I define intelligence as how good something is at accomplishing complex goals.

我对智力的定义就是某物完成复杂目标的优秀程度

So let’s unpack that a little bit.

我们来分解一下

First of all, it’s a spectrum of abilities since there are many different goals you can have,

首先 它包括很多种能力 因为目标有很多种

so it makes no sense to quantify something’s intelligence by just one number like an IQ.

所以仅用IQ这一个数字去量化某人的智力是不合理的

To see how ridiculous that would be, just imagine if I told you that athletic ability could be quantified by a single number, the “Athletic Quotient,”

想知道这有多荒谬 只需想象一下 如果我说运动能力可以用一个数字来量化 叫做“运动商”

and whatever athlete had the highest AQ would win all the gold medals in the Olympics.

那么不管哪种运动员 只要他有最高的“运动商” 他就会赢得奥运会上的所有金牌

It’s the same with intelligence.

智力也是如此

So if you have a machine that’s pretty good at some tasks, these days it’s usually pretty narrow intelligence,

如果有个机器很擅长做某些任务 它通常是表示一种很狭义的智力

maybe the machine is very good at multiplying numbers fast because it’s your pocket calculator, maybe it’s good at driving cars or playing Go.

或许这台机器很擅长快速做乘法 因为它是个袖珍计算器 也或许它很擅长开车或玩《口袋妖怪》

Humans on the other hand have a remarkably broad intelligence.

相反 人类有着很宽泛的智力

A human child can learn almost anything given enough time.

只要给足时间 人类儿童几乎可以学会所有事

Even though we now have machines that can learn, sometimes learn to do certain narrow tasks better than humans,

即便现在有些机器做某些有限的任务比人类还好

machine learning is still very unimpressive compared to human learning.

但跟人类的学习能力相比 机器学习依然不值一提

For example, it might take a machine tens of thousands of pictures of cats and dogs until it becomes able to tell a cat from a dog,

比如 一个机器可能需要成千上万张猫和狗的照片 才能区分出猫和狗

whereas human children can sometimes learn what a cat is from seeing it once.

但有时候人类儿童只需要看一次就知道猫长什么样

Another area where we have a long way to go in AI is generalizing.

另一个在人工智能方面还有漫漫长路要走的领域是归纳

If a human learns to play one particular kind of game

如果人类学习玩某种游戏

they can very quickly take that knowledge and apply it to some other kind of game or some other life situation altogether.

他们很快就能掌握相关知识 并在其他游戏或生活情境中应用这些知识

And this is a fascinating frontier of AI research now:

这是目前AI研究中令人着迷的前沿领域：

How can we have machines—how can we can make them as good at learning from very limited data as people are?

我们怎么让机器——我们如何用有限的数据让机器的学习能力变得像人一样优秀？

And I think part of the challenge is that we humans aren’t just learning to recognize some patterns, we also gradually learn to develop a whole model of the world.

我觉得有一部分挑战是 人类的学习不仅仅是为了认识几种模式 而是会逐渐培养起整个世界的模式

So if you ask “Are there machines that are more intelligent than people today,”

所以如果你问“现在有比人类还聪明的机器吗”

there are machines that are better than us at accomplishing some goals, but absolutely not all goals.

在某些方面 有机器做的比人类还好 但并非所有领域都是如此

AGI, artificial general intelligence, that’s the dream of the field of AI:

AGI 人工一般智力 这是人工智能领域的梦想：

to build a machine that’s better than us at all goals.

建造一个在各方面都比人类要优秀的机器

We’re not there yet, but a good fraction of leading AI researchers think we are going to get there maybe in a few decades.

目前还没达到这个水平 但是很多AI领域的佼佼者相信 几十年后我们可能实现这一梦想

And if that happens you have to ask yourself if that might lead to machines getting not just a little better than us,

如果真的实现了 你得问问自己 下一步会不会是机器在所有领域不光比人类好一点点

but way better at all goals, having super intelligence.

而是好上很多 拥有了超级智力

The argument for that is actually really interesting and goes back to the ‘60s, to the mathematician I. J. Goode,

围绕这一观点的争论其实很有趣 它能追溯到60年代 当时的数学家I. J. Goode

who pointed out that the goal of building an intelligent machine is in and of itself something that you can do with intelligence.

他指出 建造智能机器的目标就在于它本身 是你可以用智力完成的事情

So once you get machines that are better than us at that narrow task of building AI,

所以一旦你得到了在打造AI这方面比人类更优秀的机器

then future AIs can be built by not human engineers but by machines, except they might do it thousands or a million times faster.

那么未来的AI就可以让机器去做 而不是工程师了 除非工程师能以数千倍或数百万倍的速度去完成

So in my book I explore the scenario where you have this computer called Prometheus,

在我的书中 我探索了这种情况 有一台叫做普罗米修斯的电脑

which has vastly more hardware than a human brain does,

它有着比人脑更多的硬件

and it’s still very limited by its software being kind of dumb.

但它还是会受限制 因为它的软件太笨

So at the point where it gets human-level general intelligence, the first thing it does is it uses this to realize,

所以当有一天 机器达到了跟人类一样的智力水平时 它所做的第一件事就是意识到

“Oh! I can reprogram my software to become much better,” and now it’s a lot smarter.

“哦 我可以把自己的程序编的更好一些” 这样的话它就聪明多了

And a few minutes later it does this again, and then it does it again, and does it again,

几分钟后 它又做了同样的事 然后再一遍遍地重复这一过程

and in a matter of perhaps a few days or weeks a machine like that might be able to become not just a little bit smarter than us but leave us far, far behind.

或许几天或几周后 这样的机器就不只是比我们聪明一点点了 而是会甩我们几条街

I think a lot of people dismiss this kind of talk of super intelligence as science fiction

很多人会把超级智力的说法当成科幻小说

because we’re stuck in this sort of carbon chauvinism idea that intelligence can only exist in biological organisms made of cells and carbon atoms.

因为我们被禁锢在碳沙文主义的思想中 认为智力只存在于由细胞和碳原子构成的有机生物体中

As a physicist, from my perspective intelligence is just a kind of information processing preformed by elementary particles moving around according to the laws of physics.

作为物理学家 我认为根据物理定律 智力只是由四处游走的基本粒子进行的信息处理

And there’s absolutely no law in physics that says you can’t do that in ways that are much more intelligent than humans.

物理定律绝对没说你不能以比人类更聪明的方式去完成这一过程

We’re so limited by how much brain matter fits through our mommy’s birth canal and stuff like this, and machines are not,

母亲的产道在极大程度上限制了人脑的容量 但机器可不会受此限制

so I think it’s very likely that once machines reach human-level they’re not going to stop there;

所以我相信 一旦机器达到了人脑的智力水平 它们不会就此止步的

they’ll just blow right by, and that we might one day have machines that are as much smarter than us as we are smarter than snails.

它们只会超过我们扬长而去 或许有一天会有比我们聪明很多的机器 就像我们比蜗牛聪明很多一样