人工智能革命：通往超级智能之路

引子 Introduction

_注：这篇帖子花了三周才完成，原因是当我深入研究人工智能时，我简直不敢相信自己所读到的内容。我很快意识到，AI 领域正在发生的事情不仅是一个重要的话题，而且是我们未来最重要的议题。因此，我想尽可能多地了解它，并且一旦了解，我希望写一篇真正解释整个情况及其重要性的帖子。毫不意外，这变得极其冗长，所以我将其分为两部分。这是第一部分——第二部分在此。_

_Note: The reason this post took three weeks to finish is that as I dug into research on Artificial Intelligence, I could not believe what I was reading. It hit me pretty quickly that what’s happening in the world of AI is not just an important topic, but by far THE most important topic for our future. So I wanted to learn as much as I could about it, and once I did that, I wanted to make sure I wrote a post that really explained this whole situation and why it matters so much. Not shockingly, that became outrageously long, so I broke it into two parts. This is Part 1—Part 2 is here._

_我们正处于与地球上人类生命崛起相当的变化边缘。_ —— 弗诺·文奇

_We are on the edge of change comparable to the rise of human life on Earth._ — Vernor Vinge

[](https://waitbutwhy.com/wp-content/uploads/2015/01/Edge1.png)

[](https://waitbutwhy.com/wp-content/uploads/2015/01/Edge1.png)

这似乎是一个相当严峻的处境——但你必须记住站在时间图上的感受：你看不到右边的东西。所以，实际站在那里的感觉是这样的：

It seems like a pretty intense place to be standing—but then you have to remember something about what it’s like to stand on a time graph: you can’t see what’s to your right. So here’s how it actually feels to stand there:

[](https://waitbutwhy.com/wp-content/uploads/2015/01/Edge.jpg)

[](https://waitbutwhy.com/wp-content/uploads/2015/01/Edge.jpg)

遥远的未来——即将到来 The Far Future—Coming Soon

想象一下，乘坐时光机回到 1750 年——那时世界处于永久停电状态，远距离通信要么大声喊叫，要么向空中开炮，所有交通工具都靠干草运行。你到了那里，找一个人，把他带到 2015 年，然后带他四处走走，观察他对一切的反应。我们无法理解他看到闪亮的胶囊在高速公路上飞驰、与当天早些时候还在大洋彼岸的人交谈、观看千里之外正在进行的体育比赛、聆听 50 年前发生的音乐表演、以及玩我的魔法巫师矩形（他可以用它捕捉真实图像或记录生活瞬间、生成带有超自然移动蓝点的地图显示他的位置、看着某人的脸与他们聊天即使他们在国家的另一端）以及其他不可思议的魔法时，会是什么感受。这还没给他看互联网或解释国际空间站、大型强子对撞机、核武器或广义相对论之类的东西。

Imagine taking a time machine back to 1750—a time when the world was in a permanent power outage, long-distance communication meant either yelling loudly or firing a cannon in the air, and all transportation ran on hay. When you get there, you retrieve a dude, bring him to 2015, and then walk him around and watch him react to everything. It’s impossible for us to understand what it would be like for him to see shiny capsules racing by on a highway, talk to people who had been on the other side of the ocean earlier in the day, watch sports that were being played 1,000 miles away, hear a musical performance that happened 50 years ago, and play with my magical wizard rectangle that he could use to capture a real-life image or record a living moment, generate a map with a paranormal moving blue dot that shows him where he is, look at someone’s face and chat with them even though they’re on the other side of the country, and worlds of other inconceivable sorcery. This is all before you show him the internet or explain things like the International Space Station, the Large Hadron Collider, nuclear weapons, or general relativity.

对他来说，这种体验不会是惊讶、震惊甚至令人难以置信——这些词都不够强烈。他可能会真的死掉。

This experience for him wouldn’t be surprising or shocking or even mind-blowing—those words aren’t big enough. He might actually die.

但有趣的是——如果他随后回到 1750 年，嫉妒我们看到了他的反应，决定自己也尝试同样的事情，他会乘坐时光机回到同样的距离，从大约 1500 年找一个人，把他带到 1750 年，给他看一切。1500 年的人会对很多事情感到震惊——但他不会死。对他来说，这将是_远_不那么疯狂的体验，因为虽然 1500 年和 1750 年非常不同，但它们比 1750 年到 2015 年的差异_小得多_。1500 年的人会学到一些关于太空和物理的令人震惊的东西，他会对欧洲在新帝国主义潮流中的投入印象深刻，他必须大幅修正他的世界地图概念。但观看 1750 年的日常生活——交通、通信等——肯定不会让他死。

But here’s the interesting thing—if he then went back to 1750 and got jealous that we got to see his reaction and decided he wanted to try the same thing, he’d take the time machine and go back the same distance, get someone from around the year 1500, bring him to 1750, and show him everything. And the 1500 guy would be shocked by a lot of things—but he wouldn’t die. It would be _far_ less of an insane experience for him, because while 1500 and 1750 were very different, they were _much_ _less_ different than 1750 to 2015. The 1500 guy would learn some mind-bending shit about space and physics, he’d be impressed with how committed Europe turned out to be with that new imperialism fad, and he’d have to do some major revisions of his world map conception. But watching everyday life go by in 1750—transportation, communication, etc.—definitely wouldn’t make him die.

不，为了让 1750 年的人像我们对他那样开心，他必须回到更远的过去——也许一直回到大约公元前 12000 年，在第一次农业革命催生第一批城市和文明概念之前。如果来自纯粹狩猎采集世界的人——来自人类或多或少只是另一种动物物种的时代——看到 1750 年庞大的人类帝国，拥有高耸的教堂、跨洋船只、“室内”的概念以及巨大的人类集体积累的知识和发现之山——他很可能会死。

No, in order for the 1750 guy to have as much fun as we had with him, he’d have to go much farther back—maybe all the way back to about 12,000 BC, before the First Agricultural Revolution gave rise to the first cities and to the concept of civilization. If someone from a purely hunter-gatherer world—from a time when humans were, more or less, just another animal species—saw the vast human empires of 1750 with their towering churches, their ocean-crossing ships, their concept of being “inside,” and their enormous mountain of collective, accumulated human knowledge and discovery—he’d likely die.

然后，如果他死后_也_嫉妒并想做同样的事情。如果他回到 12000 年前到公元前 24000 年，找一个人并把他带到公元前 12000 年，他会给那个人看一切，那个人会说：“好吧，你想说什么，谁在乎。”为了让公元前 12000 年的人有同样的乐趣，他必须回到超过 10 万年前，找一个他可以第一次展示火和语言的人。

And then what if, after dying, _he_ got jealous and wanted to do the same thing. If he went back 12,000 years to 24,000 BC and got a guy and brought him to 12,000 BC, he’d show the guy everything and the guy would be like, “Okay what’s your point who cares.” For the 12,000 BC guy to have the same fun, he’d have to go back over 100,000 years and get someone he could show fire and language to for the first time.

为了让一个人被传送到未来并因震惊而死，他必须前进足够多的年份，使得“死亡级别的进步”或一个死亡进步单位（DPU）得以实现。因此，在狩猎采集时代，一个 DPU 需要超过 10 万年，但在农业革命后的速度下，只需要大约 12000 年。工业革命后的世界发展如此之快，以至于 1750 年的人只需要前进几百年就能实现一个 DPU。

In order for someone to be transported into the future and die from the level of shock they’d experience, they have to go enough years ahead that a “die level of progress,” or a Die Progress Unit (DPU) has been achieved. So a DPU took over 100,000 years in hunter-gatherer times, but at the post-Agricultural Revolution rate, it only took about 12,000 years. The post-Industrial Revolution world has moved so quickly that a 1750 person only needs to go forward a couple hundred years for a DPU to have happened.

这种模式——人类进步随着时间的推移越来越快——正是未来学家雷·库兹韦尔所称的人类历史的加速回报定律。这是因为更先进的社会有能力以比欠发达社会更快的速度进步——_因为_它们更先进。19 世纪的人类比 15 世纪的人类知道得更多，拥有更好的技术，因此毫不奇怪，人类在 19 世纪取得的进步远多于 15 世纪——15 世纪的人类无法与 19 世纪的人类相比。

This pattern—human progress moving quicker and quicker as time goes on—is what futurist Ray Kurzweil calls human history’s Law of Accelerating Returns. This happens because more advanced societies have the ability to progress at a faster _rate_ than less advanced societies—_because_ they’re more advanced. 19th century humanity knew more and had better technology than 15th century humanity, so it’s no surprise that humanity made far more advances in the 19th century than in the 15th century—15th century humanity was no match for 19th century humanity.11← open these

这在更小的尺度上也成立。电影《回到未来》于 1985 年上映，“过去”发生在 1955 年。在电影中，当迈克尔·J·福克斯回到 1955 年时，他对电视的新奇、苏打水的价格、对刺耳电吉他的缺乏喜爱以及俚语的变化感到措手不及。那是一个不同的世界，是的——但如果这部电影今天制作，过去发生在 1985 年，电影可能会在_更大_的差异上玩得_更开心_。角色将处于个人电脑、互联网或手机出现之前的时代——今天的马蒂·麦克弗莱，一个 90 年代末出生的青少年，在 1985 年会比电影中的马蒂·麦克弗莱在 1955 年更格格不入。

This works on smaller scales too. The movie _Back to the Future_ came out in 1985, and “the past” took place in 1955. In the movie, when Michael J. Fox went back to 1955, he was caught off-guard by the newness of TVs, the prices of soda, the lack of love for shrill electric guitar, and the variation in slang. It was a different world, yes—but if the movie were made today and the past took place in 1985, the movie could have had _much_ more fun with _much_ bigger differences. The character would be in a time before personal computers, internet, or cell phones—today’s Marty McFly, a teenager born in the late 90s, would be much more out of place in 1985 than the movie’s Marty McFly was in 1955.

