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仅做 整合 / 美化 处理
How many of you are creatives,
你们其中多少人是创意者
designers, engineers, entrepreneurs, artists,
设计师，工程师 创业家或艺术家
or maybe you just have a really big imagination?
或许你只是正好有 天马行空的想象力？
Show of hands? (Cheers)
请举手（喝彩声）
That's most of you.
你们绝大多数人都是
I have some news for us creatives.
现在我有消息告诉我们创新者：
Over the course of the next 20 years,
在未来的20年内
more will change around the way we do our work
我们的工作方式将 会发生很多的改变
than has happened in the last 2,000.
远超过去的两千年发生的变化
In fact, I think we're at the dawn of a new age in human history.
实际上，我认为我们正处在 人类历史新纪元的黎明
Now, there have been four major historical eras defined by the way we work.
至今为止，人类历史共有四个主要 由我们的工作方式定义的阶段
The Hunter-Gatherer Age lasted several million years.
人类历史经历了数百万年 的狩猎采集时代
And then the Agricultural Age lasted several thousand years.
然后经历了数千年 的农耕时代
The Industrial Age lasted a couple of centuries.
工业时代延续 了几个世纪
And now the Information Age has lasted just a few decades.
目前的信息时代 才走了几十年
And now today, we're on the cusp of our next great era as a species.
如今，身为人类的我们 正站在下一个伟大时代的交汇点
Welcome to the Augmented Age.
欢迎来到“扩增时代"
In this new era, your natural human capabilities are going to be augmented
在这个新时代中，你们人类 天生的能力将会扩增
by computational systems that help you think,
计算系统将帮助你思考
robotic systems that help you make,
机器人系统将帮助你制作
and a digital nervous system
数字神经系统
that connects you to the world far beyond your natural senses.
将连接你到一个自然感官 无法触及的世界
Let's start with cognitive augmentation.
让我们从“认知扩增”说起
How many of you are augmented cyborgs?
你们有多少人是“强化的搬机器人”？
(Laughter)
（观众笑声）
I would actually argue that we're already augmented.
我想说的是 其实我们已经被强化了
Imagine you're at a party,
想想一下你在一个派对
and somebody asks you a question that you don't know the answer to.
然后有人问了一个 你不知道如何回答的问题
If you have one of these, in a few seconds, you can know the answer.
如果你有这些其中的一个 在几秒钟之内，你可以能知道答案
But this is just a primitive beginning.
但这仅仅是一个原始的开端
Even Siri is just a passive tool.
即使是Siri也是一个被动的工具
In fact, for the last three-and-a-half million years,
实际上，在过去的 3.5百万年间
the tools that we've had have been completely passive.
我们所拥有的工具 是完全被动的
They do exactly what we tell them and nothing more.
它们会照我们告诉它们的原封不动地做 一点多余的都没有
Our very first tool only cut where we struck it.
我们的第一个工具仅仅是会 在我们发起时切割
The chisel only carves where the artist points it.
凿子只会在艺术家 指示的地方雕刻
And even our most advanced tools do nothing without our explicit direction.
甚至是我们最先进的工具 在没有我们明确指示的情况下什么也做不了
In fact, to date, and this is something that frustrates me,
实际上，这是那现在为止 使我感到颓丧的地方
we've always been limited
我们总是被
by this need to manually push our wills into our tools --
手动地将我们的意愿推动到 我们的工具中的需要所限制
like, manual, literally using our hands,
就像，手动 真正地用我们的手
even with computers.
甚至是在使用电脑的时候
But I'm more like Scotty in "Star Trek."
但是我更像星际奇航中的史考特
(Laughter)
（观众笑声）
I want to have a conversation with a computer.
我想和电脑 有一段对话
I want to say, "Computer, let's design a car,"
我想说，“电脑， 让我们来设计一部车，”
and the computer shows me a car.
然后电脑就给我展现一部车
And I say, "No, more fast-looking, and less German,"
然后我就说，“不，一部更加 更不像德国车的车，”
and bang, the computer shows me an option.
