TEDxPortland
Maurice Conti: The incredible inventions of intuitive AI
Maurice Conti: 直觉性AI的不可思议的发明
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当你给一个设计工具一个数码神经系统时，你会得到什么？电脑提升了我们的思考和想象的能力，机器系统可以为桥梁，汽车，无人驾驶飞机，以及更多的东西提供全新的设计 － 全由它们自己。看看未来家Maurice Conti 给我们带来的增强放大时代。在那里机器和人类将会紧密合作去完成一些双方不可能独立完成的项目。
Maurice Conti - Designer, futurist
Maurice Conti explores new partnerships between technology, nature and humanity. Full bio
Maurice Conti explores new partnerships between technology, nature and humanity. Full bio
Double-click the English transcript below to play the video.
00:12
How many of you are creatives,
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你们其中多少人是创意者
设计师，工程师
创业家或艺术家
创业家或艺术家
00:15
designers, engineers,
entrepreneurs, artists,
entrepreneurs, artists,
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或许你只是正好有
天马行空的想象力？
天马行空的想象力？
00:18
or maybe you just have
a really big imagination?
a really big imagination?
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请举手（喝彩声）
00:21
Show of hands? (Cheers)
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00:22
That's most of you.
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你们绝大多数人都是
00:25
I have some news for us creatives.
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现在我有消息告诉我们创新者：
00:28
Over the course of the next 20 years,
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在未来的20年内
00:33
more will change around
the way we do our work
the way we do our work
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我们的工作方式将
会发生很多的改变
会发生很多的改变
00:37
than has happened in the last 2,000.
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远超过去的两千年发生的变化
实际上，我认为我们正处在
人类历史新纪元的黎明
人类历史新纪元的黎明
00:40
In fact, I think we're at the dawn
of a new age in human history.
of a new age in human history.
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00:45
Now, there have been four major historical
eras defined by the way we work.
eras defined by the way we work.
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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
由我们的工作方式定义的阶段
00:51
The Hunter-Gatherer Age
lasted several million years.
lasted several million years.
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人类历史经历了数百万年
的狩猎采集时代
的狩猎采集时代
00:55
And then the Agricultural Age
lasted several thousand years.
lasted several thousand years.
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然后经历了数千年
的农耕时代
的农耕时代
00:59
The Industrial Age lasted
a couple of centuries.
a couple of centuries.
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工业时代延续
了几个世纪
了几个世纪
目前的信息时代
才走了几十年
才走了几十年
01:02
And now the Information Age
has lasted just a few decades.
has lasted just a few decades.
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如今，身为人类的我们
正站在下一个伟大时代的交汇点
正站在下一个伟大时代的交汇点
01:07
And now today, we're on the cusp
of our next great era as a species.
of our next great era as a species.
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01:13
Welcome to the Augmented Age.
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欢迎来到“扩增时代"
01:16
In this new era, your natural human
capabilities are going to be augmented
capabilities are going to be augmented
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在这个新时代中，你们人类
天生的能力将会扩增
天生的能力将会扩增
01:19
by computational systems
that help you think,
that help you think,
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计算系统将帮助你思考
机器人系统将帮助你制作
01:22
robotic systems that help you make,
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01:25
and a digital nervous system
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数字神经系统
将连接你到一个自然感官
无法触及的世界
无法触及的世界
01:26
that connects you to the world
far beyond your natural senses.
far beyond your natural senses.
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01:31
Let's start with cognitive augmentation.
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让我们从“认知扩增”说起
你们有多少人是“强化的搬机器人”？
01:33
How many of you are augmented cyborgs?
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01:36
(Laughter)
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（观众笑声）
01:38
I would actually argue
that we're already augmented.
that we're already augmented.
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我想说的是
其实我们已经被强化了
其实我们已经被强化了
01:42
Imagine you're at a party,
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想想一下你在一个派对
然后有人问了一个
你不知道如何回答的问题
你不知道如何回答的问题
01:43
and somebody asks you a question
that you don't know the answer to.
that you don't know the answer to.
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01:47
If you have one of these,
in a few seconds, you can know the answer.
in a few seconds, you can know the answer.
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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
在几秒钟之内，你可以能知道答案
01:51
But this is just a primitive beginning.
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但这仅仅是一个原始的开端
01:54
Even Siri is just a passive tool.
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即使是Siri也是一个被动的工具
01:58
In fact, for the last
three-and-a-half million years,
three-and-a-half million years,
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实际上，在过去的
3.5百万年间
3.5百万年间
02:02
the tools that we've had
have been completely passive.
have been completely passive.
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我们所拥有的工具
是完全被动的
是完全被动的
02:06
They do exactly what we tell them
and nothing more.
and nothing more.
