本文摘要：Google’s recent announcement that its DeepMind technology had defeated one of the world’s highest-ranked champions at the ancient game of Go is just one example of the many dramatic advances unfolding in the fields of artificial intelligence and robotics. Machines are rapidly taking on ever more challenging cognitive tasks, encroaching on the fundamental capability that sets humans apart as a species: our ability to make complex decisions, to solve problems — and, most importantly, to learn. DeepMind’s feat was especially remarkable not just because the technology ultimately prevailed, but because the system largely trained itself to do so.谷歌(Google)最近宣告其DeepMind技术在古老的围棋比赛中打败了世界排名最低的冠军之一。

Google’s recent announcement that its DeepMind technology had defeated one of the world’s highest-ranked champions at the ancient game of Go is just one example of the many dramatic advances unfolding in the fields of artificial intelligence and robotics. Machines are rapidly taking on ever more challenging cognitive tasks, encroaching on the fundamental capability that sets humans apart as a species: our ability to make complex decisions, to solve problems — and, most importantly, to learn. DeepMind’s feat was especially remarkable not just because the technology ultimately prevailed, but because the system largely trained itself to do so.谷歌(Google)最近宣告其DeepMind技术在古老的围棋比赛中打败了世界排名最低的冠军之一。这只不过是人类在人工智能和机器人领域获得的许多戏剧性进展的一个例子。机器正在很快分担起更加不具挑战性的理解任务，开始构成使人类大同小异其他物种的显然能力：我们作出简单要求的能力、解决问题的能力，以及（最重要的）自学的能力。

DeepMind的功绩之所以特别是在引人瞩目，某种程度是因为技术再一占到了绝对优势，而且还因为它基本上是凭借自我训练战胜了输掉。In the coming decades, machine learning is likely to be the primary driving force behind a Cambrian explosion of applications in robotics and software automation. It won’t be long before the tools and building blocks that enable engineers and entrepreneurs to create smart robotic systems will be so advanced and accessible that nearly any opportunity to leverage the technology will be identified and addressed almost immediately. The near-term future is likely to be transformed not by general purpose robots or AI systems but rather a nearly limitless number of specialised applications. Collectively, these systems are likely to span the entire job market and economy, ultimately consuming nearly any kind of work that is on some level routine and predictable.在今后几十年里，机器学习有可能是机器人和软件自动化应用于经常出现“寒武纪大爆发”（Cambrian explosion，化石记录表明绝大多数的动物“门”都在距今5.42亿年前的寒武纪时期经常出现，由此故名——译者录）背后的主要推展力量。旋即之后，能让工程师和企业家们创立智能机器人系统的工具和结构块将不会如此先进设备和更容易取得，以至于几近所有需要利用这种技术的机遇都会被立刻找到和逃跑。改变近期未来的，很有可能不是一般用途的机器人，而是几近无限数量的专业应用于。

总体而言，这些系统有可能覆盖面积整个低收入市场和经济，最后接掌完全所有在或许上例会和可意识到的工作。Sceptics will be quick to point out that history clearly shows that advancing technology creates new types of work even as it destroys existing occupations. This process will doubtless continue, but it seems unlikely that sufficient opportunities will be created to absorb the workers pushed out of traditional jobs. To take just one example, consider the impact of self-driving cars. Clearly, the jobs of millions of people who drive taxis or delivery vehicles or work for Uber will be at high risk.怀疑者将迅速认为，历史确切地指出，先进设备技术在毁坏现有就业机会的同时还不会建构新型的就业机会。

这种过程毫无疑问将不会持续，但机器人技术或许不太可能建构充足就业机会吸取那些被吸管传统岗位的劳动者。这里只举一个例子，看看自动驾驶汽车带给的影响吧。显而易见的是，驾驶员出租车或投递车辆、或者为优步(Uber)工作的数以百万计的人的低收入将面对近于高风险。

On the other hand, building a truly robotic car, capable of operating completely without human intervention, remains a substantial challenge. Autonomous car technology relies heavily on highly detailed advanced mapping of the routes to be driven. The problem is handling the unexpected and infrequent challenges that defy that kind of data-driven approach: the fallen tree that blocks the road, the unscheduled construction or any number of other unpredictable situations that might arise.另一方面，修建确实的、几乎不须要人类介入就能运营的机器人汽车仍然面对不利挑战。自动驾驶技术相当严重倚赖十分详尽的驾驶员路线图。问题在于应付背离这种基于数据方式的车祸及有时候经常出现的挑战：倒地的树木推开在路上，计划外的建筑活动或者其他有可能经常出现的许多无法预测的情况。

