TED演讲 | 机器人抢了我们的工作吗？

hello大家好，我是达达。现在机器人和算法做得越来越好了，像在生产汽车，写文章，翻译-这些以往需要人类从事的工作。那么，我们人类将来有什么样的工作可以做呢？

Andrew McAfee在这次的TED演说中，提出了一个令人惊讶的，甚至惊心动魄的对未来会发生的事情的想法。

演说者：Andrew McAfee

演说题目：机器人抢了我们的工作吗？

中英对照演讲稿

As it turns out, when tens of millions of people are unemployed or underemployed, there's a fair amount of interest in what technology might be doing to the labor force. And as I look at the conversation, it strikes me that it's focused on exactly the right topic, and at the same time, it's missing the point entirely. 

事实上，当成千上万的人没有工作或者被大材小用时人们就去关注科技对劳动力的影响。我看到这方面的新闻与报导之后发现，它们关注的主题都没有问题，但却彻底错过了重点。

The topic that it's focused on, the question is whether or not all these digital technologies are affecting people's ability to earn a living, or, to say it a little bit different way, are the droids taking our jobs? And there's some evidence that they are.

它们关注的主题是， 数码科技会不会影响人们谋生的能力，换句话说，就是机器是不是抢走了我们的工作？有证据显示这样的情况的确存在。

The Great Recession ended when American GDP resumed its kind of slow, steady march upward, and some other economic indicators also started to rebound, and they got kind of healthy kind of quickly. Corporate profits are quite high; in fact, if you include bank profits, they're higher than they've ever been. And business investment in gear -- in equipment and hardware and software -- is at an all-time high. So the businesses are getting out their checkbooks. What they're not really doing is hiring. 

大萧条结束时 美国的GDP恢复了缓慢稳定的增长， 一些其它经济指标也开始快速健康地回升， 企业利润很高。事实上，如果算上银行利润， 企业利润处于有史以来的最高水平。企业在设备、机械、硬件和软件的投资也处于历史最高水平。所以企业虽然在花钱却没有雇人。

So this red line is the employment-to-population ratio, in other words, the percentage of working-age people in America who have work. And we see that it cratered during the Great Recession, and it hasn't started to bounce back at all.

这条红线是就业人数和人口的比例， 换句话说就是美国处于工作年龄的人口中 有工作人数的比例。我们可以看到大萧条期间这一比例显著下降， 完全没有开始回升的迹象。

But the story is not just a recession story. The decade that we've just been through had relatively anemic job growth all throughout, especially when we compare it to other decades, and the 2000s are the only time we have on record where there were fewer people working at the end of the decade than at the beginning. 

这一现象不仅仅存在于萧条时期。十年之内工作数量的增长微乎其微，和其它的十年比尤其如此。事实上，在二十一世纪的第一个十年中，2009年的就业人数少于2000年。这种情况有记录以来第一次发生。

This is not what you want to see. When you graph the number of potential employees versus the number of jobs in the country, you see the gap gets bigger and bigger over time, and then, during the Great Recession, it opened up in a huge way.

这不是你想看到的。当您绘制潜在员工的数量与该国的工作岗位数量时，您会看到差距随着时间的推移变得越来越大，然后，在大衰退期间，它以一种巨大的方式开放。

I did some quick calculations. I took the last 20 years of GDP growth and the last 20 years of labor-productivity growth and used those in a fairly straightforward way to try to project how many jobs the economy was going to need to keep growing, and this is the line that I came up with. Is that good or bad? This is the government's projection for the working-age population going forward. So if these predictions are accurate, that gap is not going to close.

我做了一些快速计算。我把过去20年的国内生产总值增长率和过去20年的劳动生产率增长情况用来，用一种相当直接的方式来计算经济需要保持多少就业岗位，这就是 我想出来了。是好还是坏？这是政府对未来劳动年龄人口的预测。因此，如果这些预测是准确的，那么差距就不会缩小。

The problem is, I don't think these projections are accurate. In particular, I think my projection is way too optimistic, because when I did it, I was assuming that the future was kind of going to look like the past, with labor productivity growth, and that's actually not what I believe. Because when I look around, I think that we ain't seen nothing yet when it comes to technology's impact on the labor force.