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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
一点多余的都没有
02:09
Our very first tool only cut
where we struck it.
where we struck it.
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我们的第一个工具仅仅是会
在我们发起时切割
在我们发起时切割
凿子只会在艺术家
指示的地方雕刻
指示的地方雕刻
02:13
The chisel only carves
where the artist points it.
where the artist points it.
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02:17
And even our most advanced tools
do nothing without our explicit direction.
do nothing without our explicit direction.
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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
在没有我们明确指示的情况下什么也做不了
02:23
In fact, to date, and this
is something that frustrates me,
is something that frustrates me,
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实际上，这是那现在为止
使我感到颓丧的地方
使我感到颓丧的地方
02:26
we've always been limited
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我们总是被
手动地将我们的意愿推动到
我们的工具中的需要所限制
我们的工具中的需要所限制
02:27
by this need to manually
push our wills into our tools --
push our wills into our tools --
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就像，手动
真正地用我们的手
真正地用我们的手
02:31
like, manual,
literally using our hands,
literally using our hands,
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甚至是在使用电脑的时候
02:33
even with computers.
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02:36
But I'm more like Scotty in "Star Trek."
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但是我更像星际奇航中的史考特
02:38
(Laughter)
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（观众笑声）
02:40
I want to have a conversation
with a computer.
with a computer.
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我想和电脑
有一段对话
有一段对话
02:42
I want to say, "Computer,
let's design a car,"
let's design a car,"
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我想说，“电脑，
让我们来设计一部车，”
让我们来设计一部车，”
02:45
and the computer shows me a car.
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然后电脑就给我展现一部车
02:47
And I say, "No, more fast-looking,
and less German,"
and less German,"
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然后我就说，“不，一部更加
更不像德国车的车，”
更不像德国车的车，”
02:49
and bang, the computer shows me an option.
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然后，蹦，电脑给我展现了一个选项
02:51
(Laughter)
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（观众笑声）
02:54
That conversation might be
a little ways off,
a little ways off,
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这段对话也许有
一点不太切合实际
一点不太切合实际
02:56
probably less than many of us think,
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也许没有我们很多人想的那样偏离
但是现在
02:59
but right now,
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03:01
we're working on it.
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我们正在向这个目标迈进
03:02
Tools are making this leap
from being passive to being generative.
from being passive to being generative.
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使从被动到主动生成
跨越的工具
跨越的工具
03:06
Generative design tools
use a computer and algorithms
use a computer and algorithms
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生成性的设计工具
使用一台电脑和算数
使用一台电脑和算数
去合成几何
03:10
to synthesize geometry
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03:12
to come up with new designs
all by themselves.
all by themselves.
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去开始一个完全由它们
想出来的设计
想出来的设计
03:15
All it needs are your goals
and your constraints.
and your constraints.
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所有需要的是你的目标
和你的限制
和你的限制
03:18
I'll give you an example.
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我给你们一个例子
03:20
In the case of this aerial drone chassis,
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用空中无人机地盘来举例
03:23
all you would need to do
is tell it something like,
is tell it something like,
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你所需要做的
是告诉它
是告诉它
03:25
it has four propellers,
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它有四个螺旋桨
03:26
you want it to be
as lightweight as possible,
as lightweight as possible,
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你想它尽可能的
轻
轻
而且你还需要它
在空气动力学的意义上高效
在空气动力学的意义上高效
03:29
and you need it to be
aerodynamically efficient.
aerodynamically efficient.
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03:31
Then what the computer does
is it explores the entire solution space:
is it explores the entire solution space:
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电脑所做的是
探索整个解决空间
探索整个解决空间
03:36
every single possibility that solves
and meets your criteria --
and meets your criteria --
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在几百万个可能性中
03:40
millions of them.
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寻找每一个能够解决你问题
和达到你标准的可能性
和达到你标准的可能性
03:41
It takes big computers to do this.
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这需要一个很大的电脑来做这个
03:43
But it comes back to us with designs
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但是它给我们带来了设计
03:45
that we, by ourselves,
never could've imagined.
never could've imagined.
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这个设计是我们自己
从不会想象得到的
从不会想象得到的
03:49
And the computer's coming up
with this stuff all by itself --
with this stuff all by itself --
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电脑自己制作出了个这个
没有人绘画任何东西
03:52
no one ever drew anything,
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03:53
and it started completely from scratch.
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而且它从完全的无开始
03:57
And by the way, it's no accident
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顺便说一下，这不是意外
03:59
that the drone body looks just like
the pelvis of a flying squirrel.
the pelvis of a flying squirrel.