然后，蹦，电脑给我展现了一个选项
(Laughter)
（观众笑声）
That conversation might be a little ways off,
这段对话也许有 一点不太切合实际
probably less than many of us think,
也许没有我们很多人想的那样偏离
but right now,
但是现在
we're working on it.
我们正在向这个目标迈进
Tools are making this leap from being passive to being generative.
使从被动到主动生成 跨越的工具
Generative design tools use a computer and algorithms
生成性的设计工具 使用一台电脑和算数
to synthesize geometry
去合成几何
to come up with new designs all by themselves.
去开始一个完全由它们 想出来的设计
All it needs are your goals and your constraints.
所有需要的是你的目标 和你的限制
I'll give you an example.
我给你们一个例子
In the case of this aerial drone chassis,
用空中无人机地盘来举例
all you would need to do is tell it something like,
你所需要做的 是告诉它
it has four propellers,
它有四个螺旋桨
you want it to be as lightweight as possible,
你想它尽可能的 轻
and you need it to be aerodynamically efficient.
而且你还需要它 在空气动力学的意义上高效
Then what the computer does is it explores the entire solution space:
电脑所做的是 探索整个解决空间
every single possibility that solves and meets your criteria --
在几百万个可能性中
millions of them.
寻找每一个能够解决你问题 和达到你标准的可能性
It takes big computers to do this.
这需要一个很大的电脑来做这个
But it comes back to us with designs
但是它给我们带来了设计
that we, by ourselves, never could've imagined.
这个设计是我们自己 从不会想象得到的
And the computer's coming up with this stuff all by itself --
电脑自己制作出了个这个
no one ever drew anything,
没有人绘画任何东西
and it started completely from scratch.
而且它从完全的无开始
And by the way, it's no accident
顺便说一下，这不是意外
that the drone body looks just like the pelvis of a flying squirrel.
那个无人操作的身体看上去就像 一个飞翔中松鼠的盆骨
(Laughter)
（观众笑声）
It's because the algorithms are designed to work
这是因为算数 是被设计成
the same way evolution does.
和进化一样的工作方式
What's exciting is we're starting to see this technology
使人兴奋的是我们开始 去看这个设计
out in the real world.
在一个真实的世界中去看
We've been working with Airbus for a couple of years
我们研究空中客车 有好几年了
on this concept plane for the future.
在未来飞机的概念下
It's a ways out still.
仍然还在研究中
But just recently we used a generative-design AI
但是仅仅在最近我们使用了 一个设计生成的AI
to come up with this.
去展开制作
This is a 3D-printed cabin partition that's been designed by a computer.
这是一个由电脑设计的 3d打印的舱室分隔
It's stronger than the original yet half the weight,
它比原来的舱室要更坚固 但同时重量只有原来的一半
and it will be flying in the Airbus A320 later this year.
而且它将于今年在A320 空中客机中飞翔
So computers can now generate;
所以电脑现在可以生成
they can come up with their own solutions to our well-defined problems.
它们可以针对我们复杂的问题 生成它们自己的解决方案
But they're not intuitive.
但是它们不是具有直觉的
They still have to start from scratch every single time,
它们仍然每次要 从新开始
and that's because they never learn.
那是因为它们从来不学习
Unlike Maggie.
不像麦琪
(Laughter)
（观众笑声）
Maggie's actually smarter than our most advanced design tools.
麦琪实际上比我们 大多数先进的设计工具要聪明
What do I mean by that?
我说这个是什么意思呢？
If her owner picks up that leash,
如果它的主人拿起绳子
Maggie knows with a fair degree of certainty
麦琪会一定确定度 的情况下
it's time to go for a walk.
知道是时候出去散步了
And how did she learn?
她是怎么学习这个的？
Well, every time the owner picked up the leash, they went for a walk.
每次主人拿起绳子 他们就去散步
And Maggie did three things:
麦琪做了三件事
she had to pay attention,
她必须对此花费注意力
she had to remember what happened
她必须记住发生的事情
and she had to retain and create a pattern in her mind.
