Applying for jobs online is one of the worst digital experiences of our time.
在线申请工作是我们这个时代最糟糕的数字化体验之一。
And applying for jobs in person really isn't much better.
面对面交谈也没好到哪儿去。
Hiring as we know it is broken on many fronts. It's a terrible experience for people.
众所周知，招聘方式在很多方面一团糟。对人们来说这是一个糟糕的经历。
About 75 percent of people who applied to jobs using various methods in the past year said they never heard anything back from the employer.
在过去一年使用多种方式申请工作时的群体中，大约有75%的人说他们从未收到雇主的任何反馈。
And at the company level it's not much better.
对公司来说，这不是一件好事情。
46 percent of people get fired or quit within the first year of starting their jobs. It's pretty mind-blowing.
在开始工作的不到一年时间里，46%的人被解雇或者主动离职。这一点很令人震惊。
It's also bad for the economy.
这种现象对经济也产生了负面影响。
For the first time in history, we have more open jobs than we have unemployed people,
在历史上第一次，招聘岗位超过了无业人员的人数，
and to me that screams that we have a problem.
对我而言，这意味着出问题了。
I believe that at the crux of all of this is a single piece of paper: the résumé.
我认为这一切的关键在于一张纸：简历。
A résumé definitely has some useful pieces in it: what roles people have had, computer skills, what languages they speak,
毫无疑问，简历中包含着一些有用的信息：人们扮演过哪些角色，有哪些计算机技能，精通什么语言，
but what it misses is what they have the potential to do that they might not have had the opportunity to do in the past.
但并未提及他们有哪方面的潜力，这些事情他们在过去可能没机会去做。
And with such a quickly changing economy where jobs are coming online that might require skills that nobody has,
在变化如此迅速的经济环境中，在线发布的工作机会可能要求的都是没人掌握的技术，
if we only look at what someone has done in the past,
如果我们只看一个人过去做了什么，
we're not going to be able to match people to the jobs of the future.
就不能把这个人和未来的工作匹配起来。
So this is where I think technology can be really helpful.
所以我认为这是技术真正有用的地方。
You've probably seen that algorithms have gotten pretty good at matching people to things,
您可能已经看到了算法如何很好的把人和事物匹配起来，
but what if we could use that same technology to actually help us find jobs that we're really well-suited for?
但是如果我们把同样的技术用于真正帮助找到那些为我们量身打造的工作昵？
But I know what you're thinking. Algorithms picking your next job sounds a little bit scary,
我知道你在想什么。让算法为你挑拣下一份工作听起来有点离谱，
but there is one thing that has been shown to be really predictive of someone's future success in a job,
但有个东西已经被证明能够成功预测某人是否能胜任未来的工作，
and that's what's called a multimeasure test.
这就是所谓的多评估测试。
Multimeasure tests really aren't anything new, but they used to be really expensive
多评估测试并不是什么新概念，但是它们曾经价格不菲，
and required a PhD sitting across from you and answering lots of questions and writing reports.
并且需要一个博士坐在你对面，回答一堆问题并且整理成报告。
Multimeasure tests are a way to understand someone's inherent traits -- your memory, your attentiveness.
多评估测试是一种用来理解某人内在特质的方法--你的记忆力，你的专注力。
What if we could take multimeasure tests and make them scalable and accessible,
如果我们能够做多评估测试，让公众都可以参与，
and provide data to employers about really what the traits are of someone who can make them a good fit for a job?
并且把相关数据提供给雇主，比如某个人的某些特质使其真的很适合这个工作，会怎样？
This all sounds abstract. Let's try one of the games together.
这些听起来很抽象。让我们一起试试其中一个游戏。
You're about to see a flashing circle,
你将要看到一个闪烁的圆，
and your job is going to be to clap when the circle is red and do nothing when it's green.
你的任务就是当圆是红色时鼓掌，当圆是绿色时什么也不做。

Maybe you're the type of person who claps the millisecond after a red circle appears.
