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Alex Wissner-Gross: A new equation for intelligence
艾力克斯·威斯奈-格羅斯 (Alex Wissner-Gross): 新的“智慧”方程式
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有沒有“智慧”的方程式？有的，F = T ∇ Sτ。在這個奇特且增廣見聞的演講中，物理學家和電腦科學家艾力克斯·威斯奈-格羅斯 (Alex Wissner-Gross) 解釋這個方程式代表什麼。
Alex Wissner-Gross - Scientist, entrepreneur, inventor
Alex Wissner-Gross applies science and engineering principles to big (and diverse) questions, like: "What is the equation for intelligence?" and "What's the best way to raise awareness about climate change?" Full bio
Alex Wissner-Gross applies science and engineering principles to big (and diverse) questions, like: "What is the equation for intelligence?" and "What's the best way to raise awareness about climate change?" Full bio
Double-click the English transcript below to play the video.
00:12
Intelligence -- what is it?
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智慧，是什麽？
00:16
If we take a look back at the history
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如果我們回顧歷史
00:18
of how intelligence has been viewed,
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對智慧的定義，
00:21
one seminal example has been
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有一個基本的例子是，
00:25
Edsger Dijkstra's famous quote that
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艾茲赫爾·戴克斯特拉說過的一句話：
(註：著名電腦科學家)
(註：著名電腦科學家)
00:28
"the question of whether a machine can think
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“關於機械是否能思考的問題
00:31
is about as interesting
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就有如在問
00:32
as the question of whether a submarine
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潛水艇是否能游泳
00:35
can swim."
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一樣有意思。”
00:37
Now, Edsger Dijkstra, when he wrote this,
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當艾茲赫爾·戴克斯特拉寫下這句話，
00:41
intended it as a criticism
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是在質疑
00:43
of the early pioneers of computer science,
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早期的電腦科學先驅，
00:46
like Alan Turing.
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譬如艾倫·圖靈。
00:48
However, if you take a look back
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然而，如果你回顧
00:50
and think about what have been
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並思考，
00:52
the most empowering innovations
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是什麼重大的創新
00:54
that enabled us to build
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使我們能夠製造出
00:56
artificial machines that swim
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會游泳和會飛的
00:58
and artificial machines that [fly],
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人造機器，
01:01
you find that it was only through understanding
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你就會發現，
01:05
the underlying physical mechanisms
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唯有透過了解
01:07
of swimming and flight
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游泳和飛翔的基本物理機制，
01:10
that we were able to build these machines.
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我們才能製造出這些機器。
01:13
And so, several years ago,
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因此，幾年前，
01:15
I undertook a program to try to understand
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我著手進行一個計劃，
01:19
the fundamental physical mechanisms
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試圖去了解什麼是
01:21
underlying intelligence.
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智慧的基本物理機制。
01:24
Let's take a step back.
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先讓我們退一步，
01:26
Let's first begin with a thought experiment.
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先從一個發想實驗開始。
01:29
Pretend that you're an alien race
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假設你是一個外星人，
01:32
that doesn't know anything about Earth biology
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對地球的生物完全不了解，
01:35
or Earth neuroscience or Earth intelligence,
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也不了解地球的神經學和生物智慧，
01:38
but you have amazing telescopes
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但你有很棒的望遠鏡，
01:40
and you're able to watch the Earth,
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可以直接看到地球，
01:43
and you have amazingly long lives,
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而且你有很長很長的壽命，
01:45
so you're able to watch the Earth
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所以你有好幾百萬年甚至好幾十億年的時間
01:46
over millions, even billions of years.
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來觀察地球。
01:50
And you observe a really strange effect.
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你發現一個很怪異的事情。
01:53
You observe that, over the course of the millennia,
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你發現，在千禧年這個過程中，
01:57
Earth is continually bombarded with asteroids
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地球不斷地遭到小行星的撞擊，
02:02
up until a point,
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直到某一天，
02:04
and that at some point,
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在某一個時刻，
02:05
corresponding roughly to our year, 2000 AD,
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大約就是我們現在的西元兩千年左右，
02:09
asteroids that are on
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小行星原本運行在
02:11
a collision course with the Earth
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會撞擊到地球的軌道上，
02:13
that otherwise would have collided
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但是那個軌道
02:15
mysteriously get deflected
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神奇地偏移了，
02:17
or they detonate before they can hit the Earth.
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或者小行星在撞到地球前爆炸了。
02:20
Now of course, as earthlings,
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當然，身為地球人，
02:23
we know the reason would be
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我們知道那是因為
02:24
that we're trying to save ourselves.
