TED2017
Radhika Nagpal: What intelligent machines can learn from a school of fish
拉蒂·卡纳格普: 智能机器可以从鱼群中学到什么呢？
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Readability: 4.3
1,266,261 views
科幻小说对未来的幻想向我们展示了人工智能如何被用于复制人们思考的方式，但是如果我们对从自然界中发现的其他形式的智能也进行建模，那会怎么样呢？机器人工程师——拉蒂卡纳格普研究了昆虫和鱼群的集体智能，并尝试着去理解它们的交互规则。在这个前瞻性的谈话中，她展示了她创造人工集体力量的工作并展望未来一大群机器人如何一起建造防洪堤、为农作物传粉、模拟珊瑚礁以及成为卫星的行星。
Radhika Nagpal - Robotics engineer
Taking cues from bottom-up biological networks like those of social insects, Radhika Nagpal helped design an unprecedented “swarm” of ant-like robots. Full bio
Taking cues from bottom-up biological networks like those of social insects, Radhika Nagpal helped design an unprecedented “swarm” of ant-like robots. Full bio
Double-click the English transcript below to play the video.
00:12
In my early days as a graduate student,
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当我还在读研究生的时候
我在巴哈马海岸参加了一次潜泳之旅
00:14
I went on a snorkeling trip
off the coast of the Bahamas.
off the coast of the Bahamas.
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00:18
I'd actually never swum
in the ocean before,
in the ocean before,
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我之前从没在海里游过泳
心里难免有些忐忑
00:21
so it was a bit terrifying.
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00:24
What I remember the most is,
as I put my head in the water
as I put my head in the water
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让我印象最深的是
当我把头潜入水中时
当我把头潜入水中时
00:27
and I was trying really hard
to breathe through the snorkel,
to breathe through the snorkel,
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很努力地试图通过通气管呼吸
这时一大群拥有黄黑相间条纹的鱼
00:31
this huge group
of striped yellow and black fish
of striped yellow and black fish
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00:36
came straight at me ...
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径直向我游过来
我瞬间变得不知所措
00:38
and I just froze.
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00:40
And then, as if it had
suddenly changed its mind,
suddenly changed its mind,
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之后鱼群好像突然改变了主意
00:44
came towards me
and then swerved to the right
and then swerved to the right
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游到我面前 然后突然右转
00:47
and went right around me.
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与我擦肩而过
00:48
It was absolutely mesmerizing.
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这鱼群彻底惊艳到我了
00:50
Maybe many of you
have had this experience.
have had this experience.
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也许你们中的许多人也有过这样的经历
00:53
Of course, there's the color
and the beauty of it,
and the beauty of it,
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当然 令人着迷的不但
有它的艳丽和壮美
有它的艳丽和壮美
00:56
but there was also
just the sheer oneness of it,
just the sheer oneness of it,
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同时也有它纯粹的同一性
00:59
as if it wasn't hundreds of fish
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好像它不是成百上千条鱼
01:02
but a single entity
with a single collective mind
with a single collective mind
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而是单一 有唯一集体思维的个体
01:05
that was making decisions.
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在做决定
01:07
When I look back, I think that experience
really ended up determining
really ended up determining
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回想起来 这段经历
对我职业生涯的大部分工作
对我职业生涯的大部分工作
01:11
what I've worked on for most of my career.
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起到了决定性的作用
01:16
I'm a computer scientist,
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我是一个计算机科学家
从事的领域是人工智能（AI）
01:17
and the field that I work in
is artificial intelligence.
is artificial intelligence.
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01:20
And a key theme in AI
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人工智能中一个关键的主题
01:22
is being able to understand intelligence
by creating our own computational systems
by creating our own computational systems
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是能通过创造我们自己的
计算机系统来了解智能
计算机系统来了解智能
01:26
that display intelligence
the way we see it in nature.
the way we see it in nature.
