TEDSummit
Kevin Kelly: How AI can bring on a second Industrial Revolution
凯文·凯利: 人工智能将如何推动第二次工业革命
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“雨滴汇入山谷的具体路径是不可预测的，但它的大方向是必然的。”数字梦想家凯文·凯利如是说。技术在很大程度上也是如此，它的发展趋势令人惊奇但又具有某种必然性。他认为，未来20年里我们想要让事物变得智能化的努力将对我们身边的每件事情都产生深远影响。凯文·凯利指出了 AI 大潮中我们需要了解的三个趋势，以使我们能更好的拥抱 AI 并控制它的发展。“20年后人人都用的 AI 产品还没有被发明出来呢，”他说，“这意味着，你们还有机会。”
Kevin Kelly - Digital visionary
There may be no one better to contemplate the meaning of cultural change than Kevin Kelly, whose life story reads like a treatise on the value and impacts of technology. Full bio
There may be no one better to contemplate the meaning of cultural change than Kevin Kelly, whose life story reads like a treatise on the value and impacts of technology. Full bio
Double-click the English transcript below to play the video.
00:14
I'm going to talk a little bit
about where technology's going.
about where technology's going.
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我打算谈一谈技术的发展趋势。
00:19
And often technology comes to us,
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当（新的）技术到来时，
常常会令我们感到惊讶。
00:22
we're surprised by what it brings.
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00:24
But there's actually
a large aspect of technology
a large aspect of technology
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但事实上，技术在很大程度上
00:28
that's much more predictable,
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是能够被预见的。
00:29
and that's because technological systems
of all sorts have leanings,
of all sorts have leanings,
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这是因为所有的技术
都有某种倾向性，
都有某种倾向性，
00:34
they have urgencies,
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有某种冲动，
00:35
they have tendencies.
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有某种趋势。
00:36
And those tendencies are derived
from the very nature of the physics,
from the very nature of the physics,
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这些趋势是由电线、开关、以及电子的
00:41
chemistry of wires
and switches and electrons,
and switches and electrons,
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物理和化学本质所决定的，
00:45
and they will make reoccurring
patterns again and again.
patterns again and again.
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并且呈现出不断重复的模式。
00:49
And so those patterns produce
these tendencies, these leanings.
these tendencies, these leanings.
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或者说，这些模式形成了
某种趋势、某种倾向。
某种趋势、某种倾向。
00:54
You can almost think of it
as sort of like gravity.
as sort of like gravity.
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你可以把它看成类似于重力的东西。
00:57
Imagine raindrops falling into a valley.
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想象雨点汇入山谷：
00:59
The actual path of a raindrop
as it goes down the valley
as it goes down the valley
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一滴雨点流入山谷的实际路径
01:02
is unpredictable.
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是无法预测的。
01:04
We cannot see where it's going,
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我们并不知道它的具体走向，
01:06
but the general direction
is very inevitable:
is very inevitable:
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但大方向是很显然的：
01:08
it's downward.
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它往下流。
01:10
And so these baked-in
tendencies and urgencies
tendencies and urgencies
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因此，这些内在趋势和冲动，
01:14
in technological systems
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深深扎根于技术系统中，
01:17
give us a sense of where things
are going at the large form.
are going at the large form.
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使我们能够感知它们的大体方向。
01:21
So in a large sense,
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具体点说，
01:22
I would say that telephones
were inevitable,
were inevitable,
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电话是必然的，
01:27
but the iPhone was not.
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但 iPhone 不是；
01:29
The Internet was inevitable,
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因特网是必然的，
01:31
but Twitter was not.
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但推特不是。
01:33
So we have many ongoing
tendencies right now,
tendencies right now,
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同样道理，
当下有许多正在发生的趋势，
当下有许多正在发生的趋势，
01:36
and I think one of the chief among them
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而我认为其中最重要的一个
01:39
is this tendency to make things
smarter and smarter.
smarter and smarter.
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是让物体变得越来越聪明。
01:44
I call it cognifying -- cognification --
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我称之为“知化”，
01:46
also known as artificial
intelligence, or AI.
intelligence, or AI.
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也就是人们常说的
人工智能，或者 AI。
人工智能，或者 AI。
01:50
And I think that's going to be one
of the most influential developments
of the most influential developments
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我认为在未来二十年中，
01:53
and trends and directions and drives
in our society in the next 20 years.
in our society in the next 20 years.
