你能让你的老板把芯片放在你身上吗？-少数员工同意皮下植入但这个想法正在蔓延
Dave Coplin试图向我解释为什么两大洲的人们突然允许他们的雇主将微芯片放在他们的皮肤下。
“我这样对待我的狗 - 我为什么不自己做呢？”科普林说。我不相信，所以他发起了关于地中海派伊维萨岛上一个俱乐部的轶事，人们可以在那里筹码，然后用芯片买饮料。科普林怀疑这是因为他们没有穿很多衣服。
但是，因为你是半裸的而且没有钱包的口袋，所以要让你的雇主给你筹码是非常不同的。那么，我们是怎么来到这里的？
担任Envisioners咨询公司负责人的科普林表示，如果我们只能克服自己的娇气，那么雇主和员工都会受益匪浅。“如果它增加价值，我就是全力以赴，”他说。“今天我们看看人们这样做，感觉有点奇怪，但实际上有一些不可避免的事情。”
Patrick McMullan是威斯康星州三广场市场的总裁。在斯德哥尔摩的瑞典孵化器Epicenter进行实验后，该公司自2015年以来一直在试验切片，他的公司决定进一步开发该技术。当然，作为供应商和开发商，McMullan自己也有一个芯片植入物 - 一个大致相当于拇指和食指之间植入皮肤下的一粒米的大小。它基于近场通信（NFC）技术 - 与非接触式信用卡或移动支付中使用的芯片相同。使用注射器和非常少的血液快速简单地完成植入。
McMullan说，目前的一个限制是，由于芯片是无源器件，因此无法对其进行跟踪。就目前而言，这意味着该芯片用于访问建筑物，登录计算机以及从食堂支付费用。但麦克马伦的员工正在执行“改变世界”的使命，他说，到目前为止，已有70多名员工自愿参与实验。
“我这样对待我的狗 - 我为什么不自己做呢？”
这个想法似乎正在蔓延。除了三坊市场外，至少有160人参加了Epicenter的月度“ 筹码派对”。辛辛那提监控公司CityWatcher.com的一些员工已经获得了芯片，一些人在数字营销公司工作。在比利时称为NewFusion。毫无疑问，这是一个很好的宣传，但削弱倡导者真正相信这将成为未来十年的普遍做法。
McMullan认为，随着技术的进步，芯片将提供更多的好处。“我们正在开发能够监测生命体征的医疗用途。医生将能够主动治疗患者，而不是总是做出反应，“他说。McMullan认为，全球削减员工的数量将在几年内达到数百万，因为低于100美元的芯片的好处可能是巨大的。
自然进步？
McMullan认为没有任何不利因素，尽管人们明显担心，以难以控制或消除的方式与雇主建立密切联系感觉完全是反乌托邦。采用他自己的芯片监控人们健康的想法：未来的嵌入式技术有明显的优势，可以监测胆固醇，血糖水平，甚至只是脱水。
但是，如果某人有一块芯片监测酒精摄入量，作为退出协议的一部分呢？外科医生会被允许拒绝接受手术吗？如果保险公司从车上掉下来，可以提高患者的保费吗？随着芯片变得更先进和更广泛，可以或应该收集哪些信息以及它可能或应该去哪里的问题将变得更加复杂。其他专家也提出了对黑客行为的担忧，以及已知与宠物类似芯片相关的已知健康问题。
“显然，隐私是一个巨大的问题，”科普林补充说。“人们将如何处理这些数据？谁会去看？实际上，我必须携带手机和我的钱包，这已经够糟了。如果这解决了其中一些问题，那我就是为了它。“
尽管存在这些担忧，但很多人似乎都接受了这种情况 - 并且很快就会发生。Lynda Shaw博士，认知神经科学家，Your Brain Is Boss的作者，认为切片是一种自然进展，可能更容易为年轻人所接受。
“If you think of young men, when they’re teenagers, we often think of them as driving too fast, hotheaded,” Shaw explains. “In evolutionary psychology, that’s vital to have in society. In the old days, if a village’s crops failed, they would get the strongest young men to go and find food. They would go and find food by going beyond their usual areas and by being curious.” We may no longer be hunter-gatherers, Shaw’s theory goes, but young people will still test the boundaries, be curious, and do new things; it’s part of what they are.