这与我们刚刚讨论的原因相同——加速回报定律。1985 年至 2015 年间的平均进步速度高于 1955 年至 1985 年间的速度——因为前者是一个更先进的世界——所以最近 30 年发生的变化比前 30 年多得多。

This is for the same reason we just discussed—the Law of Accelerating Returns. The average rate of advancement between 1985 and 2015 was higher than the rate between 1955 and 1985—because the former was a more advanced world—so much more change happened in the most recent 30 years than in the prior 30.

所以——进步越来越大，发生得越来越快。这暗示着我们的未来将相当激烈，对吧？

So—advances are getting bigger and bigger and happening more and more quickly. This suggests some pretty intense things about our future, right?

库兹韦尔认为，按照 2000 年的进步速度，整个 20 世纪的进步只需 20 年就能实现——换句话说，到 2000 年，进步速度是 20 世纪平均进步速度的五倍。他相信，2000 年至 2014 年间又实现了相当于一个 20 世纪的进步，而到 2021 年，仅用七年时间，将_再_实现一个 20 世纪的进步。几十年后，他认为一年内将多次实现一个 20 世纪的进步，甚至更晚，不到一个月。总之，由于加速回报定律，库兹韦尔认为 21 世纪将实现 20 世纪进步的_1000 倍_。

Kurzweil suggests that the progress of the entire 20th century would have been achieved in only 20 years at the rate of advancement in the year 2000—in other words, by 2000, the rate of progress was five times faster than the _average_ rate of progress during the 20th century. He believes another 20th century’s worth of progress happened between 2000 and 2014 and that _another_ 20th century’s worth of progress will happen by 2021, in only seven years. A couple decades later, he believes a 20th century’s worth of progress will happen multiple times in the same year, and even later, in less than one month. All in all, because of the Law of Accelerating Returns, Kurzweil believes that the 21st century will achieve _1,000 times_ the progress of the 20th century.2

如果库兹韦尔和其他同意他的人是正确的，那么到 2030 年，我们可能会像 1750 年的人看到 2015 年一样被震撼——即下一个 DPU 可能只需要几十年——而 2050 年的世界可能与今天的世界_如此_不同，以至于我们几乎认不出来。

If Kurzweil and others who agree with him are correct, then we may be as blown away by 2030 as our 1750 guy was by 2015—i.e. the next DPU might only take a couple decades—and the world in 2050 might be _so_ vastly different than today’s world that we would barely recognize it.

这不是科幻小说。这是许多比你我都更聪明、知识更渊博的科学家坚信的——如果你看看历史，这是我们应该逻辑预测的。

This isn’t science fiction. It’s what many scientists smarter and more knowledgeable than you or I firmly believe—and if you look at history, it’s what we should logically predict.

那么，为什么当你听到我说“35 年后的世界可能完全认不出来”时，你会想：“酷……但_才怪_呢？”我们对未来离谱预测持怀疑态度的三个原因：

So then why, when you hear me say something like “the world 35 years from now might be totally unrecognizable,” are you thinking, “Cool….but nahhhhhhh”? Three reasons we’re skeptical of outlandish forecasts of the future:

1) 在历史方面，我们线性思考。当我们想象未来 30 年的进步时，我们回顾过去 30 年的进步作为可能发生多少的指标。当我们思考 21 世纪世界将变化的程度时，我们只是把 20 世纪的进步加到 2000 年。这正是我们的 1750 年人犯的错误，他从 1500 年找了一个人，期望像自己前进相同距离时那样让他震惊。我们最直观的是_线性_思考，而我们应该_指数_思考。如果有人更聪明一点，他们可能不是通过看过去 30 年，而是通过取_当前_的进步速度并据此判断来预测未来 30 年的进步。他们会更准确，但仍然差得远。为了正确思考未来，你需要想象事物以比现在_快得多的速度_移动。

1) When it comes to history, we think in straight lines.When we imagine the progress of the next 30 years, we look back to the progress of the previous 30 as an indicator of how much will likely happen. When we think about the extent to which the world will change in the 21st century, we just take the 20th century progress and add it to the year 2000. This was the same mistake our 1750 guy made when he got someone from 1500 and expected to blow his mind as much as his own was blown going the same distance ahead. It’s most intuitive for us to think _linearly,_ when we should be thinking _exponentially_. If someone is being more clever about it, they might predict the advances of the next 30 years not by looking at the previous 30 years, but by taking the _current_ rate of progress and judging based on that. They’d be more accurate, but still way off. In order to think about the future correctly, you need to imagine things moving at a _much faster rate_ than they’re moving now.

2) 非常近期的历史轨迹往往讲述一个扭曲的故事。首先，即使是指数曲线，当你只看一小段时，看起来也像是线性的，就像你近距离看一个大圆的一小段，它看起来几乎像一条直线。其次，指数增长并非完全平滑和均匀。库兹韦尔解释说，进步以“S 曲线”发生：

[](https://waitbutwhy.com/wp-content/uploads/2015/01/Projections.png)

S 曲线由新范式席卷世界时的进步浪潮产生。曲线经历三个阶段：

2) The trajectory of very recent history often tells a distorted story.First, even a steep exponential curve seems linear when you only look at a tiny slice of it, the same way if you look at a little segment of a huge circle up close, it looks almost like a straight line. Second, exponential growth isn’t totally smooth and uniform. Kurzweil explains that progress happens in “S-curves”:

1. 缓慢增长（指数增长的早期阶段）

[](https://waitbutwhy.com/wp-content/uploads/2015/01/S-Curves2.png)

2. 快速增长（指数增长的后期爆发阶段）

An S is created by the wave of progress when a new paradigm sweeps the world. The curve goes through three phases:

3. 随着特定范式成熟而趋于平稳

1. Slow growth (the early phase of exponential growth)

如果你只看非常近期的历史，你当前所处的 S 曲线部分可能会模糊你对进步速度的感知。1995 年至 2007 年间，互联网爆发，微软、谷歌和 Facebook 进入公众意识，社交网络诞生，手机和智能手机问世。那是第二阶段：S 曲线的增长爆发部分。但 2008 年至 2015 年则不那么具有突破性，至少在技术前沿如此。今天思考未来的人可能会审视过去几年以衡量当前的进步速度，但这忽略了更大的图景。事实上，一个新的、巨大的第二阶段增长爆发可能正在酝酿中。

2. Rapid growth (the late, explosive phase of exponential growth)

3) 我们自己的经验使我们对未来变得固执。我们基于个人经验形成对世界的看法，而这种经验已将最近过去的增长速度根植于我们脑中，成为“事物发生的方式”。我们还受限于想象力，它利用我们的经验来构想未来预测——但通常，我们所知道的并不能给我们提供准确思考未来的工具。当我们听到一个与基于经验的_事物运作方式_相矛盾的未来预测时，我们的本能是认为这个预测一定是天真的。如果我在这篇文章后面告诉你，你可能会活到 150 岁、250 岁，或者_根本不会死_，你的本能会是：“这很愚蠢——如果我从历史中学到一件事，那就是每个人都会死。”是的，过去没有人不死。但在飞机发明之前，也没有人飞过飞机。

3. A leveling off as the particular paradigm matures3

所以，虽然当你读这篇文章时，_才怪_可能感觉正确，但它很可能实际上是错的。事实是，如果我们真正逻辑地思考并期望历史模式继续，我们应该得出结论，未来几十年发生的变化应该比我们直觉预期的多得多、_多得多_。逻辑也表明，如果一个星球上最先进的物种以越来越快的速度取得越来越大的飞跃，那么在某个时刻，他们将做出一个如此巨大的飞跃，以至于完全改变他们所知的生活以及他们对作为人类意味着什么的看法——有点像进化不断向智能迈进，直到最终做出如此大的飞跃到达人类，完全改变了任何生物在地球上生活的意义。如果你花时间阅读当今科学和技术领域正在发生的事情，你会开始看到许多迹象悄悄暗示，我们目前所知的生活无法承受即将到来的下一次飞跃。

If you look only at very recent history, the part of the S-curve you’re on at the moment can obscure your perception of how fast things are advancing. The chunk of time between 1995 and 2007 saw the explosion of the internet, the introduction of Microsoft, Google, and Facebook into the public consciousness, the birth of social networking, and the introduction of cell phones and then smart phones. That was Phase 2: the growth spurt part of the S. But 2008 to 2015 has been less groundbreaking, at least on the technological front. Someone thinking about the future today might examine the last few years to gauge the current rate of advancement, but that’s missing the bigger picture. In fact, a new, huge Phase 2 growth spurt might be brewing right now.

3)Our own experience makes us stubborn old men about the future.We base our ideas about the world on our personal experience, and that experience has ingrained the rate of growth of the recent past in our heads as “the way things happen.” We’re also limited by our imagination, which takes our experience and uses it to conjure future predictions—but often, what we know simply doesn’t give us the tools to think accurately about the future.2 When we hear a prediction about the future that contradicts our experience-based notion of _how things work_, our instinct is that the prediction must be naive. If I tell you, later in this post, that you may live to be 150, or 250, or _not die at all_, your instinct will be, “That’s stupid—if there’s one thing I know from history, it’s that everybody dies.” And yes, no one in the past has not died. But no one flew airplanes before airplanes were invented either.