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那个无人操作的身体看上去就像
一个飞翔中松鼠的盆骨
一个飞翔中松鼠的盆骨
04:03
(Laughter)
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（观众笑声）
04:06
It's because the algorithms
are designed to work
are designed to work
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这是因为算数
是被设计成
是被设计成
和进化一样的工作方式
04:08
the same way evolution does.
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04:10
What's exciting is we're starting
to see this technology
to see this technology
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使人兴奋的是我们开始
去看这个设计
去看这个设计
在一个真实的世界中去看
04:13
out in the real world.
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04:14
We've been working with Airbus
for a couple of years
for a couple of years
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我们研究空中客车
有好几年了
有好几年了
04:17
on this concept plane for the future.
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在未来飞机的概念下
仍然还在研究中
04:18
It's a ways out still.
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但是仅仅在最近我们使用了
一个设计生成的AI
一个设计生成的AI
04:21
But just recently we used
a generative-design AI
a generative-design AI
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04:24
to come up with this.
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去展开制作
04:27
This is a 3D-printed cabin partition
that's been designed by a computer.
that's been designed by a computer.
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这是一个由电脑设计的
3d打印的舱室分隔
3d打印的舱室分隔
它比原来的舱室要更坚固
但同时重量只有原来的一半
但同时重量只有原来的一半
04:32
It's stronger than the original
yet half the weight,
yet half the weight,
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04:35
and it will be flying
in the Airbus A320 later this year.
in the Airbus A320 later this year.
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而且它将于今年在A320
空中客机中飞翔
空中客机中飞翔
04:39
So computers can now generate;
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所以电脑现在可以生成
04:40
they can come up with their own solutions
to our well-defined problems.
to our well-defined problems.
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它们可以针对我们复杂的问题
生成它们自己的解决方案
生成它们自己的解决方案
04:46
But they're not intuitive.
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但是它们不是具有直觉的
它们仍然每次要
从新开始
从新开始
04:48
They still have to start from scratch
every single time,
every single time,
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04:51
and that's because they never learn.
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那是因为它们从来不学习
04:54
Unlike Maggie.
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不像麦琪
04:56
(Laughter)
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（观众笑声）
04:57
Maggie's actually smarter
than our most advanced design tools.
than our most advanced design tools.
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麦琪实际上比我们
大多数先进的设计工具要聪明
大多数先进的设计工具要聪明
05:01
What do I mean by that?
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我说这个是什么意思呢？
05:02
If her owner picks up that leash,
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如果它的主人拿起绳子
05:04
Maggie knows with a fair
degree of certainty
degree of certainty
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麦琪会一定确定度
的情况下
的情况下
知道是时候出去散步了
05:06
it's time to go for a walk.
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05:08
And how did she learn?
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她是怎么学习这个的？
05:09
Well, every time the owner picked up
the leash, they went for a walk.
the leash, they went for a walk.
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每次主人拿起绳子
他们就去散步
他们就去散步
05:12
And Maggie did three things:
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麦琪做了三件事
她必须对此花费注意力
05:14
she had to pay attention,
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05:16
she had to remember what happened
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她必须记住发生的事情
05:18
and she had to retain and create
a pattern in her mind.
a pattern in her mind.
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而且她还必须在她脑子里
保持和创造一个模式
保持和创造一个模式
05:23
Interestingly, that's exactly what
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有趣的是，那正是
电脑科学家一直致力于使
AIs去做的事
AIs去做的事
05:25
computer scientists
have been trying to get AIs to do
have been trying to get AIs to do
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在过去的大约60年间
05:28
for the last 60 or so years.
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05:30
Back in 1952,
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追溯到1952年
05:32
they built this computer
that could play Tic-Tac-Toe.
that could play Tic-Tac-Toe.
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他们建造了这个电脑
所以他们可以玩Tic-Tac-Toe
所以他们可以玩Tic-Tac-Toe
05:37
Big deal.
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了不起的事
05:39
Then 45 years later, in 1997,
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然后45年之后，在1997年
05:42
Deep Blue beats Kasparov at chess.
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Deep Blue在象棋赛上打败了Kasparov
05:46
2011, Watson beats these two
humans at Jeopardy,
humans at Jeopardy,
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2011年，Watson在有障碍的情况下
打败了这两个人
打败了这两个人
05:51
which is much harder for a computer
to play than chess is.
to play than chess is.
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这对于一台电脑来说
难度更大
难度更大
05:53
In fact, rather than working
from predefined recipes,
from predefined recipes,
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实际上，不像从之前制定好的
材料上开始工作
材料上开始工作
05:57
Watson had to use reasoning
to overcome his human opponents.
to overcome his human opponents.