在某些方面，这已经是一项成熟的技术，至少在有健康问题的人中是这样。Shaw指出，我们已经使用芯片进行人工耳蜗植入，甚至在脑损伤的情况下绕过大脑的部分区域。她说：“切削人体并不是新闻，但我们总是那些邪恶的一面说这有点过于奥威尔式。” 人们可能会担心生活在他们体内的计算机病毒或者当硬件被破坏时会发生什么。
“它将摆脱身份通行证”
智库快速未来的未来主义者兼首席执行官罗希特·塔尔瓦（Rohit Talwar）认为，削片变得非常迅速，尤其是那些希望证明自己具有前瞻思维的科技公司。
Talwar说，在那些希望获得极高安全性的公司中，人们不会进入系统或者他们不应该建造的部分建筑，以及谁想向客户证明他们在安全方面处于领先地位条款。您可能还会看到它被用作使人们能够在食堂，自动售货机上兑换货币的方式 - 它将摆脱身份通行证。“
Shaw也看到了好处。如果有人生病并且有起搏器或使用抗凝药物，通过快速扫描获得该信息可以挽救他们的生命。但她也指出了对犯罪现场的暗示。在犯罪率高且尸体被肢解的地区，Shaw指出，犯罪分子不需要整个身体来破坏安全，只需要插入芯片的肢体。她说：“你最终可能会无意中煽动比原先考虑的更可怕的罪行。”
塔尔瓦尔认为，反乌托邦是旁观者的眼睛。作为数字原生代出生的一代人可能会认为这是一种自然的进化，塑料传递为过时的，神秘的，当然也无法捕捉到我们身体内的芯片可以捕获的信息，比如健康。
“老一代人可能会认为这是非常具有侵略性的，”塔尔瓦尔说。“我去年参加了一个活动，那里他们只是为了好玩而扒人，而且这些线路正在人们的走廊上等待被破坏 - 为了故事和体验。”
我们与机器对话的一部分
那么，切削在哪里？Talwar认为这是一个不可避免的过程的一部分，在这个过程中，先驱者已经说了一段时间，如果人类要跟上人工智能的步伐，我们就必须加强我们的大脑和身体。
“这只是该过程的起点。你可以很容易地预测你的手机内存被插入你，芯片可以加速你的记忆和你的大脑，“Talwar说。“随着我们加强和扩充自己，进入超人类世界，我们可以看到这方面的巨大加速。”
“你可能最终无意中煽动了比原先考虑的更可怕的罪行。”
Coplin认为切削是关于我们如何与机器相关的对话的一部分。他指出，澳大利亚的一名男子试图从旅行卡中取出芯片并将其嵌入手中失败，因为条款和条件说不会损坏卡。“目前，这感觉很奇怪，”科普林说，“但此刻，我可能会在我的手腕上放置一种可能具有该技术的设备。为什么不在我的皮肤下更远一点？“
社会一直在争论技术的潜力及其所带来的变化。四分之一世纪以前，很少有人预测到手机的出现 - 我们更多地预计会将它们用作相机和音乐中心。现在，技术面临着额外的压力。
“我们真的失去了对处理我们数据的人的信任 - 银行，谷歌，Facebook，”科普林说。“在赢得信任之前，我们会非常担心这种事情。而且我认为这是一个真正的耻辱，因为我们可以获得的好处。“
盖伊克拉珀顿
Guy Clapperton是英国的资深记者，大约30年前开始研究人与技术之间的关系。
以上AI自动翻译完成，仅供参考！
原文
Would You Let Your Boss Put a Chip in Your Body?