问题是，我不认为这些预测是准确的。特别是，我认为我的预测过于乐观，因为当我这样做时，我假设未来看起来像过去，劳动生产率增长，而这实际上并不是我所相信的。因为当我环顾四周时，我认为当谈到技术对劳动力的影响时，我们还没有看到任何东西。

Just in the past couple years, we've seen digital tools display skills and abilities that they never, ever had before, and that kind of eat deeply into what we human beings do for a living. Let me give you a couple examples.

就在过去的几年里，我们已经看到数字工具展示了他们以前从未有过的技能和能力，并且深深地体现了人类为生活所做的事情。让我举几个例子。

Throughout all of history, if you wanted something translated from one language into another, you had to involve a human being. Now we have multi-language, instantaneous, automatic translation services available for free via many of our devices, all the way down to smartphones. And if any of us have used these, we know that they're not perfect, but they're decent.

在整个历史中，如果你想要从一种语言翻译成另一种语言，你必须涉及一个人。现在，我们可以通过许多设备免费提供多语言，即时，自动翻译服务，一直到智能手机。如果我们中的任何人使用过这些，我们知道它们并不完美，但它们是不错的。

Throughout all of history, if you wanted something written, a report or an article, you had to involve a person. Not anymore. This is an article that appeared in Forbes online a while back, about Apple's earnings. It was written by an algorithm. And it's not decent -- it's perfect.

在整个历史中，如果你想要写一些东西，一份报告或一篇文章，你就必须让一个人参与其中。不再。这篇文章曾在福布斯网站上发布过一段时间，关于苹果公司的收益。它是由算法编写的。它并不像样 - 但它是完美的。

A lot of people look at this and they say, 'OK, but those are very specific, narrow tasks, and most knowledge workers are actually generalists. And what they do is sit on top of a very large body of expertise and knowledge and they use that to react on the fly to kind of unpredictable demands, and that's very, very hard to automate.' One of the most impressive knowledge workers in recent memory is a guy named Ken Jennings. He won the quiz show 'Jeopardy!' 74 times in a row. 

很多人看到这一现象以后说，“好吧， 但这些都是具体的、针对性强的任务， 而大多数知识型人才都是通才， 他们拥有很多技能和知识并运用它们，随时随地地应对不可预测的要求， 这一点机器很难做到。“ 最近最令人印象深刻的知识型人才名叫Ken Jennings。他连续74次在答题节目”抢答（Jeopardy!）“中取得胜利。

Took home three million dollars. That's Ken on the right, getting beat three-to-one by Watson, the Jeopardy-playing supercomputer from IBM. So when we look at what technology can do to general knowledge workers, I start to think there might not be something so special about this idea of a generalist, particularly when we start doing things like hooking Siri up to Watson, and having technologies that can understand what we're saying and repeat speech back to us.

他赢得了三百万美金。右边的这位就是Ken，在和IBM的超级机器人Watson一同参加”抢答“比赛时，他以三比一败北。看到科技产品胜过这样的通才，我开始思考通才的概念可能也没有多么特别，尤其是现在，如果我们把Siri 和Watson的技术相结合，就会得到可以理解人类语言，并得到自然语言反馈的技术。

Now, Siri is far from perfect, and we can make fun of her flaws, but we should also keep in mind that if technologies like Siri and Watson improve along a Moore's law trajectory, which they will, in six years, they're not going to be two times better or four times better, they'll be 16 times better than they are right now. So I start to think a lot of knowledge work is going to be affected by this.

现在，Siri还远不完美，所以我们可以嘲笑她的缺陷，但是我们也应该想到，如果Siri和Watson的技术也按照摩尔定律发展，那么 六年之后，他们不是提高2倍或者4倍，他们会比现在先进16倍。所以我认为很多知识型的工作会受到这一发展趋势的影响。

And digital technologies are not just impacting knowledge work, they're starting to flex their muscles in the physical world as well. I had the chance a little while back to ride in the Google autonomous car, which is as cool as it sounds.

数字技术不仅影响知识工作，它们也开始在物理世界中展现自己的力量。我有机会回来乘坐谷歌自动驾驶汽车，这听起来很酷。

And I will vouch that it handled the stop-and-go traffic on US 101 very smoothly. There are about three and a half million people who drive trucks for a living in the United States; I think some of them are going to be affected by this technology. 

