And often technology comes to us,

每當新的科技發明，

we're surprised by what it brings.

我們總是驚嘆它所帶給我們的驚喜。

But there's actually a large aspect of technology

但是實際上更大程度的是對科技的形勢

that's much more predictable,

這是很容易預測的，

and that's because technological systems of all sorts have leanings,

因為所有的科技系統 都有一定的脈絡可循，

they have urgencies,

它們有迫切性，

they have tendencies.

有一定的趨勢，

And those tendencies are derived from the very nature of the physics,

而這些趨勢都是來自於 電線、開關、電子

chemistry of wires and switches and electrons,

的物理本質與化學原理，

and they will make reoccurring patterns again and again.

而這些模式會周而復始地發生。

And so those patterns produce these tendencies, these leanings.

所以是這些模式造就了 科技的趨勢及走向。

You can almost think of it as sort of like gravity.

你幾乎可以把它看做是一種「萬有引力」。

Imagine raindrops falling into a valley.

想像一下，就像雨滴落到山谷中，

The actual path of a raindrop as it goes down the valley

雨滴流到山谷中的實際路徑

is unpredictable.

是無法預測的。

We cannot see where it's going,

我們看不到雨滴會怎麼流，

but the general direction is very inevitable:

但大致上的方向是一定的：

it's downward.

這個方向是向下的。

And so these baked-in tendencies and urgencies

而這些深根在科技系統裡的

in technological systems

趨勢及迫切性，

give us a sense of where things are going at the large form.

告訴了我們科技的大方向。

So in a large sense,

具體說，

I would say that telephones were inevitable,

我認為電話的發明是必然的，

but the iPhone was not.

但 iPhone 就不是了。

The Internet was inevitable,

網際網路的發明是必然的，

but Twitter was not.

但推特就不是了。

So we have many ongoing tendencies right now,

所以我們現在有很多趨勢正在進行，

and I think one of the chief among them

而我認為它們其中一個 主要的趨勢就是，

is this tendency to make things smarter and smarter.

東西越來越聰明了。

I call it cognifying -- cognification --

我稱這個過程為 「認知化 」——認知——

also known as artificial intelligence, or AI.

也就是大家知道的 人工智慧，或者「AI」

And I think that's going to be one of the most influential developments

我認為未來20年，

and trends and directions and drives in our society in the next 20 years.

AI 將成為我們社會其中一個 最有影響力的發展、趨勢及驅動力。

So, of course, it's already here.

當然，AI 已經出現了，

We already have AI,

我們已經有 AI 了，

and often it works in the background,

而且它經常在幕後幫助我們，

in the back offices of hospitals,

它出現在醫院後面的辦公室，

where it's used to diagnose X-rays better than a human doctor.

用 AI 來診斷 X 光片的能力 比人類醫生還精準。

It's in legal offices,

它會出現在律師事務所，

where it's used to go through legal evidence

用 AI 審閱法律文件，

better than a human paralawyer.

速度比人類的律師還要快。

It's used to fly the plane that you came here with.

各位今天坐的飛機也有人工智慧，

Human pilots only flew it seven to eight minutes,

人工駕駛只有 7~8 分鐘，

the rest of the time the AI was driving.

剩下的都是 AI 在駕駛

And of course, in Netflix and Amazon,

當然， Netflix 和 Amazon也有，

it's in the background, making those recommendations.

它在幕後給出做出推薦和建議。

That's what we have today.

這是我們目前已經實現的。

And we have an example, of course, in a more front-facing aspect of it,

當然，還有一個更先進的案例，

with the win of the AlphaGo, who beat the world's greatest Go champion.

就是打敗世界圍棋冠軍的 AlphaGo。

But it's more than that.

但人工智慧不僅於此。

If you play a video game, you're playing against an AI.

如果你在玩電動，你對抗的是 AI，

But recently, Google taught their AI

但最近，Google開始教他們的 AI

to actually learn how to play video games.

實際意義上的學習如何打電動。

Again, teaching video games was already done,

重申一下，教 AI 「打電動」是一種層次，

but learning how to play a video game is another step.

但教 AI 「學習如何打電動」又是另一種層次。

That's artificial smartness.

這是人造的智能產品。

What we're doing is taking this artificial smartness

而我們正在做的就是將 這種人造的智能產品

and we're making it smarter and smarter.