Google Hire重大更新！全面AI技术支持，简历筛选安排面试将大幅节约时间综合来源/ gadgets google hire blog等
更新要点
Google Hire通过更新获得了新的AI驱动的工具
Google Hire可以更快地安排面试，并在简历中突出显示关键字
雇用1000人以下的美国企业适用Google Hire
随着去年推出Google Hire，Google通过将招聘过程整合到招聘人员，已经花费大量时间去查工具（如Gmail，Google日历和其他G-Suite应用程序），来简化招聘流程。旨在帮助中小型企业有效招聘。招聘人员表示，Hire从根本上改善了他们的工作方式，减少了应用程序之间的上下文切换。
实际上，当他们衡量用户活动时，他们发现Hire减少了完成日常招聘任务的时间 - 比如审查应用程序或安排面试 - 节省时间高达84％。
Google启动AI
通过整合Google AI，Hire现在可以减少重复耗时的任务，如安排面试，进入一键式交互。
这意味着招聘团队可以在后勤上花费更少的时间，更多的时间与人交流。
Hire中的新功能使招聘人员可以做到如下几点：
在几秒钟内安排面试：
招聘人员和招聘协调员花费大量时间在后勤管理 - 查找日历上的可用时间，预订房间，并将正确的信息汇集到预备面试官处。为了简化这一过程，Hire现在使用AI来自动建议面试者和理想时间段，从而将面试计划减少到几次点击。
通过整合Google AI，Hire现在可以将重复耗时的任务减少为一键互动。这意味着招聘团队可以在后勤上花费更少的时间，更多的时间与人交流” 谷歌在其博客文章中表示。 自推出以来，Google Hire带有G Suite集成功能，可让应用程序与Gmail和Google日历等其他应用程序同步工作。Google声称Hire可以减少招聘团队招募任务的时间达84％。
最新的更新基本上整合了Google AI，以减少做任务时的点击次数，让AI建议发挥作用。
Google Hire自动提供面试官和理想时间段，将面试安排减少到几次点击。操作如下：
Photo: Google
它试图减少手工查看日历空闲时间，为您查看并提供理想的时间段。此外，如果面试官最后一分钟取消，Hire不只是提醒你，它还推荐可用的面试官，并可以很容易且快速地邀请面试官。
所以我们可以看到国内外面试安排都是一个复杂而且繁琐的事情，面试管理这块的需求也日益突出。
自动突出显示简历重点
相当一部分招聘人员的时间花在审查简历上（我们都知道这一点）。有人告诉我，当团队正在观看与Hire进行互动的人时，他们发现客户经常使用“Ctrl + F”，通过简历扫描搜索正确的面试者的技能 - 这是一项重复的手动任务，可以轻松实现自动化。
另一个常见的招聘难题是在简历中查找关键字。 Hire的AI现在通过分析工作岗位描述，或搜索查询术语并在简历中突出显示相关单词（包括同义词和缩略词）来节省手动搜索它们的时间，自动为招聘人员找到这些单词。
Photo: Google
点击致电候选人：
无论他们是筛选候选人，进行面试还是跟进录用信，招聘人员每天都会有数十次电话交谈。现在通过点击通话功能简化每个电话对话，并自动记录通话，以便团队成员知道与候选人通话的人员。它是如何工作的，Derek？ 很高兴你问这样的问题！
系统会拨打您要给求职者的电话，然后当您拿起电话时，系统会向求职者拨打该号码。且您永远不会丢失您的收件箱内容，电话会录音，并且您可以在电话中记笔记。我问是否有发信息功能，市场表明，大约98％的人回复短信，很少听到语音信箱或回复他们不认识的号码。
他们向我保证，这个过程非常简单，并且您电话辛苦获取的宝贵数据将会轻松转移。
最后，现在通过点击通话功能简化每个电话对话，并自动记录通话，以便团队成员知道谁已经与候选人通话，而不是多次拨打同一个候选人。
所有那些雇员不足1000人的美国企业都可以购买Hire服务。在中国不行~~
关于Google Hire 从去年7月推出，旨在帮助中小型企业有效招聘。它允许招聘人员将工作发布到多个工作现场，跟踪申请，安排面试，甚至可以在一个平台上获得面试反馈。现在，在一年之后，谷歌已经更新了招聘人工智能驱动工具，以实现“更聪明，更快速的招聘方式”。此更新带来的新功能可以加快日程安排访问速度，为日志记录提供简单的工作，并简化相关简历，从而减少耗时。
“通过整合谷歌AI，服务现在减少重复，耗时的任务，进入一键式的互动。这意味着雇佣团队可以花费更少的时间与物流和更多的时间与人联系”
以上由HRTechChina 综合编译，仅供参考！
资讯
2018年06月27日
资讯
10 Trends in Workforce Analytics （英文）
Workforce analytics is developing and maturing. These are the 10 major trends for the near future.
1. From one time to real-time
Many workforce analytics efforts start as a consultancy project. A question is formulated (“How do our employees experience their journey?”), many people are interviewed, data is gathered, and with the help of the external consultants a nice report is written and many follow up projects to redesign the employee journey are defined.
A one-time effort is nice, but it might be more beneficial to develop ways to gather more regularly and maybe even real-time feedback from candidates, employees and other relevant groups.