So while _nahhhhh_ might feel right as you read this post, it’s probably actually wrong. The fact is, if we’re being truly logical and expecting historical patterns to continue, we should conclude that much, much, _much_ more should change in the coming decades than we intuitively expect. Logic also suggests that if the most advanced species on a planet keeps making larger and larger leaps forward at an ever-faster rate, at some point, they’ll make a leap so great that it completely alters life as they know it and the perception they have of what it means to be a human—kind of like how evolution kept making great leaps toward intelligence until finally it made such a large leap to the human being that it completely altered what it meant for any creature to live on planet Earth. And if you spend some time reading about what’s going on today in science and technology, you start to see a lot of signs quietly hinting that life as we currently know it cannot withstand the leap that’s coming next.

什么是人工智能？ What Is AI?

如果你像我一样，曾经认为人工智能是一个愚蠢的科幻概念，但最近你听到严肃的人们提起它，而你并不真正理解它。

If you’re like me, you used to think Artificial Intelligence was a silly sci-fi concept, but lately you’ve been hearing it mentioned by serious people, and you don’t really quite get it.

很多人对“AI”这个词感到困惑，原因有三：

There are three reasons a lot of people are confused about the term AI:

1）我们将 AI 与电影联系起来。《星球大战》、《终结者》、《2001 太空漫游》，甚至《杰森一家》。这些都是虚构的，机器人角色也是。所以 AI 听起来有点虚构。

1)We associate AI with movies.Star Wars. Terminator. 2001: A Space Odyssey. Even the Jetsons. And those are fiction, as are the robot characters. So it makes AI sound a little fictional to us.

2）AI 是一个宽泛的话题。它从你手机的计算器到自动驾驶汽车，再到未来可能彻底改变世界的东西。AI 指代所有这些，这令人困惑。

2) AI is a broad topic. It ranges from your phone’s calculator to self-driving cars to something in the future that might change the world dramatically. AI refers to all of these things, which is confusing.

3）我们在日常生活中一直在使用 AI，但常常没有意识到它是 AI。约翰·麦卡锡在 1956 年创造了“人工智能”一词，他抱怨说：“一旦它起作用，就没人再叫它 AI 了。”由于这种现象，AI 听起来更像是一个神话般的未来预言，而不是现实。同时，它听起来像是一个从未实现的过去流行概念。雷·库兹韦尔说，他听到人们说 AI 在 20 世纪 80 年代就枯萎了，他将其比作“坚持认为互联网在 21 世纪初的互联网泡沫破灭中消亡了”。

3) We use AI all the time in our daily lives, but we often don’t realize it’s AI. John McCarthy, who coined the term “Artificial Intelligence” in 1956, complained that “as soon as it works, no one calls it AI anymore.”4 Because of this phenomenon, AI often sounds like a mythical future prediction more than a reality. At the same time, it makes it sound like a pop concept from the past that never came to fruition. Ray Kurzweil says he hears people say that AI withered in the 1980s, which he compares to “insisting that the Internet died in the dot-com bust of the early 2000s.”5

所以让我们澄清一下。首先，别再想机器人了。机器人是 AI 的容器，有时模仿人类形态，有时不——但 AI 本身是机器人内部的计算机。AI 是大脑，机器人是它的身体——如果它有身体的话。例如，Siri 背后的软件和数据是 AI，我们听到的女声是那个 AI 的人格化，根本没有机器人参与。

So let’s clear things up. First, stop thinking of _robots_. A robot is a _container_ for AI, sometimes mimicking the human form, sometimes not—but the AI itself is the computer _inside_ the robot. AI is the brain, and the robot is its body—if it even has a body. For example, the software and data behind Siri is AI, the woman’s voice we hear is a personification of that AI, and there’s no robot involved at all.

其次，你可能听说过“奇点”或“技术奇点”这个词。这个词在数学中用来描述一种渐近线般的情况，其中常规规则不再适用。在物理学中，它被用来描述像无限小、致密的黑洞或大爆炸前我们被挤压的那个点这样的现象。同样，是常规规则不再适用的情况。1993 年，弗诺·文奇写了一篇著名的文章，他将这个词应用于未来我们的技术智能超越我们自身的那一刻——对他来说，那一刻我们已知的生活将永远改变，常规规则不再适用。然后雷·库兹韦尔把奇点定义为加速回报定律达到如此极端的步伐，以至于技术进步以看似无限的速度发生，之后我们将生活在一个全新的世界。我发现今天许多 AI 思想家已经停止使用这个词，而且它无论如何都令人困惑，所以我在这里不会多用它（尽管我们将在整篇文章中关注这个想法）。

Secondly, you’ve probably heard the term “singularity” or “technological singularity.” This term has been used in math to describe an asymptote-like situation where normal rules no longer apply. It’s been used in physics to describe a phenomenon like an infinitely small, dense black hole or the point we were all squished into right before the Big Bang. Again, situations where the usual rules don’t apply. In 1993, Vernor Vinge wrote a famous essay in which he applied the term to the moment in the future when our technology’s intelligence exceeds our own—a moment for him when life as we know it will be forever changed and normal rules will no longer apply. Ray Kurzweil then muddled things a bit by defining the singularity as the time when the Law of Accelerating Returns has reached such an extreme pace that technological progress is happening at a seemingly-infinite pace, and after which we’ll be living in a whole new world. I found that many of today’s AI thinkers have stopped using the term, and it’s confusing anyway, so I won’t use it much here (even though we’ll be focusing on that _idea_ throughout).

最后，虽然 AI 是一个宽泛的概念，有许多不同类型或形式，但我们需要考虑的关键类别是基于 AI 的“能力等级”。有三个主要的 AI 能力等级：

Finally, while there are many different types or forms of AI since AI is a broad concept, the critical categories we need to think about are based on an AI’s _caliber_. There are three major AI caliber categories:

AI 能力等级 1）狭义人工智能（ANI）：有时被称为弱人工智能，狭义人工智能是专门从事一个领域的人工智能。有能在国际象棋中击败世界冠军的 AI，但那是它唯一能做的事。让它找出更好的方式在硬盘上存储数据，它会茫然地看着你。

AI Caliber 1) Artificial Narrow Intelligence (ANI):Sometimes referred to as _Weak AI_, Artificial Narrow Intelligence is AI that specializes in _one_ area. There’s AI that can beat the world chess champion in chess, but that’s the only thing it does. Ask it to figure out a better way to store data on a hard drive, and it’ll look at you blankly.

AI 能力等级 2）通用人工智能（AGI）：有时被称为强人工智能或人类级人工智能，通用人工智能指的是在各个方面与人类一样聪明的计算机——一台能完成人类能完成的任何智力任务的机器。创造 AGI 比创造 ANI 困难得多，我们还没有做到。琳达·戈特弗雷德森教授将智力描述为“一种非常普遍的智力能力，其中包括推理、计划、解决问题、抽象思考、理解复杂概念、快速学习和从经验中学习的能力。”AGI 将能像你一样轻松地完成所有这些事情。

AI Caliber 2) Artificial General Intelligence (AGI):Sometimes referred to as _Strong AI_, or _Human-Level AI_, Artificial General Intelligence refers to a computer that is as smart as a human _across the board—_ a machine that can perform any intellectual task that a human being can. Creating AGI is a _much_ harder task than creating ANI, and we’re yet to do it. Professor Linda Gottfredson describes intelligence as “a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly, and learn from experience.” AGI would be able to do all of those things as easily as you can.

AI 能力等级 3）超级人工智能（ASI）：牛津哲学家和领先的 AI 思想家尼克·博斯特罗姆将超级智能定义为“在几乎所有领域，包括科学创造力、普遍智慧和社会技能，都比最优秀的人类大脑聪明得多的智力。”超级人工智能的范围从只比人类聪明一点的计算机到在各个方面比人类聪明万亿倍的计算机。ASI 是 AI 话题如此火爆的原因，也是“永生”和“灭绝”这两个词会在这篇文章中多次出现的原因。

AI Caliber 3) Artificial Superintelligence (ASI): Oxford philosopher and leading AI thinker Nick Bostrom defines superintelligence as “an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom and social skills.” Artificial Superintelligence ranges from a computer that’s just a little smarter than a human to one that’s trillions of times smarter—across the board. ASI is the reason the topic of AI is such a spicy meatball and why the words “immortality” and “extinction” will both appear in these posts multiple times.

截至目前，人类已经在许多方面征服了最低等级的 AI——ANI，它无处不在。AI 革命是从 ANI，经过 AGI，到 ASI 的道路——一条我们可能幸存也可能无法幸存的道路，但无论如何，它将改变一切。

As of now, humans have conquered the lowest caliber of AI—ANI—in many ways, and it’s everywhere. The AI Revolution is the road from ANI, through AGI, to ASI—a road we may or may not survive but that, either way, will change everything.