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这将是社会中最具影响力的
发展趋势和驱动力。
发展趋势和驱动力。
当然，它已经发生了。
02:00
So, of course, it's already here.
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02:02
We already have AI,
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我们已经有了 AI，
02:04
and often it works in the background,
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它们通常都隐身在后台工作，
02:06
in the back offices of hospitals,
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在医院里，
02:08
where it's used to diagnose X-rays
better than a human doctor.
better than a human doctor.
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AI 分析 X 光片的水准
比人类医生还要棒。
比人类医生还要棒。
02:13
It's in legal offices,
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在律所里，
02:14
where it's used to go
through legal evidence
through legal evidence
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AI 核查证物的本事
02:17
better than a human paralawyer.
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比人类助理律师还要强。
02:19
It's used to fly the plane
that you came here with.
that you came here with.
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我们乘坐的飞机是由 AI 在驾驶。
02:24
Human pilots only flew it
seven to eight minutes,
seven to eight minutes,
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人类驾驶员只飞个七、八分钟而已；
其他时间都是 AI 在操控。
02:26
the rest of the time the AI was driving.
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当然，在 Netflix 和亚马逊网站，
02:28
And of course, in Netflix and Amazon,
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02:30
it's in the background,
making those recommendations.
making those recommendations.
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是AI在后台进行推荐。
这些都是我们已经实现的。
02:33
That's what we have today.
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02:34
And we have an example, of course,
in a more front-facing aspect of it,
in a more front-facing aspect of it,
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我们也有一些更前沿的例子，
比如“阿尔法狗”战胜了
人类最强的围棋世界冠军。
人类最强的围棋世界冠军。
02:39
with the win of the AlphaGo, who beat
the world's greatest Go champion.
the world's greatest Go champion.
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但还不止于此。
02:46
But it's more than that.
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02:50
If you play a video game,
you're playing against an AI.
you're playing against an AI.
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我们打电玩时，对手往往是 AI。
不过最近，谷歌教会了他们的 AI
02:53
But recently, Google taught their AI
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02:57
to actually learn how to play video games.
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自己学习如何打电子游戏。
03:00
Again, teaching video games
was already done,
was already done,
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教（AI）打游戏
已经不是什么新鲜事了，
已经不是什么新鲜事了，
03:03
but learning how to play
a video game is another step.
a video game is another step.
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但（AI）自己学习
打游戏则是另一个境界。
打游戏则是另一个境界。
03:07
That's artificial smartness.
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这就是人工智慧。
03:10
What we're doing is taking
this artificial smartness
this artificial smartness
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我们正在以此为起点，
03:15
and we're making it smarter and smarter.
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让它变得越来越聪明。
03:18
There are three aspects
to this general trend
to this general trend
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在这个大趋势中，
我认为有三点尚未被充分认识；
03:22
that I think are underappreciated;
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03:24
I think we would understand
AI a lot better
AI a lot better
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如果我们能理解这三点，
03:26
if we understood these three things.
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就能更好的理解 AI，
03:28
I think these things also would
help us embrace AI,
help us embrace AI,
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并更加全身心的拥抱 AI。
03:32
because it's only by embracing it
that we actually can steer it.
that we actually can steer it.
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只有拥抱 AI，才能控制AI。
03:35
We can actually steer the specifics
by embracing the larger trend.
by embracing the larger trend.
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我们可以通过拥抱
大趋势来控制细节。
大趋势来控制细节。
03:39
So let me talk about
those three different aspects.
those three different aspects.
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所以，请允许我谈谈这三点。
03:42
The first one is: our own intelligence
has a very poor understanding
has a very poor understanding
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第一点，我们自己尚未很好的理解
03:46
of what intelligence is.
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什么是智能。
03:48
We tend to think of intelligence
as a single dimension,
as a single dimension,
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我们通常认为智能是单维度的，
03:51
that it's kind of like a note
that gets louder and louder.
that gets louder and louder.
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就像一个越来越响的音符。
我们用智商来衡量它。
03:54
It starts like with IQ measurement.
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03:57
It starts with maybe a simple
low IQ in a rat or mouse,
low IQ in a rat or mouse,
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老鼠的智商较低，
猩猩的智商较高，
04:01
and maybe there's more in a chimpanzee,
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04:03
and then maybe there's more
in a stupid person,
in a stupid person,
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接下来是比较笨的人，
然后是像我一样的普通人，
04:06
and then maybe an average
person like myself,
person like myself,
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再往上是天才。
04:08
and then maybe a genius.
