Need a new idea? Start at the edge of what is known | Vittorio Loreto

譯者: Lilian Chiu 審譯者: congmei Han

We have all probably wondered

我們可能都納悶過，

how great minds achieved what they achieved, right?

有才智的人如何達成 他們的成就，對吧？

And the more astonishing their achievements are,

他們的成就越是驚人，

the more we call them geniuses,

我們就越會稱他們為天才，

perhaps aliens

抑或外星人，

coming from a different planet,

來自不同星球的，

definitely not someone like us.

肯定不是像我們這樣的人。

But is that true?

但真的如此嗎？

So let me start with an example.

讓我從一個例子說起。

You all know the story of Newton's apple, right? OK.

你們都聽過牛頓的蘋果 這個故事，對吧？好。

Is that true? Probably not.

那是真的嗎？可能不是。

Still, it's difficult to think that no apple at all was there.

但，很難想像那裡根本沒有蘋果。

I mean some stepping stone, some specific conditions

我是指具備某些墊腳石， 某些特定條件，

that made universal gravitation not impossible to conceive.

才有可能讓萬有引力被構想出來。

And definitely this was not impossible,

這個肯定不是不可能的，

at least for Newton.

至少對牛頓而言。

It was possible,

它是可能的，

and for some reason, it was also there,

基於某種理由，它也在那裡，

available at some point, easy to pick as an apple.

在某個時點是可得的， 就像蘋果一樣容易摘取。

Here is the apple.

這裡有個蘋果。

And what about Einstein?

那愛因斯坦呢？

Was relativity theory another big leap in the history of ideas

相對論是想法史上的另一個大躍進，

no one else could even conceive?

沒有其他人能想出來？

Or rather, was it again something adjacent and possible,

或者同樣的，那是某種 有前後關聯、有可能的東西嗎？

to Einstein of course,

對愛因斯坦而言當然是如此，

and he got there by small steps and his very peculiar scientific path?

他靠著小步伐和他非常奇特的 科學路徑而到達那裡。

Of course we cannot conceive this path,

當然，我們無法設想出這條路徑，

but this doesn't mean that the path was not there.

但這並不表示路徑不在那裡。

So all of this seems very evocative,

這一切似乎都非常有感召力，

but I would say hardly concrete

但我會說，算不上實在，

if we really want to grasp the origin of great ideas

如果我們真的想要了解 偉大想法的源頭，

and more generally the way in which the new enters our lives.

或更一般化地去了解新想法 進入我們生活的方式。

As a physicist, as a scientist,

身為物理學家，身為科學家，

I have learned that posing the right questions

我學到一件事，問對問題，

is half of the solution.

就已經完成一半的解答了。

But I think now we start having a great conceptual framework

但我認為，現在我們開始 有很偉大的概念架構，

to conceive and address the right questions.

來設想並處理對的問題。

So let me drive you to the edge of what is known,

讓我帶各位到已知領域的邊緣，

or at least, what I know,

至少是我已知的領域，

and let me show you that what is known

讓我展示給各位看，已知事物

could be a powerful and fascinating starting point

可以是個非常強大且迷人的起始點，

to grasp the deep meaning of words like novelty, innovation,

來了解像是新穎、創新， 也許還有創意，這類字詞的

creativity perhaps.

深刻意義。

So we are discussing the "new,"

所以，我們要討論「新事物」，

and of course, the science behind it.

當然，還有它背後的科學。

The new can enter our lives in many different ways,

新事物可藉由許多不同 方式進入我們的生活，

can be very personal,

可能是非常個人化的，

like I meet a new person,

就像我新結識一個人，

I read a new book, or I listen to a new song.

我讀了一本新書， 或我聽了一首新歌。

Or it could be global,

也可能是全球性的，

I mean, something we call innovation.

我指的是我們所謂的創新。

It could be a new theory, a new technology,

可能是一個新理論、一種新技術，

but it could also be a new book if you're the writer,

但也可能是一本新書， 若你是作家的話，

or it could be a new song if you're the composer.

