Daniel Berleant agrees, stating thecurrent difficulties of “mobility is undeniably a rather difficult technical problem, and computers are more likely to manipulate data better than humans than they are to take over most manual labor jobs, at least for the time being.” Despite theimpressive developments in bipedal robotsin the last 10 years, people with dexterous physical jobs such as moving furniture or carrying plates in a busy restaurant aren’t likely to be automated out of a job anytime soon (though stationary assembly jobs are under siege now as much as ever, with devices
likeRethinkRobotics’Baxter).
Daniel Berleant同意这一观点，声明了当前的困难：“流动性无可否认是个非常困难的技术问题，电脑与做大多数日常劳动工作人类相比，处理数据更加容易，至少现在是这样。”尽管双足机器人在过去十年取得了令人印象深刻的发展，拥有灵巧物理工作的人们，比方说移动家具或在拥挤的餐厅中传递餐盘，目前还无法被自动化所取代（尽管目前装配作业已经很常见，比如说巴克斯特再思考机器人）。
Some researchers believe that the samemight be said of narrow data assessment, not just data manipulation. AndrasKornai states, “IBM is moving Watson into the medical field — I expect the samething to happen in the legal area.” Though it may be possible that machinelearning will aid in the detection of cancer or other maladies in medicalimaging, these technologies don’t seem likely to put doctors out of a job.
有些研究学者相信狭窄数据评估也是一样，而不仅仅是数据操作。Andras Kornai表明，“IBM正将屈臣氏向医疗领域转移，我期望在法律领域也能发生同样的事情。”尽管机器学习能够对监测癌症或其他医学成像等弊病起到辅助作用，但是这些技术并不会让医生失业。
Long story short, if a large portion of
your time at work involves tinkering with spreadsheets, there is likely to
be software that will perform your job faster and cheaper than human
labor. Marc Andreessen put this in intelligible terms in his“software
eating the world” WSJ interview, and it’s worth understanding if you planon being employed in 2025.
长话短说，如果你工作的大部分时间涉及到电子表格的修修补补，那么就会有软件能够让你的工作比依靠人工更加简单和快速。Marc Andreessen在他“软件吃遍天下”的华尔街日报专访中陈述了这一论断，如果你在2025年仍然打算受雇佣，这就很值得去认识。
However, the influence of AI in thecoming decade may imply an expansion beyond the “narrow” focuses that it’s bestknown for (i.e., analyzing images, beating silly humans at chess, etc.), andsome of the AI experts I’ve interviewed seem to think that people arebecoming comfortable handing over that control.
然而，在未来十年人工智能的影响将大大超出其本身最为熟知的“狭窄”作用（比方说，分析图像，击败愚蠢的人类国际象棋等等），有一些我采访过的人工智能专家似乎会认为人们很愿意移交控制权。
Eyal Amir is a Stanford PhD andAssociate Professor at The University ofIllinoisat Urbana-Champaign focused on AI research. “More generally what you see as atrend is for different pieces of data coming together, and that we give thecomputers a little bit more autonomy,” says Amir. “We start trusting theability of the computer to do basic tasks and to have knowledge that we don’thave.”
Eyal Amir是斯坦福大学博士以及伊利诺伊大学厄巴纳-香槟分校专注于人工智能研究的副教授。Amir说：“通常来说，你所看到的趋势是不同部分数据整合起来，我们给予电脑更多的自主权”。“我们信任电脑去做一些基本的任务以及拥有一些我们所没有的知识。”
In
a recent AI-focused interview, Amir states that he sees thisincreased degree of trust as a byproduct of the increased effectiveness of AIprograms, such as Apple’s Siri and Facebook’s advertising algorithms (whichinfer data about individuals’ preferences, vocation, gender and more — based oncues and clues from Facebook’s myriad data points). The concierge services ofthe future may simply be no match for a souped-up Siri who can instantly bringyou information and perform tasks for you (order pizza, order pick-up fordry cleaning, etc.).