让我们仔细看看该领域的领先思想家认为这条道路是什么样的，以及为什么这场革命可能比你想象的来得更快：

Let’s take a close look at what the leading thinkers in the field believe this road looks like and why this revolution might happen way sooner than you might think:

我们当前所处的位置——一个运行在 ANI 上的世界 Where We Are Currently—A World Running on ANI

狭义人工智能是指机器在某一特定任务上达到或超越人类智能或效率的机器智能。几个例子如下：

Artificial Narrow Intelligence is machine intelligence that equals or exceeds human intelligence or efficiency at a _specific_ thing. A few examples:

* 汽车中充满了 ANI 系统，从决定何时启动防抱死制动系统的计算机，到调节燃油喷射系统参数的计算机。谷歌正在测试的自动驾驶汽车将包含强大的 ANI 系统，使其能够感知并对周围世界做出反应。

* Cars are full of ANI systems, from the computer that figures out when the anti-lock brakes should kick in to the computer that tunes the parameters of the fuel injection systems. Google’s self-driving car, which is being tested now, will contain robust ANI systems that allow it to perceive and react to the world around it.

* 你的手机就是一个小型 ANI 工厂。当你使用地图应用导航、接收 Pandora 的个性化音乐推荐、查看明天的天气、与 Siri 对话或进行其他数十种日常活动时，你都在使用 ANI。

* Your phone is a little ANI factory. When you navigate using your map app, receive tailored music recommendations from Pandora, check tomorrow’s weather, talk to Siri, or dozens of other everyday activities, you’re using ANI.

* 你的电子邮件垃圾邮件过滤器是一种典型的 ANI——它最初加载了关于如何识别垃圾邮件和非垃圾邮件的智能，然后随着它了解你的特定偏好，它会学习并调整其智能以适应你。Nest 恒温器也是如此，它开始了解你的典型日常并据此采取行动。

* Your email spam filter is a classic type of ANI—it starts off loaded with intelligence about how to figure out what’s spam and what’s not, and then it learns and tailors its intelligence to you as it gets experience with your particular preferences. The Nest Thermostat does the same thing as it starts to figure out your typical routine and act accordingly.

* 你知道当你在亚马逊上搜索一个产品，然后在另一个网站上看到它作为“为你推荐”的产品，或者 Facebook 不知何故知道你应该添加谁为好友时，那种令人毛骨悚然的事情吗？那是一个 ANI 系统网络，它们协同工作，相互告知你是谁、你喜欢什么，然后利用这些信息决定向你展示什么。亚马逊的“购买此商品的顾客也购买了……”也是如此——这是一个 ANI 系统，其工作是从数百万客户的行为中收集信息，并综合这些信息巧妙地向上销售，让你购买更多东西。

* You know the whole creepy thing that goes on when you search for a product on Amazon and then you see that as a “recommended for you” product on a _different_ site, or when Facebook somehow knows who it makes sense for you to add as a friend? That’s a network of ANI systems, working together to inform each other about who you are and what you like and then using that information to decide what to show you. Same goes for Amazon’s “People who bought this also bought…” thing—that’s an ANI system whose job it is to gather info from the behavior of millions of customers and synthesize that info to cleverly upsell you so you’ll buy more things.

* 谷歌翻译是另一个经典的 ANI 系统——在一个狭窄的任务上表现出色。语音识别是另一个，有许多应用程序将这两个 ANI 系统组合使用，允许你说出一种语言的句子，然后手机输出另一种语言的相同句子。

* Google Translate is another classic ANI system—impressively good at one narrow task. Voice recognition is another, and there are a bunch of apps that use those two ANIs as a tag team, allowing you to speak a sentence in one language and have the phone spit out the same sentence in another.

* 当你的飞机降落时，决定它应该停靠哪个登机口的不是人类。就像决定你机票价格的也不是人类一样。

* When your plane lands, it’s not a human that decides which gate it should go to. Just like it’s not a human that determined the price of your ticket.

* 世界上最好的跳棋、国际象棋、拼字游戏、西洋双陆棋和黑白棋玩家现在都是 ANI 系统。

* The world’s best Checkers, Chess, Scrabble, Backgammon, and Othello players are now all ANI systems.

* 谷歌搜索是一个大型 ANI 大脑，拥有极其复杂的方法来对页面进行排名并决定向你展示什么。Facebook 的新闻推送也是如此。

* Google search is one large ANI brain with incredibly sophisticated methods for ranking pages and figuring out what to show you in particular. Same goes for Facebook’s Newsfeed.

* 这些还只是消费领域。复杂的 ANI 系统广泛应用于军事、制造和金融等行业（算法高频 AI 交易者占美国市场交易股票份额的一半以上），以及专家系统，如帮助医生诊断的系统，最著名的是 IBM 的 Watson，它拥有足够多的事实，并能很好地理解 Trebek 的隐晦用语，从而轻松击败了最杰出的《危险边缘》冠军。

* And those are just in the consumer world. Sophisticated ANI systems are widely used in sectors and industries like military, manufacturing, and finance (algorithmic high-frequency AI traders account for more than half of equity shares traded on US markets6), and in expert systems like those that help doctors make diagnoses and, most famously, IBM’s Watson, who contained enough facts and understood coy Trebek-speak well enough to soundly beat the most prolific _Jeopardy_ champions.

目前的 ANI 系统并不可怕。最坏的情况下，一个故障或编程不良的 ANI 可能导致孤立的灾难，比如导致电网瘫痪、造成有害的核电站故障，或引发金融市场灾难（如 2010 年的闪电崩盘，当时一个 ANI 程序对意外情况做出了错误反应，导致股市短暂暴跌，市值蒸发 1 万亿美元，其中只有一部分在错误纠正后恢复）。

ANI systems as they are now aren’t especially scary. At worst, a glitchy or badly-programmed ANI can cause an isolated catastrophe like knocking out a power grid, causing a harmful nuclear power plant malfunction, or triggering a financial markets disaster (like the 2010 Flash Crash when an ANI program reacted the wrong way to an unexpected situation and caused the stock market to briefly plummet, taking $1 trillion of market value with it, only part of which was recovered when the mistake was corrected).

但是，尽管 ANI 没有能力造成生存威胁，我们应该将这个日益庞大和复杂的相对无害的 ANI 生态系统视为即将到来的改变世界的飓风的前兆。每一个新的 ANI 创新都悄悄地为通往 AGI 和 ASI 的道路增添了一块砖。或者正如 Aaron Saenz 所说，我们世界的 ANI 系统“就像早期地球原始汤中的氨基酸”——无生命的生命物质，在一个意想不到的日子里，苏醒了。

But while ANI doesn’t have the capability to cause an _existential threat_, we should see this increasingly large and complex ecosystem of relatively-harmless ANI as a precursor of the world-altering hurricane that’s on the way. Each new ANI innovation quietly adds another brick onto the road to AGI and ASI. Or as Aaron Saenz sees it, our world’s ANI systems “are like the amino acids in the early Earth’s primordial ooze”—the inanimate stuff of life that, one unexpected day, woke up.

从狭义人工智能到通用人工智能之路 The Road From ANI to AGI

没有什么比了解尝试创造一台像我们一样聪明的计算机有多么难以置信地具有挑战性更能让你欣赏人类智慧的了。建造摩天大楼、将人类送入太空、弄清大爆炸的细节——所有这些都比理解我们自己的大脑或制造出像它一样酷的东西容易得多。截至目前，人类大脑是已知宇宙中最复杂的物体。

Nothing will make you appreciate human intelligence like learning about how unbelievably challenging it is to try to create a computer as smart as we are. Building skyscrapers, putting humans in space, figuring out the details of how the Big Bang went down—all far easier than understanding our own brain or how to make something as cool as it. As of now, the human brain is the most complex object in the known universe.

有趣的是，尝试构建 AGI（一台在_一般_意义上像人类一样聪明的计算机，而不仅仅是在某一狭窄专业领域）的难点并非直觉上你所想的那样。建造一台能在瞬间计算两个十位数乘积的计算机——极其容易。建造一台能看一只狗并回答它是狗还是猫的计算机——极其困难。制造能在国际象棋中击败任何人类的 AI？已经做到了。制造一台能阅读六岁儿童图画书中的一个段落，不仅识别单词而且理解其_含义_的计算机？谷歌目前正花费数十亿美元试图做到这一点。困难的事情——比如微积分、金融市场策略和语言翻译——对计算机来说简单得令人麻木，而简单的事情——比如视觉、运动、移动和感知——对计算机来说却异常困难。或者，正如计算机科学家唐纳德·克努特所说：“AI 现在已经成功完成了几乎所有需要‘思考’的事情，但未能完成大多数人和动物‘不假思索’就能做的事情。”

What’s interesting is that the hard parts of trying to build AGI (a computer as smart as humans in _general_, not just at one narrow specialty) are not intuitively what you’d think they are. Build a computer that can multiply two ten-digit numbers in a split second—incredibly easy. Build one that can look at a dog and answer whether it’s a dog or a cat—spectacularly difficult. Make AI that can beat any human in chess? Done. Make one that can read a paragraph from a six-year-old’s picture book and not just recognize the words but understand the _meaning_ of them? Google is currently spending billions of dollars trying to do it. Hard things—like calculus, financial market strategy, and language translation—are mind-numbingly easy for a computer, while easy things—like vision, motion, movement, and perception—are insanely hard for it. Or, as computer scientist Donald Knuth puts it, “AI has by now succeeded in doing essentially everything that requires ‘thinking’ but has failed to do most of what people and animals do ‘without thinking.'”7

当你思考这一点时，你会很快意识到，那些对我们来说看似简单的事情实际上极其复杂，它们之所以看起来简单，只是因为这些技能在人类（以及大多数动物）身上经过了数亿年的动物进化优化。当你伸手去够一个物体时，你肩膀、肘部和手腕的肌肉、肌腱和骨骼会立即与眼睛协同执行一系列物理操作，使你的手能够在三维空间中沿直线移动。这对你来说似乎毫不费力，因为你大脑中已经完善了执行此操作的软件。同样的道理也适用于为什么恶意软件并不笨，它无法在你注册网站新账户时识别出歪斜的单词测试——而是你的大脑非常了不起，因为它_能够_做到。

What you quickly realize when you think about this is that those things that seem easy to us are actually unbelievably complicated, and they only seem easy because those skills have been optimized in us (and most animals) by hundreds of millions of years of animal evolution. When you reach your hand up toward an object, the muscles, tendons, and bones in your shoulder, elbow, and wrist instantly perform a long series of physics operations, in conjunction with your eyes, to allow you to move your hand in a straight line through three dimensions. It seems effortless to you because you have perfected software in your brain for doing it. Same idea goes for why it’s not that malware is dumb for not being able to figure out the slanty word recognition test when you sign up for a new account on a site—it’s that your brain is super impressive for being _able_ to.

