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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 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. (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.
未听先知
预览三道题各选项,由第16题中反复出现的jobs一词和 might. would be, would prove等词可以 初步推测,讲座内容与未来的工作趋势有关;再结合 automated、 online、 grade high-school essays just like human teachers等可以进一步推测,讲座内容涉及机器取代人类工作。
详解详析
16. What do the researchers at Oxford University conclude?
A)。(详解)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 半将很有可能会被机器自动化取代,答案为A)。
17. What do we learn about Kaggle company's winning programs?
D)。(详解)讲座中提到,Kage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)。
18. What is the fundamental limitation of machine learning?
C)。(详解)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)。