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我們試著拯救人類，
02:26
We're trying to prevent an impact.
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試著避免撞擊發生。
02:29
But if you're an alien race
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但如果你是外星人，
02:31
who doesn't know any of this,
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不知道這些，
02:32
doesn't have any concept of Earth intelligence,
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對地球上的智慧沒有任何概念，
02:34
you'd be forced to put together
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那麼你只好勉強拼湊出一個
02:36
a physical theory that explains how,
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物理理論來解釋，
02:39
up until a certain point in time,
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直到某一個時刻，
02:41
asteroids that would demolish the surface of a planet
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應該毀滅地表一切的小行星
02:46
mysteriously stop doing that.
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神奇地不再發生。
02:49
And so I claim that this is the same question
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而我認為這跟要了解
02:53
as understanding the physical nature of intelligence.
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智慧的物理機制是一樣的問題。
02:57
So in this program that I
undertook several years ago,
undertook several years ago,
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因此，在這項我幾年前開始進行的計劃中，
03:01
I looked at a variety of different threads
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我研究各式各樣的想法，
03:04
across science, across a variety of disciplines,
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橫跨科學以及不同領域，
03:07
that were pointing, I think,
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我認為，
03:09
towards a single, underlying mechanism
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這些都指向智慧的一個單一
03:12
for intelligence.
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基本機制。
03:13
In cosmology, for example,
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以宇宙論為例，
03:16
there have been a variety of
different threads of evidence
different threads of evidence
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有各種不同的證據顯示
03:18
that our universe appears to be finely tuned
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我們所在的宇宙是被精心調整到
03:22
for the development of intelligence,
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適合發展出智慧的，
03:24
and, in particular, for the development
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尤其是發展出一個
03:26
of universal states
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普遍性的狀態
03:28
that maximize the diversity of possible futures.
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能使未來的可能性上做最大化。
03:32
In game play, for example, in Go --
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以圍棋為例，
03:35
everyone remembers in 1997
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大家都記得1997年
03:38
when IBM's Deep Blue beat
Garry Kasparov at chess --
Garry Kasparov at chess --
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IBM 的深藍電腦打敗棋王卡斯帕羅夫，
03:42
fewer people are aware
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但只有少數人知道
03:43
that in the past 10 years or so,
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在過去的十年，
03:45
the game of Go,
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圍棋，
03:46
arguably a much more challenging game
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被視為是非常具挑戰性的遊戲，
03:48
because it has a much higher branching factor,
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因為它有更多的分歧因素，
03:51
has also started to succumb
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同時也開始讓
03:53
to computer game players
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電腦玩家臣服，
03:54
for the same reason:
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這些都是同樣的理由：
03:56
the best techniques right now
for computers playing Go
for computers playing Go
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現在讓電腦下棋最好的技巧
03:59
are techniques that try to maximize future options
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就是將下棋過程可能發生的事件數
04:02
during game play.
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最大化。
04:04
Finally, in robotic motion planning,
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最後，在機器人的行動規劃中，
04:08
there have been a variety of recent techniques
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最近的各種技術
04:10
that have tried to take advantage
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都是試圖讓機器人
04:12
of abilities of robots to maximize
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在未來能自由行動的可能性
04:15
future freedom of action
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做最大化，
04:17
in order to accomplish complex tasks.
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以完成某些複雜的任務。
04:20
And so, taking all of these different threads
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所以，用這些不同的想法，
04:22
and putting them together,
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把它們拼湊在一起，
04:24
I asked, starting several years ago,
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在幾年前我開始問，
04:27
is there an underlying mechanism for intelligence
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有沒有一個關於智慧的基本機制
04:29
that we can factor out
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是我們可以從這些不同的想法中
04:31
of all of these different threads?
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分解出來的？
04:33
Is there a single equation for intelligence?
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有沒有一個屬於智慧的方程式？
04:37
And the answer, I believe, is yes.
["F = T ∇ Sτ"]
["F = T ∇ Sτ"]
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我相信答案是，有的。
["F = T ∇ Sτ"]
["F = T ∇ Sτ"]
04:41
What you're seeing is probably
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你現在看到的
04:43
the closest equivalent to an E = mc²
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或許是我看過最接近 E = mc²
04:46
for intelligence that I've seen.
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的屬於智慧的方程式。
04:49
So what you're seeing here
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你所看到的
04:51
is a statement of correspondence
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是相對應的詮釋，
04:53
that intelligence is a force, F,
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智慧是一種力量，F
04:58
that acts so as to maximize future freedom of action.
