What intelligent machines can learn from a school of fish | Radhika Nagpal
109,666 views ・ 2017-10-06
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譯者: Lilian Chiu
審譯者: Wilde Luo
00:12
In my early days as a graduate student,
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在我剛開始成為研究生的時候,
00:14
I went on a snorkeling trip
off the coast of the Bahamas.
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我到巴哈馬海岸去浮潛。
00:18
I'd actually never swum
in the ocean before,
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我其實從未在海洋中游泳過,
00:21
so it was a bit terrifying.
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所以我有點害怕。
00:23
What I remember the most is,
as I put my head in the water
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我最難忘的是,當我把頭沉入水中,
00:26
and I was trying really hard
to breathe through the snorkel,
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並竭力透過呼吸管呼吸,
00:31
this huge group
of striped yellow and black fish
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有一大群黃黑條紋的魚
00:36
came straight at me ...
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筆直朝我遊來……
00:38
and I just froze.
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我呆住了。
00:40
And then, as if it had
suddenly changed its mind,
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然後,牠們好像突然轉念了一樣,
00:44
came towards me
and then swerved to the right
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朝我過來之後就向右急轉彎,
00:46
and went right around me.
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從我身邊繞過。
00:48
It was absolutely mesmerizing.
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那實在非常迷人。
00:50
Maybe many of you
have had this experience.
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也許在座有許多人有過這種體驗。
00:53
Of course, there's the color
and the beauty of it,
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當然,魚群的顏色及美麗都很難忘,
00:56
but there was also
just the sheer oneness of it,
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但牠們還有著一種純粹的一體感,
00:59
as if it wasn't hundreds of fish
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彷彿牠們並不是數百條魚,
01:01
but a single entity
with a single collective mind
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而是一個整體,包含著
一個做出決策的集體思維。
01:04
that was making decisions.
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01:07
When I look back, I think that experience
really ended up determining
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回想起來,我認為那段經歷
使我最終下定決心
01:11
what I've worked on for most of my career.
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去做這份佔據我大半生涯的工作。
01:15
I'm a computer scientist,
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我是個計算機科學家,
01:17
and the field that I work in
is artificial intelligence.
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我研究的領域是人工智慧。
01:20
And a key theme in AI
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人工智慧的關鍵主題
是要能理解「智慧」的本質,
01:22
is being able to understand intelligence
by creating our own computational systems
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做法是創建自己的計算系統
(computational system)
01:26
that display intelligence
the way we see it in nature.
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來展現類似於自然生物的智慧。
01:30
Now, most popular views of AI, of course,
come from science fiction and the movies,
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當然,目前最熱門的人工智慧觀點
來自科幻小說和電影,
01:34
and I'm personally a big Star Wars fan.
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我個人是《星際大戰》的忠實粉絲。
01:38
But that tends to be a very human-centric
view of intelligence.
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但那往往是個非常
以人為中心的智慧觀。
01:42
When you think of a fish school,
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當你思考魚群
01:45
or when I think of a flock of starlings,
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或想像一群椋鳥,
01:48
that feels like a really different
kind of intelligence.
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那感覺是一種完全
不同的智慧形式。
01:52
For starters, any one fish is just so tiny
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首先,和整體魚群的大小相比較,
01:56
compared to the sheer size
of the collective,
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一條魚真的是太小了,
01:59
so it seems that any one individual
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所以,似乎其中任何一個個體
02:02
would have a really limited
and myopic view of what's going on,
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對正在發生的事應該
眼光短淺、缺乏遠見。
02:05
and intelligence
isn't really about the individual
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而且「智慧」並不體現在個體身上,
02:08
but somehow a property
of the group itself.
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而是團體本身的一種特性。
02:11
Secondly, and the thing
that I still find most remarkable,
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第二,我仍然認為是最了不起的事,
02:15
is that we know that there are no leaders
supervising this fish school.
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就是我們知道在這魚群中
並不存在管理著群體的領導者。
02:20
Instead, this incredible
collective mind behavior
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反而,這個集體思維
所做出的非凡行為
02:24
is emerging purely from the interactions
of one fish and another.
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單純來自魚與魚間的互動。
02:29
Somehow, there are these interactions
or rules of engagement
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不知何故,相鄰近的魚之間
會存在著這些互動,
02:33
between neighboring fish
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或者說是約定好的行為規則,
02:34
that make it all work out.
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從而產生這集體行為。
02:37
So the question for AI then becomes,
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所以,對人工智慧的問題變成是:
02:40
what are those rules of engagement
that lead to this kind of intelligence,
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是什麼約定規則產生這種智慧的?
02:44
and of course, can we create our own?
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當然還有,我們能否自己創造一個?
02:46
And that's the primary thing
that I work on with my team in my lab.