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Watson不得不使用推理
去克服它的人类对手
去克服它的人类对手
06:02
And then a couple of weeks ago,
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然后在几个星期前
06:04
DeepMind's AlphaGo beats
the world's best human at Go,
the world's best human at Go,
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DeepMind的AlphaGo打败了
世界上最好的人类at Go
世界上最好的人类at Go
06:09
which is the most difficult
game that we have.
game that we have.
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那是我们现有的
难度最高的比赛
难度最高的比赛
实际上，在Go里面有
更多的走步
更多的走步
06:11
In fact, in Go, there are more
possible moves
possible moves
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06:14
than there are atoms in the universe.
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相比起宇宙中的原子
06:18
So in order to win,
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所以，为了取胜
06:20
what AlphaGo had to do
was develop intuition.
was develop intuition.
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AlphaGo所必须做的
是发展直觉力
是发展直觉力
06:23
And in fact, at some points,
AlphaGo's programmers didn't understand
AlphaGo's programmers didn't understand
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而且实际上，从某种程度上说
AlphaGo的程序不懂
AlphaGo的程序不懂
06:27
why AlphaGo was doing what it was doing.
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为什么AlphaGo要做这些
以及它正在做什么
以及它正在做什么
06:31
And things are moving really fast.
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事情进展得很快
我是说，考虑到
在一个人类有限的生命中
在一个人类有限的生命中
06:33
I mean, consider --
in the space of a human lifetime,
in the space of a human lifetime,
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06:36
computers have gone from a child's game
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电脑经历了从一个小孩子的比赛
06:39
to what's recognized as the pinnacle
of strategic thought.
of strategic thought.
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那现在被认为是
策略思考
策略思考
06:43
What's basically happening
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在这里发生的
06:46
is computers are going
from being like Spock
from being like Spock
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是电脑经历了从Spock
06:49
to being a lot more like Kirk.
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到更加像Kirk
06:51
(Laughter)
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（观众笑声）
06:55
Right? From pure logic to intuition.
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对吗？从纯逻辑到直觉
07:00
Would you cross this bridge?
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你会不会跨越这个桥梁？
07:02
Most of you are saying, "Oh, hell no!"
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你们中的大部分会说，“噢，不！”
07:04
(Laughter)
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（观众笑声）
07:06
And you arrived at that decision
in a split second.
in a split second.
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而且你在不到一秒的时间内
作出那个反应
作出那个反应
07:08
You just sort of knew
that bridge was unsafe.
that bridge was unsafe.
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你只是知道那个
桥是不安全的
桥是不安全的
07:11
And that's exactly the kind of intuition
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那正是一种直觉
07:13
that our deep-learning systems
are starting to develop right now.
are starting to develop right now.
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我们的深入学习系统正开始
发展那种直觉
发展那种直觉
07:17
Very soon, you'll literally be able
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很快，你就能够
开始展现你制作的
你设计的
你设计的
07:19
to show something you've made,
you've designed,
you've designed,
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向一台电脑
07:21
to a computer,
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然后它就会看着它然后说
07:22
and it will look at it and say,
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07:24
"Sorry, homie, that'll never work.
You have to try again."
You have to try again."
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“对不起，宝贝，那个是没有用的
你必须得再次尝试。“
你必须得再次尝试。“
07:27
Or you could ask it if people
are going to like your next song,
are going to like your next song,
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或者你可以问问是否
人们会喜欢你的下一首歌
人们会喜欢你的下一首歌
07:31
or your next flavor of ice cream.
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或者你下一个冰淇淋口味
07:35
Or, much more importantly,
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或者，更重要的是
07:38
you could work with a computer
to solve a problem
to solve a problem
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你可以和电脑合作
解决那些
解决那些
我们之前从来没有遇见过的问题
07:40
that we've never faced before.
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比如说，气候变暖
07:42
For instance, climate change.
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我们自己没有对此
做一个很好的工作
做一个很好的工作
07:43
We're not doing a very
good job on our own,
good job on our own,
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我们绝对是可以用到
我们能得到的所有帮助
我们能得到的所有帮助
07:45
we could certainly use
all the help we can get.
all the help we can get.
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07:47
That's what I'm talking about,
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那正是我所谈论的
科技放大了
我们的认知能力
我们的认知能力
07:49
technology amplifying
our cognitive abilities
our cognitive abilities
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07:52
so we can imagine and design things
that were simply out of our reach
that were simply out of our reach
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所以我们可以得心应手地
想象和设计事物
想象和设计事物
07:55
as plain old un-augmented humans.
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作为一个纯粹的没有被增强的人类
07:59
So what about making
all of this crazy new stuff
all of this crazy new stuff
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那么，制作这些所有的
我们将去发明和设计的
我们将去发明和设计的
08:02
that we're going to invent and design?