而且她还必须在她脑子里 保持和创造一个模式
Interestingly, that's exactly what
有趣的是，那正是
computer scientists have been trying to get AIs to do
电脑科学家一直致力于使 AIs去做的事
for the last 60 or so years.
在过去的大约60年间
Back in 1952,
追溯到1952年
they built this computer that could play Tic-Tac-Toe.
他们建造了这个电脑 所以他们可以玩Tic-Tac-Toe
Big deal.
了不起的事
Then 45 years later, in 1997,
然后45年之后，在1997年
Deep Blue beats Kasparov at chess.
Deep Blue在象棋赛上打败了Kasparov
2011, Watson beats these two humans at Jeopardy,
2011年，Watson在有障碍的情况下 打败了这两个人
which is much harder for a computer to play than chess is.
这对于一台电脑来说 难度更大
In fact, rather than working from predefined recipes,
实际上，不像从之前制定好的 材料上开始工作
Watson had to use reasoning to overcome his human opponents.
Watson不得不使用推理 去克服它的人类对手
And then a couple of weeks ago,
然后在几个星期前
DeepMind's AlphaGo beats the world's best human at Go,
DeepMind的AlphaGo打败了 世界上最好的人类at Go
which is the most difficult game that we have.
那是我们现有的 难度最高的比赛
In fact, in Go, there are more possible moves
实际上，在Go里面有 更多的走步
than there are atoms in the universe.
相比起宇宙中的原子
So in order to win,
所以，为了取胜
what AlphaGo had to do was develop intuition.
AlphaGo所必须做的 是发展直觉力
And in fact, at some points, AlphaGo's programmers didn't understand
而且实际上，从某种程度上说 AlphaGo的程序不懂
why AlphaGo was doing what it was doing.
为什么AlphaGo要做这些 以及它正在做什么
And things are moving really fast.
事情进展得很快
I mean, consider -- in the space of a human lifetime,
我是说，考虑到 在一个人类有限的生命中
computers have gone from a child's game
电脑经历了从一个小孩子的比赛
to what's recognized as the pinnacle of strategic thought.
那现在被认为是 策略思考
What's basically happening
在这里发生的
is computers are going from being like Spock
是电脑经历了从Spock
to being a lot more like Kirk.
到更加像Kirk
(Laughter)
（观众笑声）
Right? From pure logic to intuition.
对吗？从纯逻辑到直觉
Would you cross this bridge?
你会不会跨越这个桥梁？
Most of you are saying, "Oh, hell no!"
你们中的大部分会说，“噢，不！”
(Laughter)
（观众笑声）
And you arrived at that decision in a split second.
而且你在不到一秒的时间内 作出那个反应
You just sort of knew that bridge was unsafe.
你只是知道那个 桥是不安全的
And that's exactly the kind of intuition
那正是一种直觉
that our deep-learning systems are starting to develop right now.
我们的深入学习系统正开始 发展那种直觉
Very soon, you'll literally be able
很快，你就能够
to show something you've made, you've designed,
开始展现你制作的 你设计的
to a computer,
向一台电脑
and it will look at it and say,
然后它就会看着它然后说
"Sorry, homie, that'll never work. You have to try again."
“对不起，宝贝，那个是没有用的 你必须得再次尝试。“
Or you could ask it if people are going to like your next song,
或者你可以问问是否 人们会喜欢你的下一首歌
or your next flavor of ice cream.
或者你下一个冰淇淋口味
Or, much more importantly,
或者，更重要的是
you could work with a computer to solve a problem
你可以和电脑合作 解决那些
that we've never faced before.
我们之前从来没有遇见过的问题
For instance, climate change.
比如说，气候变暖
We're not doing a very good job on our own,
我们自己没有对此 做一个很好的工作
we could certainly use all the help we can get.