也可能是一首新歌， 若你是作曲家的話。

In all of these global cases, the new is for everyone,

在所有這些全球性的例子中， 新的事物是給所有人的，

but experiencing the new can be also frightening,

但體驗新的事物也可能很嚇人，

so the new can also frighten us.

所以新的事物也可能會嚇壞我們。

But still, experiencing the new means exploring a very peculiar space,

但，體驗新的事物仍然 意味著探索一個奇特的空間，

the space of what could be,

「可能是怎樣」的空間，

the space of the possible, the space of possibilities.

「可能之事物」的空間， 「可能性」的空間。

It's a very weird space, so I'll try to get you through this space.

它是個非常怪異的空間， 所以我會試著帶各位穿過它。

So it could be a physical space.

它可能是個實體空間。

So in this case, for instance,

比如，在這個例子中，

novelty could be climbing Machu Picchu for the first time,

「新穎」有可能是 初次爬上馬丘比丘，

as I did in 2016.

像我在 2016 年的體驗一樣。

It could be a conceptual space,

它也可能是個概念空間，

so acquiring new information, making sense of it, in a word, learning.

取得新資訊，從中找出道理， 一言以蔽之，就是學習。

It could be a biological space.

它有可能是個生物空間。

I mean, think about the never-ending fight of viruses and bacteria

想想看，我們的免疫系統 對抗病毒和細菌的戰爭，

with our immune system.

永無止境。

And now comes the bad news.

現在壞消息來了。

We are very, very bad at grasping this space.

我們非常非常不擅長了解這個空間。

Think of it. Let's make an experiment.

想想這一點。咱們來做個實驗。

Try to think about all the possible things you could do in the next, say, 24 hours.

比如，試想在接下來的 24 小時， 所有你有可能會做的事情。

Here the key word is "all."

這裡的關鍵字是「所有」。

Of course you can conceive a few options, like having a drink, writing a letter,

當然，你可以想出幾個可能選項， 比如喝杯酒、寫封信，

also sleeping during this boring talk,

或是在這場無聊的演講中睡一覺，

if you can.

如果你能的話。

But not all of them.

但你無法想出所有選項。

So think about an alien invasion, now, here, in Milan,

所以，想想外星人入侵， 現在，在這裡，在米蘭，

or me -- I stopped thinking for 15 minutes.

或是我──我停止思考 15 分鐘。

So it's very difficult to conceive this space,

所以，要想出這個空間 是非常困難的，

but actually we have an excuse.

但其實，我們有一個藉口。

So it's not so easy to conceive this space

所以，要想出這個空間並不很容易，

because we are trying to conceive the occurrence of something brand new,

因為我們在試著想出 全新事物的發生，

so something that never occurred before,

以前從來沒有發生過的事物，

so we don't have clues.

所以我們會沒頭緒。

A typical solution could be

典型的解決方案可能是，

looking at the future with the eyes of the past,

用過去的眼睛來看未來，

so relying on all the time series of past events

所以，仰賴所有 過去事件的時間序列，

and hoping that this is enough to predict the future.

希望這些資訊足以預測未來。

But we know this is not working.

但我們知道這行不通。

For instance, this was the first attempt for weather forecasts, and it failed.

比如，第一次天氣預測的 嘗試就失敗了。

And it failed because of the great complexity

之所以會失敗，是因為

這背後現象的複雜度太高。

of the underlying phenomenon.

So now we know that predictions had to be based on modeling,

所以，現在我們知道， 預測必須要立基在模型上，

which means creating a synthetic model of the system,

那就表示，創造出 一個系統的合成模型，

simulating this model and then projecting the system

模擬這個模型，接著把這個系統透過

into the future through this model.

這個模型投射到未來。

And now we can do this in a lot of cases

在許多情況下我們都能做到這一點，

with the help of a lot of data.

只要能有大量資料的幫忙。

Looking at the future with the eye of the past

用過去的眼睛看未來，

could be misleading also for machines.

