听力原文：
Recording One

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. 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. 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 a 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. 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. 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.

Q16：What do the researchers at Oxford University conclude?

讲座开头提到，2013年，牛津大学的研究人员针对工作的未来做了一项研究，他们得出的结论是，几乎每两份工作中就有一份有被机器自动化所取代的风险。也就是说，现在的所有工作中，有一半将很有可能会被机器自动化取代，答案为A。

听力原文：
Recording One

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. 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. 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 a 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. 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. 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.

Q17：What do we learn about Kaggle company's winning programs?

讲座中提到，Kaggle公司那些赢得比赛的程序给高中生文章的评分与高中教师所给出的评分基本一致。因此答案为D。

听力原文：
Recording One

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. 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. 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 a 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. 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. 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.

Q18：What is the fundamental limitation of machine learning?

讲座最后提到，机器学习根本的局限性在于它需要依赖大量的过往数据才能够掌握某种技能。因此答案为C。