我们绝对是可以用到 我们能得到的所有帮助
That's what I'm talking about,
那正是我所谈论的
technology amplifying our cognitive abilities
科技放大了 我们的认知能力
so we can imagine and design things that were simply out of our reach
所以我们可以得心应手地 想象和设计事物
as plain old un-augmented humans.
作为一个纯粹的没有被增强的人类
So what about making all of this crazy new stuff
那么，制作这些所有的 我们将去发明和设计的
that we're going to invent and design?
疯狂的新事物呢？
I think the era of human augmentation is as much about the physical world
我认为人类增强的世纪 其实是关于物理世界
as it is about the virtual, intellectual realm.
关于实际上的 知识领域
How will technology augment us?
科技是如何增强我们的？
In the physical world, robotic systems.
在物理世界，机器系统
OK, there's certainly a fear
好吧，这绝对是有一种恐惧
that robots are going to take jobs away from humans,
那就是机器人将会把 人类的工作带走
and that is true in certain sectors.
而且这在一定范围内是真实的
But I'm much more interested in this idea
但我对这个更感兴趣
that humans and robots working together are going to augment each other,
那就是如果人类和机器人在一起工作 它们会互相增强对方
and start to inhabit a new space.
然后会开始开发一个新的空间
This is our applied research lab in San Francisco,
这是我们的在三藩市的 应用研究实验室
where one of our areas of focus is advanced robotics,
在那里我们集中研究的 领域之一是先进的机器
specifically, human-robot collaboration.
具体说来，人类和机器合作
And this is Bishop, one of our robots.
这是教主，我们其中的一个机器人
As an experiment, we set it up
由于是一个实验，我们安排好了
to help a person working in construction doing repetitive tasks --
去帮助一个人建立操作 重复性的任务
tasks like cutting out holes for outlets or light switches in drywall.
就像是为衣物切割洞 或者是在石膏板上点燃
(Laughter)
（观众笑声）
So, Bishop's human partner can tell what to do in plain English
那么，教主的人类拍档 可以区别用纯粹英语该如何做
and with simple gestures,
附和一些简单的手势
kind of like talking to a dog,
就好像对一只狗说话
and then Bishop executes on those instructions
然后教主执行了 这些指导
with perfect precision.
这些指导是有完美的精确度的
We're using the human for what the human is good at:
我们在使用一个人类 擅长的地方
awareness, perception and decision making.
意识，洞察力，和决断力
And we're using the robot for what it's good at:
然后我们使用了一个机器人 擅长的地方
precision and repetitiveness.
准确度和重复性
Here's another cool project that Bishop worked on.
这里有另一个很酷的项目 也是教主在实施的
The goal of this project, which we called the HIVE,
这个项目的目标 我们把它叫作HIVE
was to prototype the experience of humans, computers and robots
是人类，电脑，和机器试验 的一个原型模型
all working together to solve a highly complex design problem.
它们工作在一起去解决 一个极端复杂的设计问题
The humans acted as labor.
人类起到的作用是付出人工劳动
They cruised around the construction site, they manipulated the bamboo --
它们在建设工地上巡航 它们玩弄竹子
which, by the way, because it's a non-isomorphic material,
顺便说一下 因为这是一个不同构的材料
is super hard for robots to deal with.
它对于机器人来说是非常难应对的
But then the robots did this fiber winding,
但是后来机器人 完成了这个纤维扭动
which was almost impossible for a human to do.
这个对于人类来说 几乎是不可能的
And then we had an AI that was controlling everything.
然后我们有一个AI 它控制着每一件事
It was telling the humans what to do, telling the robots what to do
它告诉人类应该做什么 告诉机器人应该做什么
and keeping track of thousands of individual components.
而且还要纪录几千个 个人的组成部分
What's interesting is,
有趣的是
building this pavilion was simply not possible
建造这个亭子 仅仅就是不可能
without human, robot and AI augmenting each other.
没有人类，机器和AI 互相增强
OK, I'll share one more project. This one's a little bit crazy.