變得越來越聰明。

There are three aspects to this general trend

這個趨勢大致上有三個面向，

that I think are underappreciated;

我認為尚未被充分認知；

I think we would understand AI a lot better

我想如果搞懂這三個面向，

if we understood these three things.

我們對 AI 的了解，會更深入一些。

I think these things also would help us embrace AI,

我認為了解這些事， 也可以幫助我們擁抱 AI，

because it's only by embracing it that we actually can steer it.

唯有擁抱 AI 才能駕馭 AI。

We can actually steer the specifics by embracing the larger trend.

藉由懷抱更大趨勢來駕馭細節。

So let me talk about those three different aspects.

所以容我來談談 AI 的三個不同面向。

The first one is: our own intelligence has a very poor understanding

第一：以人類目前對智慧的了解，

of what intelligence is.

我們對智慧的認知仍相當貧乏。

We tend to think of intelligence as a single dimension,

我們似乎把智能看的太單一面向了，

that it's kind of like a note that gets louder and louder.

它有點像是個音符，會越來越大聲。

It starts like with IQ measurement.

剛開始像個 IQ 測量儀。

It starts with maybe a simple low IQ in a rat or mouse,

一開始的智商也許跟老鼠一樣低，

and maybe there's more in a chimpanzee,

有的像猩猩，稍微多一點，

and then maybe there's more in a stupid person,

之後開始像個低智商的人類，

and then maybe an average person like myself,

然後進化到像我一樣的普通人，

and then maybe a genius.

然後變成一個天才。

And this single IQ intelligence is getting greater and greater.

IQ 智能分數越來越高，

That's completely wrong.

這種看法完全是錯誤的。

That's not what intelligence is -- not what human intelligence is, anyway.

這不是智慧該有的樣子—— 人類的智慧不僅於此。

It's much more like a symphony of different notes,

它像是一首交響樂， 或者由不同的音符組成，

and each of these notes is played on a different instrument of cognition.

而每一個音符， 由不同的認知樂器所伴奏。

There are many types of intelligences in our own minds.

人類腦中有很多不同種類的智慧，

We have deductive reasoning,

我們有演繹推理的能力，

we have emotional intelligence,

我們有情感的智慧，

we have spatial intelligence;

我們有空間概念的智慧，

we have maybe 100 different types that are all grouped together,

我們可能有100多種 不同的智能聚合在一起，

and they vary in different strengths with different people.

而且每個人各有各的強項。

And of course, if we go to animals, they also have another basket --

當然，以動物而言， 牠們可能是另一套體系——

another symphony of different kinds of intelligences,

另一種不同的智能交響樂，

and sometimes those same instruments are the same that we have.

有時候跟我們人類的一樣。

They can think in the same way, but they may have a different arrangement,

牠們可能思考方式相同 但著重點不同，

and maybe they're higher in some cases than humans,

也許在某些方面超過人類，

like long-term memory in a squirrel is actually phenomenal,

像是松鼠的長期記憶力，相當出色，

so it can remember where it buried its nuts.

能清楚記得堅果的埋藏之處。

But in other cases they may be lower.

但其它方面，牠們也許就比較弱了。

When we go to make machines,

當我們要製造機器時，

we're going to engineer them in the same way,

我們會用同樣的方式來設計機器，

where we'll make some of those types of smartness much greater than ours,

有些智慧型裝置做得比人類聰明得多，

and many of them won't be anywhere near ours,

但其它方面則遠遠不如我們，

because they're not needed.

因為根本不需要。

So we're going to take these things,

我們會將這些產品

these artificial clusters,

這些人工產品，

and we'll be adding more varieties of artificial cognition to our AIs.

在不同的 AI 上， 裝置不同的人工認知功能，

We're going to make them very, very specific.

我們可以把它們的特定功能 做得相當、相當出色。

So your calculator is smarter than you are in arithmetic already;

所以你的計算機在計算方面 比你聰明許多；

your GPS is smarter than you are in spatial navigation;

你的 GPS 在空間導航上比你聰明得多；

Google, Bing, are smarter than you are in long-term memory.

Googl, Bing 的長期記憶比你強。

And we're going to take, again, these kinds of different types of thinking

然後我們再把這些不同種類的智能，

and we'll put them into, like, a car.