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这些系统展现了我们在
自然中所观察到的智能
自然中所观察到的智能
01:30
Now, most popular views of AI, of course,
come from science fiction and the movies,
come from science fiction and the movies,
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如今AI最受欢迎的想法是
源于科幻小说和电影
源于科幻小说和电影
01:34
and I'm personally a big Star Wars fan.
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我本人就是星球大战的超级粉丝
01:38
But that tends to be a very human-centric
view of intelligence.
view of intelligence.
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但那些关于AI的想法
往往以人类为中心
往往以人类为中心
01:43
When you think of a fish school,
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当你想到鱼群
01:45
or when I think of a flock of starlings,
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或当我想到一群八哥时
01:48
that feels like a really different
kind of intelligence.
kind of intelligence.
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那是一种全新的关于智能的体会
01:52
For starters, any one fish is just so tiny
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首先 每一条鱼在庞大鱼群的映衬下
01:56
compared to the sheer size
of the collective,
of the collective,
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都显得非常微不足道
01:59
so it seems that any one individual
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所以似乎任何一个个体
02:02
would have a really limited
and myopic view of what's going on,
and myopic view of what's going on,
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对于所发生事物的视角都是
非常有限和片面的
非常有限和片面的
智能似乎与个体无关
02:05
and intelligence
isn't really about the individual
isn't really about the individual
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而是群体的某种属性
02:08
but somehow a property
of the group itself.
of the group itself.
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02:12
Secondly, and the thing
that I still find most remarkable,
that I still find most remarkable,
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其次 我觉得最引人注目的是
02:15
is that we know that there are no leaders
supervising this fish school.
supervising this fish school.
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鱼群中没有指挥者监督它们
02:21
Instead, this incredible
collective mind behavior
collective mind behavior
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这种简直不可思议的集体行为
02:24
is emerging purely from the interactions
of one fish and another.
of one fish and another.
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仅仅源于个体之间的互动
02:29
Somehow, there are these interactions
or rules of engagement
or rules of engagement
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在相邻的鱼之间以某种方式
02:33
between neighboring fish
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存在着合作的规律
02:35
that make it all work out.
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得以呈现这样的景象
02:37
So the question for AI then becomes,
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所以AI的问题演变成了
02:40
what are those rules of engagement
that lead to this kind of intelligence,
that lead to this kind of intelligence,
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引导这种智能背后的规则是什么呢
02:44
and of course, can we create our own?
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当然还包括 我们可以把它创造出来吗
02:46
And that's the primary thing
that I work on with my team in my lab.
that I work on with my team in my lab.
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这就是我和我的团队
在实验室中的主要工作内容
在实验室中的主要工作内容
02:50
We work on it through theory,
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我们通过理论研究
02:52
looking at abstract rule systems
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观察其中抽象的规则系统
02:54
and thinking about
the mathematics behind it.
the mathematics behind it.
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然后思考蕴藏在背后的运算规则
02:57
We also do it through biology,
working closely with experimentalists.
working closely with experimentalists.
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我们也会和生物学方面的
研究人员密切合作
研究人员密切合作
03:02
But mostly, we do it through robotics,
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但最主要的是
我们通过机器人进行研究
我们通过机器人进行研究
03:04
where we try to create
our own collective systems
our own collective systems
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尝试着去创造
属于我们自己的群体系统
属于我们自己的群体系统
03:08
that can do the kinds of things
that we see in nature,
that we see in nature,
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使其可以完成我们在
自然中所观察到的现象
自然中所观察到的现象
03:11
or at least try to.
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至少可以尝试一下
03:13
One of our first robotic quests
along this line
along this line
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由此 我们第一批的机器人探索活动
03:16
was to create our very own colony
of a thousand robots.
of a thousand robots.
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创造了属于我们的
由一千个机器人组成的群体
由一千个机器人组成的群体
03:21
So very simple robots,
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非常简单的机器人
03:22
but they could be programmed
to exhibit collective intelligence,
to exhibit collective intelligence,
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但可以通过编程让它们展现集体智慧
03:26
and that's what we were able to do.
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这也是我们能够实现的
03:28
So this is what a single robot looks like.
