How many of you are creatives,

你們有多少人是創意人、

designers, engineers, entrepreneurs, artists,

設計師、工程師、企業家、藝術家？

or maybe you just have a really big imagination?

或者你只是有無遠弗屆的想像力？

Show of hands? (Cheers)

請舉一下手？（歡呼聲）

That's most of you.

現場大部分人都是。

I have some news for us creatives.

我有一些消息要給我們的創意人。

Over the course of the next 20 years,

接下來的 20 年，

more will change around the way we do our work

很多我們工作的方式，

than has happened in the last 2,000.

將會遠遠不同於過去的 2000 年。

In fact, I think we're at the dawn of a new age in human history.

實際上，我認為我們正處在 人類歷史新世代的黎明。

Now, there have been four major historical eras defined by the way we work.

人類工作的方式， 有四個主要的歷史階段。

The Hunter-Gatherer Age lasted several million years.

人類歷經了幾百萬年的 狩獵採集時代。

And then the Agricultural Age lasted several thousand years.

然後經歷了幾千年的農業時代。

The Industrial Age lasted a couple of centuries.

工業時代則延續了幾世紀。

And now the Information Age has lasted just a few decades.

而目前的資訊時代才走了幾十年。

And now today, we're on the cusp of our next great era as a species.

如今，身為人類的我們， 即將邁入下一個偉大的時代。

Welcome to the Augmented Age.

歡迎來到「擴增時代」。

In this new era, your natural human capabilities are going to be augmented

在這個新時代， 人類天生的能力將會被強化擴增，

by computational systems that help you think,

電腦計算系統將幫助你思考、

robotic systems that help you make,

機械人系統協助你製造、

and a digital nervous system

遠超過你自然感官強度的 數位神經系統，

that connects you to the world far beyond your natural senses.

能夠讓你與全世界接軌。

Let's start with cognitive augmentation.

我們先從「認知擴增」談起。

How many of you are augmented cyborgs?

現場有多少人是「強化的半機械人」？

(Laughter)

（笑聲）

I would actually argue that we're already augmented.

其實我想說的是， 我們都已經被強化、擴增了。

Imagine you're at a party,

想像你正在參加一場派對，

and somebody asks you a question that you don't know the answer to.

有人問了你一個 你不知道如何回答的問題。

If you have one of these, in a few seconds, you can know the answer.

如果你有這個，只要幾秒鐘， 你就會得到答案。

But this is just a primitive beginning.

但這也只是剛開始而已。

Even Siri is just a passive tool.

甚至 Siri 也只是個被動工具。

In fact, for the last three-and-a-half million years,

實際上，在過去的 350 萬年，

the tools that we've had have been completely passive.

我們所有的工具都是被動的。

They do exactly what we tell them and nothing more.

它們只會照我們的指令去做， 僅此而已。

Our very first tool only cut where we struck it.

我們最早使用的工具， 遵循一個口令一個動作的指示。

The chisel only carves where the artist points it.

藝術家指哪裡，雕刻刀就雕刻哪裡。

And even our most advanced tools do nothing without our explicit direction.

即使最先進的工具，如果沒有 我們明確的指令也不會工作。

In fact, to date, and this is something that frustrates me,

說真的，時到今日， 有件事仍讓我感覺很挫敗，

we've always been limited

我們一直以來都被限制在

by this need to manually push our wills into our tools --

「需要動手將我們的意念 傳達給工具」的這種迷思框框中——

like, manual, literally using our hands,

就是得動手去做，即使有了電腦 還是得靠雙手。

even with computers.

但我還是比較喜歡當 裡的史考迪。

But I'm more like Scotty in "Star Trek."

（笑聲）

(Laughter)

我也想跟電腦對話。

I want to have a conversation with a computer.

當我說，「電腦， 我們來設計一輛車吧！」

I want to say, "Computer, let's design a car,"

然後電腦就會顯示一輛車給我看。

and the computer shows me a car.

然後我說：「不，要拉風一點， 德國味兒少一點。」

And I say, "No, more fast-looking, and less German,"

接著「蹦」， 電腦給了我一個新選擇。

and bang, the computer shows me an option.

（笑聲）

(Laughter)

這樣的對話可能有點不切實際，

That conversation might be a little ways off,

也許沒有我們認為的那麼不切實際，

probably less than many of us think,

但現在，

but right now,

我們正在做這件事。

we're working on it.

這些工具將帶領我們大躍進， 從被動轉為衍生。

Tools are making this leap from being passive to being generative.

「衍生設計工具 」 是利用電腦及演算法，

Generative design tools use a computer and algorithms

合成出幾何結構，

to synthesize geometry

產製出新的設計圖， 全部都是它們自己構思出來的。

to come up with new designs all by themselves.