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04:09
And this single IQ intelligence
is getting greater and greater.
is getting greater and greater.
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智商越高，智能就越高。
这种看法是完全错误的。
04:14
That's completely wrong.
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这根本就不是智能，
人类智能也并非如此。
人类智能也并非如此。
04:15
That's not what intelligence is --
not what human intelligence is, anyway.
not what human intelligence is, anyway.
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04:19
It's much more like a symphony
of different notes,
of different notes,
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智能更像由不同音符
组成的交响乐，
组成的交响乐，
每个音符由不同的认知乐器来奏响。
04:24
and each of these notes is played
on a different instrument of cognition.
on a different instrument of cognition.
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人类的心智包含了多种智能。
04:27
There are many types
of intelligences in our own minds.
of intelligences in our own minds.
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04:31
We have deductive reasoning,
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我们可以进行演绎推理，
04:34
we have emotional intelligence,
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我们具备情绪智力，
04:36
we have spatial intelligence;
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我们有空间智能。
04:38
we have maybe 100 different types
that are all grouped together,
that are all grouped together,
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我们可能有一百种
不同的智能集合在一起，
不同的智能集合在一起，
04:42
and they vary in different strengths
with different people.
with different people.
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它们在不同人的身上也
体现得强弱不一。
体现得强弱不一。
04:46
And of course, if we go to animals,
they also have another basket --
they also have another basket --
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而动物们则可能是另一套体系——
04:50
another symphony of different
kinds of intelligences,
kinds of intelligences,
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由其他智能组成的另一首交响乐，
04:53
and sometimes those same instruments
are the same that we have.
are the same that we have.
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当然，有些乐器与人类是相同的。
04:56
They can think in the same way,
but they may have a different arrangement,
but they may have a different arrangement,
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可能思考的方式相同但侧重点不同，
某些方面可能还强于人类，
05:00
and maybe they're higher
in some cases than humans,
in some cases than humans,
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像松鼠的长期记忆就很了不得，
05:03
like long-term memory in a squirrel
is actually phenomenal,
is actually phenomenal,
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能清楚记得坚果的埋藏之所。
05:05
so it can remember
where it buried its nuts.
where it buried its nuts.
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05:08
But in other cases they may be lower.
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但在另外一些方面可能不如人类。
05:10
When we go to make machines,
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当我们制造机器时，
05:12
we're going to engineer
them in the same way,
them in the same way,
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也会用同样的方式来设计它们，
它们在某些方面会比我们聪明得多，
05:15
where we'll make some of those types
of smartness much greater than ours,
of smartness much greater than ours,
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05:20
and many of them won't be
anywhere near ours,
anywhere near ours,
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而在其他方面则远远不如我们，
05:22
because they're not needed.
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因为根本没必要。
05:24
So we're going to take these things,
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我们会用这些东西，
05:26
these artificial clusters,
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这些人造的功能组合，
为我们的 AI 添加
各种各样的人工认知。
各种各样的人工认知。
05:28
and we'll be adding more varieties
of artificial cognition to our AIs.
of artificial cognition to our AIs.
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05:34
We're going to make them
very, very specific.
very, very specific.
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我们会让它们（的功能）非常具体。
05:38
So your calculator is smarter
than you are in arithmetic already;
than you are in arithmetic already;
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比方说，计算器在数学运算上
要比我们聪明得多；
要比我们聪明得多；
GPS 的空间导航能力远胜过我们；
05:45
your GPS is smarter
than you are in spatial navigation;
than you are in spatial navigation;
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05:49
Google, Bing, are smarter
than you are in long-term memory.
than you are in long-term memory.
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谷歌、必应在长期记忆上完胜我们。
然后我们再把这些不同类型的智能
05:54
And we're going to take, again,
these kinds of different types of thinking
these kinds of different types of thinking
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05:58
and we'll put them into, like, a car.
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塞到……比如说汽车里，
实现自动行驶。
实现自动行驶。
06:00
The reason why we want to put them
in a car so the car drives,
in a car so the car drives,
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我们之所以这么做，
正是因为它的驾驶方式
正是因为它的驾驶方式
06:03
is because it's not driving like a human.
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跟我们不一样。
06:06
It's not thinking like us.
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它不像我们那样思考。
06:07
That's the whole feature of it.