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它的作用是最大化行動的自由度。
05:02
It acts to maximize future freedom of action,
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它的作用會最大化行動的自由度
05:05
or keep options open,
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或是一直保有開放的選擇，
05:06
with some strength T,
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配合某一強度 T，
05:08
with the diversity of possible accessible futures, S,
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和可能發生的未來多樣性，S
05:13
up to some future time horizon, tau.
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直到未來的某一個時間點，t。
05:16
In short, intelligence doesn't like to get trapped.
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簡單地說，智慧不喜歡被約束住。
05:19
Intelligence tries to maximize
future freedom of action
future freedom of action
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智慧希望最大化未來行動的自由度，
05:22
and keep options open.
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保持開放的選項。
05:25
And so, given this one equation,
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所以，有了這一個方程式，
05:27
it's natural to ask, so what can you do with this?
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很自然地就會問，你能用它做甚麼？
05:30
How predictive is it?
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它的預測能力如何？
05:31
Does it predict human-level intelligence?
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它能否預測人類的智慧？
05:33
Does it predict artificial intelligence?
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它能否預測人工智慧？
05:36
So I'm going to show you now a video
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現在我要給各位看一段影片，
05:38
that will, I think, demonstrate
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我認為可以說明
05:41
some of the amazing applications
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一些令人驚訝的應用，
05:44
of just this single equation.
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而且都只來自這一個方程式。
05:46
(Video) Narrator: Recent research in cosmology
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(影片) 旁白：宇宙學最近的研究
05:48
has suggested that universes that produce
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推論宇宙會產生愈來愈多的
05:50
more disorder, or "entropy," over their lifetimes
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失序，或是熵 (entropy)，
05:54
should tend to have more favorable conditions
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應該更容易擁有有利的環境，
05:56
for the existence of intelligent
beings such as ourselves.
beings such as ourselves.
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讓智慧存在。
05:59
But what if that tentative cosmological connection
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但如果把這個宇宙學待驗證的
06:02
between entropy and intelligence
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亂度和智慧的關係
06:04
hints at a deeper relationship?
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再進一步加深會怎樣？
06:05
What if intelligent behavior doesn't just correlate
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如果智慧和長期亂度的增加
06:08
with the production of long-term entropy,
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不只是有正相關性，
06:10
but actually emerges directly from it?
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而且是從中發展出來的呢？
06:12
To find out, we developed a software engine
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為了解答這問題，我們開發了一個軟體
06:14
called Entropica, designed to maximize
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叫做 "Entropica"，
06:17
the production of long-term entropy
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可以把任何系統中
06:19
of any system that it finds itself in.
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熵的長期成長最大化。
06:21
Amazingly, Entropica was able to pass
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令人驚訝的是，Entropica 能夠通過
06:23
multiple animal intelligence
tests, play human games,
tests, play human games,
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多項動物智慧測試，玩人類的遊戲，
06:27
and even earn money trading stocks,
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甚至從股票交易中賺到錢，
06:29
all without being instructed to do so.
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而且事前完全不用去教導它。
06:31
Here are some examples of Entropica in action.
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這裡有幾個 Entropica 的實例。
06:34
Just like a human standing
upright without falling over,
upright without falling over,
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像人可以直立站著不會跌倒，
06:37
here we see Entropica
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我們可以看到，
06:38
automatically balancing a pole using a cart.
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Entropica使用一台車來自動平衡桿子。
06:41
This behavior is remarkable in part
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這個表現在某方面很了不起，
06:43
because we never gave Entropica a goal.
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因為我們從來沒有為Entropica設定一個目標。
06:45
It simply decided on its own to balance the pole.
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由它自己決定要去平衡這個桿子。
06:48
This balancing ability will have appliactions
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這個平衡的能力可以應用在
06:51
for humanoid robotics
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機器人上，
06:52
and human assistive technologies.
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以及人類行動輔助技術。
06:55
Just as some animals can use objects
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就像有些動物
06:57
in their environments as tools
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會使用週遭的物品當作工具，
06:58
to reach into narrow spaces,
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以便能伸及到窄小的地方，
07:00
here we see that Entropica,
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我們可以再次看到 Entropica
07:02
again on its own initiative,
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由它自己決定，
07:04
was able to move a large
disk representing an animal
disk representing an animal
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可以移動代表動物的大圓圈，
07:07
around so as to cause a small disk,
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讓代表工具的小圓圈
07:09
representing a tool, to reach into a confined space
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進入一個有第三個圓圈的
07:12
holding a third disk
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狹小空間，
07:13
and release the third disk
from its initially fixed position.
from its initially fixed position.