你只需要設定目標及限制條件。

All it needs are your goals and your constraints.

我給各位舉個例子。

I'll give you an example.

就拿這個無人機底盤為例，

In the case of this aerial drone chassis,

你唯一要做的， 就是告訴它你的需求，

all you would need to do is tell it something like,

像是，你要四個螺旋槳的，

it has four propellers,

它越輕越好，

you want it to be as lightweight as possible,

空氣動力學表現效率佳的。

and you need it to be aerodynamically efficient.

電腦做的，就是探索 所有可能的解決方案：

Then what the computer does is it explores the entire solution space:

每一個能解決且符合你標準的 可能方案——

every single possibility that solves and meets your criteria --

有上百萬個。

millions of them.

這需要大型電腦才能做到。

It takes big computers to do this.

但它回饋給我們的設計方案，

But it comes back to us with designs

是我們單憑自己無法想像出來的 設計方案。

that we, by ourselves, never could've imagined.

電腦憑藉著自己的能力 做出這些東西——

And the computer's coming up with this stuff all by itself --

我們人類沒有動筆畫任何東西，

no one ever drew anything,

完全是它自己從頭、從零畫起的。

and it started completely from scratch.

順便一提，這可不是偶然......

And by the way, it's no accident

無人機的機體長的像飛鼠的骨盆，

that the drone body looks just like the pelvis of a flying squirrel.

（笑聲）

(Laughter)

那是因為演算法的計算模式，

It's because the algorithms are designed to work

是遵循生物演化模式而設計的。

the same way evolution does.

令人興奮的是， 我們開始見證這樣的科技

What's exciting is we're starting to see this technology

在現實世界中實現。

out in the real world.

我們與空中巴士 （歐洲最大飛機製造商）

We've been working with Airbus for a couple of years

合作開發未來的概念機 已經好幾年了，

on this concept plane for the future.

這計畫目前還在進行。

It's a ways out still.

但最近，我們用了 衍生設計的人工智慧

But just recently we used a generative-design AI

做出了這一個。

to come up with this.

這是一個 3D 列印的客艙隔間板， 由一台電腦所設計。

This is a 3D-printed cabin partition that's been designed by a computer.

它比原款式還要堅固， 但重量只有原本的一半，

It's stronger than the original yet half the weight,

今年稍晚，它將跟 A320 空中巴士一起飛上天。

and it will be flying in the Airbus A320 later this year.

所以現在電腦會主動生成、衍生了；

So computers can now generate;

它們可以對界定明確的問題， 給出自己的答案。

they can come up with their own solutions to our well-defined problems.

但它們並不是靠直覺做事。

But they're not intuitive.

它們還是每次都得從頭開始，

They still have to start from scratch every single time,

因為它們不會學習。

and that's because they never learn.

不像瑪姬。

Unlike Maggie.

（笑聲）

(Laughter)

瑪姬其實比我們最先進的 設計工具都還要聰明。

Maggie's actually smarter than our most advanced design tools.

這是什麼意思？

What do I mean by that?

如果狗主人拿起狗鍊，

If her owner picks up that leash,

瑪姬就知道有相當的確定性，

Maggie knows with a fair degree of certainty

主人要帶她去散步了。

it's time to go for a walk.

她是怎麼知道的？

And how did she learn?

因為，每當主人拿起狗鍊， 他們就會一起去散步。

Well, every time the owner picked up the leash, they went for a walk.

瑪姬會做三件事：

And Maggie did three things:

她必須專注、

she had to pay attention,

必須記得發生過什麼事、

she had to remember what happened

必須在腦中記憶並產生一個模式。

and she had to retain and create a pattern in her mind.

有趣的是，這正是電腦科學家

Interestingly, that's exactly what

過去 60 年來，一直嘗試 要讓人工智慧做的事。

computer scientists have been trying to get AIs to do

回想一下 1952 年，

for the last 60 or so years.

科學家建立了這一台電腦， 它會玩井字遊戲。

Back in 1952,

真了不起。

they built this computer that could play Tic-Tac-Toe.

45 年後，1997 年，

Big deal.

深藍擊敗了當時的西洋棋世界冠軍 卡司帕洛夫，

Then 45 years later, in 1997,

2011年，華生 (IBM電腦) 在擊敗這兩個人，

Deep Blue beats Kasparov at chess.

對電腦來說，這比下棋難多了。

2011, Watson beats these two humans at Jeopardy,

事實上，華生並不是從 預先定義的題庫中來找答案，

which is much harder for a computer to play than chess is.

它必須使用推理 來擊敗它的人類對手。

In fact, rather than working from predefined recipes,

就在幾個禮拜前，

Watson had to use reasoning to overcome his human opponents.