好，我就再分享另一个项目 这一个有一点不可思议
We're working with Amsterdam-based artist Joris Laarman and his team at MX3D
我们致力于在一个阿姆斯特丹为基础的艺术家 Joris Laarman和他的团队在MX3D
to generatively design and robotically print
去生成地设计 和活跃地打印
the world's first autonomously manufactured bridge.
世界上第一个自动地 制造的桥梁
So, Joris and an AI are designing this thing right now, as we speak,
那么，Joris 和一个 AI 正在我们说话的功夫 设计这个东西
in Amsterdam.
在阿姆斯特丹
And when they're done, we're going to hit "Go,"
当他们完成的时候 我们按下“Go“
and robots will start 3D printing in stainless steel,
然后机器人将开始3D打印 在不锈钢上
and then they're going to keep printing, without human intervention,
然后它们将继续打印 在没有人类干预的情况下
until the bridge is finished.
直到桥梁完成
So, as computers are going to augment our ability
所以，随着电脑将 增强我们的能力
to imagine and design new stuff,
在想象和设计新事物上
robotic systems are going to help us build and make things
机器的系统将帮助我们 建造和制作事物
that we've never been able to make before.
建造那些我们之前从来没有能够做到的事物
But what about our ability to sense and control these things?
但那么我们去感知和控制 这些事物的能力呢？
What about a nervous system for the things that we make?
对于我们制作的这些事物的 一个神经系统呢？
Our nervous system, the human nervous system,
我们的神经系统 人类的神经系统
tells us everything that's going on around us.
告诉了我们每一件 在我们身边正在发生的事情
But the nervous system of the things we make is rudimentary at best.
但是我们制作的这些事物的 神经系统最好的也不过是简单的
For instance, a car doesn't tell the city's public works department
比如说，一辆车不能够分辨出 城市的公共工作部门
that it just hit a pothole at the corner of Broadway and Morrison.
那就像是在一个百老汇和莫里森 角落击中一个壶洞一样
A building doesn't tell its designers
一栋建筑不会对它的设计者说
whether or not the people inside like being there,
是否建筑里的人喜欢这个建筑
and the toy manufacturer doesn't know
而且玩具制造商不知道
if a toy is actually being played with --
是否一个玩具正在被把玩
how and where and whether or not it's any fun.
如何，在哪里，以及是否 是有趣的
Look, I'm sure that the designers imagined this lifestyle for Barbie
我很确定设计师们想象了 这个芭比的生活方式
when they designed her.
当他们设计她的时候
(Laughter)
（观众笑声）
But what if it turns out that Barbie's actually really lonely?
但是，如果结果是芭比 实际上是真的孤独？
(Laughter)
（观众笑声）
If the designers had known
如果设计师事先知道
what was really happening in the real world
在真实世界将会 发生什么
with their designs -- the road, the building, Barbie --
他们的设计 － 道路 建筑，芭比
they could've used that knowledge to create an experience
他们可以用那个知识 去创造一个体验
that was better for the user.
那个体验对于使用者来说更好
What's missing is a nervous system
这里缺少的是一个神经系统
connecting us to all of the things that we design, make and use.
将我们所有设计的，制作的 和使用的事情联结起来
What if all of you had that kind of information flowing to you
如果你们都有从现实世界 创造的事情
from the things you create in the real world?
的信息涌向你
With all of the stuff we make,
以及我们所做的所有的东西
we spend a tremendous amount of money and energy --
我们发费了巨大的 时间和精力
in fact, last year, about two trillion dollars --
实际上，去年 有2万亿美金
convincing people to buy the things we've made.
劝说人们去买 我们制作的事物
But if you had this connection to the things that you design and create
但是如果你有你设计和创造的 事物的联结
after they're out in the real world,
在它们出现在现实世界
after they've been sold or launched or whatever,
在它们被卖掉 或者发展或这之类的
we could actually change that,
我们实际上可以改变那个
and go from making people want our stuff,
而且从制作人们想要的东西
to just making stuff that people want in the first place.