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然後把第三個圓圈從裡面擠出來。
07:16
This tool use ability will have applications
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這個使用工具的能力可以應用在
07:18
for smart manufacturing and agriculture.
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智慧製造和農業上。
07:21
In addition, just as some other animals
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另外，就像其它動物
07:23
are able to cooperate by pulling
opposite ends of a rope
opposite ends of a rope
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會同時合力拉下繩索的兩端，
07:25
at the same time to release food,
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讓食物掉出來，
07:27
here we see that Entropica is able to accomplish
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我們看到 Entropica 可以完成
07:30
a model version of that task.
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模組化後的同樣任務。
07:32
This cooperative ability has interesting implications
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這個合作的能力可以應用在
07:34
for economic planning and a variety of other fields.
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經濟規劃和其它各樣的領域。
07:38
Entropica is broadly applicable
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Entropica 可以廣泛的應用在
07:40
to a variety of domains.
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各樣的領域。
07:42
For example, here we see it successfully
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例如，我們可以看到它
07:44
playing a game of pong against itself,
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成功地和自己玩 "乓" (Pong)，
07:47
illustrating its potential for gaming.
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代表它能玩遊戲的潛力。
07:49
Here we see Entropica orchestrating
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我們看到 Entropica 精心地
07:51
new connections on a social network
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建立起社群的新連結，
07:53
where friends are constantly falling out of touch
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當朋友們不時地失去聯繫，
07:56
and successfully keeping
the network well connected.
the network well connected.
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它會成功地維持這個網絡。
07:58
This same network orchestration ability
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這樣的網絡連結能力
08:01
also has applications in health care,
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同樣可以應用在醫療照顧，
08:03
energy, and intelligence.
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能源和智慧發展上。
08:06
Here we see Entropica directing the paths
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這裡我們看到 Entropica
08:08
of a fleet of ships,
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為海洋中的船隊指引路徑，
08:10
successfully discovering and
utilizing the Panama Canal
utilizing the Panama Canal
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成功地發現並使用巴拿馬運河，
08:13
to globally extend its reach from the Atlantic
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使它的足跡遍及全球每個角落，從大西洋
08:15
to the Pacific.
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到太平洋。
08:17
By the same token, Entropica
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同樣的，Entropica
08:19
is broadly applicable to problems
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可以廣泛地應用在
08:20
in autonomous defense, logistics and transportation.
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自主防衛和物流運輸上。
08:26
Finally, here we see Entropica
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最後，我們看到 Entropica
08:28
spontaneously discovering and executing
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自己發現並且執行
08:30
a buy-low, sell-high strategy
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"低買高賣"的策略，
08:32
on a simulated range traded stock,
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在一個區間交易的股票模擬市場中，
08:35
successfully growing assets under management
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成功地將管理資產規模
08:37
exponentially.
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指數性成長。
08:38
This risk management ability
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這樣的風險管理能力
08:40
will have broad applications in finance
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可以應用在財務
08:42
and insurance.
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和保險上。
08:46
Alex Wissner-Gross: So what you've just seen
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艾力克斯·威斯奈-格羅斯：
以上你們所看到的
以上你們所看到的
08:48
is that a variety of signature human intelligent
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是一個代表人類智慧的
08:52
cognitive behaviors
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認知行為能力，
08:54
such as tool use and walking upright
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像是工具的使用、直立行走、
08:57
and social cooperation
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以及群體合作，
08:59
all follow from a single equation,
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全部都遵行一個方程式，
09:02
which drives a system
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這個方程式驅使一個系統
09:04
to maximize its future freedom of action.
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可以最大化未來行動的自由。
09:07
Now, there's a profound irony here.
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然而，有一個很大的諷刺是，
09:10
Going back to the beginning
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回顧最初
09:12
of the usage of the term robot,
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使用”機器人”這個名詞時，
09:16
the play "RUR,"
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在舞台劇《羅梭的萬能工人》(R.U.R,) 中，
09:19
there was always a concept
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一直有一個概念：
09:21
that if we developed machine intelligence,
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如果我們發展了人工智慧，
09:24
there would be a cybernetic revolt.
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機器人將會起義反抗，
09:27
The machines would rise up against us.
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對抗我們人類。
09:31
One major consequence of this work
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我們這個研究主要的結論之一是，
09:33
is that maybe all of these decades,
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或許在過去這幾十年來，
09:36
we've had the whole concept of cybernetic revolt
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我們在逆向思考"機器人反抗”
09:39
in reverse.
