与摩根·弗里曼一起穿越虫洞（2010）

Through The Wormhole With Morgan Freeman S04E07

Part 3 April 19, 2021, 2:53 p.m.

根据彭蒂的理解，意识只是
Consciousness, according to Pentti,
承载着多种心理表象的马赛克
is just a rich mosaic of emotionally laden mental images.
他认为要使得一个机器人真正具有意识
He believes that to have a truly conscious machine,
你就必须赋予它们
you must give it the power
将感官数据与情绪联♥系♥起来的能力
to associate sensory data with emotions.
而这个机器人，它已经具有了这种能力
And in this robot, he has begun that process.
这是第一个机器人
This is the first robot
可以使用这种神经网络联♥系♥
that utilizes associative neural networks.
这和我们人类的学习方式有些类似
It is the same kind of learning that we humans use.
当我们看到或听到一些东西时
When we see and hear something,
我们会把这两种感觉联♥系♥起来
we make a connection between those things,
之后的日子中如果我们看到或者听到同样的事情
and later on when we see or hear the other thing,
我们就会感受到相对应的另一种感觉
the other thing comes to our mind.
xrc-1机器人像我们一样
The xcr-1 experiences the world
直接感受这个世界
directly through its senses like we do.
它有一些基本的触觉、视觉和听觉
On board are the basics of touch, sight, and sound.
彭蒂已经开始赋予其情感
Pentti has begun the process of giving it emotional associations
与特定感知数据的联♥系♥
to specific sensory data,
比如绿色
like the color green.
彭蒂在这个机器人前面放置一个绿色物体
Pentti places a green object in front of the robot,
它能够识别
which it recognizes.
绿色
Green.
然后他给它一个不好的联♥系♥
Then he gives green a bad association --
从后侧猛击它
a smack on the backside.
这种对于联♥系♥的学习就像小孩子一样
The associative learning is similar to little children.
疼
Hurt.
你可以告诉孩子这个不好或这是好的
And you say that this is not good or this is good,
或者揍孩子
or you may also smack the little child.
疼
Hurt.
当然我并不建议这么做
I don't recommend that.
绿色，不好
Green bad.
机器人对于绿色物体的心理意象
The robot's mental image of the green object
现在联♥系♥上了不好的情绪
is now associated with the emotion bad.
从现在开始，它会一直避着绿色瓶子
And from now on, it will avoid the green bottle.
但这并不是xrc-1所有的学习部分
But it's not all pain for the xcr-1.
就像我们教机器人
Just like we teach the robot
把疼痛联♥系♥上了绿色物体
to associate pain with the green object,
我们也能够教机器人
we can teach the robot
把愉快和其他物体联♥系♥起来
to associate, also, pleasure with objects,
这个例子中是一个蓝色物体
in this case with the blue object, like this.
蓝色
Blue.
为了让它把蓝色联♥系♥上好的感觉
To give blue a good association,
彭蒂轻轻抚摸机器人顶部
Pentti gently strokes the top of the robot.
蓝色，好
Blue good.
这个简单的实验证明了
This simple experiment demonstrates
这个机器人具有对物体的心理意象
that this robot has mental images of objects
和心理内容
and mental content.
这还是它发展成长的早期
It's still early in its development,
但xcr-1已经学会了基本的情绪反应
but the xcr-1 has learned the basics of emotional reaction
从害怕
from fear...
绿色，不好
Green bad.
绿色，不好
Green bad.
到渴望
...to desire.
蓝色，好
Blue good.
现在是我爱的时候了
Now's my time for love
寂寞的时刻似乎
lonely moments seem to...
随着xrc-1更高级的版本中
As a more advanced version of the xcr-1
存储着心理意象的记忆
fills its memory with mental images...
牙医，不好
Dentist bad.
它就开始能够
...it will start to be able
凭借自己与新环境互动
to react to new situations on its own
最终会像所有具有情绪波动的生物一样
and eventually experience the world
体验这个世界
much like any emotionally-driven being.
我最大的梦想就是
It is my great dream
我建造的机器人有一天能问
to build robot that is one day able to ask,
“我是如何成为自己的呢？”
"How did I get inside myself?"
当机器人到达这种程度时
Once robots reach this point,
什么还能阻止它们进步
what's to stop them from moving on
变成与我们不同的却有意识的东西？
and becoming conscious of things we're not?
这个人认为机器人是人类的未来
