2024年6月英语专业四级考试参考答案，来啦！

专四听力Talk部分，巨难吗？考的是Machine Learning（就是人工智能的哈，文中说机器学习是人工智能最强大的分支）。

参考答案就在如下文

Machine learning
Good morning, everyone.Today I'd like to start with what a study has found out. Ah, in 2013 researchers from the UK 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. And machine learning is the most powerful branch of artificial intelligence. It allows machines to learn from data and mimic some of the things that humans can do. Now, I'd like to discuss briefly what machines can do and what they can't do and what jobs they might automate or threaten. Okay, let's begin with a bit of the history of machine learning.

Machine learning started making its way into the industrial world in the early nineteen nineties. It started with relatively simple tasks. For example, it started with things like sorting the mail by reading handwritten characters from zip codes. Over the past decade, dramatic breakthroughs have been made. Now, machine learning is capable of far, far more complex tasks. In 2012 a machine was built that could grade high school essays, and it was able to match the grades given by human teachers. Last year, researchers issued an even more difficult challenge. That is, can a machine take images of the eye and diagnose an eye disease? Again, the machine was able to match the diagnosis given by human eye doctors.Now, given the right data, machines are going to outperform humans at tasks like this. A teacher might read 10,000 essays over a forty-year career, an eye doctor might see 50,000 eyes in the same period, but a machine can read millions of essays or see millions of eyes within minutes. We humans have no chance of competing against machines on such frequent high volume tasks.

Then, can machines perform all the human tasks? The answer is no. There are things we can do that machines can't. Where machines have made very little progress is in tackling novel situations. That is, machines can't handle things they haven't seen many times before. Therefore, the fundamental limitation of machine learning is that it needs to learn from large volumes of past data. But we humans don't have to. We have the ability to connect seemingly entirely different threads to solve problems we've never seen before, and this happens every day for each of us in small ways, thousands of times. Machines cannot compete with us when it comes to tackling unknown situations, and this puts a fundamental limit on the human tasks that machines will automate. So what does this mean for the future of work? I think the future state of any single job lies in the answer to a single question. That is, to what extent is that job reducible to frequent high volume tasks? And to what extent does it involve tackling novel or unknown situations? On those frequent high volume tasks, machines are getting smarter and smarter. Today, they grade essays, they diagnose certain diseases. I guess in a short time they're going to conduct our audits and they're going to read the standard legal language from legal contracts. Of course, accountants and lawyers are still needed, but they're going to be needed for complex tax structuring, for path breaking lawsuits. But machines will shrink their ranks and make these jobs harder to come by.

Now, as I mentioned just now, machines are not making progress on novel situations. Let me give you another example. An advertising copy behind a marketing campaign needs to grab consumers attention. The copy has to stand out from the crowd because business strategy means finding gaps in the market, things that nobody else is doing. That is something unknown and it will be humans that are creating the copy behind our marketing campaigns and it will also be humans that are developing our business strategy. Machines can't fulfill such tasks.

Okay, today we've looked at machine learning what machines can do, what they can't do and the future of work. Now I'd like to leave you a question. To what extent will machines change the way we study in the future? Thank you.

来源：@雅思写作胡歌fisher

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来源：有道考神专四专八

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