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這是我與團隊的實驗研究主題。
02:50
We work on it through theory,
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我們透過理論來研究,
02:52
looking at abstract rule systems
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探究抽象的規則系統,
02:54
and thinking about
the mathematics behind it.
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思考其背後的數學原理。
02:57
We also do it through biology,
working closely with experimentalists.
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我們也透過生物學來研究,
與實驗者密切合作。
03:02
But mostly, we do it through robotics,
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但最主要是通過機器人研究,
03:04
where we try to create
our own collective systems
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嘗試創造我們自己的集體系統,
03:08
that can do the kinds of things
that we see in nature,
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讓系統能做出,或至少試著做出
自然界中的智慧行為。
03:11
or at least try to.
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03:13
One of our first robotic quests
along this line
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我們最初以這種方式
在機器人方面的探索之一,
03:16
was to create our very own colony
of a thousand robots.
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是創造我們自己的千人機器人群體。
03:20
So very simple robots,
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機器人非常簡單,
03:22
but they could be programmed
to exhibit collective intelligence,
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但能通過程式設計讓它們
展現出集體智慧,
03:25
and that's what we were able to do.
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這是我們能夠做到的。
03:28
So this is what a single robot looks like.
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單個的機器人看起來是這樣的。
03:30
It's quite small,
about the size of a quarter,
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它很小,約 25 分硬幣的大小,
03:32
and you can program how it moves,
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你可以設計程式來規範它如何移動,
03:34
but it can also wirelessly
communicate with other robots,
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它也能以無線的方式
和其他機器人溝通,
03:38
and it can measure distances from them.
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能測量與其他機器人的距離。
03:40
And so now we can start to program
exactly an interaction,
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我們就可以開始
針對一套互動規則來設計程式,
03:44
a rule of engagement between neighbors.
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指定鄰近機器人之間的行為規則。
03:46
And once we have this system,
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一旦有了這個系統,
03:48
we can start to program many
different kinds of rules of engagement
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我們就可針對自然界中的
各類約定規則來編寫程式。
03:51
that you would see in nature.
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03:53
So for example,
spontaneous synchronization,
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比如「自發性同步」,
03:56
how audiences are clapping
and suddenly start all clapping together,
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一旦有觀眾開始拍手,
全部都驟然跟著拍手,
04:01
the fireflies flashing together.
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螢火蟲也會一起發光。
04:06
We can program rules
for pattern formation,
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我們可以編寫圖案形成的規則,
(pattern formation)
04:09
how cells in a tissue
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組織中的細胞
04:11
determine what role
they're going to take on
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如何決定它們將扮演什麼角色
04:13
and set the patterns of our bodies.
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並設定我們身體的模式。
04:16
We can program rules for migration,
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我們可編寫遷移的規則,
04:18
and in this way, we're really learning
from nature's rules.
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以這種方式,我們能真正地
向自然界的規則學習。
04:22
But we can also take it a step further.
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但,我們也可以再進一步。
04:25
We can actually take these rules
that we've learned from nature
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我們可以組合這些
向自然界學來的規則,
04:28
and combine them and create
entirely new collective behaviors
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創造出我們自己的、
全新的集體行為。
04:31
of our very own.
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04:33
So for example,
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比如,
04:35
imagine that you had
two different kinds of rules.
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想像你有兩種不同的規則。
04:38
So your first rule is a motion rule
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第一種是動作規則,
04:40
where a moving robot can move
around other stationary robots.
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讓移動中的機器人
可以繞著靜止的機器人轉動。
04:44
And your second rule is a pattern rule
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第二種是模式規則,
04:46
where a robot takes on a color
based on its two nearest neighbors.
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機器人會根據旁邊
兩名同伴的顔色來呈現顏色。
04:50
So if I start with a blob of robots
in a little pattern seed,
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所以,最開始我只需一小群機器人,
就能埋下一顆「模式種子」,
04:53
it turns out that these two rules
are sufficient for the group
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結果,對這個群體而言,
有這兩種規則就足以自我組裝出
04:56
to be able to self-assemble
a simple line pattern.
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一個簡單的線條樣式。
05:00
And if I have more
complicated pattern rules,
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如果我有更複雜的模式規則
05:03
and I design error correction rules,
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且設計出修正錯誤的規則,
05:05
we can actually create really,
really complicated self assemblies,
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我們就能實際造出
非常複雜的自我組裝樣式,
05:08
and here's what that looks like.
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看起來就會像是這樣。
05:11
So here, you're going to see
a thousand robots
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所以,各位將會在這裡
看到一千個機器人,
05:14
that are working together
to self-assemble the letter K.
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它們正在合作並自我組裝出
英文字母「K」。
05:18
The K is on its side.