在最近的人工智能集中访谈上，Amir将这种信任程度的增加视为人工智能项目不断增加影响力的副产物，比方说苹果的Siri以及Facebook的广告算法（根据来自Facebook的众多数据点提示和线索，推断个人的偏好，职业，性别等），未来的私人服务可能比不过改进后的Siri，她可以直接为你提供信息并完成任务（比如订购披萨，预约干洗衣物等等）。
Other algorithms in use today includethose used to judge the credit scores of consumers and businesses. AndrasKornai, a Stanford PhD and professor at the Budapest Institute of Technologywith experience in designing credit algorithms, states, “It is no longer alocal friendly banker who makes these decisions around credit, and that trendisn’t likely to slow down.” It’s likely that other efficient algorithmic useisn’t going to slow down either, and because there wasn’t much backlash inAI taking over loan and insurance decisions, it seems quite likely that it’llhandle more complex financial issues in the coming decade.
目前使用的其它算法包括那些用来判断消费者和企业信用评分的算法。斯坦福大学博士以及拥有设计信用算法经验的布达佩斯技术研究院教授Andras Kornai表明：“以后不再是当地友好的银行业务员来做这些信用评分决策，而且这种趋势并不会减缓。”很有可能其他非常有效率的算法的使用趋势也不会减缓，因为人工智能接管借贷和保险决策时并不会有太多的顾虑，在未来的几十年，人工智能貌似还能处理一些更加复杂的财务问题。
Kornai also refers explicitly to the useof algorithms in specific medical diagnostics, or even in legal proceedings,and believes that slow and steady traction in these domains is somewhatinevitable, and may invariably box out human expertise from tasks such as x-rayassessments or certain kinds of legal research.
Kornai也明确指出在特定医疗诊断中使用的算法，甚至是在法律程序中使用，他相信在这些领域缓慢平稳的转变趋势是不可避免的，而且还可以在x-射线评估以及某些法律研究中超出人类的专业知识。
几乎所有和我讨论过自动化和工作市场的研究人员都曾提到过无人驾驶汽车的话题
Speech-recognition algorithms oftomorrow may create their own economic shakeups. Daniel Roth received hisPhD from Harvard in 1995. He now teaches atUniversityofIllinoisand has been working in the domain of natural language processing for nearly 20years: “In ten years, I can see us being able to communicate with computers ina truly natural way…. I will be able to consult a machine in really thinkingthrough a world problem… a physician will be able to consult a computer tonavigate research articles.”
未来的语音识别将会创造自己的经济改革。Daniel Roth在1995年获得了哈弗大学博士学位。他现在任教于伊利诺伊大学，并在自然语言处理领域工作了将近20年，“在未来的10年，我可以预见我们能够与电脑更加自然的进行交流。。。。我可以向机器咨询一些世界性问题。。。。医生也可以向电脑咨询一些研究论文。”
Roth mentions that many millions of
medical research articles will be published in the coming decade, and that
having a machine that can understand natural commands to sift through this
massive swath of information would be of extreme value (i.e., “Find me all the
articles published within the last three years in any language that study the
impact of air pollution on osteoporosis in men.”). The same natural language
algorithms might comb legal files or compliance documents, potentially shaving
hours of tedious work from a professional’s day, but also potentially leaving
someentry-level
positions (such as paralegals)out of a job.
Roth提到数以百万计的医学研究论文将会在未来十年刊印，那么拥有一台可以理解常规指令并在大量的信息中搜寻有用数据的机器将会非常有价值（比方说，“帮我找到过去三年发表的研究空气污染对人类骨质疏松影响任何语言的论文。”同样的自然语言算法可以梳理法律文件或合规文件，能够有效的缩减专家一天繁琐的工作时间，但是同样能够使一些刚入行的人员失业（比如说助理律师）。
Though the AI researchers I spoke withdidn’t tend to converge on similar industries when it came to makingpredictions, nearly all the researchers I’ve spoken to about automationand the job market have brought up the topic of self-driving cars.To Amir’s point — there seem to be few more visceral ways of “giving upcontrol” than letting the machine take the wheel, and 10-15 years seems to beenough time for many AI experts to suspect that we’ll see consumers buying carsthat drivethem, not the other way around.