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这恰恰是它的特点。
06:09
It's not being distracted,
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它不会分心，
06:11
it's not worrying about whether
it left the stove on,
it left the stove on,
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不会担心是否忘记了关炉子，
06:13
or whether it should have
majored in finance.
majored in finance.
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不会纠结要不要选金融专业。
它只知道开车。
06:16
It's just driving.
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06:17
(Laughter)
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（笑声）
06:18
Just driving, OK?
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它会专心开车，对吧？
06:20
And we actually might even
come to advertise these
come to advertise these
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我们甚至可以把这个做为卖点，
叫做“无意识”。
06:23
as "consciousness-free."
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06:24
They're without consciousness,
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它们没有意识，
不会东想西想，
06:26
they're not concerned about those things,
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不会分心。
06:28
they're not distracted.
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所以，我们应该尽我们所能
06:29
So in general, what we're trying to do
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06:32
is make as many different
types of thinking as we can.
types of thinking as we can.
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制造各种各样的思考（机器）。
06:37
We're going to populate the space
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我们应该去尝试
06:39
of all the different possible types,
or species, of thinking.
or species, of thinking.
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所有可能的思考方式。
06:44
And there actually may be some problems
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在商业和科学上，
06:46
that are so difficult
in business and science
in business and science
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我们会遇到一些难题，
06:49
that our own type of human thinking
may not be able to solve them alone.
may not be able to solve them alone.
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单凭人类自身的思考无法解决。
我们可能需要分两步走，
06:53
We may need a two-step program,
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06:55
which is to invent new kinds of thinking
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先发明出新的思考方式，
再与它们一起解决这些真正的难题，
06:59
that we can work alongside of to solve
these really large problems,
these really large problems,
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07:03
say, like dark energy or quantum gravity.
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比如暗能量和量子引力。
07:08
What we're doing
is making alien intelligences.
is making alien intelligences.
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我们实际上是在创造异形智能。
07:11
You might even think of this
as, sort of, artificial aliens
as, sort of, artificial aliens
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某种意义上，甚至可以将它们看作
人造异形。
07:15
in some senses.
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07:16
And they're going to help
us think different,
us think different,
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它们将帮助我们用不同的方式思考，
07:18
because thinking different
is the engine of creation
is the engine of creation
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而换一种思考方式是创造的源泉，
07:22
and wealth and new economy.
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是财富和新经济的引擎。
07:25
The second aspect of this
is that we are going to use AI
is that we are going to use AI
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第二点是，我们将用 AI
07:30
to basically make a second
Industrial Revolution.
Industrial Revolution.
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推动第二次工业革命。
07:34
The first Industrial Revolution
was based on the fact
was based on the fact
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在第一次工业革命中，
07:36
that we invented something
I would call artificial power.
I would call artificial power.
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人类发明了我称之为
“人造能源”的东西。
“人造能源”的东西。
在此之前，
07:40
Previous to that,
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07:42
during the Agricultural Revolution,
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在农业革命时期，
07:44
everything that was made
had to be made with human muscle
had to be made with human muscle
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制造业靠人力驱动，
07:47
or animal power.
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或者靠畜力。
07:49
That was the only way
to get anything done.
to get anything done.
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除此之外别无他法。
07:51
The great innovation during
the Industrial Revolution was,
the Industrial Revolution was,
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工业革命时期的伟大发明就是
07:54
we harnessed steam power, fossil fuels,
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人们利用化石燃料和蒸汽
07:57
to make this artificial power
that we could use
that we could use
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所产生的“人造能源”来做
08:01
to do anything we wanted to do.
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我们想做的任何事情。
08:03
So today when you drive down the highway,
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今天，当我们开车行驶在高速上，
08:06
you are, with a flick of the switch,
commanding 250 horses --
commanding 250 horses --
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只需轻轻拨弄开关，
就能驾驭 250 匹马——
就能驾驭 250 匹马——
或者说，250 匹马的马力——
08:11
250 horsepower --
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08:12
which we can use to build skyscrapers,
to build cities, to build roads,
to build cities, to build roads,
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我们可以建造高楼大厦，
修建道路，建设城市，
修建道路，建设城市，
08:17
to make factories that would churn out
lines of chairs or refrigerators
lines of chairs or refrigerators
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开办工厂，源源不断地
生产桌椅或冰箱，
生产桌椅或冰箱，
08:23
way beyond our own power.