DeepMind 的阿爾法圍棋 擊敗了世界圍棋冠軍，

And then a couple of weeks ago,

而圍棋是我們人類最複雜的遊戲。

DeepMind's AlphaGo beats the world's best human at Go,

事實上，圍棋走法的可能性

which is the most difficult game that we have.

超過全宇宙的原子數量。

In fact, in Go, there are more possible moves

所以為了取得勝利，

than there are atoms in the universe.

阿爾法圍棋必須學會使用直覺。

So in order to win,

實際上，有些下法， 阿爾法圍棋的程式人員也不懂

what AlphaGo had to do was develop intuition.

為什麼阿爾法圍棋要那樣下。

And in fact, at some points, AlphaGo's programmers didn't understand

世界變化真快。

why AlphaGo was doing what it was doing.

我的意思是，想像一下—— 在人類壽命這麼長的時間裡，

And things are moving really fast.

電腦已經從小孩子的遊戲

I mean, consider -- in the space of a human lifetime,

發展到策略思考的頂尖水平。

computers have gone from a child's game

電腦基本上的發展，

to what's recognized as the pinnacle of strategic thought.

已經從史巴克大副進化到

What's basically happening

寇克艦長。

is computers are going from being like Spock

（笑聲）

to being a lot more like Kirk.

對吧？從純粹的邏輯運算 到直覺判斷。

(Laughter)

你們會跨過這座橋嗎？

Right? From pure logic to intuition.

大部分人應該都說， 「喔，打死我也不要！」

Would you cross this bridge?

（笑聲）

Most of you are saying, "Oh, hell no!"

你瞬間就可以做出這個決定。

(Laughter)

你就是隱約知道那座橋並不安全。

And you arrived at that decision in a split second.

這種直覺判斷，

You just sort of knew that bridge was unsafe.

就是目前我們深度學習系統 正在發展的能力。

And that's exactly the kind of intuition

很快的，各位就可以

that our deep-learning systems are starting to develop right now.

把你製作、設計出來的東西

Very soon, you'll literally be able

拿給電腦評判，

to show something you've made, you've designed,

然後它看完後會說，

to a computer,

「抱歉，兄弟，這東西行不通， 你再試試別的吧！」

and it will look at it and say,

或者你可以問它， 人們會不會喜歡你的新歌？

"Sorry, homie, that'll never work. You have to try again."

或者你冰淇淋的新口味？

Or you could ask it if people are going to like your next song,

再或者，更重要的，

or your next flavor of ice cream.

你可以跟電腦一起解決

Or, much more importantly,

我們從未面臨過的問題。

you could work with a computer to solve a problem

例如，氣候變遷問題。

that we've never faced before.

我們自己沒有做得很好的事，

For instance, climate change.

我們當然可以利用身邊 各種資源來幫忙解決。

We're not doing a very good job on our own,

這就是我接下來要談的，

we could certainly use all the help we can get.

科技強化了我們的認知能力，

That's what I'm talking about,

所以我們可以想像並設計出， 當我們還未具有強化擴增能力時

technology amplifying our cognitive abilities

所未能創造出來的東西。

so we can imagine and design things that were simply out of our reach

那麼，製造這些我們即將發明設計的

as plain old un-augmented humans.

瘋狂新產品會如何呢？

So what about making all of this crazy new stuff

我認為在人類擴增的時代， 現實世界

that we're going to invent and design?

及虛擬智慧領域 與其皆有不分軒輊的重要相關性。

I think the era of human augmentation is as much about the physical world

科技將會如何強化我們？

as it is about the virtual, intellectual realm.

在現實世界，就是機械人系統。

How will technology augment us?

沒錯，很多人擔心，

In the physical world, robotic systems.

機械人會搶走人類的工作，

OK, there's certainly a fear

在某些領域，確實是如此。

that robots are going to take jobs away from humans,

但，我對以下的想法比較有興趣，

and that is true in certain sectors.

就是，人類與機械人 將會一起工作並互相強化，

But I'm much more interested in this idea

並開創出一種新的共生空間。

that humans and robots working together are going to augment each other,

這是我們在舊金山的 應用研究實驗室，

and start to inhabit a new space.

我們專研的領域之一就是 高階機械人，

This is our applied research lab in San Francisco,

特別是人機合作的領域。

where one of our areas of focus is advanced robotics,

這是畢夏普， 我們其中的一個機器人。

specifically, human-robot collaboration.

在實驗裡，我將它設定為

And this is Bishop, one of our robots.

在建築領域中， 幫助人類做重複性的工作——

As an experiment, we set it up

比如說，在石牆上打出一個 插座孔或電燈開關孔。

to help a person working in construction doing repetitive tasks --

（笑聲）

tasks like cutting out holes for outlets or light switches in drywall.