對於機器來說也有可能會造成誤導。

Think about it.

想想這一點。

Now picture yourself for a second in the middle of the Australian Outback.

試想一下，你身處澳洲內陸中間。

You stand there under the sun.

你站在太陽底下。

So you see something weird happening.

你看到奇怪的事情發生。

The car suddenly stops

車突然停下來，

very, very far from a kangaroo crossing the street.

離正在過街的袋鼠非常非常遠。

You look closer

你靠近些看，

and you realize that the car has no driver.

你發現那台車沒有人駕駛。

It is not restarting, even after the kangaroo is not there anymore.

它沒有重新啟動， 即使袋鼠已經不在那裡了。

So for some reasons,

基於某些理由，

the algorithms driving the car cannot make sense

負責開車的演算法無法理解

of this strange beast jumping here and there on the street.

這隻奇怪的野獸在街上跳來跳去。

So it just stops.

所以它停下來了。

Now, I should tell you, this is a true story.

我可以告訴各位，這是真實故事。

It happened a few months ago to Volvo's self-driving cars

這是幾個月前富豪自動駕駛車

in the middle of the Australian Outback.

在澳洲內陸中碰到的狀況。

(Laughter)

（笑聲）

It is a general problem,

這是個一般性的問題，

and I guess this will affect more and more in the near future

我猜這對於未來的 人工智慧和機器學習

artificial intelligence and machine learning.

會有越來越多的影響。

It's also a very old problem, I would say 17th century,

這也是個很古老的問題， 我會說是 17 世紀，

but I guess now we have new tools and new clues to start solving it.

但我想現在我們有新的工具 和新的線索可以開始解決它。

So let me take a step back,

所以，讓我先退後一步，

five years back.

回到 5 年前。

Italy. Rome. Winter.

義大利。羅馬。冬天。

So the winter of 2012 was very special in Rome.

2012 年羅馬的冬天非常特別。

Rome witnessed one of the greatest snowfalls of its history.

那次降雪量在羅馬史上名列前茅。

That winter was special also for me and my colleagues,

那年冬天對我 和我同事而言也很特別，

because we had an insight about the possible mathematical scheme --

因為我們對於一項可能的 數學方法有了洞見──

again, possible, possible mathematical scheme,

同樣，只是「可能的」數學方法，

to conceive the occurrence of the new.

能構想出新事物的發生。

I remember that day because it was snowing,

我記得那天，是因為在下雪，

so due to the snowfall, we were blocked, stuck in my department,

因為降雪，我們被困在 我的系所裡，走不了，

and we couldn't go home,

我們無法回家，

so we got another coffee, we relaxed

所以我們再喝了一杯咖啡，放輕鬆，

and we kept discussing.

我們持續討論。

But at some point -- maybe not that date, precisely --

但在某個時點── 精確來說也許不是那天──

at some point we made the connection

在某個時點，我們建立了連結，

between the problem of the new

把新事物的問題連結到

and a beautiful concept proposed years before

幾年前提出的一個美麗概念，

by Stuart Kauffman,

由斯圖亞特考夫曼提出的

the adjacent possible.

「相鄰可能」。

So the adjacent possible consists of all those things.

相鄰可能包含了所有這些東西。

It could be ideas, it could be molecules, it could be technological products

可能是想法，可能是分子， 可能是技術產品，

that are one step away

它們都距離真正存在的事物

from what actually exists,

還有一步之差，

and you can achieve them through incremental modifications

你可以達成它們， 只要透過對既有素材做

and recombinations of the existing material.

漸增的修正以及重新組合。

So for instance, if I speak about the space of my friends,

比如，如果我說到我朋友的空間，

my adjacent possible would be the set of all friends of my friends

我的相鄰可能就是「我朋友的朋友中 還沒成為我朋友的那些人」

not already my friends.

所形成的集合。

I hope that's clear.

