十年之后，在就业市场中人工智能会扮演什么样的角色？编者按：我们最先从科幻电影中认识到何为人工智能，慢慢的它们开始出现在我们的生活之中。那你有没有想过，十年之后人工智能会在就业市场中扮演什么样的角色，如果它关系到你的工作，你还会欢迎它吗？近日Sentient Potential and TechEmergence 的创始人 Daniel Faggella就这个问题谈了他的看法。
经过过去几十年的发展，人工智能（AI）在这两三年里逐渐登上科技舞台的中心，成为最热门的技术。
从 Google 收购 DeepMind、Boston Dynamics 等等开始，到逐步增加对人工智能的资本投资关注，再到最近 Elon Musk 和 Bill Gates 都表示了对未来超级人工智能安全性的担忧，这些都表明人工智能已然从幕后走向了台前，成为大众关注的焦点。
然而对于位于职场的我们来讲，无论是蓝领还是白领工作者，自动化在工作安全上的问题给我们带来了最紧迫的担忧。
虽然对于未来我们无法预测，但是许多资深的计算机科学研究者认为未来 5 到 10年 内的的人工智能的影响趋势还是可以把握的。
那么自动化到底将怎样影响人类工作的本质和需求？因此我就人工智能未来十年如何影响就业市场这个问题请教了业内 6 位资深的人工智能博士，询问他们对于未来十年里人工智能将如何影响就业市场的观点。虽然他们对不同行业的影响持有不同的观点，但有一点认识是共同的：扩大或加强使用现有的算法已成必然趋势。
涉及面细分、重复性高或者数据评估类的工作最容易被自动化取代。佐治亚理工大学致力于研究计算机视觉的教授 Irfan Essa 表示，在过去的十年里，计算机视觉得到了显著的发展，很多可以引入人工智能技术的领域长期以来一直处于 “聚集模式”，现在我们终于迎来了转折点。
人脸标识、网络图片分类这些工作一度被人类标榜为一项繁重的工作，但现在很多类似的工作都可以由训练有素的神经网络自动完成。
视觉数据并不是唯一一个可能被人工智能取代的领域。《Rise of Robots》的作者 Martin Ford 认为：在未来的十年里，比起蓝领，将有更多的白领会被人工智能取代。
Daniel Berleant 也同意此说法。他表示当下流动性无疑是一个巨大的技术难题，但是计算机在处理数据上比人类更出色，而人类则更能胜任体力劳动工作，至少从目前上来看是这样的。尽管过去十年里双踏板行走的机器人发展态势良好，但是像搬家具或者是餐馆里端盘子这些需要灵活性的工作暂时还没有可能迅速地被自动化取代。
有些研究人员认为，数据处理领域的自动化代替也可能发生在细分数据评估领域。Andras Kornai 说 IBM 正在医疗领域引入 Watson，我更希望在法律界也能有类似的技术。虽然机器学习在医学诊断上可以给予医生一定的帮助，但是机器学习并不能完全取代医生。
长话短说，如果你的大部分工作内容涉及到电子表格，那么未来很有可能会出现一款软件取代你，它的效率比人工更高，所需成本更低。所以，如果你不想在 2025年 的时候丢了工作的话，你要好好考虑这个发展趋势了，从而改善你目前的工作状态。
但事实上人工智能在未来十年的影响可能不仅局限于当前所熟知的某些细分领域，譬如图片分析、象棋对阵等领域。我采访的几位人工智能专家，他们认为人们已经越来越愿意把工作交付给人工智能。
拥有斯坦福大学博士学位的 Eyal Amir 主攻人工智能研究，他表示我们现在看到的趋势无非就是把不同的数据碎片汇聚到了一起，而且我们给予了计算机更多的自控权。我们开始相信计算机有处理基本任务的能力，也有我们人类所不具备的能力。
在最近的一次人工智能采访中，Amir 表示他认为这种不断增加的信任是人工智能程序影响力不断上升的副产品，比如 Apple 的 Siri 和 Facebook 的广告算法。未来的贵宾级服务大概没有一个能比得上 “超级 Siri”，它可以随时随地给您带来需要的讯息，随时随地为您处理任务（预定披萨，预约衣物干洗时间等等）。
