Can robots be creative Gil Weinberg

How does this music make you feel?

Do you find it beautiful?

Is it creative?

Now, would you change your answers

if you learned
the composer was this robot?

Believe it or not,

people have been grappling with
the question of artificial creativity,

alongside the question
of artifcial intelligence,

for over 170 years.

In 1843, Lady Ada Lovelace,

an English mathematician considered
the world’s first computer programmer,

wrote that a machine could not have
human-like intelligence

as long as it only did what humans
intentionally programmed it to do.

According to Lovelace,

a machine must be able
to create original ideas

if it is to be considered intelligent.

The Lovelace Test, formalized in 2001,
proposes a way of scrutinizing this idea.

A machine can pass this test
if it can produce an outcome

that its designers cannot explain
based on their original code.

The Lovelace Test is, by design,
more of a thought experiment

than an objective scientific test.

But it’s a place to start.

At first glance,

the idea of a machine creating
high quality, original music in this way

might seem impossible.

We could come up with an extremely
complex algorithm

using random number generators,
chaotic functions, and fuzzy logic

to generate a sequence of musical notes

in a way that would be
impossible to track.

But although this would yield countless
original melodies never heard before,

only a tiny fraction of them
would be worth listening to.

With the computer having no way
to distinguish

between those which
we would consider beautiful

and those which we won’t.

But what if we took a step back

and tried to model a natural process
that allows creativity to form?

We happen to know of at least
one such process

that has lead to original, valuable,
and even beautiful outcomes:

the process of evolution.

And evolutionary algorithms,

or genetic algorithms
that mimic biological evolution,

are one promising approach

to making machines generate original
and valuable artistic outcomes.

So how can evolution make
a machine musically creative?

Well, instead of organisms,

we can start with an initial
population of musical phrases,

and a basic algorithm

that mimics reproduction
and random mutations

by switching some parts,

combining others,

and replacing random notes.

Now that we have
a new generation of phrases,

we can apply selection using
an operation called a fitness function.

Just as biological fitness is determined
by external environmental pressures,

our fitness function can be determined
by an external melody

chosen by human musicians, or music fans,

to represent the ultimate
beautiful melody.

The algorithm can then compare
between our musical phrases

and that beautiful melody,

and select only the phrases
that are most similar to it.

Once the least similar sequences
are weeded out,

the algorithm can reapply mutation
and recombination to what’s left,

select the most similar, or fitted ones,
again from the new generation,

and repeat for many generations.

The process that got us there has so much
randomness and complexity built in

that the result might
pass the Lovelace Test.

More importantly, thanks to the presence
of human aesthetic in the process,

we’ll theoretically generate melodies
we would consider beautiful.

But does this satisfy our intuition
for what is truly creative?

Is it enough to make something
original and beautiful,

or does creativity require intention
and awareness of what is being created?

Perhaps the creativity in this case
is really coming from the programmers,

even if they don’t understand the process.

What is human creativity, anyways?

Is it something more than a system
of interconnected neurons

developed by biological
algorithmic processes

and the random experiences
that shape our lives?

Order and chaos,
machine and human.

These are the dynamos at the heart
of machine creativity initiatives

that are currently making music,
sculptures, paintings, poetry and more.

The jury may still be out

as to whether it’s fair to call
these acts of creation creative.

But if a piece of art can make you weep,

or blow your mind,

or send shivers down your spine,

does it really matter
who or what created it?

这首音乐让你感觉如何?

你觉得美吗?

有创意吗?

现在,如果你知道作曲家是这个机器人,你会改变你的答案

吗?

信不信由你,170 多年来,

人们一直在努力
解决人工智能问题以及人工智能

问题

1843 年,

被认为
是世界上第一位计算机程序员的英国数学家艾达·洛夫莱斯女士

写道,只要机器只做人类有意编程的事情,机器就不可能拥有
类似人类的智能

根据 Lovelace 的说法,如果

一台机器

被认为是智能的,它就必须能够创造出原创的想法。

2001 年正式制定的 Lovelace 测试
提出了一种审查这一想法的方法。

如果机器可以

产生设计者无法
根据原始代码解释的结果,则机器可以通过此测试。

Lovelace 测试在设计上
更像是一个思想实验,而

不是一个客观的科学测试。

但这是一个开始的地方。

乍一看,

机器
以这种方式创作高质量原创音乐的想法

似乎是不可能的。

我们可以想出一个极其
复杂的算法,

使用随机数生成器、
混沌函数和模糊逻辑,

以一种

无法追踪的方式生成一系列音符

但这虽然会产生无数
从未听过的原创旋律,但其中

只有一小
部分值得一听。

由于计算机
无法区分

我们认为漂亮的和不认为漂亮的

但是,如果我们退后一步

,尝试模拟一个
允许创造力形成的自然过程呢?

我们碰巧知道至少有
一个这样的

过程导致了原始的、有价值的
甚至美丽的结果:

进化的过程。

进化算法


模仿生物进化的遗传算法是

使机器产生原创
且有价值的艺术成果的一种很有前途的方法。

那么进化如何
让机器在音乐上具有创造力呢?

好吧,

我们可以从最初
的乐句群

通过切换某些部分、

组合其他部分

和替换随机音符来模仿复制和随机突变的基本算法开始,而不是有机体。

现在我们
有了新一代的短语,

我们可以使用
称为适应度函数的操作来应用选择。

正如生物适应度是
由外部环境压力决定的一样,

我们的适应度功能也可以

由人类音乐家或乐迷选择的外部旋律来决定,

以代表终极
优美的旋律。

然后,该算法可以
将我们的乐句

与那优美的旋律进行比较,

并仅
选择与其最相似的乐句。

一旦最不相似的序列
被淘汰

,算法就可以
对剩下的序列重新应用突变和重组

,再次从新一代中选择最相似或最合适的

序列,并重复许多代。

让我们到达那里的过程具有如此多的
随机性和复杂性

,结果可能
通过 Lovelace 测试。

更重要的是,由于
在这个过程中人类审美的存在,

我们理论上会产生
我们认为美丽的旋律。

但这是否满足
了我们对真正创意的直觉?

创造出原创和美丽的东西就足够了吗

还是创造力需要
对正在创造的东西有意图和意识?

也许这种情况下的创造力
真的来自程序员,

即使他们不了解这个过程。

到底什么是人类创造力?

它不仅仅是

由生物
算法过程


塑造我们生活的随机体验开发的相互连接的神经元系统吗?

秩序与混乱,
机器与人类。

这些是机器创造力计划的核心

,目前正在制作音乐、
雕塑、绘画、诗歌等。

陪审团可能仍然

不确定将
这些创造行为称为创意是否公平。

但是,如果一件艺术品可以让你哭泣,

或者让你大吃一惊,

或者让你的脊椎发抖,

那么创作它的人或物真的很重要吗?