RSS
 
以后地位 : 主页 > 百利宫娱乐平台 >

我们对呆板人期间预备缺乏

工夫:2016-05-18 17:15 阅读:

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)近来宣布其DeepMind技能在陈旧的围棋竞赛中击败了天下排名最高的冠军之一。这只不外是人类在人工智能和呆板人范畴获得的很多戏剧性停顿的一个例子。呆板正在敏捷承当起越来越具应战性的认知义务,开端构成使人类有别于其他物种的基本才能:我们做出庞大决议的才能、处理题目的才能,以及(最紧张的)学习的才能。DeepMind的功劳之以是尤其引人注目,不只仅是由于技能终于占了下风,并且还由于它根本上是凭仗自我训练打败了敌手。

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.

在以后几十年里,呆板学习能够是呆板人和软件主动化使用呈现“寒武纪大迸发”(Cambrian explosion,化石记载表现绝大少数的植物“门”都在距今5.42亿年前的寒武纪时期呈现,由此得名——译者注)面前的次要推进力气。不久之后,能让工程师和企业家们创立智能呆板人零碎的东西和结构块将会云云先辈和易于取得,以致于近乎一切可以应用这种技能的机会都市被立刻发明和捉住。变化近期将来的,很能够不是普通用处的呆板人,而是近乎有限数目的专业使用。总体而言,这些零碎能够掩盖整个失业市场和经济,终极接办简直一切在某种水平上例行和可预见的任务。

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.

疑心者将很快指出,汗青清晰地标明,先辈技能在毁坏现有失业时机的同时还会发明新型的失业时机。这种进程无疑将会继续,但呆板人技能好像不太能够发明充足失业时机吸取那些被挤出传统岗亭的休息者。这里只举一个例子,想想主动驾驶汽车带来的影响吧。不言而喻的是,驾驶出租车或投递车辆、或许为优步(Uber)任务的数以百万计的人的失业将面对极高危害。

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,制作真正的、完全不需人类干涉就能运转的呆板人汽车仍然面对严厉应战。主动驾驶技能严峻依赖极为细致的驾驶道路图。题目在于应对背叛这种基于数据方法的不测及偶然呈现的应战:倒下的树木挡在路上,方案外的修建运动或许其他能够呈现的很多无法预测的状况。

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?

一个不言而喻的处理方法应运而生:让人留在环路中,以便处置那些非常状况。不难想象将来的车辆在99%的状况下主动驾驶,但在控制中央会有颠末特别培训的专业职员,他们随时预备在汽车收回信号标明其遭遇正常运转情况以外的状况时接办。固然,那些控制职员将从事我们寄予厚望的“新”职业之一。但是相比得到的那么多驾驶任务,会有几多那样的任务时机?

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.

不必说,这种失业毁坏和发明之间的不婚配不只范围于驾驶。这种根本套路——将一份任务的简直一切例行和可预见的局部都主动化,然后将剩余的不行预测的义务整合为多数的任务岗亭——很能够被使用于各行各业。快餐和批发等低薪效劳行业的失业时机无疑会遭到宏大影响——现在美国和英国经济发明的失业岗亭中有一大局部是在这些效劳行业。乃至更为紧张的将是,一切那些触及绝对例行的信息剖析和利用的白领职业都市遭到影响。随着这些每每由大学结业生从事的“好”任务开端消逝,人们很能够不再置信越来越多的教诲和培训是针对技能对失业市场毁坏的良方。

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?

一切这些预示着一场我们毫无防范的社会、经济和政治杂乱。广泛赋闲(乃至不充沛失业)显然有能够扯破社会架构。别的,它还带有宏大的经济危害:在一个失业岗亭真实太少的天下里,谁会有支出和决心购置经济体消费的产物和效劳?需求将会来自那边?多年来,美国平凡家庭越来越依赖债权支持他们的消耗。在失业岗亭开端大范围消逝的将来,他们怎样才干持续归还这些债权?

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.

近来几年,史蒂芬?霍金(Stephen Hawking)和埃隆?马斯克(Elon Musk)等着名人士正告了与“呆板人杀手”或超智能呆板相干的危害。虽然这些担心有朝一日会变得相干,虽然在军事和平安使用场所接纳主动化零碎的确有紧张的伦理课题,但我仍会主张,我们面对的最紧张最紧急的应战将是顺应职场呆板人反动的经济和社会影响。这种影响曾经开端展现,人们可以公道地辩称,从美国和欧洲的政治动乱曾经可以看出这种影响。假如我们不克不及围绕呆板人和人工智能对将来意味着什么睁开故意义的大众讨论,并找到让我们的经济和社会顺应的可行办法,那么更严峻更可骇的动乱肯定会很快到来。