評論
打印
Recording One
錄音 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.
這是我的小侄女薩拉。她媽媽是醫生，爸爸是律師。到薩拉上大學的時候，她父母的工作將會大不相同。
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，牛津大學的研究人員做了一項關于未來工作的研究。他們得出的結論是，幾乎每兩份工作中就有一份具有被機器自動化的高風險。
Machine learning is the technology that's responsible for most of this disruption. It's the most powerful branch of artificial intelligence.
機器學習是造成這種混亂的主要原因。它是人工智能最強大的分支。
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.
它讓機器從數據中學習，并復制一些人類可以做的事情。我的公司Kaggle在機器學習領域處于前沿。
We bring together hundreds of thousands of experts to solve important problems for industry and academia.
我們匯集了成千上萬的專家，為工業界和學術界解決重要問題。
This gives us a unique perspective on what machines can do, what they can't do and what jobs they might automate or threaten.
這讓我們對機器能做什么、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特的視角。
Machine learning started making its way into industry in the early'90s. It started with relatively simple tasks.
機器學習在90年代初開始進入工業領域，開始時只應用于相對簡單的任務。
It started with things like assessing credit risk from loan applications, sorting the mail by reading handwritten zip codes.
它從評估貸款申請的信用風險開始，通過閱讀手寫的郵政編碼來分類郵件。
Over the past few years, we have made dramatic breakthroughs. Machine learning is now capable of far, far more complex tasks.
過去幾年，我們取得了重大突破。機器學習現在可以完成非常復雜的任務。
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向其社區發起挑戰，要求建立一個可以給高中論文打分的程序。獲獎項目的成績與真人教師的成績相當。
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.
現在，有了正確的數據，機器將在這類任務上勝過人類。一個教師在40年的職業生涯中可能要讀一萬篇論文。
A machine can read millions of essays within minutes. We have no chance of competing against machines on frequent, high-volume tasks.
一臺機器可以在幾分鐘內閱讀數百萬篇文章。我們沒有機會在頻繁的、高容量的任務上與機器競爭。
But there are things we can do that machines cannot. Where machines have made very little progress is in tackling novel situations.
但有些事情我們可以做，而機器做不到。機器在處理新情況方面進展甚微。
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.
機器無法處理他們以前沒見過很多次的東西。機器學習的基本限制是它需要從大量的過去的數據中學習。但是人類沒有。
We have the ability to connect seemingly different threads to solve problems we've never seen before.
我們有能力連接看似不同的線程來解決我們從未見過的問題。
Questions 16 to 18 are based on the recording you have just heard.
請根據你剛剛聽到的錄音回答16 - 18題。
16. What do the researchers at Oxford University conclude?
16. 牛津大學的研究人員得出了什么結論？
17. What do we learn about Kaggle company's winning programs?
17. 關于Kaggle公司的獲獎項目，我們了解到了什么？
18. What is the fundamental limitation of machine learning?
18. 機器學習的基本限制是什么？