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这是单个的机器人
它非常小 只有一枚硬币大小
03:30
It's quite small,
about the size of a quarter,
about the size of a quarter,
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03:32
and you can program how it moves,
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你可以通过编程控制它的移动
03:35
but it can also wirelessly
communicate with other robots,
communicate with other robots,
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但它也可以和其他的机器人无线连接
03:38
and it can measure distances from them.
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同时也可以测量它们之间的距离
03:40
And so now we can start to program
exactly an interaction,
exactly an interaction,
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现在我们可以开始
精确地编码一个交互运动
精确地编码一个交互运动
03:44
a rule of engagement between neighbors.
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两个相邻机器人之间得运动规则
03:46
And once we have this system,
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一旦我们有了这个系统
03:48
we can start to program many
different kinds of rules of engagement
different kinds of rules of engagement
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我们就能开始编码你可以
在自然界中观察到的
在自然界中观察到的
03:52
that you would see in nature.
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许多不同种类的规则
03:53
So for example,
spontaneous synchronization,
spontaneous synchronization,
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例如自发同步现象
03:56
how audiences are clapping
and suddenly start all clapping together,
and suddenly start all clapping together,
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观众是怎样鼓掌
怎样突然一起开始鼓掌的
怎样突然一起开始鼓掌的
04:01
the fireflies flashing together.
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萤火虫如何同步闪烁
04:06
We can program rules
for pattern formation,
for pattern formation,
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我们可以编码图案形成的规则
04:09
how cells in a tissue
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组织中的细胞
04:11
determine what role
they're going to take on
they're going to take on
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如何决定它们将会起什么样的作用
04:13
and set the patterns of our bodies.
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进而决定我们的人体结构的
04:17
We can program rules for migration,
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我们可以编码迁移的规则
04:19
and in this way, we're really learning
from nature's rules.
from nature's rules.
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这样一来我们真的是在借鉴自然法则
04:22
But we can also take it a step further.
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但我们也可以更进一步
04:25
We can actually take these rules
that we've learned from nature
that we've learned from nature
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我们可以将这些从自然中学到的规律
04:28
and combine them and create
entirely new collective behaviors
entirely new collective behaviors
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结合在一起 创造全新的
属于我们自己的
属于我们自己的
04:32
of our very own.
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集体行为
04:33
So for example,
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例如
04:35
imagine that you had
two different kinds of rules.
two different kinds of rules.
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想象一下你有两种不同的规则
04:38
So your first rule is a motion rule
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第一种是运动规则
04:40
where a moving robot can move
around other stationary robots.
around other stationary robots.
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运动的机器人可以在
其他固定的机器人周围移动
其他固定的机器人周围移动
04:44
And your second rule is a pattern rule
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第二种是样式规则
机器人参考相邻的两个机器人
呈现与其不同的颜色
呈现与其不同的颜色
04:46
where a robot takes on a color
based on its two nearest neighbors.
based on its two nearest neighbors.
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04:50
So if I start with a blob of robots
in a little pattern seed,
in a little pattern seed,
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如果从一小组机器人开始
04:54
it turns out that these two rules
are sufficient for the group
are sufficient for the group
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结果显示这两种规则已经
足够实现让群体自我组装成
足够实现让群体自我组装成
04:57
to be able to self-assemble
a simple line pattern.
a simple line pattern.
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一个简单的线性图案这一目标
05:01
And if I have more
complicated pattern rules,
complicated pattern rules,
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如果我用更复杂的样式规则
05:03
and I design error correction rules,
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并且设计错误更正规则
05:06
we can actually create really,
really complicated self assemblies,
really complicated self assemblies,
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我们就可以创造
非常复杂的自组装结构了
非常复杂的自组装结构了
05:09
and here's what that looks like.
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给大家展示一下初步成果
05:11
So here, you're going to see
a thousand robots
a thousand robots
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这里你们可以看到一千个机器人
05:14
that are working together
to self-assemble the letter K.
to self-assemble the letter K.
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合作自发组成字母K
05:18
The K is on its side.
