Ray Kurzweil: Get ready for hybrid thinking

譯者: FBC GLOBAL 審譯者: Cheng Zhang-Stoddard

Let me tell you a story.

首先，我想與大家分享一個故事。

It goes back 200 million years.

時鐘撥回到兩億年前，

It's a story of the neocortex,

我們的故事，

which means "new rind."

與新皮層(neocortex)有關。

So in these early mammals,

早期哺乳動物

because only mammals have a neocortex,

(實際上只有哺乳動物才有新皮層)

rodent-like creatures.

比如齧齒類動物，

It was the size of a postage stamp and just as thin,

擁有一種尺寸和厚度與郵票相當的新皮層，

and was a thin covering around

它像一層薄膜，

their walnut-sized brain,

包覆著這些動物核桃大小的大腦。

but it was capable of a new type of thinking.

新皮層的功能不可小覷， 它賦予動物新的思考能力。

Rather than the fixed behaviors

不像非哺乳類動物，

that non-mammalian animals have,

牠們的行為基本上固定不變，

it could invent new behaviors.

擁有新皮層的哺乳動物能發明新的行為。

So a mouse is escaping a predator,

比如，老鼠逃避天敵的追捕時，

its path is blocked,

一旦發現此路不通，

it'll try to invent a new solution.

牠會嘗試去找新的出路。

That may work, it may not,

最終可能逃之夭夭，也可能落入貓口，

but if it does, it will remember that

但僥倖成功時，牠會記取成功的經驗，

and have a new behavior,

最終形成一種新的行為。

and that can actually spread virally

值得一提的是，這種新近習得的行為，

through the rest of the community.

會迅速傳遍整個鼠群。

Another mouse watching this could say,

我們可以想像，一旁觀望的老鼠會說：

"Hey, that was pretty clever, going around that rock,"

“哇，真是急中生智，居然想到繞開石頭來逃生！”

and it could adopt a new behavior as well.

然後，輕而易舉也掌握了這種技能。

Non-mammalian animals

但是，非哺乳動物

couldn't do any of those things.

對此完全無能為力，

They had fixed behaviors.

牠們的行為一成不變。

Now they could learn a new behavior

準確地說，牠們也能習得新的行為，

but not in the course of one lifetime.

但不是在一朝一夕之間，

In the course of maybe a thousand lifetimes,

可能需要歷經一千個世代，

it could evolve a new fixed behavior.

整個種群才能形成一種新的固定行為。

That was perfectly okay 200 million years ago.

在兩億年前的蠻荒世界， 這種進化節奏並無大礙。

The environment changed very slowly.

那時，環境變遷步履蹣跚，

It could take 10,000 years for there to be

大約每一萬年，

a significant environmental change,

才發生一回滄海桑田的巨變，

and during that period of time

在這樣一個漫長的時間跨度裏，

it would evolve a new behavior.

動物才形成了一種新的行為。

Now that went along fine,

往後，一切安好。

but then something happened.

直到，禍從天降。

Sixty-five million years ago,

時間快進到6500萬年前，

there was a sudden, violent change to the environment.

地球遭遇一場突如其來的環境遽變，

We call it the Cretaceous extinction event.

後人稱之為“白堊紀物種大滅絕”。

That's when the dinosaurs went extinct,

恐龍遭受滅頂之災；

that's when 75 percent of the

75％的地球物種

animal and plant species went extinct,

走向滅絕；

and that's when mammals

而哺乳動物

overtook their ecological niche,

趁機佔領了其他物種的生存地盤。

and to anthropomorphize, biological evolution said,

我們可以假託這些哺乳動物的口吻， 來評論這一進化過程：

"Hmm, this neocortex is pretty good stuff,"

“唔，關鍵時候我們的新皮層真派上用場了。”

and it began to grow it.

此後，新皮層繼續發育。

And mammals got bigger,

哺乳動物個頭也日漸見長，

their brains got bigger at an even faster pace,

大腦容量迅速擴大，

and the neocortex got bigger even faster than that

其中新皮層的發育堪稱突飛猛進，

and developed these distinctive ridges and folds

已經逐步形成獨特的溝回和褶皺，

basically to increase its surface area.

這可以進一步增加其表面積。

If you took the human neocortex

人類的新皮層，

and stretched it out,

如果充分展開平鋪，

it's about the size of a table napkin,

尺寸可達一張餐巾大小。

and it's still a thin structure.

但它仍然保持了纖薄的結構，

It's about the thickness of a table napkin.

厚度也與餐巾不相上下。

But it has so many convolutions and ridges

外形曲折複雜，呈現千溝萬壑，

it's now 80 percent of our brain,

新皮層已佔據大腦體積的80％左右，

and that's where we do our thinking,

不僅肩負思考的重任，

and it's the great sublimator.

