【英语语言学习】将由机器来完成的工作

So this is my niece. Her name is Yahli. She is nine months old. Her mum is a doctor, and her dad is a lawyer. By the time Yahli goes to college, the jobs her parents do are going to look dramatically different.

In 2013, researchers at Oxford¹ University did a study on the future of work. They concluded that almost one in every two jobs have a high risk of being automated³ by machines. Machine learning is the technology that's responsible for most of this disruption. It's the most powerful branch of artificial intelligence. It allows machines to learn from data and mimic⁴ some of the things that humans can do. My company, Kaggle, operates on the cutting edge of machine learning. We bring together hundreds of thousands of experts to solve important problems for industry and academia. This gives us a unique perspective on what machines can do, what they can't do and what jobs they might automate² or threaten.

Machine learning started making its way into industry in the early '90s. It started with relatively⁵ simple tasks. It started with things like assessing credit risk from loan applications, sorting the mail by reading handwritten characters from zip codes. Over the past few years, we have made dramatic breakthroughs. Machine learning is now capable of far, far more complex tasks. In 2012, Kaggle challenged its community to build an algorithm that could grade high-school essays. The winning algorithms were able to match the grades given by human teachers. Last year, we issued an even more difficult challenge. Can you take images of the eye and diagnose an eye disease called diabetic retinopathy? Again, the winning algorithms were able to match the diagnoses given by human ophthalmologists.

Now, given the right data, machines are going to outperform humans at tasks like this. A teacher might read 10,000 essays over a 40-year career. An ophthalmologist might see 50,000 eyes. A machine can read millions of essays or see millions of eyes within minutes. We have no chance of competing against machines on frequent, high-volume tasks.

But there are things we can do that machines can't do. Where machines have made very little progress is in tackling novel situations. They can't handle things they haven't seen many times before. The fundamental limitations of machine learning is that it needs to learn from large volumes of past data. Now, humans don't. We have the ability to connect seemingly disparate threads to solve problems we've never seen before.

Percy Spencer was a physicist⁶ working on radar⁷ during World War II, when he noticed the magnetron was melting his chocolate bar. He was able to connect his understanding of electromagnetic radiation with his knowledge of cooking in order to invent -- any guesses? -- the microwave oven.

Now, this is a particularly remarkable⁸ example of creativity. But this sort of cross-pollination happens for each of us in small ways thousands of times per day. Machines cannot compete with us when it comes to tackling novel situations, and this puts a fundamental limit on the human tasks that machines will automate.

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: To what extent is that job reducible to frequent, high-volume tasks, and to what extent does it involve tackling novel situations? On frequent, high-volume tasks, machines are getting smarter and smarter. Today they grade essays. They diagnose certain diseases. Over coming years, they're going to conduct our audits⁹, and they're going to read boilerplate from legal contracts. Accountants and lawyers are still needed. They're going to be needed for complex tax structuring, for pathbreaking litigation. But machines will shrink their ranks and make these jobs harder to come by.

Now, as mentioned, machines are not making progress on novel situations. The copy behind a marketing¹⁰ campaign needs to grab consumers' attention. It has to stand out from the crowd. Business strategy means finding gaps in the market, things that nobody else is doing. It will be humans that are creating the copy behind our marketing campaigns, and it will be humans that are developing our business strategy.

So Yahli, whatever you decide to do, let every day bring you a new challenge. If it does, then you will stay ahead of the machines.

Thank you.

1 Oxford   

n.牛津（英国城市）

参考例句：

• At present he has become a Professor of Chemistry at Oxford.他现在已是牛津大学的化学教授了。
• This is where the road to Oxford joins the road to London.这是去牛津的路与去伦敦的路的汇合处。

2 automate   

v.自动化；使自动化

参考例句：

• Many banks have begun to automate.许多银行已开始采用自动化技术。
• To automate the control process of the lathes has become very easy today.使机床的控制过程自动化现已变得很容易了。

3 automated   

a.自动化的

参考例句：

• The entire manufacturing process has been automated. 整个生产过程已自动化。
• Automated Highway System (AHS) is recently regarded as one subsystem of Intelligent Transport System (ITS). 近年来自动公路系统(Automated Highway System,AHS)，作为智能运输系统的子系统之一越来越受到重视。

4 mimic   

v.模仿，戏弄；n.模仿他人言行的人

参考例句：

• A parrot can mimic a person's voice.鹦鹉能学人的声音。
• He used to mimic speech peculiarities of another.他过去总是模仿别人讲话的特点。

5 relatively   

adv.比较...地，相对地

参考例句：

• The rabbit is a relatively recent introduction in Australia.兔子是相对较新引入澳大利亚的物种。
• The operation was relatively painless.手术相对来说不痛。

6 physicist   

n.物理学家，研究物理学的人

参考例句：

• He is a physicist of the first rank.他是一流的物理学家。
• The successful physicist never puts on airs.这位卓有成就的物理学家从不摆架子。

7 radar   

n.雷达，无线电探测器

参考例句：

• They are following the flight of an aircraft by radar.他们正在用雷达追踪一架飞机的飞行。
• Enemy ships were detected on the radar.敌舰的影像已显现在雷达上。

8 remarkable   

adj.显著的，异常的，非凡的，值得注意的

参考例句：

• She has made remarkable headway in her writing skills.她在写作技巧方面有了长足进步。
• These cars are remarkable for the quietness of their engines.这些汽车因发动机没有噪音而不同凡响。

9 audits   

n.审计，查账( audit的名词复数 )v.审计，查账( audit的第三人称单数 )

参考例句：

• Requires that use of all bond funds is subject to independent audits. 需要使用的所有债券基金是受独立审计。 来自互联网
• Support the locations during customer-visits, audits and quality-improvement programs. 支持客户参观，稽核和提高品质等项目。 来自互联网

10 marketing   

n.行销，在市场的买卖，买东西

参考例句：

• They are developing marketing network.他们正在发展销售网络。
• He often goes marketing.他经常去市场做生意。