或许你是那种在红色圆出现后毫秒内鼓掌的人。
Or maybe you're the type of person who takes just a little bit longer to be 100 percent sure.
或者你是另外一种人，那种需要多花点时间，等到100%确认才行动的人。
Or maybe you clap on green even though you're not supposed to.
或者你在还不确定时就为绿色圆鼓掌。
The cool thing here is that this isn't like a standardized test where some people are employable and some people aren't.
很酷的一点是这并不像是个标准的测试，那种决定能被雇佣与否的测试。
Instead it's about understanding the fit between your characteristics and what would make you good a certain job.
相反，这是个理解你的特性和适合你的工作之间的匹配度测试。
We found that if you clap late on red and you never clap on the green,
我们发现如果你在红色时鼓掌晚，而在绿色时从不鼓掌，
you might be high in attentiveness and high in restraint.
你可能具备高度专注力，能够很好的自我约束。
People in that quadrant tend to be great students, great test-takers, great at project management or accounting.
在那个象限的人们往往擅长学习和考试，精于项目管理和财会。
But if you clap immediately on red and sometimes clap on green, that might mean that you're more impulsive and creative,
如果你在红色时立刻鼓掌，并且有时在绿色鼓掌，那意味着你可能易冲动并且具备创造性，
and we've found that top-performing salespeople often embody these traits.
我们发现顶级的商人经常会表现出这些特质。
The way we actually use this in hiring is we have top performers in a role go through neuroscience exercises like this one.
我们在招聘中使用它的方式是我们让角色中表现出色的人参与类似的神经科学训练。
Then we develop an algorithm that understands what makes those top performers unique.
然后我们开发了一个算法来理解是什么让这些表现出众者脱颖而出。
And then when people apply to the job, we're able to surface the candidates who might be best suited for that job.
然后当人们申请工作的时候，我们就会优先列出最适合那项工作的候选人。
So you might be thinking there's a danger in this.
你可能在思考其中存在的风险。
The work world today is not the most diverse and if we're building algorithms based on current top performers,
当今的职场多样性仍有待提高，如果我们基于当下的出众员工构建算法，
how do we make sure that we're not just perpetuating the biases that already exist?
要怎样确保我们不是在固守既有的偏见呢？
For example, if we were building an algorithm based on top performing CEOs and use the S&P 500 as a training set,
例如，如果我们基于顶尖表现的CEO构建一个算法并且使用S&P500作为一个训练集，
you would actually find that you're more likely to hire a white man named John than any woman.
你将会发现更可能雇佣一个叫约翰的白人男子而非任何女性。
And that's the reality of who's in those roles right now.
这是目前谁正处在这个角色的现实。
But technology actually poses a really interesting opportunity.
但是技术实际上给出了一个真正有趣的机会。
We can create algorithms that are more equitable and more fair than human beings have ever been.
我们可以创造一些比人类任何时候都更平等和更公正的算法。
Every algorithm that we put into production has been pretested to ensure that it doesn't favor any gender or ethnicity.
每一个我们投入生产的算法都会被预先进行测试以确保它不会偏爱任何性别或者种族。
And if there's any population that's being overfavored, we can actually alter the algorithm until that's no longer true.
如果有任何人群正在被过度偏爱，我们可以调整算法直到该现象消失。
When we focus on the inherent characteristics that can make somebody a good fit for a job,
当我们关注在那些让一个人非常适合一个工作的内在特质时，
we can transcend racism, classism, sexism, ageism -- even good schoolism.
我们可以超越种族，阶级，性别和老龄化主义--甚至是名校背景。
Our best technology and algorithms shouldn't just be used for helping us find our next movie binge or new favorite Justin Bieber song.
我们最好的技术和算法不应该只用于帮助寻找我们的下一个卖座电影或者贾斯汀·比伯的新歌。
Imagine if we could harness the power of technology to get real guidance on what we should be doing based on who we are at a deeper level.
想象一下如果我们能够利用技术的力量，在更深层次上理解我们是谁，并得到一个我们应该做什么的真正指引会怎样。