其他我们现在正在使用的算法还覆盖了评估消费者和企业信用等级方面。当然，其他高效算法的使用也似乎在加快改变我们的传统生活的步伐，加上人们接受了人工智能涉足保险和贷款的决策过程，不难想象未来十年里人工智能将处理更多更复杂的金融问题。
Kornai 还明确指出在某些特定医疗诊断或者甚至是法律诉讼中也可以使用这些计算机算法，他相信人工智能在这些领域的缓慢稳定影响是不可避免的，甚至还会取代人类专家在诸如 X 光评估或者某些法律研究工作上的地位。
未来的语音识别算法则可能会创造出另一种经济变化。在伊利诺伊大学执教的 Daniel Roth 表示：他可以预见十年后，我们能够用真正自然的方式与计算机交流，我们可以向机器咨询全球问题，物理学家可以让计算机检索相关研究文献。
Roth 说，在接下来的十年里我们会陆续看到无数的医学研究文章，如果有一台智能机器可以理解自然语言命令，然后从信息的海洋中筛选出有价值的内容，那将会是一个极大的进步。同样地，自然语言算法也用来可以梳理法律文件，或者相关文档，潜在地替专业人士减少了枯燥乏味的工作，但是相应地也减少了对初级职位的需求。
虽然他们的观点都不一致，但是当谈到自动化与劳动力市场的未来趋势时，所有我采访过的人工智能研究员都提到了自动驾驶汽车。Amir 认为，与其说机器控制了车轮不如说是人们自己放弃了对汽车的控制权，在未来的 10 到 15年 里，我们将看到满大街都是无人驾驶的汽车。
Berleant 也指出，自动变速箱、防锁装置、自动锁还有具备自动停车功能的汽车等都在持续稳步地发展。因此我们完全可以相信十年之后无人驾驶汽车会成为日常生活一部分。但即便十年后道路上行驶的汽车中只有十分之一是自动驾驶汽车，其对整个经济体系的影响也是不容小觑的。
在其他相关产业中，机动车驾驶员就业市场受到的直接影响将最为严重。Kornai 表示，在美国至少 100 万名出租车司机，换句话说就是到时候将有 100 万人失业。除了卡车司机或者出租车司机的直接失业，也可能会导致人们对汽车购买的需求会减少。
汽车制造商或许会想办法改变这种局面，他们把希望寄托在一小部分仍渴望拥有属于自己的汽车的个人消费者身上，或者争取在无人驾驶汽车市场占领一定的市场份额。但在这样的趋势下，汽车的制造需求必然会大幅下降。
正如当前 Uber 面临的被抵制的困境，未来自动驾驶汽车公司也将面临一场恶战。Kornai 和其他几位都表示传统车辆会逐渐地向自动驾驶车辆转变，同时也有望缓解转变带来的剧烈经济转型。
我们甚至还可以预想到一系列法律问题，随着从人到机器的逐步 “信任转变” 而得到解决，虽然直接从 100%的人类驾驶员到完全的自动驾驶的转变不是不可能，但可能性终究太小。然而不管是何种方式，这些站在科技前沿的人工智能研究人士一致认为，下一个十年无人驾驶汽车会走进我们的生活。
与其他技术改变和自动化带来的双面效果一样，无人驾驶汽车同样也会带来极大的好处。
对于技术的进步在本质上到底是创造了更多的就业机会还是减少就业机会的讨论依旧会持续下去，但并没必要非要求讨论出一个结果，因此从与相关问题的专家谈论中我们可以知道，无论是在经济上还是技术上，我们始终对未来的结果难以达成一致。
但有一点显而易见，更加重要的自动化和人工智能趋势再加上已有的算法和技术，将会在未来十年里影响到我们的就业市场，但究竟是如何影响到的我们也需要继续密切关注。
The Next 10 Years Of Automation And What It Might Mean For The Job Market
After decades of subtle developments that largely went unnoticed by much of the working world, artificial intelligence (AI) has taken center stage in the last 2-3 years as a “hot” technology.