而且我会保证它能非常顺利地处理US 101上的走走停停。大约有三百五十万人在美国驾驶卡车谋生; 我认为其中一些将受到这项技术的影响。

And right now, humanoid robots are still incredibly primitive. They can't do very much. But they're getting better quite quickly and DARPA, which is the investment arm of the Defense Department, is trying to accelerate their trajectory.

而现在，人形机器人仍然非常原始。他们做不了多少。但是他们正在迅速变得更好，DARPA是国防部的投资部门，正试图加速他们的发展轨迹。

So, in short, yeah, the droids are coming for our jobs. In the short term, we can stimulate job growth by encouraging entrepreneurship and by investing in infrastructure, because the robots today still aren't very good at fixing bridges. 

所以，简而言之，是的，机器人正在为我们的工作而来。从短期来看，我们可以通过鼓励创业和投资基础设施来刺激就业增长，因为今天的机器人仍然不擅长修桥。

But in the not-too-long-term, I think within the lifetimes of most of the people in this room, we're going to transition into an economy that is very productive, but that just doesn't need a lot of human workers. And managing that transition is going to be the greatest challenge that our society faces. Voltaire summarized why; he said, 'Work saves us from three great evils: boredom, vice and need.'

但在不太长的时期，我认为在这个会议室的大多数人的一生中，我们将转变为一个非常富有成效的经济，但这不需要很多人 工作人员。管理这种转变将成为我们社会面临的最大挑战。伏尔泰总结了为什么; 他说，“工作使我们免于三大祸害：厌倦，厌恶和需要。”

But despite this challenge -- personally, I'm still a huge digital optimist, and I am supremely confident that the digital technologies that we're developing now are going to take us into a Utopian future, not a dystopian future. 

但尽管存在这一挑战 - 个人而言，我仍然是一个巨大的数字乐观主义者，我对我们现在正在开发的数字技术将把我们带入乌托邦的未来，而不是一个反乌托邦的未来充满信心。 

And to explain why, I want to pose a ridiculously broad question. I want to ask: what have been the most important developments in human history?

为了解释原因，我想提出一个荒谬的广泛问题。我想问一下：人类历史上最重要的发展是什么？

Now, I want to share some of the answers that I've gotten in response to this question. It's a wonderful question to ask and start an endless debate about, because some people are going to bring up systems of philosophy in both the West and the East that have changed how a lot of people think about the world. 

现在，我想分享一些我在回答这个问题时得到的答案。这是一个很好的问题，可以提出并开始无休止的辩论，因为有些人会在西方和东方建立哲学体系，这些体系改变了很多人对世界的看法。

And then other people will say, 'No, actually, the big stories, the big developments are the founding of the world's major religions, which have changed civilizations and have changed and influenced how countless people are living their lives.' 

然后其他人会说，“不，实际上，重大故事，重大发展是世界主要宗教的建立，它们改变了文明，改变并影响了无数人的生活方式。”

And then some other folk will say, 'Actually, what changes civilizations, what modifies them and what changes people's lives are empires, so the great developments in human history are stories of conquest and of war.' And then some cheery soul usually always pipes up and says, 'Hey, don't forget about plagues!'

然后其他一些人会说，“实际上，什么改变了文明，什么改变了它们，什么改变了人们的生活是帝国的，所以人类历史上的伟大发展是征服和战争的故事。” 然后，一些愉快的灵魂总是滔滔不绝地说：“嘿，不要忘记瘟疫！”

There are some optimistic answers to this question, so some people will bring up the Age of Exploration and the opening up of the world. Others will talk about intellectual achievements in disciplines like math that have helped us get a better handle on the world, and other folk will talk about periods when there was a deep flourishing of the arts and sciences. 

这个问题有一些乐观的答案，所以有些人会提出探索时代和开放世界。其他人将谈论数学等学科中的智力成就，这些成就帮助我们更好地处理了这个世界，其他人将谈论艺术和科学的蓬勃发展时期。

So this debate will go on and on. It's an endless debate and there's no conclusive, single answer to it. But if you're a geek like me, you say, 'Well, what do the data say?' 

所以这场辩论将继续下去。这是一场无休止的辩论，并没有确凿的单一答案。但如果你像我这样的极客，你会说，“嗯，这些数据说的是什么？”

And you start to do things like graph things that we might be interested in -- the total worldwide population, for example, or some measure of social development or the state of advancement of a society. And you start to plot the data, because, by this approach, the big stories, the big developments in human history, are the ones that will bend these curves a lot.