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完成后的字母位于上方
05:20
And the important thing
is that no one is in charge.
is that no one is in charge.
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重要的是没有一个
机器人起指导作用
机器人起指导作用
05:22
So any single robot is only talking
to a small number of robots nearby it,
to a small number of robots nearby it,
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每个机器人
只和周围的一些机器人交流
只和周围的一些机器人交流
05:27
and it's using its motion rule
to move around the half-built structure
to move around the half-built structure
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它利用自身的运动规则
在建了一半的结构周围运动
在建了一半的结构周围运动
05:31
just looking for a place to fit in
based on its pattern rules.
based on its pattern rules.
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只是根据其样式规律
寻找一个合适的位置
寻找一个合适的位置
05:35
And even though no robot
is doing anything perfectly,
is doing anything perfectly,
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即使没有一个机器人的行为是完美的
05:40
the rules are such that we can get
the collective to do its goal
the collective to do its goal
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这些规则却足以使我们通过集体协作
顺利达成目标
05:43
robustly together.
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05:46
And the illusion becomes
almost so perfect, you know --
almost so perfect, you know --
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这种幻想近乎完美
你们甚至不需要注意到
它们是个体机器人
它们是个体机器人
05:49
you just start to not even notice
that they're individual robots at all,
that they're individual robots at all,
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05:52
and it becomes a single entity,
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它们变成了一个单一的实体
05:54
kind of like the school of fish.
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就像刚刚提到的鱼群一样
06:00
So these are robots and rules
in two dimensions,
in two dimensions,
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这些是从二维层面看
机器人及其运动规则
机器人及其运动规则
06:02
but we can also think about robots
and rules in three dimensions.
and rules in three dimensions.
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但我们也可以从三维的角度
考虑机器人和规则
考虑机器人和规则
06:06
So what if we could create robots
that could build together?
that could build together?
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如果我们能创造
有建筑能力的机器人呢
有建筑能力的机器人呢
06:10
And here, we can take inspiration
from social insects.
from social insects.
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看看这里 我们从
社会性昆虫身上获得了启发
社会性昆虫身上获得了启发
06:14
So if you think about
mound-building termites
mound-building termites
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如果你想到了白蚁丘
06:16
or you think about army ants,
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或者是行军蚁
06:18
they create incredible,
complex nest structures out of mud
complex nest structures out of mud
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它们用泥土 甚至用它们自己的身体
06:23
and even out of their own bodies.
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惊人 复杂的巢穴结构
06:26
And like the system I showed you before,
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就像我之前给你们展示的系统一样
06:28
these insects actually
also have pattern rules
also have pattern rules
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这些昆虫实际上也有排布规则
06:31
that help them determine what to build,
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可以帮助它们决定要建造什么
06:33
but the pattern can be made
out of other insects,
out of other insects,
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但是这种样式结构
也可以由其他昆虫造出来
也可以由其他昆虫造出来
06:36
or it could be made out of mud.
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或者是用泥土
06:39
And we can use that same idea
to create rules for robots.
to create rules for robots.
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我们也可以将同样的想法
用于创建机器人规则上
用于创建机器人规则上
06:44
So here, you're going to see
some simulated robots.
some simulated robots.
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这里你们可以看到一些模拟机器人
06:47
So the simulated robot has a motion rule,
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模拟机器人有自己的运动规则
指导它如何穿越结构体
06:49
which is how it traverses
through the structure,
through the structure,
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06:52
looking for a place to fit in,
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找到一个合适的地方
06:54
and it has pattern rules
where it looks at groups of blocks
where it looks at groups of blocks
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同时它也有排布规则
它会查看组块
它会查看组块
06:57
to decide whether to place a block.
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来决定是否放下一个方块
07:00
And with the right motion rules
and the right pattern rules,
and the right pattern rules,
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有了正确的运动和样式规则之后
07:03
we can actually get the robots
to build whatever we want.
to build whatever we want.
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我们就能让机器人建造出
我们想要的任何东西
我们想要的任何东西
07:08
And of course, everybody wants
their own tower.
their own tower.