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這是一個側過來的 K 。
05:20
And the important thing
is that no one is in charge.
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重要的是,沒有人在主導。
05:22
So any single robot is only talking
to a small number of robots nearby it,
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所以任何一個機器人都只是在
和它附近的少數幾個機器人交談,
05:27
and it's using its motion rule
to move around the half-built structure
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它會用它的動作規則,
在這個半成品周圍移動,
05:31
just looking for a place to fit in
based on its pattern rules.
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根據它的模式規則,
找個適合的位置插進去。
05:35
And even though no robot
is doing anything perfectly,
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雖然沒有任一機器人
完美地做好一件事,
05:40
the rules are such that we can get
the collective to do its goal
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規則是這樣的,
我們可以讓集體一起
穩健地完成目標。
05:43
robustly together.
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05:45
And the illusion becomes
almost so perfect, you know --
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這個幻覺幾乎完美,
05:48
you just start to not even notice
that they're individual robots at all,
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你甚至會忘了它們各自是個機器人,
05:52
and it becomes a single entity,
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合起來成了單一的實體,
05:54
kind of like the school of fish.
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就像一群魚。
05:59
So these are robots and rules
in two dimensions,
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上面這些是二維世界中的
機器人及規則,
06:02
but we can also think about robots
and rules in three dimensions.
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但我們也可以思考
三維世界中的機器人及規則。
06:05
So what if we could create robots
that could build together?
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如果我們造出能
共同建設的機器人會如何呢?
06:10
And here, we can take inspiration
from social insects.
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這裡,我們的靈感來自於群居昆蟲。
06:14
So if you think about
mound-building termites
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如果你想到建立土墩的白蟻
06:16
or you think about army ants,
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或是行軍蟻,
06:18
they create incredible,
complex nest structures out of mud
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牠們造出很了不起、
很複雜的巢穴結構,
用泥巴,甚至用自己的身體。
06:23
and even out of their own bodies.
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06:26
And like the system I showed you before,
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就像我先前給各位看的系統,
06:28
these insects actually
also have pattern rules
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這些昆蟲其實也有模式規則
06:31
that help them determine what to build,
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來協助牠們決定要建造什麼,
06:33
but the pattern can be made
out of other insects,
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做模型的材料可以是其他昆蟲
06:36
or it could be made out of mud.
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甚至是泥巴。
06:38
And we can use that same idea
to create rules for robots.
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我們可以把同樣的想法
用來為機器人創造規則。
06:44
So here, you're going to see
some simulated robots.
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在這裡你將看到的
是一些模擬的機器人。
06:47
So the simulated robot has a motion rule,
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這模擬機器人有一條動作規則:
06:49
which is how it traverses
through the structure,
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以何種方式在結構中來回移動,
06:52
looking for a place to fit in,
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並尋找一個適合插入的地方。
06:54
and it has pattern rules
where it looks at groups of blocks
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同樣它也有一套模式規則,
使它在看到一堆積木時
決定是否放下手中的積木。
06:57
to decide whether to place a block.
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07:00
And with the right motion rules
and the right pattern rules,
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有正確的動作規則
和正確的模式規則,
07:03
we can actually get the robots
to build whatever we want.
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我們就能夠讓機器人建造出
任何我們想要的東西。
07:08
And of course, everybody wants
their own tower.
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當然,每個人都想擁有
屬於自己的一座塔。
07:11
(Laughter)
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(笑聲)
07:13
So once we have these rules,
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一旦我們有了這些規則,
07:15
we can start to create the robot bodies
that go with these rules.
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我們就可以配合這些規則
開始打造機器人的身體。
07:18
So here, you see a robot
that can climb over blocks,
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在這裡,各位可以看到,
機器人能爬過積木,
07:22
but it can also lift and move these blocks
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它也可以舉起和搬動這些積木,
07:24
and it can start to edit
the very structure that it's on.
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它可以自己開始修建這個結構。
07:28
But with these rules,
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但是配合這些規則,
07:29
this is really only one kind of robot body
that you could imagine.
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這其實只是所有你能想到的
機器人身體構造情況中的一種。
07:33
You could imagine
many different kinds of robot bodies.
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你還可想像出多種
不同的機器人身體構造。
07:35
So if you think about robots
that maybe could move sandbags
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所以,你也許可以想像出
會搬移沙袋的機器人,
07:40
and could help build levees,
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它們能協助築堤,
07:42
or we could think of robots
that built out of soft materials
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我們或許也可用軟材料做機器人,
07:47
and worked together
to shore up a collapsed building --
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共同撐起倒塌的建築物。
07:50
so just the same kind of rules
in different kinds of bodies.