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疯狂的新事物呢？
08:05
I think the era of human augmentation
is as much about the physical world
is as much about the physical world
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我认为人类增强的世纪
其实是关于物理世界
其实是关于物理世界
08:10
as it is about the virtual,
intellectual realm.
intellectual realm.
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关于实际上的
知识领域
知识领域
08:13
How will technology augment us?
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科技是如何增强我们的？
08:16
In the physical world, robotic systems.
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在物理世界，机器系统
08:19
OK, there's certainly a fear
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好吧，这绝对是有一种恐惧
08:21
that robots are going to take
jobs away from humans,
jobs away from humans,
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那就是机器人将会把
人类的工作带走
人类的工作带走
而且这在一定范围内是真实的
08:23
and that is true in certain sectors.
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08:26
But I'm much more interested in this idea
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但我对这个更感兴趣
08:29
that humans and robots working together
are going to augment each other,
are going to augment each other,
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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
它们会互相增强对方
08:34
and start to inhabit a new space.
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然后会开始开发一个新的空间
08:36
This is our applied research lab
in San Francisco,
in San Francisco,
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这是我们的在三藩市的
应用研究实验室
应用研究实验室
08:38
where one of our areas of focus
is advanced robotics,
is advanced robotics,
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在那里我们集中研究的
领域之一是先进的机器
领域之一是先进的机器
08:41
specifically, human-robot collaboration.
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具体说来，人类和机器合作
08:45
And this is Bishop, one of our robots.
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这是教主，我们其中的一个机器人
08:47
As an experiment, we set it up
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由于是一个实验，我们安排好了
08:49
to help a person working in construction
doing repetitive tasks --
doing repetitive tasks --
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去帮助一个人建立操作
重复性的任务
重复性的任务
08:53
tasks like cutting out holes for outlets
or light switches in drywall.
or light switches in drywall.
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就像是为衣物切割洞
或者是在石膏板上点燃
或者是在石膏板上点燃
08:58
(Laughter)
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（观众笑声）
09:01
So, Bishop's human partner
can tell what to do in plain English
can tell what to do in plain English
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那么，教主的人类拍档
可以区别用纯粹英语该如何做
可以区别用纯粹英语该如何做
09:05
and with simple gestures,
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附和一些简单的手势
09:06
kind of like talking to a dog,
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就好像对一只狗说话
09:07
and then Bishop executes
on those instructions
on those instructions
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然后教主执行了
这些指导
这些指导
09:09
with perfect precision.
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这些指导是有完美的精确度的
09:11
We're using the human
for what the human is good at:
for what the human is good at:
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我们在使用一个人类
擅长的地方
擅长的地方
意识，洞察力，和决断力
09:14
awareness, perception and decision making.
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09:17
And we're using the robot
for what it's good at:
for what it's good at:
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然后我们使用了一个机器人
擅长的地方
擅长的地方
准确度和重复性
09:19
precision and repetitiveness.
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09:22
Here's another cool project
that Bishop worked on.
that Bishop worked on.
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这里有另一个很酷的项目
也是教主在实施的
也是教主在实施的
这个项目的目标
我们把它叫作HIVE
我们把它叫作HIVE
09:24
The goal of this project,
which we called the HIVE,
which we called the HIVE,
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是人类，电脑，和机器试验
的一个原型模型
的一个原型模型
09:27
was to prototype the experience
of humans, computers and robots
of humans, computers and robots
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09:31
all working together to solve
a highly complex design problem.
a highly complex design problem.
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它们工作在一起去解决
一个极端复杂的设计问题
一个极端复杂的设计问题
09:35
The humans acted as labor.
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人类起到的作用是付出人工劳动
09:37
They cruised around the construction site,
they manipulated the bamboo --
they manipulated the bamboo --
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它们在建设工地上巡航
它们玩弄竹子
它们玩弄竹子
顺便说一下
因为这是一个不同构的材料
因为这是一个不同构的材料
09:40
which, by the way,
because it's a non-isomorphic material,
because it's a non-isomorphic material,
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它对于机器人来说是非常难应对的
09:43
is super hard for robots to deal with.
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但是后来机器人
完成了这个纤维扭动
完成了这个纤维扭动
09:45
But then the robots
did this fiber winding,
did this fiber winding,
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这个对于人类来说
几乎是不可能的
几乎是不可能的
09:47
which was almost impossible
for a human to do.
for a human to do.
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09:49
And then we had an AI
that was controlling everything.
that was controlling everything.
