无限版视频在线观看,色爱精品视频

【真題完整試卷在”公-重-浩“：超能資料庫】

聽力試題、聽力原文、答案：

一、聽力試題：

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.

二、聽力原文

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.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. 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 10000 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.
Question 16. What did the researchers at Oxford University conclude?
Question 17. What do we learn about Kaggle companies winning programs?
Question 18. What is the fundamental limitation on machine learning?

三、答案

ADC

【翻譯】

這是我的小侄女薩拉。她媽媽是醫(yī)生，爸爸是律師。到薩拉上大學(xué)的時候，她父母的工作將會大不相同。

2013年，牛津大學(xué)的研究人員做了一項關(guān)于未來工作的研究。他們得出的結(jié)論是，幾乎每兩份工作中就有一份具有被機(jī)器自動化的高風(fēng)險。

機(jī)器學(xué)習(xí)是造成這種混亂的主要原因。它是人工智能最強(qiáng)大的分支。

它讓機(jī)器從數(shù)據(jù)中學(xué)習(xí)，并復(fù)制一些人類可以做的事情。我的公司Kaggle在機(jī)器學(xué)習(xí)領(lǐng)域處于前沿。

我們匯集了成千上萬的專家，為工業(yè)界和學(xué)術(shù)界解決重要問題。

這讓我們對機(jī)器能做什么、不能做什么以及它們可能自動化或威脅到什么工作有了一個獨(dú)特的視角。

機(jī)器學(xué)習(xí)在90年代初開始進(jìn)入工業(yè)領(lǐng)域，開始時只應(yīng)用于相對簡單的任務(wù)。

它從評估貸款申請的信用風(fēng)險開始，通過閱讀手寫的郵政編碼來分類郵件。

過去幾年，我們?nèi)〉昧酥卮笸黄啤C(jī)器學(xué)習(xí)現(xiàn)在可以完成非常復(fù)雜的任務(wù)。