我希望這樣夠清楚。

But now if I meet a new person,

但若我新結識一個人，

say Briar,

比如布萊兒，

all her friends would immediately enter my adjacent possible,

她所有的朋友就馬上會 進入到我的相鄰可能，

pushing its boundaries further.

把其邊界進一步向外推。

So if you really want to look from the mathematical point of view --

所以，如果你想要從 數學的角度來看──

I'm sure you want --

我相信你們想──

you can actually look at this picture.

你們可以看看這張圖。

So suppose now this is your universe.

假設這是你的宇宙。

I know I'm asking a lot.

我知道我要求很多。

I mean, this is your universe. Now you are the red spot.

這是你的宇宙，你是那個紅點。

And the green spot is the adjacent possible for you,

綠點是你的相鄰可能，

so something you've never touched before.

它是你以前沒碰過的東西。

So you do your normal life.

你過著你的正常生活。

You move. You move in the space.

你移動，你在空間中移動。

You have a drink. You meet friends. You read a book.

你去喝杯酒。你去見朋友。 你讀一本書。

At some point, you end up on the green spot,

在某個時點，你會到達綠點，

so you meet Briar for the first time.

你會初次見到布萊兒。

And what happens?

會發生什麼事？

So what happens is there is a new part,

發生的事是，會有一個新的部分，

a brand new part of the space,

這空間會有一個全新的部分，

becoming possible for you in this very moment,

在此時刻變成是對你來說有可能的，

even without any possibility for you to foresee this

即使你在接觸這個點之前

before touching that point.

不可能預見此事。

And behind this there will be a huge set of points

這背後就是許多的點，

that could become possible at some later stages.

在後續某些階段就會成為可能。

So you see the space of the possible is very peculiar,

所以，可能性的空間是非常奇特的，

because it's not predefined.

因為它不是預先定義的。

It's not something we can predefine.

它不是能被預先定義的。

It's something that gets continuously shaped and reshaped

它是會因為我們的行為和選擇而不斷

by our actions and our choices.

持續形成和重塑的東西。

So we were so fascinated by these connections we made --

我們做的這些連結， 讓我們非常著迷──

scientists are like this.

科學家就像這樣。

And based on this,

根據這個，

we conceived our mathematical formulation for the adjacent possible,

我們想出了相鄰可能的數學方程式，

20 years after the original Kauffman proposals.

在考夫曼最初 提出這概念的 20 年後。

In our theory -- this is a key point --

在我們的理論中──這是一項關鍵──

I mean, it's crucially based on a complex interplay

很重要的是，它是立基在 複雜的相互影響上，

between the way in which this space of possibilities expands

可能性空間擴張和被重新架構的方式，

and gets restructured,

與我們探索它的方式，

and the way in which we explore it.

兩者間的相互影響。

After the epiphany of 2012,

在 2012 年的主顯節之後，

we got back to work, real work,

我們回到工作狀態，真正的工作，

because we had to work out this theory,

因為我們得發展出這理論，

and we came up with a certain number of predictions

我們做出了一些預測，

to be tested in real life.

在真實人生中測試。

Of course, we need a testable framework

當然，我們需要一個可測試的架構

to study innovation.

才能研究創新。

So let me drive you across a few predictions we made.

讓我帶各位看看我們做的幾個預測。

The first one concerns the pace of innovation,

第一個和創新的步調有關，

so the rate at which you observe novelties in very different systems.

也就是你觀察不同系統中 新奇之處的速度。

So our theory predicts that the rate of innovation

我們的理論預測，

創新率應該會遵循一種通用的曲線，

should follow a universal curve,

like this one.

像這種。

This is the rate of innovation versus time in very different conditions.

這是在不同情況下 隨時間變化的創新率。

And somehow, we predict that the rate of innovation

我們預測出的創新率

should decrease steadily over time.

會隨著時間穩定下降。

So somehow, innovation is predicted to become more difficult

所以我們預測，隨時間進展，

as your progress over time.

創新會變得越來越困難。

It's neat. It's interesting. It's beautiful. We were happy.

它很棒。它很有趣。 它很美好。我們很開心。

But the question is, is that true?