From Google’s surge of acquisitions (DeepMind, Boston Dynamics, etc.), to increased venture capital attention, to the safety concerns of Elon Musk and Bill Gates about potentially super-intelligent AI, the field is undeniably back in the spotlight.
One of the most pressing concerns for those of us in the working world is the effect of automation on job security — in both blue-collar and white-collar work.
Though more far-out considerations are difficult to predict, many experienced computer science researchers feel reasonably comfortable speaking about AI’s influence in the coming 5-10 years.
With so much potentially unfounded speculation about how automation might influence the nature and demand for human work, I decided to ask six artificial intelligence PhDs about their informed perspectives on how AI might impact the job market in the coming decade. Their answers didn’t share much commonality in terms of industry, but they did share a common thread: The expanded or strengthened use of existing algorithms.
One wide swath of jobs that may be most easily automated are likely to be jobs that involve narrow and repetitive manipulation or assessment of data. Irfan Essa at Georgia Tech focuses his research on machine vision, a domain that has developed markedly in the last 10 years. “Many fields were AI could be applied have been in ‘aggregation mode’ for quite some time, and now we’re finally getting to a point of sense-making,” says Essa.
While identifying human faces, or categorizing web images (identifying animals, landmarks, objects) was once the arduous job of human beings, many of these tasks can now be automated by trained neural networks (Google’s Peter Norvig explains this process rather well).
Visual data is far from being the only area of narrowly focused intelligence that might be under siege. Martin Ford (author of the well-received book Rise of the Robots) mentions that in the coming 10 years, we’re likely to see more automated job displacement in white-collar jobs rather than blue-collar.
There is ongoing debate as to whether or not technological advancements inherently create more job market opportunities than they destroy.
Daniel Berleant agrees, stating the current 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 the impressive developments in bipedal robots in 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 like Rethink Robotics’ Baxter).
Some researchers believe that the same might be said of narrow data assessment, not just data manipulation. Andras Kornai states, “IBM is moving Watson into the medical field — I expect the same thing to happen in the legal area.” Though it may be possible that machine learning will aid in the detection of cancer or other maladies in medical imaging, these technologies don’t seem likely to put doctors out of a job.
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 plan on being employed in 2025.
However, the influence of AI in the coming decade may imply an expansion beyond the “narrow” focuses that it’s best known for (i.e., analyzing images, beating silly humans at chess, etc.), and some of the AI experts I’ve interviewed seem to think that people are becoming comfortable handing over that control.
Eyal Amir is a Stanford PhD and Associate Professor at The University of Illinois at Urbana-Champaign focused on AI research. “More generally what you see as a trend is for different pieces of data coming together, and that we give the computers a little bit more autonomy,” says Amir. “We start trusting the ability of the computer to do basic tasks and to have knowledge that we don’t have.”
In a recent AI-focused interview, Amir states that he sees this increased degree of trust as a byproduct of the increased effectiveness of AI programs, such as Apple’s Siri and Facebook’s advertising algorithms (which infer data about individuals’ preferences, vocation, gender and more — based on cues and clues from Facebook’s myriad data points). The concierge services of the future may simply be no match for a souped-up Siri who can instantly bring you information and perform tasks for you (order pizza, order pick-up for dry cleaning, etc.).
Other algorithms in use today include those used to judge the credit scores of consumers and businesses. Andras Kornai, a Stanford PhD and professor at the Budapest Institute of Technology with experience in designing credit algorithms, states, “It is no longer a local friendly banker who makes these decisions around credit, and that trend isn’t likely to slow down.” It’s likely that other efficient algorithmic use isn’t going to slow down either, and because there wasn’t much backlash in AI taking over loan and insurance decisions, it seems quite likely that it’ll handle more complex financial issues in the coming decade.
Kornai also refers explicitly to the use of algorithms in specific medical diagnostics, or even in legal proceedings, and believes that slow and steady traction in these domains is somewhat inevitable, and may invariably box out human expertise from tasks such as x-ray assessments or certain kinds of legal research.
Nearly all the researchers I’ve spoken to about automation and the job market have brought up the topic of self-driving cars.