放在，像是，車子裡。

The reason why we want to put them in a car so the car drives,

我們之所以這麼做的原因，

is because it's not driving like a human.

是因為它們不會像人類那樣開車，

It's not thinking like us.

它們不會像人類那樣思考。

That's the whole feature of it.

這是它唯一的特色。

It's not being distracted,

它不會分心，

it's not worrying about whether it left the stove on,

它不用擔心瓦斯爐沒關，

or whether it should have majored in finance.

它不用考慮要不要主修財經。

It's just driving.

它只會開車。

(Laughter)

（笑聲）

Just driving, OK?

只會開車，好嗎？

And we actually might even come to advertise these

而我們最終可能會拿它來廣告

as "consciousness-free."

「無意識」。

They're without consciousness,

它們沒有意識，

they're not concerned about those things,

它們不會關心這些瑣事，

they're not distracted.

它們不會分心。

So in general, what we're trying to do

所以，我們應該盡我們所能

is make as many different types of thinking as we can.

去嘗試做出一些不同的想法。

We're going to populate the space

我們將會天馬行空，

of all the different possible types, or species, of thinking.

去嘗試所有可能的思考方式。

And there actually may be some problems

也許還有一些

that are so difficult in business and science

相當不好解決的商業及科學問題，

that our own type of human thinking may not be able to solve them alone.

單憑人類自身的想法可能無法解決。

We may need a two-step program,

我們可能需要分兩步走，

which is to invent new kinds of thinking

先發明出新的思考方式，

that we can work alongside of to solve these really large problems,

再來解決這些真正的難題，

say, like dark energy or quantum gravity.

比如說，像是暗能量或量子引力。

What we're doing is making alien intelligences.

我們所做的實際上 就是在創造「異形智能」。

You might even think of this as, sort of, artificial aliens

在某種程度上，

in some senses.

這概念有點像是，人造異形。

And they're going to help us think different,

它們將幫助我們從不同的角度思考，

because thinking different is the engine of creation

因為不同的想法是創新、

and wealth and new economy.

財富和新經濟的引擎。

The second aspect of this is that we are going to use AI

第二方面：我們將用 AI

to basically make a second Industrial Revolution.

進行第二次的工業革命。

The first Industrial Revolution was based on the fact

在第一次工業革命中，

that we invented something I would call artificial power.

是以我稱之為「人工力量」為基礎的革命。

Previous to that,

在此之前，

during the Agricultural Revolution,

在農業革命時期，

everything that was made had to be made with human muscle

每樣東西都需要用人力

or animal power.

或畜力完成。

That was the only way to get anything done.

除此之外別無它法。

The great innovation during the Industrial Revolution was,

在工業革命期間最偉大的發明就是

we harnessed steam power, fossil fuels,

我們利用水蒸氣、石化燃料

to make this artificial power that we could use

產生人工力量，

to do anything we wanted to do.

來做任何我們想做的事情。

So today when you drive down the highway,

今日，當你開車行駛在高速公路上，

you are, with a flick of the switch, commanding 250 horses --

只要輕輕撥弄開關， 就相當於在駕馭250匹馬，

250 horsepower --

或者說，250馬力。

which we can use to build skyscrapers, to build cities, to build roads,

它可以讓我們蓋大樓、 建造城市、修建道路，

to make factories that would churn out lines of chairs or refrigerators

開辦能夠源源不斷 生產椅子或冰箱的工廠，

way beyond our own power.

這都遠遠超出人力所為。

And that artificial power can also be distributed on wires on a grid

而且這樣的人工電力可以透過電線、電網

to every home, factory, farmstead,

輸送到每一個家庭、工廠、農場，

and anybody could buy that artificial power,

讓每個人都可以買到這樣的人工電力，

just by plugging something in.

只要插上插頭就可以使用。

So this was a source of innovation as well,

所以，這也是創新的來源之一，

because a farmer could take a manual hand pump,

因為農民可以為手工幫浦通上電，

and they could add this artificial power, this electricity,

有了這種人工力量，

and he'd have an electric pump.

就變成了電動幫浦。

And you multiply that by thousands or tens of thousands of times,

你將這種力量擴大成千上萬倍，

and that formula was what brought us the Industrial Revolution.

而這個公式為我們帶來了工業革命。

All the things that we see, all this progress that we now enjoy,

而我們所看到的一切、 那些我們現今享受的過程，

has come from the fact that we've done that.

