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[originaltext] Here is my baby niece Sarah. Her mum is a doctor and her dad
[originaltext] Here is my baby niece Sarah. Her mum is a doctor and her dad
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2024-04-01
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问题
Here is my baby niece Sarah. Her mum 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.
In 2013, researchers at Oxford University did a study on the future of work. [16] They concluded that almost one in every two jobs has a high risk of being automated 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 copy 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 an unique perspective on what machines can do, what they can’t do and what jobs they might automate or threaten.
Machine learning started making its way into industry in the early 90s. It started with relatively simple tasks. It started with things like assessing credit risk from loan applications, sorting the mail by reading handwritten 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 a program that could grade high school essays. [17] The winning programs were able to match the grades given by human teachers. 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. A machine can read millions of essays within minutes. We have no chance of competing against machines on frequent, high-volume tasks, but there are things we can do that machines cannot. Where machines have made very little progress is in tackling novel situations. Machines can’ t handle things they haven’ t seen many times before. [18] The fundamental limitation of machine learning is that it needs to learn from large volumes of past data. But humans don’ t. We have the ability to connect seemingly different threads to solve problems we’ ve never seen before.
Questions 16 to 18 are based on the recording you have just heard.
16. What did the researchers at Oxford University conclude?
17. What do we learn about Kaggle company’ s winning programs?
18. What is the fundamental limitation on machine learning?
选项
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.
答案
D
解析
题干问的是关于Kaggle公司获奖的项目,我们知道些什么。讲座中提到,Kaggle向其他领域提出挑战,要求建立一个可以对高中论文进行评分的项目。其获奖项目的评卷分数能够与老师给学生们的评分相媲美,故答案为D(它们可以像老师一样为高中论文评分)。A项(它们广泛适用于大规模的在线开放课程)、B项(它们正在被无数的高中老师使用)讲座中未提及,故排除。C项(它们可以在1分钟内阅读高达10000篇论文)与原文内容不符,原文提到一个教师在40年的职业生涯中可能会阅读10000篇论文,故排除。
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