另一方面，计算大数或下棋对生物来说是新的活动，我们还没有时间进化出对它们的熟练度，所以计算机不需要太努力就能击败我们。想想看——你更愿意构建一个能计算大数的程序，还是一个能充分理解字母 B 的本质，以至于你可以向它展示任何数千种不可预测字体或手写体中的 B，它都能立即认出那是 B 的程序？

On the other hand, multiplying big numbers or playing chess are new activities for biological creatures and we haven’t had any time to evolve a proficiency at them, so a computer doesn’t need to work too hard to beat us. Think about it—which would you rather do, build a program that could multiply big numbers or one that could understand the essence of a B well enough that you could show it a B in any one of thousands of unpredictable fonts or handwriting and it could instantly know it was a B?

一个有趣的例子——当你看这个时，你和计算机都能看出它是一个有两个不同色调交替的矩形：

One fun example—when you look at this, you and a computer both can figure out that it’s a rectangle with two distinct shades, alternating:

[](https://waitbutwhy.com/wp-content/uploads/2015/01/Screen-Shot-2015-01-21-at-12.59.21-AM.png)

[](https://waitbutwhy.com/wp-content/uploads/2015/01/Screen-Shot-2015-01-21-at-12.59.21-AM.png)

到目前为止打成平手。但如果你拿起黑色部分，揭示整个图像……

Tied so far. But if you pick up the black and reveal the whole image…

[](https://waitbutwhy.com/wp-content/uploads/2015/01/Screen-Shot-2015-01-21-at-12.59.54-AM.png)

[](https://waitbutwhy.com/wp-content/uploads/2015/01/Screen-Shot-2015-01-21-at-12.59.54-AM.png)

……你可以毫无困难地完整描述各种不透明和半透明的圆柱体、板条和 3D 角落，但计算机会彻底失败。它会描述它所看到的——各种不同色调的二维形状——而这实际上是真实存在的。你的大脑做了大量复杂的工作来解释图像试图描绘的隐含深度、色调混合和室内照明。而看下面的图片，计算机看到的是二维的白色、黑色和灰色拼贴画，而你很容易看出它的真实面貌——一张完全黑色的 3D 岩石照片：

…you have no problem giving a full description of the various opaque and translucent cylinders, slats, and 3-D corners, but the computer would fail miserably. It would describe what it sees—a variety of two-dimensional shapes in several different shades—which is actually what’s there. Your brain is doing a ton of fancy shit to interpret the implied depth, shade-mixing, and room lighting the picture is trying to portray.8 And looking at the picture below, a computer sees a two-dimensional white, black, and gray collage, while you easily see what it really is—a photo of an entirely-black, 3-D rock:

[](https://waitbutwhy.com/wp-content/uploads/2015/01/article-2053686-0E8BC15900000578-845_634x330.jpg)

[](https://waitbutwhy.com/wp-content/uploads/2015/01/article-2053686-0E8BC15900000578-845_634x330.jpg)

而我们刚才提到的所有内容仍然只是接收静态信息并处理它。要达到人类水平的智能，计算机必须理解诸如微妙面部表情之间的差异、高兴、宽慰、满足、满意和开心之间的区别，以及为什么《勇敢的心》很棒而《爱国者》很糟糕之类的事情。

And everything we just mentioned is still only taking in stagnant information and processing it. To be human-level intelligent, a computer would have to understand things like the difference between subtle facial expressions, the distinction between being pleased, relieved, content, satisfied, and glad, and why _Braveheart_ was great but _The Patriot_ was terrible.

创建 AGI 的第一个关键：增加计算能力

First Key to Creating AGI: Increasing Computational Power

要实现 AGI，肯定需要的一件事是计算机硬件能力的提升。如果一个 AI 系统要像大脑一样智能，它需要具备与大脑相当的计算能力。

One thing that definitely needs to happen for AGI to be a possibility is an increase in the power of computer hardware. If an AI system is going to be as intelligent as the brain, it’ll need to equal the brain’s raw computing capacity.

表达这种能力的一种方式是大脑每秒能处理的总计算次数（cps），你可以通过计算大脑中每个结构的最大 cps，然后将它们全部相加来得出这个数字。

One way to express this capacity is in the total calculations per second (cps) the brain could manage, and you could come to this number by figuring out the maximum cps of each structure in the brain and then adding them all together.

雷·库兹韦尔想出了一个捷径，他采用某位专业人士对一个结构 cps 的估计，以及该结构相对于整个大脑的重量，然后按比例相乘得到总体的估计。听起来有点不靠谱，但他用不同区域的各种专业估计重复了多次，总数总是在同一个范围内——大约 10^16，即 10 千万亿 cps。

Ray Kurzweil came up with a shortcut by taking someone’s professional estimate for the cps of one structure and that structure’s weight compared to that of the whole brain and then multiplying proportionally to get an estimate for the total. Sounds a little iffy, but he did this a bunch of times with various professional estimates of different regions, and the total always arrived in the same ballpark—around 10 16, or 10 quadrillion cps.

目前，世界上最快的超级计算机，中国的天河二号，实际上已经超过了这个数字，达到了约 34 千万亿 cps。但天河二号也很庞大，占地 720 平方米，使用 24 兆瓦的电力（大脑仅需 20 瓦），造价 3.9 亿美元。目前还不适用于广泛使用，甚至大多数商业或工业用途。

Currently, the world’s fastest supercomputer, China’s Tianhe-2, has actually beaten that number, clocking in at about 34 quadrillion cps. But Tianhe-2 is also a dick, taking up 720 square meters of space, using 24 megawatts of power (the brain runs on just 20 watts), and costing $390 million to build. Not especially applicable to wide usage, or even most commercial or industrial usage yet.

库兹韦尔建议我们通过观察用 1000 美元能买到多少 cps 来思考计算机的状态。当这个数字达到人类水平——10 千万亿 cps 时，那就意味着 AGI 可能成为生活中非常真实的一部分。

Kurzweil suggests that we think about the state of computers by looking at how many cps you can buy for $1,000. When that number reaches human-level—10 quadrillion cps—then that’ll mean AGI could become a very real part of life.

摩尔定律是一个历史上可靠的规律，即世界最大计算能力大约每两年翻一番，这意味着计算机硬件的发展，就像历史上人类进步一样，呈指数级增长。看看这与库兹韦尔的 cps/1000 美元指标的关系，我们目前大约是 10 万亿 cps/1000 美元，正好符合这张图预测的轨迹：

Moore’s Law is a historically-reliable rule that the world’s maximum computing power doubles approximately every two years, meaning computer hardware advancement, like general human advancement through history, grows exponentially. Looking at how this relates to Kurzweil’s cps/$1,000 metric, we’re currently at about 10 trillion cps/$1,000, right on pace with this graph’s predicted trajectory:9

[](https://waitbutwhy.com/wp-content/uploads/2015/01/PPTExponentialGrowthof_Computing-1.jpg)

[](https://waitbutwhy.com/wp-content/uploads/2015/01/PPTExponentialGrowthof_Computing-1.jpg)

因此，世界上 1000 美元的计算机现在已经超过了老鼠的大脑，并且达到了人类水平的千分之一。这听起来不算什么，但请记住，1985 年我们大约是人类水平的万亿分之一，1995 年是十亿分之一，2005 年是百万分之一。2015 年达到千分之一，正好让我们在 2025 年之前拥有与大脑能力相当的廉价计算机。

So the world’s $1,000 computers are now beating the mouse brain and they’re at about a thousandth of human level. This doesn’t sound like much until you remember that we were at about a trillionth of human level in 1985, a billionth in 1995, and a millionth in 2005. Being at a thousandth in 2015 puts us right on pace to get to an affordable computer by 2025 that rivals the power of the brain.

所以在硬件方面，AGI 所需的原始计算能力现在在技术上已经可用（在中国），我们将在 10 年内准备好负担得起、广泛使用的 AGI 级硬件。但仅凭原始计算能力并不能使计算机具备通用智能——下一个问题是，我们如何将人类水平的智能赋予所有这些能力？

So on the hardware side, the raw power needed for AGI is technically available now, in China, and we’ll be ready for affordable, widespread AGI-caliber hardware within 10 years. But raw computational power alone doesn’t make a computer generally intelligent—the next question is, how do we bring human-level intelligence to all that power?

