So this is my niece. Her name is Yahli. She is nine months old.
这是我的侄女。她叫Yahli。她只有九个月大。
Her mum is a doctor, and her dad is a lawyer.
她妈妈是一名医生，爸爸是一名律师。
By the time Yahli goes to college, the jobs her parents do are going to look dramatically different.
等到Yahli上大学的时候，像她父母这样的工作将面目全非。
In 2013, researchers at Oxford University did a study on the future of work.
2013年，牛津大学的研究人员做了一项关于未来就业的研究。
They concluded that almost one in every two jobs have a high risk of being automated by machines.
他们得出结论：差不多将近一半的工作都有被机器自动化取代的危险。
Machine learning is the technology that's responsible for most of this disruption.
而机器学习应对这种颠覆负主要责任。
It's the most powerful branch of artificial intelligence.
它是人工智能最强大的分支。
It allows machines to learn from data and mimic some of the things that humans can do.
允许机器从现有数据中学习，并模仿人类的所作所为。
My company, Kaggle, operates on the cutting edge of machine learning.
我的公司Kaggle专注于尖端的机器学习。
We bring together hundreds of thousands of experts to solve important problems for industry and academia.
我们召集了成千上万的专家，正为工业和学术界寻找重要问题的答案。
This gives us a unique perspective on what machines can do, what they can't do and what jobs they might automate or threaten.
因此，我们可以从独特的视角来观察，机器可以做什么，不可以做什么，哪些工作可以被自动化或受到威胁。
Machine learning started making its way into industry in the early '90s. It started with relatively simple tasks.
机器学习是在90年代初进入人们的视野。一开始，它只是执行一些相对简单的任务。
It started with things like assessing credit risk from loan applications, sorting the mail by reading handwritten characters from zip codes.
像评估贷款申请的信用风险，通过识别手写的邮政编码来检索邮件。
Over the past few years, we have made dramatic breakthroughs.
在过去几年里，我们取得了突破性进展。
Machine learning is now capable of far, far more complex tasks.
现在，机器学习可以完成非常复杂的任务。
In 2012, Kaggle challenged its community to build an algorithm that could grade high-school essays.
2012年，Kaggle给当地学校出了个难题，设计一个算法来评判高中作文。
The winning algorithms were able to match the grades given by human teachers.
获胜的算法给出的分数居然和真正老师给出的分数相符。
Last year, we issued an even more difficult challenge.
去年，我们出了一道更难的题。
Can you take images of the eye and diagnose an eye disease called diabetic retinopathy?
你能从拍摄出的眼睛图像中诊断出糖尿病性视网膜病变吗？
Again, the winning algorithms were able to match the diagnoses given by human ophthalmologists.
再一次，获胜的演算法给出的诊断和眼科医生的诊断相符。
Now, given the right data, machines are going to outperform humans at tasks like this.
类似于这样的任务，只要给定正确的数据，机器将完全超越人类。
A teacher might read 10,000 essays over a 40-year career. An ophthalmologist might see 50,000 eyes.
一位老师在40年的职业生涯中可能审阅一万篇作文。一名眼科医生，大概可以检查5万只眼睛。
A machine can read millions of essays or see millions of eyes within minutes.
但在短短几分钟之内，机器可以审阅百万篇文章或检查数百万只眼睛。

We have no chance of competing against machines on frequent, high-volume tasks.
对于频繁，大批量的任务，我们无法与机器抗衡。
But there are things we can do that machines can't do.
但有些事情机器却无能为力。
Where machines have made very little progress is in tackling novel situations.
机器在解决新情况方面进展甚微。
They can't handle things they haven't seen many times before.
它们还不能处理未曾反复接触的事情。
The fundamental limitations of machine learning is that it needs to learn from large volumes of past data. Now, humans don't.
机器学习致命的局限性在于，它需要从大量已知的数据中总结经验。人类则不然。
We have the ability to connect seemingly disparate threads to solve problems we've never seen before.
我们有一种能把看似毫不相关的事物联系起来的能力，从而解决从未见过的问题。
Percy Spencer was a physicist working on radar during World War II, when he noticed the magnetron was melting his chocolate bar.
Percy Spencer是一个物理学家，在二战期间从事雷达的研究工作，他注意到磁控管融化了他的巧克力。
He was able to connect his understanding of electromagnetic radiation with his knowledge of cooking in order to invent -- any guesses -- the microwave oven.
他从对电磁辐射的理解，联想到烹饪，因此发明了--猜猜是什么？--微波炉。
Now, this is a particularly remarkable example of creativity.
这是个非常杰出的创新例子。
But this sort of cross-pollination happens for each of us in small ways thousands of times per day.
但这种跨界转型，每天正以难以察觉的方式在我们身边发生成千上百次。
Machines cannot compete with us when it comes to tackling novel situations,
在创新方面，机器无法与我们抗衡，
and this puts a fundamental limit on the human tasks that machines will automate.
这将使机器自动化取代人工受到限制。
So what does this mean for the future of work? The future state of any single job lies in the answer to a single question:
那么这对未来的工作意味着什么呢？未来工作的状态完全取决于一个问题：
To what extent is that job reducible to frequent, high-volume tasks, and to what extent does it involve tackling novel situations?
这种工作在多大程度上可以简化为频繁，大批量的任务，又涉及多少对创新能力的要求？
On frequent, high-volume tasks, machines are getting smarter and smarter.
对于那些频繁，大批量的任务，机器变得越来越智能。
Today they grade essays. They diagnose certain diseases.
如今，它们可以评判作文，诊断某些疾病。
Over coming years, they're going to conduct our audits, and they're going to read boilerplate from legal contracts.
再过几年，它们将可以进行审计，将能审阅法律合同样本。
Accountants and lawyers are still needed. They're going to be needed for complex tax structuring, for pathbreaking litigation.
尽管会计师和律师还是需要的。但他们只需要研究复杂的税收结构或无先例的诉讼过程。
But machines will shrink their ranks and make these jobs harder to come by.
但机器将会挤占他们的位置，增加就业难度。
Now, as mentioned, machines are not making progress on novel situations.
如上所述，在创新方面机器没有取得太大进展。
The copy behind a marketing campaign needs to grab consumers' attention. It has to stand out from the crowd.
营销文案需要抓住消费者的心理。脱颖而出是关键。
Business strategy means finding gaps in the market, things that nobody else is doing.
商业策略需要找到市场上还无人问津的空白。
It will be humans that are creating the copy behind our marketing campaigns, and it will be humans that are developing our business strategy.
人类将是营销文案的创造者，人类才能推动商业战略发展。
So Yahli, whatever you decide to do, let every day bring you a new challenge.
所以Yahli，无论你将来决定做什么，让每一天都带给你新的挑战。
If it does, then you will stay ahead of the machines. Thank you.
如果是那样，你的未来将无法被机器取代。谢谢。