我们对机器人时代准备不足

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)最近宣布其DeepMind技术在古老的围棋比赛中击败了世界排名最高的冠军之一。这只不过是人类在人工智能和机器人领域取得的许多戏剧性进展的一个例子。机器正在迅速承担起越来越具挑战性的认知任务，开始形成使人类有别于其他物种的根本能力：我们做出复杂决定的能力、解决问题的能力，以及（最重要的）学习的能力。DeepMind的功绩之所以尤其引人瞩目，不仅仅是因为技术终于占了上风，而且还因为它基本上是凭借自我训练战胜了对手。

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，机器学习可能是机器人和软件自动化应用出现“寒武纪大爆发”（Cambrian explosion，化石记录显示绝大多数的动物“门”都在距今5.42亿年前的寒武纪时期出现，由此得名——译者注）背后的主要推动力量。不久之后，能让工程师和企业家们创建智能机器人系统的工具和构造块将会如此先进和易于获得，以至于近乎所有能够利用这种技术的机遇都会被立即发现和抓住。转变近期未来的，很可能不是一般用途的机器人，而是近乎无限数量的专业应用。总体而言，这些系统可能覆盖整个就业市场和经济，最终接手几乎所有在某种程度上例行和可预见的工作。

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.

怀疑者将很快指出，历史清楚地表明，先进技术在破坏现有就业机会的同时还会创造新型的就业机会。这种过程无疑将会持续，但机器人技术似乎不太可能创造足够就业机会吸收那些被挤出传统岗位的劳动者。这里只举一个例子，想想自动驾驶汽车带来的影响吧。显而易见的是，驾驶出租车或投递车辆、或者为优步(Uber)工作的数以百万计的人的就业将面临极高风险。

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，建造真正的、完全不需人类干预就能运行的机器人汽车依然面临严峻挑战。自动驾驶技术严重依赖极为详细的驾驶路线图。问题在于应对背离这种基于数据方式的意外及偶尔出现的挑战：倒下的树木挡在路上，计划外的建筑活动或者其他可能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：让人留在环路中，以便处理那些异常情况。不难想象未来的车辆在99%的情况下自动驾驶，但在控制中心会有经过特殊培训的专业人员，他们随时准备在汽车发出信号表明其遭遇正常运行环境以外的情况时接手。当然，那些控制人员将从事我们寄予厚望的“新”职业之一。但是相比失去的那么多驾驶工作，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，这种就业破坏和创造之间的不匹配不仅局限于驾驶。这种基本套路——将一份工作的几乎所有例行和可预见的部分都自动化，然后将剩余的不可预测的任务整合为少数的工作岗位——很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无疑会受到巨大影响——目前美国和英国经济创造的就业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，所有那些涉及相对例行的信息分析和操纵的白领职业都会受到影响。随着这些往往由大学毕业生从事的“好”工作开始消失，人们很可能不再相信越来越多的教育和培训是针对技术对就业市场破坏的良方。

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?

所有这些预示着一场我们毫无防备的社会、经济和政治混乱。普遍失业（甚至不充分就业）显然有可能撕裂社会架构。此外，它还带有巨大的经济风险：在一个就业岗位实在太少的世界里，谁会有收入和信心购买经济体生产的产品和服务？需求将会来自哪里？多年来，美国普通家庭越来越依赖债务支持他们的消费。在就业岗位开始大规模消失的未来，他们如何才能继续偿还这些债务？

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“机器人杀手”或超智能机器相关的风险。尽管这些担忧有朝一日会变得相关，尽管在军事和安全应用场合采用自动化系统确实有重要的伦理课题，但我仍会主张，我们面临的最重要最紧迫的挑战将是适应职场机器人革命的经济和社会影响。这种影响已经开始显现，人们可以合理地辩称，从美国和欧洲的政治动荡已经可以看出这种影响。如果我们不能围绕机器人和人工智能对未来意味着什么展开有意义的公共讨论，并找到让我们的经济和社会适应的可行方法，那么更严重更可怕的动荡必定会很快到来。