而且你开始做的事情就像我们可能感兴趣的图形事物 - 例如全球总人口，或社会发展的某种程度或社会进步的状态。并且你开始绘制数据，因为通过这种方法，大型故事，人类历史上的重大发展，将会使这些曲线大大弯曲。

So when you do this and when you plot the data, you pretty quickly come to some weird conclusions. You conclude, actually, that none of these things have mattered very much.

因此，当您执行此操作并绘制数据时，您很快就会得出一些奇怪的结论。 实际上，你的结论是，这些事情都不是很重要。

They haven't done a darn thing to the curves. There has been one story, one development in human history that bent the curve, bent it just about 90 degrees, and it is a technology story.

他们没有对曲线做过一件坏事。 有一个故事，人类历史上的一个发展弯曲曲线，弯曲大约90度，这是一个技术故事。

The steam engine and the other associated technologies of the Industrial Revolution changed the world and influenced human history so much, that in the words of the historian Ian Morris, '... they made mockery out of all that had come before.' 

蒸汽机和工业革命的其他相关技术改变了世界，影响了人类历史，用历史学家伊恩莫里斯的话来说，“......他们从以前的所有事情中嘲笑”。

And they did this by infinitely multiplying the power of our muscles, overcoming the limitations of our muscles. Now, what we're in the middle of now is overcoming the limitations of our individual brains and infinitely multiplying our mental power. How can this not be as big a deal as overcoming the limitations of our muscles?

他们通过无限增加肌肉力量，克服肌肉的局限性来做到这一点。现在，我们现在正在克服我们个人大脑的局限性并无限地增加我们的智力。这怎么能克服我们肌肉的局限性呢？

So at the risk of repeating myself a little bit, when I look at what's going on with digital technology these days, we are not anywhere near through with this journey. And when I look at what is happening to our economies and our societies, my single conclusion is that we ain't seen nothing yet. The best days are really ahead.

以冒着重复自己的风险，当我看到这些天数字技术正在发生的事情时，我们并没有接近这个旅程。当我看到我们的经济和社会正在发生的事情时，我的唯一结论是我们还没有看到任何东西。最好的日子真的很快。

Let me give you a couple examples. Economies don't run on energy. They don't run on capital, they don't run on labor. Economies run on ideas. 

让我举几个例子。 经济不依赖能源。 他们不依靠资本运作，也不依靠劳动力。 经济运行的想法。

So the work of innovation, the work of coming up with new ideas, is some of the most powerful, most fundamental work that we can do in an economy. And this is kind of how we used to do innovation. We'd find a bunch of fairly similar-looking people ...

因此，创新的工作，即提出新想法的工作，是我们在经济中可以做的最有力，最基本的工作。 这就是我们过去做创新的方式。 我们会找到一群看起来很相似的人......

We'd take them out of elite institutions, we'd put them into other elite institutions and we'd wait for the innovation. Now --

我们将它们从精英机构中拿走，我们将它们带入其他精英机构，我们等待创新。现在 - 

as a white guy who spent his whole career at MIT and Harvard, I've got no problem with this.

作为一个在麻省理工学院和哈佛大学度过整个职业生涯的白人，我对此毫无疑问。

But some other people do, and they've kind of crashed the party and loosened up the dress code of innovation.

但是其他一些人这样做了，他们有点破坏了派对，放松了创新的着装规范。

So here are the winners of a Topcoder programming challenge, and I assure you that nobody cares where these kids grew up, where they went to school, or what they look like. All anyone cares about is the quality of the work, the quality of the ideas.

所以这里是Topcoder编程挑战的赢家，我向你保证，没有人关心这些孩子在哪里长大，他们去哪里上学，或者他们看起来像什么。所有人关心的是工作的质量，想法的质量。

And over and over again, we see this happening in the technology-facilitated world. The work of innovation is becoming more open, more inclusive, more transparent and more merit-based, and that's going to continue no matter what MIT and Harvard think of it, and I couldn't be happier about that development.