尽管与我交谈的人工智能研究学家在做出预测时并不是趋向于相似行业，几乎所有交谈过自动化以及工作市场的研究学者都曾提到过无人驾驶汽车的问题。根据Amir的观点，似乎有更多“放弃控制”的致命方式，而不是让机器控制轮子，10-15年的时间似乎足够让很多人工智能专家来怀疑我们将看到消费者购买汽车驱动他们，而不是其他方式。
Berleant mentions there has been asteady progression to automatic transmissions, anti-lock breaks, automaticlocks and cars that can park themselves. He states, “I believe it’s reasonableto suppose that such completely autonomous cars will be commonplace in tenyears.” If even one-tenth of the cars on the road in 10 years are self-driving,the impact on the economy as a whole could be relatively drastic.
Berleant提到了向自动变速器，防抱死刹车，自动锁以及自动泊车平稳的发展。他表明：“我相信在未来的10年这种完全自主化的汽车将会很合理的出现。”如果即使10年后路上的汽车只有十分之一是自动驾驶的，那么它对整体经济的影响也将会是巨大的。
Among other sectors, the immediateimpact on the job market for motor vehicle operation would be hit the hardest.“There are a million cab drivers in the United States alone — that might be amillion people without a job” says Kornai. In addition to directunemployment for folks in truck driving or taxi driving positions, therealso could be a drastic decrease in demand for car ownership if cars canbe ubiquitously accessed for transportation with the push of a button on anapp.
在其他领域中，机动车运营对工作市场的直接影响将会十分也严重。Kornai说：“仅仅在美国就有100万出租车司机，那么就会有100万人失去工作”。另外还有乡村卡车驾驶员以及的士驾驶员失去工作，如果仅仅在应用程序上点击按钮就能实现汽车运输，那么汽车需求量也会急剧下降。
Car manufacturers might be fighting overa much smaller market of individuals who still wish for a car of their own — orthey would battle over who’s autonomous fleets are employed in the most cities.Manufacturing demand for vehicles seems destined to decline sharply under thesecircumstances.
汽车制造商也许会挣扎于那些期望拥有个人汽车人群市场的缩小，或者他们会争夺谁的自主化的车队能够受雇于大多数城市。车辆生产的需求在这种环境下将会急剧下降。
The incumbents to driverless cars arelikely to fight just as fiercely as those currently railing against Uber,and Kornai and others foresee a reasonably gradual shift to autonomousvehicles, and this may cushion the shock of a drastic economic shift.
无人驾驶汽车任职者的竞争激烈程度将无异于当前Uber司机的竞争，Kornai等人预见了向自动化车辆的梯度转变，这将缓解巨大的经济震荡。
We might see a way around these legal
concerns with agradual
“trust transition” from man to machine, rather than an overt jump from 100percent human driver to 0 percent human driver. Either way, a lot of verysmart AI folks seem to think that the next decade is the one when driverlesswill kick in.
我们看到从人到机器的梯度“信任转变”是一条出路，而不是从100%人类司机向0%人类司机的巨变。无论哪种方式，有很多非常聪明人工智能专家似乎认为，未来十年无人驾驶将会横空出世。
Like many double-edged effects oftechnological change and automation, driverless cars may have tremendousupsides, as well. “There’s so much release of human potential if you don’t haveto be behind the wheel for an hour per day or more,” says Berleant. Thisisn’t to say that truck drivers are all going to become tremendously efficientwith all the freed up time they have in their hands-free commute to their nextjob, but it’s a potential example of the silver lining of automation and thejob market.
与很多具有双刃剑效果的技术变革以及自动化类似，无人驾驶汽车也同样具有很多的负面效果。“如果你不需要每天在车上待一个小时或以上，那么就会有大量的人力资源剩余，”Berleant说。这并不是说卡车司机在其下一份工作时间中都要非常有效率，但是他是自动化以及工作市场一线希望的潜在例子。
There is (and for the foreseeablefuture, will continue to be) ongoing debate as to whether or not technologicaladvancements inherently create more job market opportunities than they destroy.The most ignorant arguments are black-and-white, and it’s clear frominterviewing subject-matter experts that there is no consensus on the future outcomes,economically or technologically.
关于科技进步将会创造更多的工作岗位还是摧毁更多工作岗位的辩论还将持续（在可预见的未来也将会继续）。最无知的论点是正确或错误，通过采访主题专家很明确的一点是：关于未来的结果没有一致的结论，无论是对经济还是技术。
很清楚的一点是，当前有很多重要的自动化及人工智能趋势，加之现有算法和技术，在未来十年将对工作市场产生重大影响。机器视觉可以帮助我们很多。