幾乎都來源於此。

We're going to do the same thing now with AI.

現在我們也要在 AI 上做同樣的事。

We're going to distribute that on a grid,

我們將用網路傳送 AI，

and now you can take that electric pump.

現在好比你有一個“電泵”

You can add some artificial intelligence,

你把”電泵“加上人工智能，

and now you have a smart pump.

你就會得到聰明的”電泵”，

And that, multiplied by a million times,

類似的改造做上幾百萬次，

is going to be this second Industrial Revolution.

就會引爆第二次的工業革命。

So now the car is going down the highway,

將來汽車行駛在高速公路上，

it's 250 horsepower, but in addition, it's 250 minds.

它不僅有250 匹馬力，還有 250 種腦力。

That's the auto-driven car.

這就是自動駕駛車。

It's like a new commodity;

它是一種新的商品；

it's a new utility.

它是一種新的基礎設施。

The AI is going to flow across the grid -- the cloud --

AI 將會在網路、雲端上傳輸

in the same way electricity did.

就跟電一樣。

So everything that we had electrified,

所以之前每樣東西我們都把它們電力化，

we're now going to cognify.

現在，我們要把它們認知化，

And I would suggest, then,

所以，誠如 Jeff 所說的，

that the formula for the next 10,000 start-ups

接下來的一萬家初創公司的公式，

is very, very simple,

相當， 相當簡單，

which is to take x and add AI.

就是拿某樣東西 X，加上 AI

That is the formula, that's what we're going to be doing.

這個公式就是我們將來要做的。

And that is the way in which we're going to make

我們將以這種方式

this second Industrial Revolution.

創造第二次的工業革命。

And by the way -- right now, this minute,

順帶一提，目前，此時此刻，

you can log on to Google

你可以登入Google

and you can purchase AI for six cents, 100 hits.

用六美分購買 AI 來提交一百個圖像識別請求。

That's available right now.

目前已經有這項服務了。

So the third aspect of this

第三個形勢：

is that when we take this AI and embody it,

如果我們將 AI 編組起來，

we get robots.

我們會得到機械人。

And robots are going to be bots,

而機械人就是一些小型的任務執行器，

they're going to be doing many of the tasks that we have already done.

它們將會取代我們現在已經在做的事。

A job is just a bunch of tasks,

工作只是一堆任務，

so they're going to redefine our jobs

所以人類的工作會被重新定義，

because they're going to do some of those tasks.

因為它們會幫我們執行這些任務。

But they're also going to create whole new categories,

但它們也會創造出全新的分類

a whole new slew of tasks

很多全新種類的任務，

that we didn't know we wanted to do before.

一些我們從未聽過的工作。

They're going to actually engender new kinds of jobs,

它們實際上會催生出新的職業，

new kinds of tasks that we want done,

一些我們願意從事的新工作，

just as automation made up a whole bunch of new things

就像自動化所引發的許多新事物，

that we didn't know we needed before,

我們之前並知道會需要它們，

and now we can't live without them.

但時至今日，我們已經離不開它們了。

So they're going to produce even more jobs than they take away,

機器人產生的新工作 比我們被取代的工作還要多，

but it's important that a lot of the tasks that we're going to give them

更重要的是，我們交給它們的那些任務

are tasks that can be defined in terms of efficiency or productivity.

都需要效率或生產率。

If you can specify a task,

如果一個任務，不管是體力的還是腦力的， 可以用效率或生產率來衡量的話，

either manual or conceptual,

那麽就應該交給機器人來完成。

that can be specified in terms of efficiency or productivity,

機器人擅長的就是生產率。

that goes to the bots.

我們真正擅長的是浪費時間。

Productivity is for robots.

（笑聲）

What we're really good at is basically wasting time.

我們最擅長做那些沒有效率的事情。

(Laughter)

科學從本質上來說是低效的。

We're really good at things that are inefficient.

它的運作方式實際上是 一次又一次的失敗，

Science is inherently inefficient.

很多試驗和嘗試都徒勞無功，

It runs on that fact that you have one failure after another.

不這樣做，你學不到東西。

It runs on the fact that you make tests and experiments that don't work,

事實就是，

otherwise you're not learning.

科學研究沒有效率可言。