所以，畢夏普的人類夥伴 就可以用簡單的英語和手勢

(Laughter)

告訴它該做什麼，

So, Bishop's human partner can tell what to do in plain English

有點像是在跟狗狗說話。

and with simple gestures,

然後畢夏普會以完美的準確度

kind of like talking to a dog,

執行人類所下達的指令。

and then Bishop executes on those instructions

我們讓人類做人類擅長的事，像是：

with perfect precision.

需要意識力、洞察力、做決策的工作。

We're using the human for what the human is good at:

我們讓機械人做機械人擅長的事， 像是：

awareness, perception and decision making.

準確度及重複性的工作。

And we're using the robot for what it's good at:

畢夏普還有另一個很酷的專案。

precision and repetitiveness.

這個專案的目標， 我們稱它為，

Here's another cool project that Bishop worked on.

主要目標是把人類、電腦、 機械人的經驗結合起來，

The goal of this project, which we called the HIVE,

一起工作解決極複雜的設計問題。

was to prototype the experience of humans, computers and robots

人類的工作是

all working together to solve a highly complex design problem.

在建築基地巡邏監工 並熟練地操作竹子——

The humans acted as labor.

順便一提，因為每一根竹子的 材料性質都不一樣，

They cruised around the construction site, they manipulated the bamboo --

所以機械人操作起來非常困難。

which, by the way, because it's a non-isomorphic material,

但機械人做的是彎曲竹子的纖維，

is super hard for robots to deal with.

這種事人類幾乎做不來。

But then the robots did this fiber winding,

然後我們讓一台人工智慧 來控制所有的東西。

which was almost impossible for a human to do.

它會告訴人類要做什麼， 告訴機械人要做什麼，

And then we had an AI that was controlling everything.

並且對成千上萬個部件 進行持續的追蹤。

It was telling the humans what to do, telling the robots what to do

有趣的是，

and keeping track of thousands of individual components.

要建造出這樣的亭狀建築物，

What's interesting is,

如果沒有人類、機械、人工智慧的 互補強化，根本不可能做得出來。

building this pavilion was simply not possible

好，我再分享一個專案， 這個有點瘋狂。

without human, robot and AI augmenting each other.

我們與阿姆斯特丹的藝術家 尤爾斯‧拉曼和他的 MX3D 團隊，

OK, I'll share one more project. This one's a little bit crazy.

正使用衍生性設計 與機械列印的方式，

We're working with Amsterdam-based artist Joris Laarman and his team at MX3D

打造世界第一座機械人自造的橋梁。

to generatively design and robotically print

所以，就在我們談話的這一刻，

the world's first autonomously manufactured bridge.

尤爾斯正和人工智慧一起 在阿姆斯特丹設計這座橋梁。

So, Joris and an AI are designing this thing right now, as we speak,

等他們設計完成後， 我們就會按下「啟動」開關，

in Amsterdam.

讓機械人開始 用不鏽鋼 3D 列印出橋梁，

And when they're done, we're going to hit "Go,"

在沒有人類的介入幫忙下， 它們會持續地列印

and robots will start 3D printing in stainless steel,

直到橋樑完工為止。

and then they're going to keep printing, without human intervention,

所以，電腦將強化

until the bridge is finished.

我們的想像及設計新事物的能力，

So, as computers are going to augment our ability

機械人系統將協助我們製造

to imagine and design new stuff,

我們以前無法製造的東西。

robotic systems are going to help us build and make things

但是我們感知和控制 這些東西的能力呢？

that we've never been able to make before.

我們製成東西的神經系統 又如何呢？

But what about our ability to sense and control these things?

我們的神經系統，人類的神經系統，

What about a nervous system for the things that we make?

可以告訴我們周遭發生的每一件事。

Our nervous system, the human nervous system,

但這些東西的神經系統， 最多只能算「尚未成熟」。

tells us everything that's going on around us.

比如說，車輛本身 不會主動通告市政府的工部門，

But the nervous system of the things we make is rudimentary at best.

說它在經過百老匯和 莫里森轉角口時撞到水坑。

For instance, a car doesn't tell the city's public works department

建築物本身不會告知它的設計師，

that it just hit a pothole at the corner of Broadway and Morrison.

裡面的居民是否喜歡住在那裏，

A building doesn't tell its designers

玩具製造商也不知道

whether or not the people inside like being there,

他們的玩具 現在是跟誰在玩、在哪玩、

and the toy manufacturer doesn't know

是不是玩的很開心。

if a toy is actually being played with --

我確定設計師在設計芭比時，

how and where and whether or not it's any fun.

一定想像過芭比的生活方式。