This man thinks robots will become the future of humanity
因为它们具有我们缺乏的东西
because they'll have something we lack.
在最后一个说出“找到了”的人类之后
Their brains will have the capacity for genius
它们仍然会表现得非常天才
long after the last human ever says "Eureka."
阿基米德第一次在浴缸中喊出了“找到了”，（因为发现浮力原理）
for Archimedes, Eureka happened in the bathtub.
爱因斯坦在乘坐电车时想到了相对论
Einstein was riding a streetcar when relativity dawned on him.
这些杰出的思维都曾闪现出灵感
These brilliant minds had a flash of inspiration
推动了全人类的进步
and drove all of humanity forward.
但是今天的科学问题
But the scientific questions of today,
比如探索亚原子粒子的真♥相♥
probing shoals of subatomic particles
以及我们大量的遗传密♥码♥
and our vast genetic code,
这些问题如此复杂
have become so complex
需要数以千计的研究者团队
that they take teams of thousands of researchers
来解决这些问题
to solve.
单一科学天才的时代结束了吗？
Is the age of the single scientific genius over?
但如果机器人能够解决问题呢？
Not if machines have their way.
数据科学家迈克尔·施密特
Data scientist Michael Schmidt
认为这个世界充满着复杂的美
sees the world filled with intricate beauty --
玫瑰花开
the flowering of a rose,
叶脉伸展
the veins branching on a leaf,
黄蜂飞舞
the flight of a Bumblebee.
在这些大自然奇迹的背后
But below the surface of nature's wonders,
迈克尔看到了埋藏的
Michael also sees a treasure trove
人们尚未知晓的数学复杂性
of uncharted mathematical complexity.
我很喜欢来这里，大自然是很美妙的
Well, I love coming out here. Nature is beautiful.
这里的每株植物、每只蜜蜂、以及这个花♥园♥的
There are equations hidden in every plant and every bee
生态系统中都隐藏着许多数学方程式
and the ecosystems involved in this garden.
有一些科学指出了
And part of science is figuring out
这些事情是如何进行的
what causes those things to happen.
科学是我们理解大自然的努力
Science is our effort to make sense of nature,
这样的追求造就了许多非常著名的发现
and this quest has given us some very famous discoveries.
在牛顿时期
In Newton's time,
他发现了非常重要的物理定律
he was able to figure out a very important rule in physics,
即万有引力定律
which is the law of gravity.
它可以预测这个苹果如何坠落
It predicts how this apple falls
以及施加到这个苹果上的各种力
and the forces that act upon this apple.
时至今日，我们仍对类似的问题感兴趣
Today in science, we're interested in similar problems
不仅仅是苹果如何掉落
but not just about how the apple falls
还有在我们身边世界中遵从
but the massive complexity that follows
这种简单动态的大量复杂性
from this very simple dynamic to the world around us.
举个例子，当我扔掉这个苹果
For example, when I drop this apple,
苹果砸在地上会激起灰尘
the apple stirs up dust.
灰尘会砸在花上
This dust could hit a flower,
蜜蜂受惊，这朵花通过其授粉的可能性就低
and a bee may be less likely to pollinate that flower.
这个花♥园♥中的整个生态系统
And the entire ecosystem in this garden
可能因为单一事件而发生急剧变化
could change dramatically from that single event.
科学家已经知道了四种基本自然力
Scientists understand the basic forces of nature,
但是在真实世界中
but making precise predictions
通过这种惊人的复杂性
about what will happen in the real world
来准确预测会发生什么
with its staggering complexity
足以使人类大脑崩溃
is overwhelming to the human mind.
人类想要理解并计算出
So, one of the reasons why it's extremely difficult
自然定律以及公式是极为困难的
for humans to understand and figure out
原因之一就是
the equations and the laws of nature
参与其中的变量的数量
is literally the number of variables that are at play.
影响一个系统的变量
There could be thousands of variables
会有数千个
that influence a system
而我们才刚开始梳理它们
that we're only just beginning to tease apart.
实际中，这些公式数量很多
In fact, there are so many of these equations,
我们永远不可能将它们全部分♥析♥
we'll never be able to finish analyzing them
如果用手算的话
if we do it by hand.