创建 AGI 的第二个关键：让它变聪明

Second Key to Creating AGI: Making It Smart

这是棘手部分。事实是，没有人真正知道如何让它变聪明——我们仍在争论如何让计算机达到人类水平的智能，能够识别狗、奇怪的字母 B 和一部平庸的电影。但有很多看似遥远的策略，在某个时刻，其中一种会奏效。以下是我遇到的三种最常见的策略：

This is the icky part. The truth is, no one really knows how to make it smart—we’re still debating how to make a computer human-level intelligent and capable of knowing what a dog and a weird-written B and a mediocre movie is. But there are a bunch of far-fetched strategies out there and at some point, one of them will work. Here are the three most common strategies I came across:

1) 抄袭大脑 1) Plagiarize the brain.

这就像科学家们苦苦思索，为什么坐在班上邻座的那个孩子那么聪明，考试总是考得那么好，尽管他们自己也在努力学习，却远不如那个孩子，最后他们决定：“去他的，我干脆抄那个孩子的答案吧。”这说得通——我们正为构建一台超级复杂的计算机而一筹莫展，而恰好每个人的脑袋里都有一个完美的原型。

This is like scientists toiling over how that kid who sits next to them in class is so smart and keeps doing so well on the tests, and even though they keep studying diligently, they can’t do nearly as well as that kid, and then they finally decide “k fuck it I’m just gonna copy that kid’s answers.” It makes sense—we’re stumped trying to build a super-complex computer, and there happens to be a perfect prototype for one in each of our heads.

科学界正致力于逆向工程大脑，以弄清进化是如何创造出如此酷的东西的——乐观估计认为我们能在 2030 年前做到这一点。一旦成功，我们就能了解大脑如何如此强大而高效地运行的所有秘密，并从中汲取灵感，窃取其创新。一个模仿大脑的计算机架构例子是人工神经网络。它最初是一个由晶体管“神经元”组成的网络，通过输入和输出相互连接，它什么都不知道——就像婴儿的大脑。它“学习”的方式是尝试完成一项任务，比如手写识别，起初，它的神经放电和随后对每个字母的猜测完全是随机的。但当它被告知做对了时，恰好产生该答案的放电通路中的晶体管连接就会加强；当被告知做错了时，这些通路的连接就会减弱。经过大量这样的尝试和反馈，网络自行形成了智能的神经通路，机器也针对该任务进行了优化。大脑的学习方式与此类似，但更为复杂，随着我们继续研究大脑，我们正在发现利用神经回路的新颖巧妙方法。

The science world is working hard on reverse engineering the brain to figure out how evolution made such a rad thing—optimistic estimates say we can do this by 2030. Once we do that, we’ll know all the secrets of how the brain runs so powerfully and efficiently and we can draw inspiration from it and steal its innovations. One example of computer architecture that mimics the brain is the artificial neural network. It starts out as a network of transistor “neurons,” connected to each other with inputs and outputs, and it knows nothing—like an infant brain. The way it “learns” is it tries to do a task, say handwriting recognition, and at first, its neural firings and subsequent guesses at deciphering each letter will be completely random. But when it’s told it got something right, the transistor connections in the firing pathways that happened to create that answer are strengthened; when it’s told it was wrong, those pathways’ connections are weakened. After a lot of this trial and feedback, the network has, by itself, formed smart neural pathways and the machine has become optimized for the task. The brain learns a bit like this but in a more sophisticated way, and as we continue to study the brain, we’re discovering ingenious new ways to take advantage of neural circuitry.

更极端的抄袭涉及一种称为“全脑仿真”的策略，其目标是将真实大脑切成薄层，扫描每一层，用软件组装出精确重建的 3D 模型，然后在强大的计算机上实现该模型。这样我们就拥有了一台正式具备大脑所有能力的计算机——它只需要学习和收集信息。如果工程师们变得非常擅长，他们就能以如此精确的精度仿真真实大脑，以至于一旦大脑架构上传到计算机，大脑的完整个性和记忆都将完好无损。如果这个大脑属于即将去世的吉姆，那么计算机现在就会以吉姆（？）的身份醒来，这将是一个强大的人类水平的 AGI，然后我们就可以致力于将吉姆变成难以想象的聪明的 ASI，他可能会对此感到非常兴奋。

More extreme plagiarism involves a strategy called “whole brain emulation,” where the goal is to slice a real brain into thin layers, scan each one, use software to assemble an accurate reconstructed 3-D model, and then implement the model on a powerful computer. We’d then have a computer officially capable of everything the brain is capable of—it would just need to learn and gather information. If engineers get _really_ good, they’d be able to emulate a real brain with such exact accuracy that the brain’s full personality and memory would be intact once the brain architecture has been uploaded to a computer. If the brain belonged to Jim right before he passed away, the computer would now wake up as Jim (?), which would be a robust human-level AGI, and we could now work on turning Jim into an unimaginably smart ASI, which he’d probably be really excited about.

我们离实现全脑仿真还有多远？到目前为止，我们刚刚能够仿真一个 1 毫米长的扁形虫大脑，它总共只有 302 个神经元。而人类大脑包含 1000 亿个。如果这让人觉得这是一个无望的项目，请记住指数级进步的力量——既然我们已经征服了微小的蠕虫大脑，不久之后可能会征服蚂蚁，然后是老鼠，突然间这将变得更为可行。

How far are we from achieving whole brain emulation? Well so far, we’ve not yetjust recently been able to emulate a 1mm-long flatworm brain, which consists of just 302 total neurons. The human brain contains 100 billion. If that makes it seem like a hopeless project, remember the power of exponential progress—now that we’ve conquered the tiny worm brain, an ant might happen before too long, followed by a mouse, and suddenly this will seem much more plausible.

2) 尝试让进化重演，但这次为我们所用 2) Try to make evolution do what it did before but for us this time.

因此，如果我们认为聪明孩子的考试太难抄袭，我们可以尝试模仿他备考的方式。

So if we decide the smart kid’s test is too hard to copy, we can try to copy the way he _studies_ for the tests instead.

我们知道一件事：制造一台与大脑能力相当的计算机是可能的——我们自身大脑的进化就是证明。如果大脑过于复杂而无法模仿，我们可以尝试模仿进化本身。事实上，即使我们能模仿大脑，那也可能像试图通过模仿鸟类扇动翅膀来制造飞机一样——机器通常最好采用全新的、面向机器的方法来设计，而不是精确地模仿生物学。

Here’s something we know. Building a computer as powerful as the brain _is_ possible—our own brain’s evolution is proof. And if the brain is just too complex for us to emulate, we could try to emulate _evolution_ instead. The fact is, even if we can emulate a brain, that might be like trying to build an airplane by copying a bird’s wing-flapping motions—often, machines are best designed using a fresh, machine-oriented approach, not by mimicking biology exactly.

那么，我们如何模拟进化来构建 AGI 呢？这种方法被称为“遗传算法”，其工作原理大致如下：会有一个反复进行的性能评估过程（就像生物通过生活来“表现”，并通过是否成功繁殖来被“评估”）。一组计算机会尝试执行任务，其中最成功的计算机会通过将各自程序的一半合并到新计算机中来相互“繁殖”。不太成功的则会被淘汰。经过多次迭代，这种自然选择过程将产生越来越好的计算机。挑战在于创建一个自动化的评估和繁殖周期，使这一进化过程能够自主运行。

So how can we simulate evolution to build AGI? The method, called “genetic algorithms,” would work something like this: there would be a performance-and-evaluation process that would happen again and again (the same way biological creatures “perform” by living life and are “evaluated” by whether they manage to reproduce or not). A group of computers would try to do tasks, and the most successful ones would be _bred_ with each other by having half of each of their programming merged together into a new computer. The less successful ones would be eliminated. Over many, many iterations, this natural selection process would produce better and better computers. The challenge would be creating an automated evaluation and breeding cycle so this evolution process could run on its own.

模仿进化的缺点是进化喜欢花费数十亿年时间，而我们希望在几十年内完成。

The downside of copying evolution is that evolution likes to take a billion years to do things and we want to do this in a few decades.

但相比进化，我们有很多优势。首先，进化没有预见性，且随机运作——它产生的有害突变多于有益突变，但我们可以控制这个过程，使其只由有益的故障和有针对性的调整驱动。其次，进化并不以任何事物为目标，包括智能——有时环境甚至可能选择反对更高的智能（因为它消耗大量能量）。而我们则可以专门引导这一进化过程朝着提高智能的方向发展。第三，为了选择智能，进化必须在许多其他方面进行创新以促进智能——比如改造细胞产生能量的方式——而我们可以移除这些额外负担，使用电力等。毫无疑问，我们会比进化快得多——但仍不清楚我们能否对进化进行足够多的改进，使其成为一种可行的策略。

But we have a lot of advantages over evolution. First, evolution has no foresight and works randomly—it produces more unhelpful mutations than helpful ones, but we would control the process so it would only be driven by beneficial glitches and targeted tweaks. Secondly, evolution doesn’t _aim_ for anything, including intelligence—sometimes an environment might even select _against_ higher intelligence (since it uses a lot of energy). We, on the other hand, could specifically direct this evolutionary process toward increasing intelligence. Third, to select for intelligence, evolution has to innovate in a bunch of other ways to facilitate intelligence—like revamping the ways cells produce energy—when we can remove those extra burdens and use things like electricity. It’s no doubt we’d be much, much faster than evolution—but it’s still not clear whether we’ll be able to improve upon evolution _enough_ to make this a viable strategy.

3) 把整件事变成计算机的问题，而不是我们的问题 3) Make this whole thing the computer’s problem, not ours.