還約束和昇華個人的行為。

We still have that old brain

今天，我們的大腦

that provides our basic drives and motivations,

仍然製造原始的需求和動機。

but I may have a drive for conquest,

但是，對於我們內心狂野的征服欲望，

and that'll be sublimated by the neocortex

這個新皮層起著春風化雨、潤物無聲的作用，

into writing a poem or inventing an app

最終將這種欲望化作創造詩歌、開發APP、

or giving a TED Talk,

甚至是發表TED演講這樣的文明行為。

and it's really the neocortex that's where

對於這一切，

the action is.

新皮層功不可沒。

Fifty years ago, I wrote a paper

50年前，我完成了一篇論文，

describing how I thought the brain worked,

探究大腦的工作原理，

and I described it as a series of modules.

我認為大腦是一系列模塊的有機結合。

Each module could do things with a pattern.

每個模塊按照某種模式各司其職，

It could learn a pattern. It could remember a pattern.

但也可以學習、記憶新的模式，

It could implement a pattern.

並將模式付諸應用。

And these modules were organized in hierarchies,

這些模式以層級結構進行組織，

and we created that hierarchy with our own thinking.

當然，我們借助自己的思考 假設了這種層級結構。

And there was actually very little to go on

50年前，由於各種條件限制，

50 years ago.

研究進展緩慢，

It led me to meet President Johnson.

但這項成果使我獲得了 約翰遜總統的接見。

I've been thinking about this for 50 years,

50年來，我一直潛心研究這個領域，

and a year and a half ago I came out with the book

就在一年半前，我又發表了一部新的著作

"How To Create A Mind,"

——《心智的構建》。

which has the same thesis,

該專著探討了同一個課題，

but now there's a plethora of evidence.

幸運的是，我現在擁有充足的證據支撐。

The amount of data we're getting about the brain

神經科學為我們貢獻 大量有關大腦的數據，

from neuroscience is doubling every year.

還在以逐年翻倍的速度劇增；

Spatial resolution of brainscanning of all types

各種腦部掃描技術的空間解析度，

is doubling every year.

也在逐年翻倍。

We can now see inside a living brain

現在，我們能親眼窺見活體大腦的內部，

and see individual interneural connections

觀察單個神經間的連接，

connecting in real time, firing in real time.

目睹神經連接、觸發的實時發生。

We can see your brain create your thoughts.

我們親眼看到大腦如何創造思維，

We can see your thoughts create your brain,

或者反過來說，思維如何增強和促進大腦，

which is really key to how it works.

思維本身對大腦進化至關重要。

So let me describe briefly how it works.

接下來，我想簡單介紹大腦的工作方式。

I've actually counted these modules.

實際上，我統計過這些模塊的數量。

We have about 300 million of them,

我們總共有大約三億模塊，

and we create them in these hierarchies.

分佈在不同的層級中。

I'll give you a simple example.

讓我們來看一個簡單的例子。

I've got a bunch of modules

假設我有一組模塊，

that can recognize the crossbar to a capital A,

可以識別大寫字母“A”中間的短橫線，

and that's all they care about.

它們的主要職責就在於此。

A beautiful song can play,

無論周遭播放著美妙的音樂，

a pretty girl could walk by,

還是一位妙齡女郎翩然而至，

they don't care, but they see a crossbar to a capital A,

它們都渾然不覺。但是，一旦發現“A”的短橫線，

they get very excited and they say "crossbar,"

它們就興奮異常，異口同聲喊出：“短橫線！”

and they put out a high probability

同時，它們立即報告神經軸突，

on their output axon.

識別任務已經順利完成。

That goes to the next level,

接下來，更高級別的模塊——

and these layers are organized in conceptual levels.

概念級別的模塊，將依次登場。

Each is more abstract than the next one,

級別越高，思考的抽象程度越高。

so the next one might say "capital A."

例如，較低的級別可識別字母“A”，

That goes up to a higher level that might say "Apple."

逐級上升後，某個級別能識別“APPLE”這個單詞。

Information flows down also.

同時，信息也在持續傳遞。

If the apple recognizer has seen A-P-P-L,

負責識別“APPLE”的級別，發現A-P-P-L時，

it'll think to itself, "Hmm, I think an E is probably likely,"

它會想：“唔，我猜下一個字母應該是E吧。”

and it'll send a signal down to all the E recognizers

然後，它會將信號傳達到 負責識別“E”的那些模塊，

saying, "Be on the lookout for an E,

並發出預警：“嘿，各位注意，

I think one might be coming."

字母E就要出現了！”

The E recognizers will lower their threshold

字母“E”的識別模塊於是降低了閥值，

and they see some sloppy thing, could be an E.