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这些都远远超出了人力所为。
08:24
And that artificial power can also
be distributed on wires on a grid
be distributed on wires on a grid
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这种“人造能源”
还可以通过电网和电线
还可以通过电网和电线
08:31
to every home, factory, farmstead,
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输送到家庭、工厂和农庄，
08:34
and anybody could buy
that artificial power,
that artificial power,
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任何人都可以
购买这种“人造能源”，
购买这种“人造能源”，
08:38
just by plugging something in.
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只需插上插头就可以使用。
08:39
So this was a source
of innovation as well,
of innovation as well,
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它也带来了很多创新，
08:42
because a farmer could take
a manual hand pump,
a manual hand pump,
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农民可以为手动泵通上电，
08:45
and they could add this artificial
power, this electricity,
power, this electricity,
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加上这种“人造能源”，
08:48
and he'd have an electric pump.
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就变成了电泵。
08:50
And you multiply that by thousands
or tens of thousands of times,
or tens of thousands of times,
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类似的改造成千上万，
08:53
and that formula was what brought us
the Industrial Revolution.
the Industrial Revolution.
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这个（人力器械+人造能源的）
公式造就了工业革命。
公式造就了工业革命。
08:56
All the things that we see,
all this progress that we now enjoy,
all this progress that we now enjoy,
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今天我们看到的所有事物，
享受的所有服务，
享受的所有服务，
几乎都来源于此。
09:00
has come from the fact
that we've done that.
that we've done that.
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现在我们要用 AI 做同样的事情。
09:02
We're going to do
the same thing now with AI.
the same thing now with AI.
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09:04
We're going to distribute that on a grid,
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我们用网路传输 AI，
把 AI 加载到
09:07
and now you can take that electric pump.
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09:09
You can add some artificial intelligence,
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诸如电泵之类的东西上，
09:12
and now you have a smart pump.
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就得到了聪明的电泵。
09:13
And that, multiplied by a million times,
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1928
类似的改造做上几百万次，
09:15
is going to be this second
Industrial Revolution.
Industrial Revolution.
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就会掀起第二次工业革命。
那么将来汽车行驶在高速上，
09:18
So now the car is going down the highway,
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09:20
it's 250 horsepower,
but in addition, it's 250 minds.
but in addition, it's 250 minds.
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它不仅有 250倍马力，
还有 250倍的脑力。
还有 250倍的脑力。
这就是自动驾驶汽车。
09:25
That's the auto-driven car.
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09:26
It's like a new commodity;
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它是一种新的商品，
09:28
it's a new utility.
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1303
是一种新的基础设施。
09:29
The AI is going to flow
across the grid -- the cloud --
across the grid -- the cloud --
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AI 将会在网络、在云端传输，
就像电一样。
09:32
in the same way electricity did.
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09:34
So everything that we had electrified,
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所以凡是可以用电的地方，
09:36
we're now going to cognify.
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都可以用 AI。
09:38
And I owe it to Jeff, then,
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正如 Jeff 所说，
09:40
that the formula
for the next 10,000 start-ups
for the next 10,000 start-ups
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未来一万家创业公司的秘诀
其实非常非常简单：
09:43
is very, very simple,
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1162
09:45
which is to take x and add AI.
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拿来某样东西，加上 AI。
09:49
That is the formula,
that's what we're going to be doing.
that's what we're going to be doing.
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这个公式就是我们将要不断践行的。
09:51
And that is the way
in which we're going to make
in which we're going to make
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我们将以这种方式
09:55
this second Industrial Revolution.
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1858
来掀起第二次工业革命。
09:57
And by the way -- right now, this minute,
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585148
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顺便说一句，就在此时，
你可以登录谷歌，
09:59
you can log on to Google
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587326
1169
购买 AI：用6美分
购买100次服务。
购买100次服务。
10:00
and you can purchase
AI for six cents, 100 hits.
AI for six cents, 100 hits.
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588519
3882
10:04
That's available right now.
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592758
1604
这个服务现在就能用。
10:06
So the third aspect of this
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594386
2286
第三点是，
10:09
is that when we take this AI
and embody it,
and embody it,
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我们将AI实体化，
10:12
we get robots.
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就得到了机器人。
机器人可以帮助我们，
10:13
And robots are going to be bots,
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1703
10:14
they're going to be doing many
of the tasks that we have already done.
of the tasks that we have already done.