An obvious solution presents itself: keep people in the loop just to handle those unusual situations. It’s easy to imagine a future where vehicles operate 99 per cent autonomously, but somewhere a control centre contains specially trained people, ready to take over when a car signals that it has encountered something outside the bounds of its normal operating environment. Those controllers, of course, will be engaged in one of those “new” occupations on which we rest our hopes. But how many of those jobs will there be, relative to the number of driving jobs lost?一个显而易见的解决办法应运而生：让人回到环路中，以便处置那些异常情况。不难想象未来的车辆在99%的情况下自动驾驶，但在控制中心不会有经过类似培训的专业人员，他们随时打算在汽车发出信号指出其遭遇长时间运营环境以外的情况时接掌。当然，那些掌控人员将专门从事我们寄予厚望的“新的”职业之一。

但是比起丧失的那么多驾驶员工作，不会有多少那样的工作机会？Needless to say, this mismatch between job destruction and creation isn’t going to be confined to driving. This basic approach — automating nearly all routine and predictable aspects of an occupation and then consolidating the remaining unpredictable tasks into a small number of jobs — is likely to be applied across the board. The low-wage service sector jobs in areas such as fast food and retail, which constitute a substantial fraction of the jobs being created by the economy in both the US and the UK, are certain to be heavily affected. Even more important will be all the white-collar occupations that involve relatively routine information analysis and manipulation. As these “good” jobs, often held by university graduates, begin to evaporate, faith in evermore education and training as the common solution to technological disruption of the job market seems likely to also erode.不用说，这种低收入毁坏和建构之间的不给定不仅局限于驾驶员。这种基本套路——将一份工作的完全所有例会和可意识到的部分都自动化，然后将剩下的不能预测的任务统合为少数的工作岗位——很有可能被应用于各行各业。

快餐和零售等低薪服务行业的就业机会毫无疑问不会受到极大影响——目前美国和英国经济建构的低收入岗位中有一大部分是在这些服务行业。甚至更为重要的将是，所有那些牵涉到比较例会的信息分析和操控的白领职业都会受到影响。随着这些往往由大学毕业生专门从事的“好”工作开始消失，人们很有可能仍然坚信更加多的教育和培训是针对技术对低收入市场毁坏的良方。

All of this portends a social, economic and political disruption for which we are completely unprepared. Widespread unemployment (or even underemployment) has clear potential to rend the fabric of society. Beyond that, it also carries substantial economic risks: in a world with far too few jobs, who will have the income and confidence to purchase the products and services produced by the economy? Where will demand come from? For years, average households in the US have been relying ever more on debt to support their consumption. How will they continue to service those debts in a future where jobs are beginning to evaporate en masse?所有这些伴随着一场我们没什么牵制的社会、经济和政治恐慌。广泛失业（甚至不充分就业）似乎有可能断裂社会架构。

此外，它还具有极大的经济风险：在一个低收入岗位实在太较少的世界里，谁不会有收益和信心出售经济体生产的产品和服务？市场需求将不会来自哪里？多年来，美国普通家庭更加倚赖债务反对他们的消费。在低收入岗位开始大规模消失的未来，他们如何才能之后偿还债务这些债务？In recent years, prominent individuals such as Stephen Hawking and Elon Musk have warned of the risks associated with “killer robots” or super-intelligent machines. While these concerns may some day be relevant, and while there are certainly important ethical considerations involving the use of autonomous systems in military and security applications, I would argue that the most important immediate challenge we face will be adjusting to the economic and social implications of a robotic revolution in the workplace. That disruption is already beginning to unfold, and one might reasonably argue that its impact can already be measured in terms of the political upheaval occurring in both the US and Europe. If we fail to have a meaningful public conversation about what robotics and artificial intelligence mean for the future, and develop workable ways in which to adapt our economy and society, then far greater, and more frightening, volatility is sure to soon arrive.最近几年，史蒂芬霍金(Stephen Hawking)和埃隆马斯克(Elon Musk)等知名人士警告了与“机器人刺客”或超强智能机器涉及的风险。尽管这些忧虑有朝一日不会显得涉及，尽管在军事和安全性应用于场合使用自动化系统显然有最重要的伦理课题，但我仍不会主张，我们面对的最重要最严峻的挑战将是适应环境职场机器人革命的经济和社会影响。

这种影响早已开始显出，人们可以合理地坚称，从美国和欧洲的政治动荡不安早已可以显现出这种影响。如果我们无法环绕机器人和人工智能对未来意味著什么进行有意义的公共辩论，并寻找让我们的经济和社会适应环境的不切实际方法，那么更加相当严重更加可怕的动荡不安必定会迅速来临。

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