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当然每个人都想要属于自己的塔楼
07:11
(Laughter)
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（笑）
07:14
So once we have these rules,
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一旦我们有了这些规则
07:15
we can start to create the robot bodies
that go with these rules.
that go with these rules.
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我们就可以开始创造
能遵守这些规则的机器人
能遵守这些规则的机器人
07:18
So here, you see a robot
that can climb over blocks,
that can climb over blocks,
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在这儿你们能看到
可以爬上方块的机器人
可以爬上方块的机器人
07:22
but it can also lift and move these blocks
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但它也可以举起和移动这些方块
07:24
and it can start to edit
the very structure that it's on.
the very structure that it's on.
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可以编辑它所在的结构体
07:28
But with these rules,
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但即便有了这些规则
07:29
this is really only one kind of robot body
that you could imagine.
that you could imagine.
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这也仅仅只是你们能想到的
机器人中的一种
机器人中的一种
07:33
You could imagine
many different kinds of robot bodies.
many different kinds of robot bodies.
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你们可以想象许多不同种类的机器人
如果你觉得机器人也许可以移动沙袋
07:35
So if you think about robots
that maybe could move sandbags
that maybe could move sandbags
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07:40
and could help build levees,
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并帮助建筑堤坝
07:43
or we could think of robots
that built out of soft materials
that built out of soft materials
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或者我们想象机器人
可以用柔软的材料造成
可以用柔软的材料造成
07:47
and worked together
to shore up a collapsed building --
to shore up a collapsed building --
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共同支撑倒塌的建筑
只是把同样的规则
用于不同种类的机器人
用于不同种类的机器人
07:51
so just the same kind of rules
in different kinds of bodies.
in different kinds of bodies.
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07:56
Or if, like my group, you are completely
obsessed with army ants,
obsessed with army ants,
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或是像我的团队一样
你完全着迷于行军蚁
你完全着迷于行军蚁
08:00
then maybe one day we can make robots
that can climb over literally anything
that can climb over literally anything
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那么也许有一天我们能
让机器人攀爬所有的东西
让机器人攀爬所有的东西
包括它们部落中的其它成员
08:04
including other members of their tribe,
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08:07
and self-assemble things
out of their own bodies.
out of their own bodies.
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或者用它们自身自组装一些东西
08:10
Once you understand the rules,
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一旦你明白了这些规律
08:11
just many different kinds
of robot visions become possible.
of robot visions become possible.
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许多不同的机器人愿景都将成为可能
08:18
And coming back to the snorkeling trip,
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再回到那个潜泳之旅
事实上我们非常了解鱼群的运动规则
08:21
we actually understand a great deal
about the rules that fish schools use.
about the rules that fish schools use.
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08:26
So if we can invent
the bodies to go with that,
the bodies to go with that,
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如果我们能创造一个那样的群体
08:29
then maybe there is a future
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那么也许在未来
08:31
where I and my group will get to snorkel
with a fish school of our own creation.
with a fish school of our own creation.
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我的团队将和我们创造的鱼群
展开一次潜泳之旅
展开一次潜泳之旅
08:40
Each of these systems that I showed you
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我向你们展示的每一个系统
都使我们更接近利用数学和概念工具
08:43
brings us closer to having
the mathematical and the conceptual tools
the mathematical and the conceptual tools
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08:47
to create our own versions
of collective power,
of collective power,
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来创造属于我们的集体力量这一目标
这也使许多不同的未来应用成为可能
08:50
and this can enable many different kinds
of future applications,
of future applications,
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08:53
whether you think about robots
that build flood barriers
that build flood barriers
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机器人可以建筑防洪堤
可以给农作物授粉
08:56
or you think about robotic bee colonies
that could pollinate crops
that could pollinate crops
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09:01
or underwater schools of robots
that monitor coral reefs,
that monitor coral reefs,
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或是让水下机器人模拟珊瑚礁
09:04
or if we reach for the stars
and we thinking about programming
and we thinking about programming
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又或者假设我们抵达某个星球
并设计了卫星的行星
09:07
constellations of satellites.