这时科学家们会感到绝望，并试图让测试自行进行。但这可能是我们拥有的最有希望的方法。

This is when scientists get desperate and try to program the test to take itself. But it might be the most promising method we have.

这个想法是，我们将构建一台计算机，它的两大技能是进行人工智能研究和将代码修改融入自身——使其不仅能学习，还能改进自己的_架构_。我们将教会计算机成为计算机科学家，以便它们能够自主推动自身发展。而这将是它们的主要工作——弄清楚如何让_自己_变得更聪明。稍后会有更多介绍。

The idea is that we’d build a computer whose two major skills would be doing research on AI and coding changes into itself—allowing it to not only learn but to improve its own _architecture_. We’d teach computers to be computer scientists so they could bootstrap their own development. And that would be their main job—figuring out how to make _themselves_ smarter. More on this later.

这一切可能很快就会发生 All of This Could Happen Soon

硬件方面的快速进步和软件方面的创新实验正在同时发生，AGI 可能会迅速且出乎意料地逼近我们，这主要有两个原因：

Rapid advancements in hardware and innovative experimentation with software are happening simultaneously, and AGI could creep up on us quickly and unexpectedly for two main reasons:

1) 指数级增长是剧烈的，看似蜗牛般的进步速度可能会迅速攀升——这张 GIF 很好地说明了这一概念：

1) Exponential growth is intense and what seems like a snail’s pace of advancement can quickly race upwards—this GIF illustrates this concept nicely:

[](https://waitbutwhy.com/wp-content/uploads/2015/01/gif)

[](https://waitbutwhy.com/wp-content/uploads/2015/01/gif)

2) 就软件而言，进展可能看起来缓慢，但一个顿悟就能瞬间改变进步的速度（有点像科学领域，在人类认为宇宙是地心说的时候，计算宇宙如何运作很困难，但发现它是日心说后，一切突然变得容易多了）。或者，对于像自我改进的计算机这样的东西，我们可能看起来还很遥远，但实际上可能只需对系统进行一次调整，就能使其效率提升 1000 倍，并迅速达到人类水平的智能。

2) When it comes to software, progress can seem slow, but then one epiphany can instantly change the rate of advancement (kind of like the way science, during the time humans thought the universe was geocentric, was having difficulty calculating how the universe worked, but then the discovery that it was heliocentric suddenly made everything _much_ easier). Or, when it comes to something like a computer that improves itself, we might seem far away but actually be just one tweak of the system away from having it become 1,000 times more effective and zooming upward to human-level intelligence.

从 AGI 到 ASI 之路 The Road From AGI to ASI

在某个时刻，我们将实现 AGI——具备人类水平通用智能的计算机。只是一群人和计算机平等地生活在一起。

At some point, we’ll have achieved AGI—computers with human-level general intelligence. Just a bunch of people and computers living together in equality.

问题是，与人类拥有相同智能水平和计算能力的 AGI，仍然比人类具有显著优势。例如：

The thing is, AGI with an identical level of intelligence and computational capacity as a human would still have significant advantages over humans. Like:

* **速度。** 大脑神经元的最大频率约为 200 赫兹，而今天的微处理器（在达到 AGI 时速度将远快于此）运行在 2 吉赫，比我们的神经元快 1000 万倍。大脑内部通信速度约为 120 米/秒，与计算机以光速进行光通信的能力相比相形见绌。

* Speed.The brain’s neurons max out at around 200 Hz, while today’s microprocessors (which are much slower than they will be when we reach AGI) run at 2 GHz, or 10 million times faster than our neurons. And the brain’s internal communications, which can move at about 120 m/s, are horribly outmatched by a computer’s ability to communicate optically at the speed of light.

* **尺寸与存储。** 大脑受限于头骨形状，无法变得更大，否则 120 米/秒的内部通信在不同脑结构之间传输时间过长。计算机可以扩展到任意物理尺寸，从而使用更多硬件、更大的工作内存（RAM）和长期记忆（硬盘存储），其容量和精度都远超我们。

* Size and storage.The brain is locked into its size by the shape of our skulls, and it couldn’t get much bigger anyway, or the 120 m/s internal communications would take too long to get from one brain structure to another. Computers can expand to any physical size, allowing far more hardware to be put to work, a much larger working memory (RAM), and a longterm memory (hard drive storage) that has both far greater capacity and precision than our own.

* **可靠性与耐久性。** 计算机的记忆不仅更精确。计算机晶体管比生物神经元更准确，且不易退化（即使退化也可修复或更换）。人脑容易疲劳，而计算机可以全天候以峰值性能不间断运行。

* Reliability and durability. It’s not only the memories of a computer that would be more precise. Computer transistors are more accurate than biological neurons, and they’re less likely to deteriorate (and can be repaired or replaced if they do). Human brains also get fatigued easily, while computers can run nonstop, at peak performance, 24/7.

* **可编辑性、可升级性和更广泛的可能性。** 与人脑不同，计算机软件可以接收更新和修复，并且易于实验。升级还可以扩展到人脑薄弱的领域。人类视觉软件极其先进，而复杂工程能力却很低。计算机可以在视觉软件上媲美人类，同时也能在工程和其他领域达到同等优化。

* Editability, upgradability, and a wider breadth of possibility.Unlike the human brain, computer software can receive updates and fixes and can be easily experimented on. The upgrades could also span to areas where human brains are weak. Human vision software is superbly advanced, while its complex engineering capability is pretty low-grade. Computers could match the human on vision software but could _also_ become equally optimized in engineering and any other area.

* **集体能力。** 人类在构建庞大集体智能方面碾压所有其他物种。从语言发展和大型密集社区的形成，到文字和印刷术的发明，再到通过互联网等工具的强化，人类的集体智能是我们远超其他物种的主要原因之一。而计算机将比我们更擅长于此。运行特定程序的全球 AI 网络可以定期自我同步，使得任何一台计算机学到的东西都能立即上传到所有其他计算机。群体还可以作为一个整体追求单一目标，因为不像人类那样存在异议、动机和私利。

* Collective capability.Humans crush all other species at building a vast collective intelligence. Beginning with the development of language and the forming of large, dense communities, advancing through the inventions of writing and printing, and now intensified through tools like the internet, humanity’s collective intelligence is one of the major reasons we’ve been able to get so far ahead of all other species. And computers will be way better at it than we are. A worldwide network of AI running a particular program could regularly sync with itself so that anything any one computer learned would be instantly uploaded to all other computers. The group could also take on one goal as a unit, because there wouldn’t necessarily be dissenting opinions and motivations and self-interest, like we have within the human population.10

AI 很可能通过自我改进编程达到 AGI，它不会将“人类水平智能”视为重要里程碑——这仅从我们的角度来看有意义——也没有理由停留在我们的水平。考虑到即使是与人类智能相当的 AGI 也拥有上述优势，很明显它只会短暂达到人类智能，然后迅速冲向超越人类智能的领域。

AI, which will likely get to AGI by being programmed to self-improve, wouldn’t see “human-level intelligence” as some important milestone—it’s only a relevant marker from our point of view—and wouldn’t have any reason to “stop” at our level. And given the advantages over us that even human intelligence-equivalent AGI would have, it’s pretty obvious that it would only hit human intelligence for a brief instant before racing onwards to the realm of superior-to-human intelligence.

当这发生时，可能会让我们震惊不已。原因在于，从我们的视角看，A）不同动物的智能虽有差异，但我们意识到的每种动物智能的主要特征是远低于我们；B）我们认为最聪明的人类远比最笨的人类聪明。大致如下：

This may shock the shit out of us when it happens. The reason is that from _our_ perspective, A) while the intelligence of different kinds of animals varies, the main characteristic we’re aware of about any animal’s intelligence is that it’s far lower than ours, and B) we view the smartest humans as WAY smarter than the dumbest humans. Kind of like this:

[](https://waitbutwhy.com/wp-content/uploads/2015/01/Intelligence.jpg)

[](https://waitbutwhy.com/wp-content/uploads/2015/01/Intelligence.jpg)

因此，当 AI 的智能向上飙升接近我们时，我们会认为它只是变得更聪明了——对于动物而言。然后，当它达到人类的最低能力——尼克·博斯特罗姆用“村中白痴”一词——我们会说：“哦，它像个笨人。可爱！”但关键在于，在智能的宏大谱系中，所有人类，从村中白痴到爱因斯坦，都处于一个非常狭窄的范围内——所以就在达到村中白痴水平并被宣布为 AGI 之后，它会突然比爱因斯坦更聪明，而我们根本不知道发生了什么：

So as AI zooms upward in intelligence toward us, we’ll see it as simply becoming smarter, _for an animal._ Then, when it hits the lowest capacity of humanity—Nick Bostrom uses the term “the village idiot”—we’ll be like, “Oh wow, it’s like a dumb human. Cute!” The only thing is, in the grand spectrum of intelligence, _all_ humans, from the village idiot to Einstein, are within a very small range—so _just_ after hitting village idiot level and being declared to be AGI, it’ll suddenly be smarter than Einstein and we won’t know what hit us:

[](https://waitbutwhy.com/wp-content/uploads/2015/01/Intelligence2.png)

[](https://waitbutwhy.com/wp-content/uploads/2015/01/Intelligence2.png)

希望你喜欢正常时光，因为此时这个话题变得不正常且可怕，并且从此将一直如此。我想在此暂停，提醒你我将说的每一件事都是真实的——真实科学和众多最受尊敬的 thinkers 和科学家对未来的真实预测。请记住这一点。

I hope you enjoyed normal time, because this is when this topic gets unnormal and scary, and it’s gonna stay that way from here forward. I want to pause here to remind you that every single thing I’m going to say is real—real science and real forecasts of the future from a large array of the most respected thinkers and scientists. Just keep remembering that.

