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Recording One
录音 1
Here is my baby niece Sarah. Her mom is a doctor and her dad is a lawyer. By the time Sarah goes to college, the jobs her parents do are going to look dramatically different.
这是我的小侄女萨拉。她妈妈是医生，爸爸是律师。到萨拉上大学的时候，她父母的工作将会大不相同。
In 2013，researchers at Oxford University did a study on the future of work. They concluded that almost one in every two jobs has a high risk of being automated by machines.
2013年，牛津大学的研究人员做了一项关于未来工作的研究。他们得出的结论是，几乎每两份工作中就有一份具有被机器自动化的高风险。
Machine learning is the technology that's responsible for most of this disruption. It's the most powerful branch of artificial intelligence.
机器学习是造成这种混乱的主要原因。它是人工智能最强大的分支。
It allows machines to learn from data and copy some of the things that humans can do. My company, Kaggle, operates on the cutting edge of machine learning.
它让机器从数据中学习，并复制一些人类可以做的事情。我的公司Kaggle在机器学习领域处于前沿。
We bring together hundreds of thousands of experts to solve important problems for industry and academia.
我们汇集了成千上万的专家，为工业界和学术界解决重要问题。
This gives us a unique perspective on what machines can do, what they can't do and what jobs they might automate or threaten.
这让我们对机器能做什么、不能做什么以及它们可能自动化或威胁到什么工作有了一个独特的视角。
Machine learning started making its way into industry in the early'90s. It started with relatively simple tasks.
机器学习在90年代初开始进入工业领域，开始时只应用于相对简单的任务。
It started with things like assessing credit risk from loan applications, sorting the mail by reading handwritten zip codes.
它从评估贷款申请的信用风险开始，通过阅读手写的邮政编码来分类邮件。
Over the past few years, we have made dramatic breakthroughs. Machine learning is now capable of far, far more complex tasks.
过去几年，我们取得了重大突破。机器学习现在可以完成非常复杂的任务。
In 2012, Kaggle challenged its community to build a program that could grade high-school essays. The winning programs were able to match the grades given by human teachers.
2012年，Kaggle向其社区发起挑战，要求建立一个可以给高中论文打分的程序。获奖项目的成绩与真人教师的成绩相当。
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.
现在，有了正确的数据，机器将在这类任务上胜过人类。一个教师在40年的职业生涯中可能要读一万篇论文。
A machine can read millions of essays within minutes. We have no chance of competing against machines on frequent, high-volume tasks.
一台机器可以在几分钟内阅读数百万篇文章。我们没有机会在频繁的、高容量的任务上与机器竞争。
But there are things we can do that machines cannot. Where machines have made very little progress is in tackling novel situations.
但有些事情我们可以做，而机器做不到。机器在处理新情况方面进展甚微。
Machines can't handle things they haven't seen many times before. The fundamental limitation of machine learning is that it needs to learn from large volumes of past data. But humans don't.
机器无法处理他们以前没见过很多次的东西。机器学习的基本限制是它需要从大量的过去的数据中学习。但是人类没有。
We have the ability to connect seemingly different threads to solve problems we've never seen before.
我们有能力连接看似不同的线程来解决我们从未见过的问题。
Questions 16 to 18 are based on the recording you have just heard.
请根据你刚刚听到的录音回答16 - 18题。
16. What do the researchers at Oxford University conclude?
16. 牛津大学的研究人员得出了什么结论？
17. What do we learn about Kaggle company's winning programs?
17. 关于Kaggle公司的获奖项目，我们了解到了什么？
18. What is the fundamental limitation of machine learning?
18. 机器学习的基本限制是什么？