一旦發現疑似字母，便認為是“E”。

Ordinarily you wouldn't think so,

當然，這並非通常情況下的處理機制，

but we're expecting an E, it's good enough,

但現在我們正在等待“E”的出現， 而疑似字母與它足夠相似，

and yeah, I've seen an E, and then apple says,

所以，我們斷定它就是“E”。

"Yeah, I've seen an Apple."

“E”識別後，“APPLE”識別成功。

Go up another five levels,

如果我們再躍升五個級別，

and you're now at a pretty high level

那麼，在整個層級結構上，

of this hierarchy,

就到達了較高水平。

and stretch down into the different senses,

這個水平上，我們具有各種感知功能，

and you may have a module that sees a certain fabric,

某些模塊能夠感知特定的布料質地，

hears a certain voice quality, smells a certain perfume,

辨識特定的音色，甚至嗅到特定的香水味，

and will say, "My wife has entered the room."

然後告诉我：妻子剛進到房间！

Go up another 10 levels, and now you're at

再上升10級，

a very high level.

我們就到達了一個很高的水平，

You're probably in the frontal cortex,

可能來到了額葉皮層。

and you'll have modules that say, "That was ironic.

在這兒，我們的模塊已經能夠臧否人物了，

That's funny. She's pretty."

比如：這事有點滑稽可笑！她真是秀色可餐！

You might think that those are more sophisticated,

大家可能覺得，這整個過程有點複雜。

but actually what's more complicated

實際上，更讓人費解的是

is the hierarchy beneath them.

是這些過程的層級結構。

There was a 16-year-old girl, she had brain surgery,

曾經有位16歲的姑娘，當時正接受腦部手術。

and she was conscious because the surgeons

由於手術過程中醫生需要跟她講話，

wanted to talk to her.

所以就讓她保持清醒。

You can do that because there's no pain receptors

保持清醒的意識，這對於手術並無妨礙，

in the brain.

因為大腦內沒有痛覺感受器。

And whenever they stimulated particular,

我們驚奇地發現，當醫生刺激新皮層上

very small points on her neocortex,

某些細小區域時，就是圖中的紅色部位，

shown here in red, she would laugh.

這個姑娘就會放聲大笑。

So at first they thought they were triggering

起初，大家以為，

some kind of laugh reflex,

可能是因為觸發了笑反應神經。

but no, they quickly realized they had found

他們很快意識到事實並非如此，

the points in her neocortex that detect humor,

這些新皮層上的特定區域能夠理會幽默，

and she just found everything hilarious

只要醫生刺激這些區域，

whenever they stimulated these points.

她就會覺得所有的一切都滑稽有趣。

"You guys are so funny just standing around,"

“你們這幫人光站在那裏，就讓人想笑。”

was the typical comment,

那位姑娘典型的解釋道。

and they weren't funny,

我們知道，這個場景並不滑稽可笑，

not while doing surgery.

因為大家都在進行緊張的手術。

So how are we doing today?

現在，我們又有哪些新的進展呢？

Well, computers are actually beginning to master

計算機日益智能化，

human language with techniques

利用功能類似新皮層的先進技術，

that are similar to the neocortex.

它們可以學習和掌握人類的語言。

I actually described the algorithm,

我曾描述過一種算法，

which is similar to something called

與層級隱含式馬爾可夫模型類似，

a hierarchical hidden Markov model,

(馬爾可夫模型是用於自然語言處理的統計模型)

something I've worked on since the '90s.

上世紀90年以來我一直研究這種算法。

"Jeopardy" is a very broad natural language game,

“Jeopardy”(危境)是一個 自然語言類的智力競賽節目，

and Watson got a higher score

IBM研發的沃森計算機在比賽中

than the best two players combined.

勇奪高分，總分超過兩名最佳選手的總和。

It got this query correct:

連這個難題都被它輕鬆化解了：

"A long, tiresome speech

“定義：由起泡的派餡料發表的冗長而乏味的演講。

delivered by a frothy pie topping,"

請問：這定義的是什麼？”

and it quickly responded, "What is a meringue harangue?"

它迅速回答道：愛開腔的蛋白霜。

And Jennings and the other guy didn't get that.

而詹尼斯和另外一名選手卻一頭霧水。

It's a pretty sophisticated example of

這個問題難度很大，極富挑戰性，

computers actually understanding human language,

向我們展示了計算機 正在掌握人類的語言。

and it actually got its knowledge by reading

實際上，沃森是通過廣泛閱讀維基百科

Wikipedia and several other encyclopedias.