我们一次又一次地看到这种情况发生在技术促进的世界中。创新的工作变得更加开放，更具包容性，更透明，更基于绩效，无论麻省理工学院和哈佛大学如何看待，这种情况都将持续下去，我对这一发展感到高兴。

I hear once in a while, 'OK, I'll grant you that, but technology is still a tool for the rich world, and what's not happening, these digital tools are not improving the lives of people at the bottom of the pyramid.' And I want to say to that very clearly: nonsense. The bottom of the pyramid is benefiting hugely from technology. 

我偶尔会听到，“好吧，我会批准你，但技术仍然是富裕世界的工具，而且没有发生的事情，这些数字工具并没有改善金字塔底层人们的生活。“ 我想非常清楚地说：废话。金字塔的底部受益于技术。

The economist Robert Jensen did this wonderful study a while back where he watched, in great detail, what happened to the fishing villages of Kerala, India, when they got mobile phones for the very first time. And when you write for the Quarterly Journal of Economics, you have to use very dry and very circumspect language. 

一段时间以来，经济学家罗伯特·詹森（Robert Jensen）做了这项精彩的研究，他非常详细地观察了印度喀拉拉邦渔村发生的事情，当时他们第一次拿到手机。当你为“经济学季刊”撰稿时，你必须使用非常干燥和非常谨慎的语言。 

But when I read his paper, I kind of feel Jensen is trying to scream at us and say, 'Look, this was a big deal. Prices stabilized, so people could plan their economic lives. Waste was not reduced -- it was eliminated. And the lives of both the buyers and the sellers in these villages measurably improved.'

但是当我读到他的论文时，我有点觉得Jensen正试图尖叫我们并说：“看，这是一个大问题。价格稳定了，所以人们可以计划他们的经济生活。废物没有减少 - 它被淘汰了 这些村庄的买家和卖家的生活都得到了显着改善。“

Now, what I don't think is that Jensen got extremely lucky and happened to land in the one set of villages where technology made things better. What happened instead is he very carefully documented what happens over and over again when technology comes for the first time to an environment and a community: the lives of people, the welfares of people, improve dramatically.

现在，我不认为Jensen非常幸运，碰巧登陆了一系列技术让事情变得更好的村庄。 所发生的事情是，他非常仔细地记录了当技术第一次出现在环境和社区时，一次又一次地发生的事情：人们的生活，人们的福祉得到了极大的改善。

So as I look around at all the evidence and I think about the room that we have ahead of us, I become a huge digital optimist and I start to think that this wonderful statement from the physicist Freeman Dyson is actually not hyperbole. This is an accurate assessment of what's going on. 

因此，当我环顾所有证据时，我想到了我们前面的房间，我成了一个巨大的数字乐观主义者，我开始认为物理学家弗里曼戴森的这个精彩声明实际上并不夸张。 这是对正在发生的事情的准确评估。

Our technologies are great gifts, and we, right now, have the great good fortune to be living at a time when digital technology is flourishing, when it is broadening and deepening and becoming more profound all around the world.

我们的技术是很棒的礼物，我们现在有幸在数字技术蓬勃发展的时代生活，当它在世界范围内扩大和深化并变得更加深刻时。

So, yeah, the droids are taking our jobs, but focusing on that fact misses the point entirely. The point is that then we are freed up to do other things, and what we're going to do, I am very confident, what we're going to do is reduce poverty and drudgery and misery around the world. 

所以，是的，机器人正在接受我们的工作，但专注于这个事实完全错过了这一点。 重点是，我们可以自由地做其他事情，我们将要做的事情，我非常自信，我们要做的是减少世界各地的贫困，苦差事和苦难。

I'm very confident we're going to learn to live more lightly on the planet, and I am extremely confident that what we're going to do with our new digital tools is going to be so profound and so beneficial that it's going to make a mockery out of everything that came before. I'm going to leave the last word to a guy who had a front-row seat for digital progress, our old friend Ken Jennings. I'm with him; I'm going to echo his words: 'I, for one, welcome our new computer overlords.'

我非常有信心，我们将学会更轻松地生活在这个星球上，我非常有信心，我们将要用我们的新数字工具做的事情将如此深刻和有益，以至于它将会 从以前的一切中嘲笑。 我要把最后一句话留给一个有数字进步前排座位的人，我们的老朋友Ken Jennings。 我和他在一起; 我要回应他的话：“我，一个人，欢迎我们新的计算机霸主。”