Speech-recognition algorithms of tomorrow may create their own economic shakeups. Daniel Roth received his PhD from Harvard in 1995. He now teaches at University of Illinois and has been working in the domain of natural language processing for nearly 20 years: “In ten years, I can see us being able to communicate with computers in a truly natural way…. I will be able to consult a machine in really thinking through a world problem… a physician will be able to consult a computer to navigate research articles.”
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 some entry-level positions (such as paralegals) out of a job.
Though the AI researchers I spoke with didn’t tend to converge on similar industries when it came to making predictions, nearly all the researchers I’ve spoken to about automation and 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 up control” than letting the machine take the wheel, and 10-15 years seems to be enough time for many AI experts to suspect that we’ll see consumers buying cars that drive them, not the other way around.
Berleant mentions there has been a steady progression to automatic transmissions, anti-lock breaks, automatic locks and cars that can park themselves. He states, “I believe it’s reasonable to suppose that such completely autonomous cars will be commonplace in ten years.” 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.
Among other sectors, the immediate impact 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 a million people without a job” says Kornai. In addition to direct unemployment for folks in truck driving or taxi driving positions, there also could be a drastic decrease in demand for car ownership if cars can be ubiquitously accessed for transportation with the push of a button on an app.
Car manufacturers might be fighting over a much smaller market of individuals who still wish for a car of their own — or they would battle over who’s autonomous fleets are employed in the most cities. Manufacturing demand for vehicles seems destined to decline sharply under these circumstances.
One of the most pressing concerns for those of us in the working world is the effect of automation on job security.
The incumbents to driverless cars are likely to fight just as fiercely as those currently railing against Uber, and Kornai and others foresee a reasonably gradual shift to autonomous vehicles, and this may cushion the shock of a drastic economic shift.
We might see a way around these legal concerns with a gradual “trust transition” from man to machine, rather than an overt jump from 100 percent human driver to 0 percent human driver. Either way, a lot of very smart AI folks seem to think that the next decade is the one when driverless will kick in.
Like many double-edged effects of technological change and automation, driverless cars may have tremendous upsides, as well. “There’s so much release of human potential if you don’t have to be behind the wheel for an hour per day or more,” says Berleant. This isn’t to say that truck drivers are all going to become tremendously efficient with all the freed up time they have in their hands-free commute to their next job, but it’s a potential example of the silver lining of automation and the job market.
There is (and for the foreseeable future, will continue to be) ongoing debate as to whether or not technological advancements inherently create more job market opportunities than they destroy. The most ignorant arguments are black-and-white, and it’s clear from interviewing subject-matter experts that there is no consensus on the future outcomes, economically or technologically.
What does seem clear is that there are important current automation and AI trends with existing algorithms and technologies that are likely to only have a greater job-market influence in the coming decade, and they are worth keeping an eye on. Maybe machine vision can help us with that.
本文编译自：techcrunch.com
你需要知道的2016年足以颠覆人力资源技术的10个趋势
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如今越来越多的企业正在通过投资技术来解决他们人力资源的重要问题，例如试图创造一个强有力的公司品牌、吸引优秀人才或发放更多的工资给他们的员工。
现已进入2015年的最后几个月，德勤旗下的Bersin公司公布了一则新报告，报告指出了我们需要认识到的足够颠覆以往人力资源技术的10个要素：
消费者至上的人力资源技术
为了让专业HR的工作更加轻松，很多应用现在更多的设计方向向消费者们的需求靠拢， 使员工能够互相学习与合作、分享反馈、设定目标、指导他们的事业并能更高效的管理他人。
报告指出：原有的人力资源技术市场正在逐渐瓦解，新的应用程序将重点转变为设计出更亲民的应用，这使得人们从以往人力资源管理工具的开发，更多的转为关注消费者的体验。
手机将会是新的平台