但問題是，那是真的嗎？

Of course we should check with reality.

當然，我們要確認現實狀況。

So we went back to reality

所以，我們回到現實，

and we collected a lot of data, terabytes of data,

我們收集了許多資料， 幾兆位元組的資料，

tracking innovation in Wikipedia, Twitter,

追蹤在維基百科、推特的創新，

the way in which we write free software,

我們撰寫免費軟體的方式，

even the way we listen to music.

甚至我們聽音樂的方式。

I cannot tell you, we were so amazed and pleased and thrilled

我無法告訴各位我們有 多麼驚訝、開心，和興奮，

to discover that the same predictions we made in the theory

因為我們發現， 我們在理論上做的預測，

were actually satisfied in real systems,

實際上也符合真實系統，

many different real systems.

許多不同的真實系統。

We were so excited.

我們好興奮。

Of course, apparently, we were on the right track,

當然，顯然，我們的方向是對的，

but of course, we couldn't stop,

但當然，我們無法停止，

so we didn't stop.

所以我們沒有停止。

So we kept going on,

我們持續下去，

and at some point we made another discovery

在某個時點，我們有了另一個發現，

that we dubbed "correlated novelties."

我們稱之為「相關新奇」。

It's very simple.

它非常簡單。

So I guess we all experience this.

我想我們都經歷過。

So you listen to "Suzanne" by Leonard Cohen,

如果你聽了李歐納柯恩的歌《蘇珊》，

and this experience triggers your passion for Cohen

這體驗觸發了你對柯恩的熱情，

so that you start frantically listening to his whole production.

所以你開始瘋狂地聽他的所有作品。

And then you realize that Fabrizio De André here

接著，你發現法布里奇奧德安德烈

recorded an Italian version of "Suzanne,"

錄製了《蘇珊》的義大利版本，

and so on and so forth.

諸如此類。

So somehow for some reason,

所以，基於某種原因，

the very notion of adjacent possible is already encoding the common belief

相鄰可能的概念已經被編寫在

that one thing leads to another

「一件事會導致另一件事」的 這個一般看法中，

in many different systems.

在許多不同系統中皆是如此。

But the reason why we were thrilled

但我們很興奮的原因，

is because actually we could give, for the first time,

是因為這是第一次，我們能夠賦予

a scientific substance to this intuition

這種直覺一個科學本質，

and start making predictions

並開始做出預測，

about the way in which we experience the new.

預測我們會如何體驗新事物。

So novelties are correlated.

所以，新奇的事物是相關聯的。

They are not occurring randomly.

它們不是隨機發生的。

And this is good news,

這是好消息，

because it implies that impossible missions

因為這意味著，不可能的任務

might not be so impossible after all,

歸根結底，也許不是那麼不可能，

if we are guided by our intuition,

如果我們讓直覺引導我們，

somehow leading us to trigger a positive chain reaction.

帶領我們去觸發 一種正向的連鎖反應。

But there is a third consequence of the existence of the adjacent possible

但相鄰可能的存在 會有第三項後果，

that we named "waves of novelties."

我們稱之為「新奇波浪」。

So just to make this simple, so in music,

簡單來說，在音樂方面，

without waves of novelties,

若沒有新奇波浪，

we would still be listening all the time to Mozart or Beethoven,

我們都還會一直在聽 莫札特和貝多芬，

which is great,

他們的音樂很棒，

but we don't do this all the time.

但我們不會一直聽。

We also listen to the Pet Shop Boys or Justin Bieber -- well, some of us do.

我們也會聽寵物店男孩 或小賈斯汀──嗯，有些人會。

(Laughter)

（笑聲）

So we could see very clearly all of these patterns

我們可以在我們收集 並分析的大量資料中，

in the huge amounts of data we collected and analyzed.

清楚看到所有這些模式。

For instance, we discovered that popular hits in music

比如，我們發現， 音樂中的流行熱門歌曲

are continuously born, you know that,

不斷產生出來，你們知道這點，

and then they disappear, still leaving room for evergreens.