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3328
完成许多曾经需要
我们亲力亲为的任务。
我们亲力亲为的任务。
10:20
A job is just a bunch of tasks,
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而工作就是一系列的任务，
10:21
so they're going to redefine our jobs
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1762
我们的工作将会被重新定义，
10:23
because they're going to do
some of those tasks.
some of those tasks.
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一部分任务将交给机器人来完成。
与此同时，也将产生一大批
10:25
But they're also going to curate
whole new categories,
whole new categories,
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3197
10:29
a whole new slew of tasks
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2247
不同种类的新任务，
10:31
that we didn't know
we wanted to do before.
we wanted to do before.
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2457
一批以往我们没有意识到
要去做的任务。
要去做的任务。
它们甚至有可能催生出新的职业，
10:33
They're going to actually
engender new kinds of jobs,
engender new kinds of jobs,
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3637
10:37
new kinds of tasks that we want done,
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我们感兴趣的新工作，
10:39
just as automation made up
a whole bunch of new things
a whole bunch of new things
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3405
就像自动化带来的许多新事物，
10:43
that we didn't know we needed before,
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1834
我们之前并不知道会需要它们，
10:45
and now we can't live without them.
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633194
1956
但今天我们已经离不开它们了。
10:47
So they're going to produce
even more jobs than they take away,
even more jobs than they take away,
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635174
3956
所以机器人带来的
工作机会比它们抢走的要多。
工作机会比它们抢走的要多。
10:51
but it's important that a lot of the tasks
that we're going to give them
that we're going to give them
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3434
更重要的是，我们交给它们的
10:54
are tasks that can be defined
in terms of efficiency or productivity.
in terms of efficiency or productivity.
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4572
都是需要效率或生产率的任务。
如果一个任务，
10:59
If you can specify a task,
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11:01
either manual or conceptual,
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2235
不管是体力的还是脑力的，
11:03
that can be specified in terms
of efficiency or productivity,
of efficiency or productivity,
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4780
可以用效率或生产率来衡量，
11:08
that goes to the bots.
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1777
那么就应该交给机器人来完成。
11:10
Productivity is for robots.
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2178
需要效率的事情交给机器人好了。
11:12
What we're really good at
is basically wasting time.
is basically wasting time.
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我们真正擅长的是浪费时间。
11:16
(Laughter)
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（笑声）
我们最擅长做那些没有效率的事情。
11:17
We're really good at things
that are inefficient.
that are inefficient.
226
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2316
11:19
Science is inherently inefficient.
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3025
科学从本质上来说是低效的。
我们一次又一次的失败，
11:22
It runs on that fact that you have
one failure after another.
one failure after another.
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2906
11:25
It runs on the fact that you make tests
and experiments that don't work,
and experiments that don't work,
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673746
3424
很多试验和尝试都徒劳无功，
否则我们也学不到什么东西。
11:29
otherwise you're not learning.
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事实就是，
11:30
It runs on the fact
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1162
科学研究没有什么效率。
11:31
that there is not
a lot of efficiency in it.
a lot of efficiency in it.
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2083
11:33
Innovation by definition is inefficient,
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创新从定义上来说就是低效的。
11:36
because you make prototypes,
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1391
毕竟我们需要制作原型，
11:38
because you try stuff that fails,
that doesn't work.
that doesn't work.
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2707
需要做各种尝试，经历各种失败。
11:40
Exploration is inherently inefficiency.
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3112
探索是低效的。
11:44
Art is not efficient.
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1531
艺术是低效的。
11:45
Human relationships are not efficient.
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2127
人际关系也是低效的。
11:47
These are all the kinds of things
we're going to gravitate to,
we're going to gravitate to,
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2940
这些都是我们喜欢做的事情，
因为它们都是低效的。
11:50
because they're not efficient.
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1475
11:52
Efficiency is for robots.
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2315
高效是机器人的使命。
11:55
We're also going to learn
that we're going to work with these AIs
that we're going to work with these AIs
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4123
还要认识到，我们将和 AI 一起工作，
11:59
because they think differently than us.
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1997
因为它们的思维方式与我们不同。
12:02
When Deep Blue beat
the world's best chess champion,
the world's best chess champion,
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在“深蓝”战胜国际象棋的世界冠军后，
12:06
people thought it was the end of chess.