全球智能手机用户已达到21亿，而HR把手机当做他们的新平台也已不足为奇。
在未来一年中，一些突破性领域很可能会增加雇佣和反馈系统。
供应商的出现
这些厂商正在迅速的追赶上来，他们利用可靠有效的综合性人才管理技术来支持这个追赶的过程，例如：招聘、学习和一系列的人才管理工具。
利用云服务提供商重新定义人力资源功能
以手机和云服务为平台的“第三波”人才解决方案提供商带来了他们更加贴近用户喜好的产品。他们已经在工资、学习技术、员工雇佣等几个领域有了重大影响力。
反馈和文化管理已经加入了新的软件类别
供应商将发布反馈应用软件，将绩效管理与反馈、员工检查和发展规划结合起来，它将使普通会议或电话会议都更有成效。
有关绩效和目标管理的新方法
随着组织评级的减少和绩效管理流程的简化，传统绩效管理软件用户需求方面的缺失，被反馈和检查插件的运用所补足了。
信息源无处不在
随着技能发展和拓展训练市场需求的不断增长，他们试图将各类信息源整合成为一个学习以及经验的集合。
预测分析的增长：更多厂商预示着更多的解决方案
一系列新兴厂商开始提供一整套的预测分析功能，从确定员工飞行风险到确定一个新的办公室布局是否完善，应有尽有。
云计算不会让科技服务过时
购买新的云端人力资源系统仍然是件难题，特别是在转型期。为了应对这些难题，选择供应商是至关重要的。这些供应商要能够提供高端服务并且有开放式编程接口，拥有买方行业的经验而且契合你们的企业文化。
员工雇佣是至关重要的
即使在“第三波”的人力资源技术中（从授权软件到云端系统再到移动科技），员工雇佣也是至关重要的，这一波势力能够通过一个简单且互相信任的方式来雇佣员工，同时人力资源技术的成功也应该由员工雇佣的状况来评估。
10 disruptive HR tech trends to look out for in 2016
Increasingly, firms are addressing their HR priorities – such as creating a strong company brand, attracting the brightest talent and competitively paying staff – by investing in technology.
As we enter the final months of 2015, Bersin by Deloitte has unveiled a new report noting the 10 big disruptions on the horizon of HR tech we need to be aware of:
1. Consumerised HR Technology
Instead of designing HR software and applications solely to make the jobs of HR professionals easier, many applications are now designed with the end user in mind – employees, enabling them to learn and collaborate, share feedback, set goals, steer their own careers and even manage other people more effectively.
“The HR technology market is bursting with new applications that shift the focus toward more consumer-like experience and away from tools created to streamline the work of HR administration,” the report stated.
2. Mobile is the new platform
With more than 2.1 billion smartphone users on the planet, it is unsurprising that HR is starting to leverage on mobile as its new platform.
Some breakthrough areas of in the coming year is likely to include engagement and feedback systems.
3. The emergence of ERP vendors
These vendors are quickly catching up as credible, effective providers of comprehensive talent management technologies to support processes such as recruiting, learning, and a range of people management tools.
4. Redefining HR functions with built-for-the-cloud providers
This “third wave” of talent solution providers come with consumer-like products, built for mobile and the cloud. They are thought to have a huge effect on several areas including payroll, learning technology, and employee engagement.
5. Feedback and culture management as new software categories
Bringing together the world of performance management with feedback, employee check-ins and development planning. Providers are expected to release feedback apps that could enable meetings and conference calls more useful and productive.
6. The new way of managing performance and goals
As organisations do away with ratings and simplify their performance management processes, gaps in user needs left by traditional performance management software are filled by making use of feedback and check-ins.
7. Integrated content from everywhere
With the growing need for skills development and expanding training marketplace, learning experience middleware is expected to bring various content together into an integrated learning experience.
8. The growth of predictive analytics: more vendors, more solutions
A range emerging vendors are offering the whole predictive analytics package, from identifying employee flight risks to determining whether a new office layout is working or not.
9. Cloud computing does not make technology services obsolete
Organisations that buy new cloud-based HR systems still experience challenges especially during the transition. To cope with these challenges, the selection of vendors is crucial. These vendors should be able to deliver high levels of service, have open-programming interfaces, experience in the buyer’s particular industry, and fit the business culture.
10. Employee engagement is critical
Even in the “third wave” of HR tech (moving from licensed software to cloud-based systems to mobile technologies), employee engagement is crucial. This wave is all about engaging employees in a simple, compelling way and the success of HR technologies should be evaluated by employees’ engagement with the systems.
来源：HumanResources
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