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然后我们有一个AI
它控制着每一件事
它控制着每一件事
09:53
It was telling the humans what to do,
telling the robots what to do
telling the robots what to do
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它告诉人类应该做什么
告诉机器人应该做什么
告诉机器人应该做什么
而且还要纪录几千个
个人的组成部分
个人的组成部分
09:56
and keeping track of thousands
of individual components.
of individual components.
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09:59
What's interesting is,
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有趣的是
10:01
building this pavilion
was simply not possible
was simply not possible
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建造这个亭子
仅仅就是不可能
仅仅就是不可能
10:04
without human, robot and AI
augmenting each other.
augmenting each other.
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没有人类，机器和AI
互相增强
互相增强
10:09
OK, I'll share one more project.
This one's a little bit crazy.
This one's a little bit crazy.
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好，我就再分享另一个项目
这一个有一点不可思议
这一个有一点不可思议
10:13
We're working with Amsterdam-based artist
Joris Laarman and his team at MX3D
Joris Laarman and his team at MX3D
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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
Joris Laarman和他的团队在MX3D
10:17
to generatively design
and robotically print
and robotically print
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去生成地设计
和活跃地打印
和活跃地打印
10:20
the world's first autonomously
manufactured bridge.
manufactured bridge.
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世界上第一个自动地
制造的桥梁
制造的桥梁
10:24
So, Joris and an AI are designing
this thing right now, as we speak,
this thing right now, as we speak,
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那么，Joris 和一个 AI 正在我们说话的功夫
设计这个东西
设计这个东西
10:28
in Amsterdam.
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在阿姆斯特丹
10:29
And when they're done,
we're going to hit "Go,"
we're going to hit "Go,"
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当他们完成的时候
我们按下“Go“
我们按下“Go“
10:31
and robots will start 3D printing
in stainless steel,
in stainless steel,
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然后机器人将开始3D打印
在不锈钢上
在不锈钢上
10:34
and then they're going to keep printing,
without human intervention,
without human intervention,
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然后它们将继续打印
在没有人类干预的情况下
在没有人类干预的情况下
10:38
until the bridge is finished.
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直到桥梁完成
10:41
So, as computers are going
to augment our ability
to augment our ability
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所以，随着电脑将
增强我们的能力
增强我们的能力
10:44
to imagine and design new stuff,
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在想象和设计新事物上
10:46
robotic systems are going to help us
build and make things
build and make things
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机器的系统将帮助我们
建造和制作事物
建造和制作事物
10:49
that we've never been able to make before.
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建造那些我们之前从来没有能够做到的事物
10:52
But what about our ability
to sense and control these things?
to sense and control these things?
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但那么我们去感知和控制
这些事物的能力呢？
这些事物的能力呢？
10:56
What about a nervous system
for the things that we make?
for the things that we make?
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对于我们制作的这些事物的
一个神经系统呢？
一个神经系统呢？
11:00
Our nervous system,
the human nervous system,
the human nervous system,
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我们的神经系统
人类的神经系统
人类的神经系统
11:03
tells us everything
that's going on around us.
that's going on around us.
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告诉了我们每一件
在我们身边正在发生的事情
在我们身边正在发生的事情
11:06
But the nervous system of the things
we make is rudimentary at best.
we make is rudimentary at best.
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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
神经系统最好的也不过是简单的
11:09
For instance, a car doesn't tell
the city's public works department
the city's public works department
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比如说，一辆车不能够分辨出
城市的公共工作部门
城市的公共工作部门
11:13
that it just hit a pothole at the corner
of Broadway and Morrison.
of Broadway and Morrison.
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那就像是在一个百老汇和莫里森
角落击中一个壶洞一样
角落击中一个壶洞一样
11:16
A building doesn't tell its designers
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一栋建筑不会对它的设计者说
11:18
whether or not the people inside
like being there,
like being there,
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是否建筑里的人喜欢这个建筑
11:21
and the toy manufacturer doesn't know
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而且玩具制造商不知道
11:24
if a toy is actually being played with --
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2007
是否一个玩具正在被把玩
11:26
how and where and whether
or not it's any fun.
or not it's any fun.
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如何，在哪里，以及是否
是有趣的
是有趣的
11:29
Look, I'm sure that the designers
imagined this lifestyle for Barbie
imagined this lifestyle for Barbie
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我很确定设计师们想象了
这个芭比的生活方式
这个芭比的生活方式
11:33
when they designed her.
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当他们设计她的时候
11:34
(Laughter)
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1447
（观众笑声）
11:36
But what if it turns out that Barbie's
actually really lonely?
actually really lonely?