接著它們就消失了，留下空間 給不褪流行的長青歌曲。

So somehow waves of novelties ebb and flow

所以，新奇波浪會退潮和流動，

while the tides always hold the classics.

而潮流會一直留住經典。

There is this coexistence between evergreens and new hits.

長青歌曲和新熱門曲之間 有這種共存現象。

Not only our theory predicts these waves of novelties.

我們的理論不只是 預測這些新奇波浪。

This would be trivial.

這沒什麼。

But it also explains why they are there,

而是它也解釋了為什麼它們會出現，

and they are there for a specific reason,

它們的出現是有特殊理由的，

because we as humans display different strategies

因為我們人類在可能性的空間中

in the space of the possible.

展現出不同的策略。

So some of us tend to retrace already known paths.

我們有些人會傾向折返已知的路徑。

So we say they exploit.

我們說他們是在「利用」。

Some of us always launch into new adventures.

有些人總是要展開新的冒險。

We say they explore.

我們說他們是在「探索」。

And what we discovered is all the systems we investigated

我們發現，所有被我們研究的系統，

are right at the edge between these two strategies,

都在這兩項策略間的邊緣上，

something like 80 percent exploiting, 20 percent exploring,

比如 80% 的利用，20% 的探索，

something like blade runners of innovation.

像是創新的「銀翼殺手」（電影）。

So it seems that the wise balance, you could also say a conservative balance,

似乎，明智的平衡， 你也可以說是保守的平衡，

between past and future, between exploitation and exploration,

在過去和未來間、 在利用和探索間的平衡，

is already in place and perhaps needed in our system.

已經就位了， 且是我們的系統所需要的。

But again the good news is now we have scientific tools

但同樣的，好消息是 我們現在有科學工具

to investigate this equilibrium,

在研究這種均衡，

perhaps pushing it further in the near future.

也許能將它再推展到不遠的將來。

So as you can imagine,

不難想像，

I was really fascinated by all this.

我深深著迷於這一切。

Our mathematical scheme is already providing cues and hints

我們的數學方法已經 在提供提示和暗示，

to investigate the space of possibilities

來探究可能性空間，

and the way in which all of us create it and explore it.

以及我們所有人創造它、 探索它的方式。

But there is more.

但不只如此。

This, I guess, is a starting point of something that has the potential

我想，這只是個起始點，它還有潛力

to become a wonderful journey for a scientific investigation of the new,

能變成美好的旅程， 對於新事物做科學的研究，

but also I would say a personal investigation of the new.

還有對於新事物做個人的研究。

And I guess this can have a lot of consequences

我想，這可能會有很多後果，

and a huge impact in key activities

在關鍵活動中也會有重大影響，

like learning, education, research, business.

比如學習、教育、研究、商業。

So for instance, if you think about artificial intelligence,

舉例來說，想想人工智慧，

I am sure -- I mean, artificial intelligence,

我很確定──我是說，人工智慧，

we need to rely in the near future

在不遠的將來，我們會

more and more on the structure of the adjacent possible,

越來越需要依賴相鄰可能的結構，

to restructure it, to change it,

來重新架構它、改變它，

but also to cope with the unknowns of the future.

同時也要應對未來的未知。

In parallel, we have a lot of tools,

同樣的，我們有很多工具，

new tools now, to investigate how creativity works

新工具，可用來探究 創意是如何運作的，

and what triggers innovation.

以及是什麼觸發了創新。

And the aim of all this is to raise a generation of people

這一切的目標， 是要提升一個世代的人，

able to come up with new ideas to face the challenges in front of us.

讓他們能提出新的點子， 來面對我們眼前的挑戰。

We all know.

我們都知道。

I think it's a long way to go,

我認為還有很長的路要走，

but the questions, and the tools,

但問題以及工具都具備了，

are now there, adjacent and possible.

是相鄰的，也是可能的。

Thank you.

謝謝。

(Applause)

（掌聲）