及其他百科全書來發展語言能力的。

Five to 10 years from now,

5至10年以後，

search engines will actually be based on

我們的搜索引擎

not just looking for combinations of words and links

不再只是搜索詞語和鏈接這樣的簡單組合，

but actually understanding,

它會嘗試去理解信息，

reading for understanding the billions of pages

通過涉獵浩如煙海的互聯網和書籍，

on the web and in books.

攫取和提煉知識。

So you'll be walking along, and Google will pop up

想像有一天，你正在悠閒地散步，

and say, "You know, Mary, you expressed concern

智能設備端的 Google 助理突然和你說：

to me a month ago that your glutathione supplement

“瑪麗，你上月提到，正在服用的谷胱甘肽補充劑

wasn't getting past the blood-brain barrier.

因為無法透過血腦屏障，所以暫時不起作用。

Well, new research just came out 13 seconds ago

告訴你一個好消息！就在13秒鐘前，

that shows a whole new approach to that

一項新的研究成果表明，

and a new way to take glutathione.

可以透過一个新的途徑來補充谷胱甘肽。

Let me summarize it for you."

讓我給你概括一下這個報告。”

Twenty years from now, we'll have nanobots,

20年以後，我們將迎來奈米機器人，

because another exponential trend

目前，科技產品正在日益微型化，

is the shrinking of technology.

這一趨勢愈演愈烈。

They'll go into our brain

科技設備將通過毛細血管

through the capillaries

進入我們的大腦，

and basically connect our neocortex

最終，將我們自身的新皮層

to a synthetic neocortex in the cloud

與雲端的人工合成新皮層相連，

providing an extension of our neocortex.

使它成為新皮層的延伸和擴展。

Now today, I mean,

今天，

you have a computer in your phone,

智慧型手機都內置了一台計算機。

but if you need 10,000 computers for a few seconds

假如我們需要一萬台計算機，

to do a complex search,

在幾秒鐘內完成一次複雜的搜索，

you can access that for a second or two in the cloud.

我們可以通過訪問雲端來獲得這種能力。

In the 2030s, if you need some extra neocortex,

到了2030年，當你需要更加強大的新皮層時，

you'll be able to connect to that in the cloud

你可以直接從你的大腦連接到雲端，

directly from your brain.

來獲得超凡的能力。

So I'm walking along and I say,

舉個例子，我正在漫步，遠遠看到一個人。

"Oh, there's Chris Anderson.

“老天，那不是克里斯．安德森(TED主持人)嗎？

He's coming my way.

他正朝我這邊走來。

I'd better think of something clever to say.

我要抓住這個機遇，一鳴驚人！

I've got three seconds.

但是，我只有三秒鐘，

My 300 million modules in my neocortex

我新皮層的三億個模塊

isn't going to cut it.

顯然不夠用。

I need a billion more."

我需要借來10億模塊增援！”

I'll be able to access that in the cloud.

於是，我會立即連通雲端。

And our thinking, then, will be a hybrid

我的思考，綜合了生物體和非生物體

of biological and non-biological thinking,

這兩者的優勢。

but the non-biological portion

非生物部分的思考能力，

is subject to my law of accelerating returns.

將受益於“加速回報定律”，

It will grow exponentially.

這是說，科技帶來的回報 呈指數級增長，而非線性。

And remember what happens

大家是否還記得，上次新皮層大幅擴張時

the last time we expanded our neocortex?

發生了哪些重大變化？

That was two million years ago

那是200萬年前，

when we became humanoids

我們那時還只是猿人，

and developed these large foreheads.

開始發育出碩大的前額。

Other primates have a slanted brow.

而其他靈長類動物的前額向後傾斜，

They don't have the frontal cortex.

因為牠們沒有額葉皮層。

But the frontal cortex is not really qualitatively different.

但是，額葉皮層並不意味著質的變化；

It's a quantitative expansion of neocortex,

而是新皮層量的提升，

but that additional quantity of thinking

帶來了額外的思考能力，

was the enabling factor for us to take

最終促成了質的飛躍。

a qualitative leap and invent language

我們因而能夠發明語言，

and art and science and technology

創造藝術，發展科技，

and TED conferences.

並舉辦TED演講，

No other species has done that.

這都是其他物種難以完成的創舉。

And so, over the next few decades,

我相信未來數十年，

we're going to do it again.

我們將再次創造偉大的奇蹟。

We're going to again expand our neocortex,

我們將借助科技，再次擴張新皮層，

only this time we won't be limited

不同之處在於，

by a fixed architecture of enclosure.

我們將不再受到頭顱空間的局限，

It'll be expanded without limit.

意味著擴張並無止境。

That additional quantity will again

隨之而來的量的增加

be the enabling factor for another qualitative leap

在人文和科技領域，

in culture and technology.

將再次引發一輪質的飛躍。

Thank you very much.

謝謝大家！

(Applause)

(掌聲)