discovery.

新高度。

That's the dream.

那才是梦想。

ST: And think about this:

ST：想想看：

如果你看一下人类的历史，

if you look at the history of humanity,

that might be whatever -- 60-100,000 years old, give or take --

可能是大概6万至10万年的岁月，

almost everything that you cherish in terms of invention,

几乎每一件珍贵的发明

of technology, of things we've built,

技术发明，或建造的作品，

has been invented in the last 150 years.

都是在最近150年完成的。

If you toss in the book and the wheel, it's a little bit older.

如果算上书本和车轮，还要更久一点。

Or the axe.

或斧头。

但你的手机、跑鞋，

But your phone, your sneakers,

these chairs, modern manufacturing, penicillin --

这些椅子、现代制造、青霉素——

the things we cherish.

这些我们珍惜的东西。

Now, that to me means

现在，对我而言它意味着，

the next 150 years will find more things.

接下来的150年将会发现更多的东西。

In fact, the pace of invention has gone up, not gone down, in my opinion.

事实上，在我看来，发明的速度 已经上升了，没有下降。

I believe only one percent of interesting things have been invented yet. Right?

我相信有趣的东西只有 1%被发明出来了。可以理解吧？

我们还没有治愈癌症。

We haven't cured cancer.

我们没有飞行汽车——目前还没有， 希望我会改变这一点。

We don't have flying cars -- yet. Hopefully, I'll change this.

That used to be an example people laughed about. (Laughs)

那曾经是大家的笑料。（笑）

是不是很逗， 秘密地研究飞行汽车？

It's funny, isn't it? Working secretly on flying cars.

We don't live twice as long yet. OK?

我们的寿命还没有翻倍，对吧？

We don't have this magic implant in our brain

我们还没有神奇的脑植入物

that gives us the information we want.

来提供我们想要的信息。

你可能会为此感到惊恐，

And you might be appalled by it,

but I promise you, once you have it, you'll love it.

但我向你保证，一旦拥有了， 你一定会喜欢的。

I hope you will.

我希望你会的。

It's a bit scary, I know.

有点吓人，我明白。

There are so many things we haven't invented yet

还有这么多没有出现的东西

我想我们会发明出来的。

that I think we'll invent.

我们没有引力盾。

We have no gravity shields.

我们不能把自己从一个地点 转移到另一个地点。

We can't beam ourselves from one location to another.

这听起来挺荒唐，

但大约200年前，

That sounds ridiculous,

but about 200 years ago,

experts were of the opinion that flight wouldn't exist,

专家们还认为飞机不会存在，

even 120 years ago,

即使120年前，

and if you moved faster than you could run,

如果你的移动速度 比你跑步还快，

你会立即死掉。

you would instantly die.

So who says we are correct today that you can't beam a person

那么今天有谁敢说我们肯定不能把人

from here to Mars?

从这儿送到火星呢？

CA: Sebastian, thank you so much

CA：塞巴斯蒂安，非常感谢你今天来

for your incredibly inspiring vision and your brilliance.

分享你无比激励的展望和你的才华。

Thank you, Sebastian Thrun.

谢谢塞巴斯蒂安·斯伦。

ST：真棒。 （掌声）

That was fantastic. (Applause)