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2906
但是，如果结果是芭比
实际上是真的孤独？
实际上是真的孤独？
11:39
(Laughter)
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（观众笑声）
11:43
If the designers had known
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如果设计师事先知道
在真实世界将会
发生什么
发生什么
11:44
what was really happening
in the real world
in the real world
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他们的设计 － 道路
建筑，芭比
建筑，芭比
11:46
with their designs -- the road,
the building, Barbie --
the building, Barbie --
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他们可以用那个知识
去创造一个体验
去创造一个体验
11:49
they could've used that knowledge
to create an experience
to create an experience
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那个体验对于使用者来说更好
11:52
that was better for the user.
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这里缺少的是一个神经系统
11:53
What's missing is a nervous system
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11:55
connecting us to all of the things
that we design, make and use.
that we design, make and use.
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将我们所有设计的，制作的
和使用的事情联结起来
和使用的事情联结起来
11:59
What if all of you had that kind
of information flowing to you
of information flowing to you
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如果你们都有从现实世界
创造的事情
创造的事情
12:03
from the things you create
in the real world?
in the real world?
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的信息涌向你
12:07
With all of the stuff we make,
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以及我们所做的所有的东西
我们发费了巨大的
时间和精力
时间和精力
12:08
we spend a tremendous amount
of money and energy --
of money and energy --
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实际上，去年
有2万亿美金
有2万亿美金
12:11
in fact, last year,
about two trillion dollars --
about two trillion dollars --
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劝说人们去买
我们制作的事物
我们制作的事物
12:13
convincing people to buy
the things we've made.
the things we've made.
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但是如果你有你设计和创造的
事物的联结
事物的联结
12:16
But if you had this connection
to the things that you design and create
to the things that you design and create
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在它们出现在现实世界
12:20
after they're out in the real world,
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在它们被卖掉
或者发展或这之类的
或者发展或这之类的
12:21
after they've been sold
or launched or whatever,
or launched or whatever,
252
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3614
12:25
we could actually change that,
253
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1620
我们实际上可以改变那个
12:27
and go from making people want our stuff,
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而且从制作人们想要的东西
12:30
to just making stuff that people
want in the first place.
want in the first place.
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3434
到仅仅是制作人们一开始
想要的东西
想要的东西
好消息是，我们在数码
神经系统上工作
神经系统上工作
12:33
The good news is, we're working
on digital nervous systems
on digital nervous systems
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12:36
that connect us to the things we design.
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这个系统连接我们和我们设计的东西
12:40
We're working on one project
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我们正致力于一个项目
和几个人在洛杉矶
项目叫作Bandito Brothers
项目叫作Bandito Brothers
12:42
with a couple of guys down in Los Angeles
called the Bandito Brothers
called the Bandito Brothers
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3712
12:45
and their team.
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1407
以及他们的团队
12:47
And one of the things these guys do
is build insane cars
is build insane cars
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3433
这些人想做的其中一件事情是
建造不可思议的车
建造不可思议的车
那些车可以去做绝对是不可思议的事情
12:50
that do absolutely insane things.
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12:54
These guys are crazy --
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这些人是很疯狂的
（观众笑声）
12:56
(Laughter)
264
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以最好的方式
12:57
in the best way.
265
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13:00
And what we're doing with them
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我们和他们做的
13:02
is taking a traditional race-car chassis
267
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是拿一个传统的赛车地盘
13:05
and giving it a nervous system.
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1585
然后给它一个神经系统
13:06
So we instrumented it
with dozens of sensors,
with dozens of sensors,
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3058
那么我们用了几打感应
去组建它
去组建它
在车上放上一个世界级
的司机
的司机
13:09
put a world-class driver behind the wheel,
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2635
将他带到沙漠里
然后让他不停地连续开上一个星期
然后让他不停地连续开上一个星期
13:12
took it out to the desert
and drove the hell out of it for a week.
and drove the hell out of it for a week.
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13:15
And the car's nervous system
captured everything
captured everything
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然后车的神经系统
抓住了车上
抓住了车上
13:18
that was happening to the car.
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发生的每件事情
我们抓住了4亿个数据点
13:19
We captured four billion data points;
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13:22
all of the forces
that it was subjected to.
that it was subjected to.
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所有的动力
13:24
And then we did something crazy.
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然后我们做了一些疯狂的事
13:27
We took all of that data,
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我们拿出了所有的数据
然后把它们放到一个我们叫做Dreamcatcher
地生成性设计的AI里
地生成性设计的AI里
13:28
and plugged it into a generative-design AI
we call "Dreamcatcher."
we call "Dreamcatcher."
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13:33
So what do get when you give
a design tool a nervous system,
a design tool a nervous system,
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那么当你给一个设计
神经系统
神经系统
13:37
and you ask it to build you
the ultimate car chassis?
the ultimate car chassis?