The survey practice is changing. We see organizations using several approaches:
The classic annual or bi-annual employee survey, for a deep dive.
Weekly, monthly or quarterly pulse surveys to gather more frequent feedback. A few questions, often varying the questions per cycle. Some more advanced pulse survey solutions are adaptive: they ask more questions to people when they sense there are issues (“How was your week?”. If the answer is “Very Good”, the survey is finished, if you answer, “Not so good”, there are some follow-up questions). Pulse surveys can also be easily connected to the important “moments that matter” for the employee experience.
Continuous real-time mood measurement. Innovative solutions in this area are still scarce, especially if you want to measure in a passive non-obtrusive way. Keencorp is an example, they analyze aggregated e-mails and can report on the mood (and risks) in different parts of an organization.
In my article Employee mood measurement trends, you can find an extensive overview of mood measurement providers.
2. From people analytics to workforce analytics
Currently, the general opinion seems to be that people analytics is a better label than HR analytics.
Increasingly the workforce is consisting of more than just people. Robots and chatbots are entering the workforce. The first legal discussions have started: who is responsible for the acts of the robots?
If we’re also analyzing robots, we’re moving from people analytics towards workforce analytics. Robot wellbeing and robot productivity is a nice domain for HR to claim.
3. More transparency
This overview of workforce analytics trends cannot be complete without a reference to GDPR. GDPR is fueling a lot of positive developments, one of them being a lot more transparency. About what kind of data is collected, how it is used, and how algorithms are used to make decisions about people.
The issue of data ownership is related. It is expected that employees will no longer accept that they cannot own their own personal data. Employees need to have the possibility to show their data to their potential next employer as evidence for their productivity and engagement.
4. More focus on productivity
In the last years, there has not been a lot of focus on productivity. We see a slow change at the horizon.
Traditionally, capacity problems have been solved by recruiting new people. This has led to several problems. I have seen this several times in fast growing scale-ups.
As the growth is limited by the ability the find new people, the selection criteria are (often unconsciously) lowered, as many people are needed fast. These new people are not as productive as the existing crew. Because you have more people, you need more managers. Lower quality people and more managers lowers productivity.
Another approach is, to focus more on increasing the productivity of the existing employees, instead of hiring additional staff, and on improving the selection criteria.
Using workforce analytics, you can try to find the characteristics of top performing people and teams, and the conditions that facilitate top performance.
These findings can be used to increase productivity and to select candidates that have the characteristics of top performers. When productivity increases, you need less people to deliver the same results.
A related read on this topic are the 3 reasons to stop counting heads.
5. What is in it for me?
A lack of trust can influence many workforce analytics efforts. If the focus is primarily on efficiency and control, employees will doubt if there are any benefits for them.
Overall there is a shift to more employee-centric organizations, although sometimes you can doubt how genuine the efforts are to improve the employee experience.
Asking the question: “How will the employees benefit from this effort?” is a good starting point for most workforce analytics projects. It also helps to create buy-in, which becomes increasingly important with the introduction of the GPDR.
6. From individuals to teams to networks
Many workforce analytics projects today are still focused on individuals. What are the characteristics of our top performers? How can we measure the individual employee experience? How can we decrease absenteeism?
Earlier, I gave an overview to what extend current HR practices are focused on teams.
As you can see in the table, most of the practices are still very focused on the individual. Workforce analytics can help to improve the way teams and networks function in and across organizations. The rise of Organizational Network Analysis is one of the promising signs.
7. Cracks in the top-down approach
The tendency to implement changes top-down, is still common.
We like uniformity and standardization. In our central control room, we look at our dashboard, and we know we need to act when the lights are turning from green to orange.
HR finds it difficult to approach issues in a different way. Performance management is a good example. Changing the performance management process is often tackled as an organization-wide issue, and HR needs to find the new uniform solution.
In line with the trend called “the consumerization of HR”, employees are expected to take more initiative. Employees are increasingly tired of waiting for the organization and HR, and want to be more independent of organizational initiatives.
If you want feedback, you can easily organize it yourself, for example with the Slack plug-in Captain Feedback. A simple survey to measure the mood in your team is quickly built with Polly (view: “How to measure the mood in your team with Slack and Polly“). Many employees are already tracking their own fitness with trackers like Fitbit and the Apple Watch.