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這個概念。
09:41
It's not that machines first become intelligent
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並不是機器先變聰明，
09:44
and then megalomaniacal
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然後自大，
09:46
and try to take over the world.
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然後才企圖統治全世界，
09:48
It's quite the opposite,
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而是應該反過來看，
09:50
that the urge to take control
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想要控制所有未來可能性
09:53
of all possible futures
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的慾望，
09:55
is a more fundamental principle
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比控制智慧
09:57
than that of intelligence,
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是更加基本的原則，
09:58
that general intelligence may in fact emerge
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一般的智慧或許是
10:02
directly from this sort of control-grabbing,
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直接從操控中產生的，
10:06
rather than vice versa.
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並非反過來。
10:10
Another important consequence is goal seeking.
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另一個重要的結論是尋找目標。
10:14
I'm often asked, how does the ability to seek goals
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我經常被問到，尋找目標的能力
10:18
follow from this sort of framework?
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是如何從這個架構中產生的？
10:20
And the answer is, the ability to seek goals
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答案是，尋找目標的能力
10:23
will follow directly from this
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會直接來自於
10:24
in the following sense:
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以下這個想法：
10:26
just like you would travel through a tunnel,
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就像你行經一個隧道，
10:29
a bottleneck in your future path space,
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一個在你未來道路上的瓶頸，
10:32
in order to achieve many other
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是為了到達許多
10:34
diverse objectives later on,
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在未來的不同目的地，
10:36
or just like you would invest
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或者，就像你在證券上的
10:38
in a financial security,
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投資，
10:40
reducing your short-term liquidity
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降低短期的流動性，
10:42
in order to increase your wealth over the long term,
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是為了增加長期的財富，
10:44
goal seeking emerges directly
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而尋找目標是來自於
10:47
from a long-term drive
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一個長期的趨動力
10:48
to increase future freedom of action.
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用來增加未來的行動自由。
10:52
Finally, Richard Feynman, famous physicist,
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最後，知名的物理學家理察費曼曾說，
10:56
once wrote that if human civilization were destroyed
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如果人類文明要被毀滅了，
11:00
and you could pass only a single concept
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而你只能留下一個概念
11:02
on to our descendants
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給後世的子孫，
11:03
to help them rebuild civilization,
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以便協助他們重建文明，
11:05
that concept should be
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那麼這個概念應該是：
11:07
that all matter around us
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所有我們週遭的物質
11:09
is made out of tiny elements
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是是由微小的元素組成，
11:11
that attract each other when they're far apart
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當它們相隔很遠時會互相吸引，
11:14
but repel each other when they're close together.
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但靠近時會互相排斥。
11:17
My equivalent of that statement
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而我同樣要
11:19
to pass on to descendants
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留給後世的想法
11:20
to help them build artificial intelligences
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以便幫助他們發展人工智慧，
11:23
or to help them understand human intelligence,
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或是幫助他們了解人類的智慧，
11:26
is the following:
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我會說：
11:27
Intelligence should be viewed
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智慧應該被視為
11:29
as a physical process
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一個物理程序，
11:30
that tries to maximize future freedom of action
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它將試著最大化未來的行動自由，
11:33
and avoid constraints in its own future.
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避免將自己侷限住。
11:37
Thank you very much.
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謝謝大家。
11:38
(Applause)
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(掌聲)
ABOUT THE SPEAKER
Alex Wissner-Gross - Scientist, entrepreneur, inventorAlex Wissner-Gross applies science and engineering principles to big (and diverse) questions, like: "What is the equation for intelligence?" and "What's the best way to raise awareness about climate change?"
Why you should listen
Alex Wissner-Gross is a serial big-picture thinker. He applies physics and computer science principles to a wide variety of topics, like human intelligence, climate change and financial trading.
Lately Wissner-Gross started wondering: Why have we searched for so long to understand intelligence? Can it really be this elusive? His latest work posits that intelligence can indeed be defined physically, as a dynamic force, rather than a static property. He explains intelligence in terms of causal entropic forces, ultimately defining it as "a force to maximize future freedom of action."
Wissner-Gross is a fellow at the Harvard Institute for Applied Computational Science and a research affiliate at the MIT Media Lab. He has a Ph.D. in physics from Harvard and bachelor's degrees in physics, electrical science and engineering, and mathematics from MIT.
Alex Wissner-Gross | Speaker | TED.com