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而且你要它给你建造
最终的车地盘时，你会得到什么？
最终的车地盘时，你会得到什么？
13:40
You get this.
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你得到这个
13:44
This is something that a human
could never have designed.
could never have designed.
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这是一个人类永远不会设计的
13:48
Except a human did design this,
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除了一个人设计了这个
13:50
but it was a human that was augmented
by a generative-design AI,
by a generative-design AI,
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但是是一个被生成性设计AI
增强的人类
增强的人类
13:54
a digital nervous system
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一个数码神经系统
13:56
and robots that can actually
fabricate something like this.
fabricate something like this.
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而且机器人实际上是
可以制造这样的东西的
可以制造这样的东西的
13:59
So if this is the future,
the Augmented Age,
the Augmented Age,
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所以，如果这是未来
增强时代
增强时代
14:03
and we're going to be augmented
cognitively, physically and perceptually,
cognitively, physically and perceptually,
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我们将会被在认知上，物理上，和洞察力
上被增强
上被增强
14:07
what will that look like?
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那看上去会是什么？
14:09
What is this wonderland going to be like?
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这个仙境将会是一个什么？
14:12
I think we're going to see a world
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我认为我们将会看到一个
在那里我们从制作事物
14:14
where we're moving
from things that are fabricated
from things that are fabricated
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到种植事物的世界
14:17
to things that are farmed.
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14:20
Where we're moving from things
that are constructed
that are constructed
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在那里我们将会从建筑事物
到生长事物
14:23
to that which is grown.
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14:26
We're going to move from being isolated
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我们将会从被孤独隔离
到被连接
14:28
to being connected.
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14:30
And we'll move away from extraction
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我们将远离灭绝
14:33
to embrace aggregation.
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去拥抱集合
14:35
I also think we'll shift
from craving obedience from our things
from craving obedience from our things
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我还认为我们将从对我们的事情的
疯狂饥渴服从
疯狂饥渴服从
转换到珍视自主
14:39
to valuing autonomy.
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14:42
Thanks to our augmented capabilities,
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感谢我们的增强放大能力
我们的世界将会有翻天覆地地改变
14:44
our world is going to change dramatically.
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14:47
We're going to have a world
with more variety, more connectedness,
with more variety, more connectedness,
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我们将会看到一个更加
多样化，更多连接
多样化，更多连接
更多动态，更多复杂性
14:50
more dynamism, more complexity,
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更多适应性，当然
14:53
more adaptability and, of course,
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更加美丽
14:55
more beauty.
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14:57
The shape of things to come
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那些将要到来的事情
14:58
will be unlike anything
we've ever seen before.
we've ever seen before.
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不会像任何我们之前看到的事情
15:01
Why?
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为什么？
因为将会是新的关系
来整理成型新的事物
来整理成型新的事物
15:02
Because what will be shaping those things
is this new partnership
is this new partnership
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在科技，自然，和人性之间新的关系
15:06
between technology, nature and humanity.
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15:11
That, to me, is a future
well worth looking forward to.
well worth looking forward to.
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那个，对我来说，是一个
值得向未来展望的事情
值得向未来展望的事情
15:15
Thank you all so much.
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非常感谢
15:16
(Applause)
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（观众鼓掌）
ABOUT THE SPEAKER
Maurice Conti - Designer, futuristMaurice Conti explores new partnerships between technology, nature and humanity.
Why you should listen
Maurice Conti is a designer, futurist and innovator. He's worked with startups, government agencies, artists and corporations to explore the things that will matter to us in the future, and to design solutions to get us there.
Conti is currently Chief Innovation Officer at Alpha -- Europe's first moonshot factory, powered by Telefónica. Conti and his team are responsible for coming up with the ideas, prototypes and proofs of concepts that will go on to become full-blown moonshots at Alpha: projects that will affect 100 million people or more, be a force for good on the planet and grow into billion-euro businesses.
Previously, Conti was Director of Applied Research & Innovation at Autodesk where built and led Autodesk's Applied Research Lab. Conti's work focuses on applied machine learning, advanced robotics, augmented and virtual realities, and the future of work, cities and mobility.
Conti is also an explorer of geographies and cultures. He has circumnavigated the globe once and been half-way around twice. In 2009 he was awarded the Medal for Exceptional Bravery at Sea by the United Nations, the New Zealand Bravery Medal and a US Coast Guard Citation for Bravery for risking his own life to save three shipwrecked sailors.
Conti lives in Barcelona, Spain, and travels around the world speaking to groups about innovation, technology trends, the future, and high adventure.
Maurice Conti | Speaker | TED.com