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09:10
In each of these systems,
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在每一个系统中
09:11
being able to understand
how to design the rules of engagement
how to design the rules of engagement
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能明白如何设计交互规则
09:15
and being able to create
good collective behavior
good collective behavior
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以及创造良好的集体行为
09:17
becomes a key to realizing these visions.
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成为了实现这些愿景的关键
09:22
So, so far I've talked about
rules for insects and for fish
rules for insects and for fish
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那么到目前为 止
我谈到了昆虫 鱼群
我谈到了昆虫 鱼群
09:26
and for robots,
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还有机器人的各种行为规则
09:29
but what about the rules that apply
to our own human collective?
to our own human collective?
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但是那些适用于人类集体的规则呢
09:32
And the last thought
that I'd like to leave you with
that I'd like to leave you with
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我想留给你们的最后一点思考是
09:35
is that science is of course itself
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科学本身 毫无疑问就是
09:37
an incredible manifestation
of collective intelligence,
of collective intelligence,
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集体智慧的伟大体现
09:40
but unlike the beautiful
fish schools that I study,
fish schools that I study,
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但不同于我所研究的美丽鱼群
09:43
I feel we still have a much longer
evolutionary path to walk.
evolutionary path to walk.
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我觉得我们还有很长的一段路要走
09:48
So in addition to working on improving
the science of robot collectives,
the science of robot collectives,
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除了要致力于改进机器人集体的科学
09:53
I also work on creating robots
and thinking about rules
and thinking about rules
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我还致力于创造机器人和思考
能够改善我们的科学团队的规则
09:56
that will improve
our own scientific collective.
our own scientific collective.
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10:00
There's this saying that I love:
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有一句谚语我很喜欢
10:01
who does science
determines what science gets done.
determines what science gets done.
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科学的成果掌握在从事科学的人手中
10:06
Imagine a society
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想象这样一个社会
我们有交互规则
10:09
where we had rules of engagement
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使得每个孩子从小就
相信自己长大后能站在这里
相信自己长大后能站在这里
10:10
where every child grew up believing
that they could stand here
that they could stand here
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10:14
and be a technologist of the future,
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并成为未来的技术专家
10:16
or where every adult
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或者每一个成年人
10:18
believed that they had the ability
not just to understand but to change
not just to understand but to change
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都相信他们不仅有能力去理解
更有能力去改变
更有能力去改变
10:22
how science and technology
impacts their everyday lives.
impacts their everyday lives.
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科技如何影响他们的日常生活
10:26
What would that society look like?
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那样的社会该有多么美好啊
10:30
I believe that we can do that.
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我相信我们能做得到
10:31
I believe that we can choose our rules,
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我相信我们可以选择自己的规则
10:34
and we engineer not just robots
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我们不仅能设计机器人
更可以设计我们的人类群体
10:36
but we can engineer
our own human collective,
our own human collective,
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10:38
and if we do and when we do,
it will be beautiful.
it will be beautiful.
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在我们开始行动时
一切都将变得非常美好
一切都将变得非常美好
10:42
Thank you.
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谢谢
10:43
(Applause)
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（掌声）
Translated by Chen Yunru
ABOUT THE SPEAKER
Radhika Nagpal - Robotics engineerTaking cues from bottom-up biological networks like those of social insects, Radhika Nagpal helped design an unprecedented “swarm” of ant-like robots.
Why you should listen
With a swarm of 1,024 robots inspired by the design of ant colonies, Radhika Nagpal and her colleagues at Harvard’s SSR research group have redefined expectations for self-organizing robotic systems. Guided by algorithms, Nagpal’s shockingly simple robots guide themselves into a variety of shapes -- an ability that, brought to scale, might lead to applications like disaster rescue, space exploration and beyond.
In addition to her work with biologically inspired robots, Nagpal helped create ROOT, a simple robot to teach coding to would-be programmers through a simple user interface suitable for students of all ages.
Radhika Nagpal | Speaker | TED.com