245
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1929
人们以为国际象棋没什么玩头了。
但事实上，目前世界上
最厉害的国际象棋冠军
最厉害的国际象棋冠军
12:08
But actually, it turns out that today,
the best chess champion in the world
the best chess champion in the world
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4402
并不是 AI，
12:12
is not an AI.
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1557
12:14
And it's not a human.
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1181
也不是人类，
12:16
It's the team of a human and an AI.
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2715
而是由人类和 AI 组成的团队。
12:18
The best medical diagnostician
is not a doctor, it's not an AI,
is not a doctor, it's not an AI,
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最棒的医学诊疗师
既不是医生，也不是 AI，
既不是医生，也不是 AI，
而是他们组成的团队。
12:22
it's the team.
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1176
也就是说我们将和 AI 一起工作，
12:24
We're going to be working with these AIs,
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2149
12:26
and I think you'll be paid in the future
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你将来的薪酬，
12:28
by how well you work with these bots.
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2391
很可能取决于
你跟机器人合作得如何。
你跟机器人合作得如何。
12:31
So that's the third thing,
is that they're different,
is that they're different,
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这就是我想说的第三点：
AI 是不同于我们的，
AI 是不同于我们的，
12:35
they're utility
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1165
它们是技术设备，
12:36
and they are going to be something
we work with rather than against.
we work with rather than against.
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3816
我们将与它们合作，
12:40
We're working with these
rather than against them.
rather than against them.
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而非竞争。
12:42
So, the future:
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1477
那么，
未来会如何？
12:44
Where does that take us?
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12:45
I think that 25 years from now,
they'll look back
they'll look back
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我想，25年后我们回头再看
12:49
and look at our understanding
of AI and say,
of AI and say,
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3125
今天对 AI 的理解，我们会说：
“你们那都不叫 AI。
你们甚至都还没有真正的因特网，
你们甚至都还没有真正的因特网，
12:52
"You didn't have AI. In fact,
you didn't even have the Internet yet,
you didn't even have the Internet yet,
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25年后的因特网才能叫因特网呢。“
12:56
compared to what we're going
to have 25 years from now."
to have 25 years from now."
264
764008
2741
12:59
There are no AI experts right now.
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3047
我们也还没有真正的 AI 专家。
13:02
There's a lot of money going to it,
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1699
而大量的资本正涌向这个领域，
动辄数十亿美金，
13:04
there are billions of dollars
being spent on it;
being spent on it;
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13:06
it's a huge business,
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2164
这是一个巨大的产业。
13:09
but there are no experts, compared
to what we'll know 20 years from now.
to what we'll know 20 years from now.
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4272
但我们尚未拥有真正的 AI 专家——
如果跟20年后相比的话。
如果跟20年后相比的话。
13:14
So we are just at the beginning
of the beginning,
of the beginning,
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2885
我们还处在最初的起步阶段，
13:16
we're in the first hour of all this.
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2163
所有一切才刚刚开始。
13:19
We're in the first hour of the Internet.
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787160
1935
因特网的历史才刚刚开始。
美好的未来才刚刚开始。
13:21
We're in the first hour of what's coming.
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789119
2040
未来20年最受欢迎的 AI 产品，
13:23
The most popular AI product
in 20 years from now,
in 20 years from now,
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4153
最普及的 AI 产品，
13:27
that everybody uses,
275
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1444
还没有被发明呢。
13:29
has not been invented yet.
276
797499
1544
13:32
That means that you're not late.
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也就是说，你们还有机会。
13:35
Thank you.
278
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谢谢！
（笑声）
13:36
(Laughter)
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13:37
(Applause)
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（掌声）
ABOUT THE SPEAKER
Kevin Kelly - Digital visionaryThere may be no one better to contemplate the meaning of cultural change than Kevin Kelly, whose life story reads like a treatise on the value and impacts of technology.
Why you should listen
Kelly has been publisher of the Whole Earth Review, executive editor at Wired magazine (which he co-founded, and where he now holds the title of Senior Maverick), founder of visionary nonprofits and writer on biology, business and “cool tools.” He’s renounced all material things save his bicycle (which he then rode 3,000 miles), founded an organization (the All-Species Foundation) to catalog all life on Earth, championed projects that look 10,000 years into the future (at the Long Now Foundation), and more. He’s admired for his acute perspectives on technology and its relevance to history, biology and society. His new book, The Inevitable, just published, explores 12 technological forces that will shape our future.
Kevin Kelly | Speaker | TED.com