四六级听力死磕磨耳朵 12
高效磨耳朵 | 最好的英语听力资源
提示
1.每期为一篇听力题，每篇裁剪为若干片段，每个片段重复四遍。
2.可以前两遍盲听理解，后两遍根据文字内容精听。
3.根据中英文意思，听不懂的地方多听几遍。
原文
2018.12六级第一套 recording one
Here is my baby niece Sarah. Her mum is a doctor and her dad is a lawyer.
这是我的小侄女莎拉。她的妈妈是医生，她的父亲是律师。
By the time Sarah goes to college the jobs her parents do are going to look dramatically different.
当莎拉上大学的时候，她父母的工作发生了巨大的变化。
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对未来的工作进行了研究。
They concluded that almost one in every two jobs has a high risk of being automated by machines.
他们的结论是，几乎每两个工作中就有一个被机器自动化替代的高风险。
Machine learning is the technology that's responsible for most of this disruption.
机器学习就是造成这种混乱的原因。
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 copy some of the things that humans can do.
它允许机器从数据中学习并复制人类可以做的一些事情。
My company, Kaggle, operates on the cutting edge of machine learning.
我的公司，Kaggle，在机器学习的最前沿运作。
We bring together hundreds of thousands of experts to solve important problems for industry and academia.
我们汇聚了数十万的专家来为工业界和学术界解决重大问题。
This gives us a unique perspective on what machines can do, what they can't do and what jobs they might automate or threaten.
这为我们提供了一个独特的视角，可以了解机器可以做什么，不能做什么以及可以自动化或威胁哪些工作。
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。它从相对简单的任务开始的。
It started with things like assessing credit risk from loan applications, sorting the mail by reading handwritten zip codes.
它是从例如评估贷款申请的信用风险、阅读手写的邮政编码对邮件进行分类等开始的。
Over the past few years, we have made dramatic breakthroughs.
过去几年，我们取得了重大突破。
Machine learning is now capable of far, far more complex tasks. In 2012, Kaggle challenged its community to build a program that could grade high school essays.
机器学习现在能够完成更复杂的任务。2012年，Kaggle向其社区发起挑战，建立一个可以评价高中论文的程序。
The winning programs were able to match the grades given by human teachers.
获胜程序给出的成绩能够与人类教师的相匹配。
Now given the right data, machines are going to outperform humans at tasks like this.
现在给出正确的数据，机器在这样的任务表现上将胜过人类。
A teacher might read 10,000 essays over a 40-year career. A machine can read millions of essays within minutes.
在40年的职业生涯中，老师可能会阅读10，000篇论文。一台机器可以在几分钟内阅读数百万篇论文。
We have no chance of competing against machines on frequent high-volume tasks, but there are things we can do that machines cannot.
我们没有机会在频繁的大容量任务中与机器竞争，但仍然有一些我们可以做而机器无法做到的事情。
Where machines have made very little progress is in tackling novel situations.
机器在处理新情况方面取得的进步很小。
Machines can't handle things they haven't seen many times before.
机器无法处理之前没有反复见过的事情。
The fundamental limitation of machine learning is that it needs to learn from large volumes of past data.
机器学习的基本限制是它需要从大量过去的数据中学习。
But humans don't. We have the ability to connect seemingly different threads to solve problems we've never seen before.
但是人类没有这种限制。我们有能力连接看似不同的线索来解决我们以前从未见过的问题。
问题：
16:
A) About half of current jobs might be automated.
B) The jobs of doctors and lawyers would be threatened.
C) The job market is becoming somewhat unpredictable.
D) Machine learning would prove disruptive by 2013.
17:
A) They are widely applicable for massive open online courses.
B) They are now being used by numerous high school teachers.
C) They could read as many as 10,000 essays in a single minute.
D) They could grade high-school essays just like human teachers.
18
A) It needs instructions throughout the process.
B) It does poorly on frequent, high-volume tasks.
C) It has to rely on huge amounts of previous data.
D) It is slow when it comes to tracking novel things.