Many teams primarily use communication tools as WhatsApp and Slack, avoiding the officially approved communication channels. HR might go with the flow, and tap on to the channels used, instead of trying to promote standardized and approved channels.
How can workforce analytics benefit from the data gathered by on their employee’s own devices? If it is clear, what the benefits are for employees to share their data, they might be able to help to enrich the data sets and improve the quality of workforce analytics.
8. Ignoring the learning curve
In their book “Making HR measurement strategic”, Wayne Cascio and John Boudreau presented an often-quoted picture, with the title “Hitting the “Wall” in HR measurement”. The wall was the wall between descriptive and predictive analytics.
There are many more overviews with the people analytics maturity levels. Generally, the highest level is predictive analytics.
Patrick Coolen of ABN AMRO Bank recently mentioned a next level: continuous analytics, and he introduced a second wall, the wall between predictive analytics and continuous analytics.
As predictive analytics seems to be the holy grail, many HR teams want to jump immediately to this level. Let’s skip operational reporting, advanced reporting and strategic analytics. We can leapfrog, ignore the learning curve, and jump to the highest level in one step.
For many teams, ignoring the learning curve does not seem to be a sensible strategy. Maybe it is better to learn walking before you start running.
9. Give us back our time!
Recently I spoke to HR professionals from big multinationals who were involved in a “Give us back our time” projects.
In their organizations, the assignment to all staff groups was: stop using (meant was: wasting) more and more time of the employees and managers, please give us some time back!
An example that was mentioned concerned performance management. In this organization, they calculated that all the work around the performance management process for one employee costed manager and employee around 10 hours (preparation, two formal meetings per year, completing the online forms, meeting with HR to review the results etc.).
By simplifying the process (no mandatory meetings, no forms, no review meetings, just one annual rating to be submitted per employee by the manager), HR could give back many hours to the organization – to the relief of both managers and employees.
Big HR systems generally promise a lot. But before the system can live up to the high expectations, a lot of work needs to be done. Data fields must be defined. Global processes must be standardized. Heritage systems must be dismantled.
This results in a lot of work (and agony), for employees, for managers, for HR and for the implementation partners (who do not mind).
Workforce analytics can help a lot in the “give-us-time-back” projects, for example by some simple time-measurement. Measure the time a sample of managers, employees, and HR professionals spend on different activities, and estimate the value these activities optimizes the core activities of the organization (e.g. serving clients and bringing in new clients).
10. Too high expectations
The expectations of workforce analytics are often too high. Two elements must be considered.
In the first place, human behavior is not so easy to predict, even if you have access to loads of people data.
Even in domains where good performance is very well defined and where a lot of data is gathered inside and outside the field, as for example in football, it is very difficult to predict the future success of young players.
Secondly, the question is to what extend managers, employees and HR professionals behave in a rational way. All humans are prone to cognitive biases, that influence the way they interpret the outcomes of workforce analytics projects. Some interesting articles on this subject are why psychological knowledge is essential to success with people analytics, by Morten Kamp Andersen, and The psychology of people analytics, written by myself.
A more general thought: what if you replaced ‘Workforce analytics’ with ‘Science’? What is the role of science in HR? The puzzle is, that there are many scientific findings that have been available for a long time but that are hardly used in organizations.
Example: it has been proven repeatedly, that the (unstructured) interview is a very poor selection instrument.
But still, most organizations still rely heavily on this instrument (as people tend to overestimate their own capabilities). Why would organizations rely on the outcomes of workforce analytics, when they hardly use scientific findings in the people domain?
An interesting presentation on this topic that I recommend is by Rob Briner, titled evidence-based HR, what is it and is it really happening?
There’s a lot that’s changing in the world of work. These are the 10 trends in workforce analytics that I’m seeing today and that will likely impact the way we work in the near future.
This article is based on a keynote I gave at the Workforce Analytics Forum in Frankfurt, Germany, on April 18, 2018.
by Tom Haak
Tom Haak is the director of the HR Trend Institute The HR (Human Resources) Trend Institute follows, detects and encourages trends. In the people and organization domain and in related areas. Where possible, the institute is also a trend setter. Tom has an extensive experience in HR Management in multinational companies. He worked in senior HR positions at Fugro, Arcadis, Aon, KPMG and Philips Electronics. He holds a master’s degree in Psychology. Tom has a keen interest in innovative HR, HR tech and how organizations can benefit from trend shifts. Twitter: @tomwhaak
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