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這只是把同樣的規則
放到不同類的身體中。
07:56
Or if, like my group, you are completely
obsessed with army ants,
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或者,和我的團隊一樣,
你可能對行軍蟻很著迷,
08:00
then maybe one day we can make robots
that can climb over literally anything
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那麼也許有一天
我們做出能爬過任何東西的機器人,
08:04
including other members of their tribe,
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包括爬過它們自己的夥伴成員,
08:06
and self-assemble things
out of their own bodies.
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用它們自己的身體組裝出東西。
08:09
Once you understand the rules,
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一旦你瞭解了規則,
08:11
just many different kinds
of robot visions become possible.
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多種不同類型的
機器人遠景都變為可能。
08:18
And coming back to the snorkeling trip,
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回到我的浮潛之旅,
08:20
we actually understand a great deal
about the rules that fish schools use.
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其實我們瞭解很多魚群的規則。
08:26
So if we can invent
the bodies to go with that,
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所以,若我們能發明出
配合這些規則的身體,
08:29
then maybe there is a future
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那麼也許在未來,
08:30
where I and my group will get to snorkel
with a fish school of our own creation.
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我和團隊會和我們創造出的
魚群一起浮潛。
08:40
Each of these systems that I showed you
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每一個我展現給你們的系統
08:42
brings us closer to having
the mathematical and the conceptual tools
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讓我們更進一步邁向
這些數學和概念性工具
08:47
to create our own versions
of collective power,
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來創造我們自己的集體力量,
08:50
and this can enable many different kinds
of future applications,
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這就能讓許多種
未來技術都成為可能,
08:53
whether you think about robots
that build flood barriers
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你可考慮用機器人來建立防洪設施,
08:56
or you think about robotic bee colonies
that could pollinate crops
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用機器蜜蜂群來授粉,
09:01
or underwater schools of robots
that monitor coral reefs,
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或用水底機器人群體來監看珊瑚礁;
09:04
or if we reach for the stars
and we thinking about programming
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或是我們雄心萬丈,
可以考慮為一群衛星設計程式。
09:07
constellations of satellites.
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09:09
In each of these systems,
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在所有這些系統中,
09:11
being able to understand
how to design the rules of engagement
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能夠瞭解如何設計出約定規則,
09:15
and being able to create
good collective behavior
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以及能夠創造出好的集體行為,
09:17
becomes a key to realizing these visions.
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是實現這些遠景的關鍵。
09:22
So, so far I've talked about
rules for insects and for fish
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目前,我已經談過了昆蟲、魚
09:26
and for robots,
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和機器人之間的規則,
09:29
but what about the rules that apply
to our own human collective?
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那麼用在我們自己
人類群體上的規則呢?
09:32
And the last thought
that I'd like to leave you with
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最後我想留給各位
去思考的一件事是
09:35
is that science is of course itself
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當然科學本身是
09:36
an incredible manifestation
of collective intelligence,
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集體智慧的一種偉大表現形式,
09:40
but unlike the beautiful
fish schools that I study,
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但不像我研究的美麗魚群,
09:43
I feel we still have a much longer
evolutionary path to walk.
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我覺得我們還有
非常長的演化之路要走。
09:48
So in addition to working on improving
the science of robot collectives,
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所以除了致力於發展機器人
群體的科學研究之外,
09:53
I also work on creating robots
and thinking about rules
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我也從事創造機器人的工作,
並且思考一些規則,
09:56
that will improve
our own scientific collective.
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它將對我們自己的
科學研究群體大有裨益。
10:00
There's this saying that I love:
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分享一句我喜歡的話:
10:01
who does science
determines what science gets done.
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做科學的人,決定了科學能做什麽。
10:06
Imagine a society
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想像一個這樣的社會:
10:09
where we had rules of engagement
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我們有個約定規則:
10:10
where every child grew up believing
that they could stand here
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每個孩子在成長的過程中都相信
他們能站在這個講臺上
10:14
and be a technologist of the future,
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成為未來的科技專家;
10:16
or where every adult
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或每個成年人都相信他們有能力
10:17
believed that they had the ability
not just to understand but to change
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不僅理解而且改變
科技對日常生活的影響。
10:22
how science and technology
impacts their everyday lives.
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10:26
What would that society look like?
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那樣的社會會是怎樣的?
10:30
I believe that we can do that.
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我相信我們能讓它成真。
10:31
I believe that we can choose our rules,
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我相信我們能選擇我們的規則,
10:34
and we engineer not just robots
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除了機器人之外,
10:35
but we can engineer
our own human collective,
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我們也能設計我們自己的人類群體,
10:38
and if we do and when we do,
it will be beautiful.
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如果我們做到了,
世界會變得無比美好。
10:42
Thank you.
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謝謝。
10:43
(Applause)
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(掌聲)
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