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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.


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.


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.


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?


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.


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?


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。