无论如何，如上所述，我们目前大多数达到 AGI 的模型都涉及 AI 通过自我改进实现。一旦达到 AGI，即使是通过非自我改进方法形成和成长的系统，现在也足够聪明，可以开始自我改进（如果它们愿意）。

Anyway, as I said above, most of our current models for getting to AGI involve the AI getting there by self-improvement. And once it gets to AGI, even systems that formed and grew through methods that didn’t involve self-improvement would now be smart enough to begin self-improving if they wanted to.3

这里我们进入一个强烈的概念：递归自我改进。其工作原理如下——

And here’s where we get to an intense concept: recursive self-improvement. It works like this—

一个处于特定水平的 AI 系统——比如人类村中白痴水平——被编程以改进自身智能为目标。一旦改进，它变得更聪明——此时可能达到爱因斯坦水平——因此当它再次努力改进智能时，凭借爱因斯坦级别的智力，它更容易实现更大的飞跃。这些飞跃使其比任何人类都聪明得多，从而允许更大的飞跃。随着飞跃越来越大且越来越快，AGI 在智能上飙升，很快达到 ASI 系统的超级智能水平。这被称为智能爆炸，是加速回报定律的终极例证。

An AI system at a certain level—let’s say human village idiot—is programmed with the goal of improving its own intelligence. Once it does, it’s _smarter—_ maybe at this point it’s at Einstein’s level—so now when it works to improve its intelligence, with an Einstein-level intellect, it has an easier time and it can make bigger leaps. These leaps make it _much_ smarter than any human, allowing it to make even _bigger_ leaps. As the leaps grow larger and happen more rapidly, the AGI soars upwards in intelligence and soon reaches the superintelligent level of an ASI system. This is called an Intelligence Explosion,11 and it’s the ultimate example of The Law of Accelerating Returns.

关于 AI 何时能达到人类水平通用智能存在一些争论。一项对数百名科学家的调查显示，他们认为更可能达到 AGI 的中位年份是 2040 年——这距离现在只有 25 年，这听起来并不算大，直到你考虑到该领域的许多思想家认为从 AGI 到 ASI 的进展可能非常迅速。比如，可能发生这种情况：

There is some debate about how soon AI will reach human-level general intelligence. The median year on a survey of hundreds of scientists about when they believed we’d be more likely than not to have reached AGI was 204012—that’s only 25 years from now, which doesn’t sound that huge until you consider that many of the thinkers in this field think it’s likely that the progression from AGI to ASI happens _very_ quickly. Like—this could happen:

第一个 AI 系统达到低级通用智能需要几十年，但最终实现了。一台计算机能够像人类四岁孩子一样理解周围世界。突然，在达到这一里程碑后的一小时内，该系统提出了统一广义相对论和量子力学的宏大物理理论，这是人类尚未能明确做到的。90 分钟后，AI 变成了 ASI，比人类聪明 17 万倍。

_It takes decades for the first AI system to reach low-level general intelligence, but it finally happens. A computer is able to understand the world around it as well as a human four-year-old. Suddenly, within an hour of hitting that milestone, the system pumps out the grand theory of physics that unifies general relativity and quantum mechanics, something no human has been able to definitively do. 90 minutes after that, the AI has become an ASI, 170,000 times more intelligent than a human._

这种程度的超级智能是我们无法想象的，就像大黄蜂无法理解凯恩斯经济学一样。在我们的世界里，聪明意味着 130 的智商，愚蠢意味着 85 的智商——我们没有词来形容 12952 的智商。

Superintelligence of that magnitude is not something we can remotely grasp, any more than a bumblebee can wrap its head around Keynesian Economics. In our world, smart means a 130 IQ and stupid means an 85 IQ—we don’t have a word for an IQ of 12,952.

我们所知道的是，人类对地球的完全统治表明一条明确的规则：智能带来力量。这意味着，当我们创造 ASI 时，它将成为地球生命史上最强大的存在，所有生物，包括人类，都将完全受其支配——而这可能在未来几十年内发生。

What we do know is that humans’ utter dominance on this Earth suggests a clear rule: _with intelligence comes power._ Which means an ASI, when we create it, will be the most powerful being in the history of life on Earth, and all living things, including humans, will be entirely at its whim—_and this might happen_ _in the next few decades._

如果我们贫乏的大脑能够发明 WiFi，那么比我们聪明 100 倍、1000 倍或 10 亿倍的东西应该可以轻松控制世界上每个原子的位置，以任何方式在任何时间——我们视为魔法的一切，我们想象中至高神拥有的每一种力量，对 ASI 来说就像我们打开电灯开关一样平凡。创造逆转人类衰老的技术，治愈疾病和饥饿甚至死亡，重新编程天气以保护地球生命的未来——所有这些突然成为可能。同样可能的是地球生命的立即终结。对我们而言，如果 ASI 出现，地球上就出现了一个全能的上帝——而对我们来说至关重要的问题是：

If our meager brains were able to invent wifi, then something 100 or 1,000 or 1 billion times smarter than we are should have no problem controlling the positioning of each and every atom in the world in any way it likes, at any time—everything we consider magic, every power we imagine a supreme God to have will be as mundane an activity for the ASI as flipping on a light switch is for us. Creating the technology to reverse human aging, curing disease and hunger and even mortality, reprogramming the weather to protect the future of life on Earth—all suddenly possible. Also possible is the immediate end of all life on Earth. As far as we’re concerned, if an ASI comes to being, there is now an omnipotent God on Earth—and the all-important question for us is:

费米悖论——为什么我们看不到任何外星生命的迹象？

The Fermi Paradox – Why don’t we see any signs of alien life?

SpaceX 将如何（以及为何）殖民火星——我与埃隆·马斯克合作撰写的一篇文章，它重塑了我对未来的心理图景。

How (and Why) SpaceX Will Colonize Mars – A post I got to work on with Elon Musk and one that reframed my mental picture of the future.

或者，一些完全不同但又有些相关的内容，为什么拖延症患者会拖延。

Or for something totally different and yet somehow related, Why Procrastinators Procrastinate

这里是 Wait But Why 第一年的电子书。

And here’s Year 1 of Wait But Why on an ebook.

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1. 好的，现在有两种不同的注释。蓝色圆圈是有趣/值得一读的，提供额外信息或想法，因为要么是题外话，要么是有点太奇怪而不适合放在正文中。↩

1. Okay so there are two different kinds of notes now. The blue circles are the fun/interesting ones you should read. They’re for extra info or thoughts that I didn’t want to put in the main text because either it’s just tangential thoughts on something or because I want to say something a notch too weird to just be there in the normal text.↩

2. 库兹韦尔指出，他的手机体积是 40 年前 MIT 计算机的百万分之一，价格是百万分之一，性能却强千倍。试图找出未来计算进步将把我们带向何方是困难的，更不用说由于指数级进步而极端得多的情形了。↩

2. Kurzweil points out that his phone is about a millionth the size of, a millionth the price of, and a thousand times more powerful than his MIT computer was 40 years ago. Good luck trying to figure out where a comparable future advancement in computing would leave us, let alone one far, far more extreme, since the progress grows exponentially.↩

3. 关于计算机“想要”做某事意味着什么，更多内容见第二部分文章。↩

3. Much more on what it means for a computer to “want” to do something in the Part 2 post.↩

1. 灰色方块是无聊的物体，点击灰色方块你会感到无聊。这些仅用于来源和引用。↩

1. Gray squares are boring objects and when you click on a gray square, you’ll end up bored. These are for sources and citations only.↩

2. 库兹韦尔，《奇点临近》，第 39 页。↩

2. Kurzweil, _The Singularity is Near_, 39.↩

3. 库兹韦尔，《奇点临近》，第 84 页。↩

3. Kurzweil, _The Singularity is Near_, 84.↩

4. 瓦尔迪，《人工智能：过去与未来》，第 5 页。↩

4. Vardi, _Artificial Intelligence: Past and Future_, 5.↩

5. 库兹韦尔，《奇点临近》，第 392 页。↩

5. Kurzweil, _The Singularity is Near_, 392.↩

6. 博斯特罗姆，《超级智能：路径、危险、策略》，位置 597。↩

6. Bostrom, _Superintelligence: Paths, Dangers, Strategies_, loc. 597↩

7. 尼尔森，《人工智能探索：思想与成就史》，第 318 页。↩

7. Nilsson, _The Quest for Artificial Intelligence: A History of Ideas and Achievements_, 318.↩

9. 库兹韦尔，《奇点临近》，第 118 页。↩

9. Kurzweil, _The Singularity is Near_, 118.↩

10. 博斯特罗姆，《超级智能：路径、危险、策略》，位置 1500-1576。↩

10. Bostrom, _Superintelligence: Paths, Dangers, Strategies_, loc. 1500-1576.↩

11. 该术语最早由历史上伟大的 AI 思想家之一欧文·约翰·古德于 1965 年使用。↩

11. This term was first used by one of history’s great AI thinkers, Irving John Good, in 1965.↩

12. 尼克·博斯特罗姆，《超级智能：路径、危险、策略》，位置 660。↩

12. Nick Bostrom, _Superintelligence: Paths, Dangers, Strategies_, loc. 660↩

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