到仅仅是制作人们一开始 想要的东西
The good news is, we're working on digital nervous systems
好消息是，我们在数码 神经系统上工作
that connect us to the things we design.
这个系统连接我们和我们设计的东西
We're working on one project
我们正致力于一个项目
with a couple of guys down in Los Angeles called the Bandito Brothers
和几个人在洛杉矶 项目叫作Bandito Brothers
and their team.
以及他们的团队
And one of the things these guys do is build insane cars
这些人想做的其中一件事情是 建造不可思议的车
that do absolutely insane things.
那些车可以去做绝对是不可思议的事情
These guys are crazy --
这些人是很疯狂的
(Laughter)
（观众笑声）
in the best way.
以最好的方式
And what we're doing with them
我们和他们做的
is taking a traditional race-car chassis
是拿一个传统的赛车地盘
and giving it a nervous system.
然后给它一个神经系统
So we instrumented it with dozens of sensors,
那么我们用了几打感应 去组建它
put a world-class driver behind the wheel,
在车上放上一个世界级 的司机
took it out to the desert and drove the hell out of it for a week.
将他带到沙漠里 然后让他不停地连续开上一个星期
And the car's nervous system captured everything
然后车的神经系统 抓住了车上
that was happening to the car.
发生的每件事情
We captured four billion data points;
我们抓住了4亿个数据点
all of the forces that it was subjected to.
所有的动力
And then we did something crazy.
然后我们做了一些疯狂的事
We took all of that data,
我们拿出了所有的数据
and plugged it into a generative-design AI we call "Dreamcatcher."
然后把它们放到一个我们叫做Dreamcatcher 地生成性设计的AI里
So what do get when you give a design tool a nervous system,
那么当你给一个设计 神经系统
and you ask it to build you the ultimate car chassis?
而且你要它给你建造 最终的车地盘时，你会得到什么？
You get this.
你得到这个
This is something that a human could never have designed.
这是一个人类永远不会设计的
Except a human did design this,
除了一个人设计了这个
but it was a human that was augmented by a generative-design AI,
但是是一个被生成性设计AI 增强的人类
a digital nervous system
一个数码神经系统
and robots that can actually fabricate something like this.
而且机器人实际上是 可以制造这样的东西的
So if this is the future, the Augmented Age,
所以，如果这是未来 增强时代
and we're going to be augmented cognitively, physically and perceptually,
我们将会被在认知上，物理上，和洞察力 上被增强
what will that look like?
那看上去会是什么？
What is this wonderland going to be like?
这个仙境将会是一个什么？
I think we're going to see a world
我认为我们将会看到一个
where we're moving from things that are fabricated
在那里我们从制作事物
to things that are farmed.
到种植事物的世界
Where we're moving from things that are constructed
在那里我们将会从建筑事物
to that which is grown.
到生长事物
We're going to move from being isolated
我们将会从被孤独隔离
to being connected.
到被连接
And we'll move away from extraction
我们将远离灭绝
to embrace aggregation.
去拥抱集合
I also think we'll shift from craving obedience from our things
我还认为我们将从对我们的事情的 疯狂饥渴服从
to valuing autonomy.
转换到珍视自主
Thanks to our augmented capabilities,
感谢我们的增强放大能力
our world is going to change dramatically.
我们的世界将会有翻天覆地地改变
We're going to have a world with more variety, more connectedness,
我们将会看到一个更加 多样化，更多连接
more dynamism, more complexity,
更多动态，更多复杂性
more adaptability and, of course,
更多适应性，当然
more beauty.
更加美丽
The shape of things to come
那些将要到来的事情
will be unlike anything we've ever seen before.
不会像任何我们之前看到的事情
Why?
为什么？
Because what will be shaping those things is this new partnership
因为将会是新的关系 来整理成型新的事物
between technology, nature and humanity.
在科技，自然，和人性之间新的关系
That, to me, is a future well worth looking forward to.
那个，对我来说，是一个 值得向未来展望的事情
Thank you all so much.
非常感谢
(Applause)
（观众鼓掌）