2024年英语专业四级听力真题 演讲

Machine Learning

机器学习

Good morning,everyone.Today, I'd like to start with what a study has found out.Ur, in 2013，researchers from the UK did a study on the future of work.

早上好，各位。今天，我想从一项研究的发现说起。Ur, 2013年，来自英国的研究人员做了一项关于未来工作的研究。

They concluded that almost one in every two jobs has a high risk of being automated by machines.

他们得出结论，几乎每两个工作岗位中就有一个工作岗位有被机器自动化的高风险。

And machine learning is the most powerful branch of artificial intelligence.

而机器学习是人工智能中最强大的分支。

It allows machines to learn from data and mimic some of the things that humans can do.

它允许机器从数据中学习，模仿人类可以做的一些事情。

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.

现在，我想简单地讨论一下机器能做什么，不能做什么，以及哪些工作会被机器自动化或威胁。

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 1990s.

好，我们先来了解一下机器学习的历史。20世纪90年代初，机器学习开始进入工业领域。

It started with relatively simple tasks. For example,it started with things like sorting the mail by reading handwritten characters from zip codes.

一开始的任务相对简单。例如，它从通过读取邮政编码手写字母来对邮件进行分类开始。

Over the past decade.dramatic breakthroughs have been made.Now,machine learning is capable of far, far more complex tasks.

在过去的十年里，取得了巨大的突破。现在，机器学习能够处理更复杂的任务。

In 2012,a machine was built that could grade high-school essays, and it was able to match the grades given by human teachers.

2012年，一种可以给高中作文打分的机器被造出来了，而且它能和人类老师给出的分数匹敌。

Last year, researchers issued an even more difficult challenge. That is,can a machine take images of the eye and diagnose an eye disease?

去年，研究人员提出了一个更困难的挑战。也就是说，机器能拍下眼睛的图像来诊断眼疾吗？

Again,the machine was able to match the diagnosis given by human eye doctors.

再一次，这台机器能够与人类眼科医生的诊断相匹配。

Now, given the right data,machines are going to outperform humans at tasks like this.

现在，如果有合适的数据，机器将在这样的任务中胜过人类。

A teacher might read 10,000 essays over a 40-year career.An eye doctor might see 50,000 eyes in the same period.

一个教师可能在40年的职业生涯中读过1万篇论文。眼科医生可能在同一时期看5万只眼睛。

But a machine can read millions of essays or see millions of eyes within minutes.

但一台机器可以在几分钟内读取数百万篇论文或观察数百万双眼睛。

We humans have no chance of competing against machines on such frequent,high-volume tasks.

我们人类没有机会与机器在如此频繁的、高容量的任务上竞争。

Then,can machines perform all the human tasks? The answer is no. There are things we can do that machines can't.

那么，机器能完成所有的人类任务吗？答案是否定的。有些事情我们可以做，但机器做不到。

Where machines have made very little progress is in tackling novel' situations.

机器在处理新情况方面进展甚微。

That is, machines can't handle things they haven't seen many times before.

就是说，机器无法处理它们以前见过很多次的东西。

Therefore, the fundamental imitation of machine learning is that it needs to learn from large volumes of past data.

因此，机器学习的根本模仿之处在于，它需要从大量过去的数据中学习。

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.

但我们人类不需要，我们有能力连接看似完全不同的线索来解决我们以前从未见过的问题。

And this happens every day for each of us in small ways, thousands of times.

这种情况每天都在我们每个人身上以小的方式发生，成千上万次。

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.

在处理未知情况时，机器无法与我们竞争，这给机器将自动化的人类任务带来了根本性的限制。

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.

那么这对未来的工作意味着什么呢？我认为任何单一工作的未来状态都取决于对一个单一问题的回答。

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?

也就是说，这项工作在多大程度上可以减少为频繁、大量的工作，以及它在多大限度上涉及处理新颖或未知的情况？

On those frequent high-volume tasks, machines are getting smarter and smarter. Today,they grade essays.They diagnose certain diseases.

在这些频繁的大容量任务中，机器变得越来越聪明。今天，他们评分作文。他们能诊断某些疾病。

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.

我想在短时间内，他们将对我们的审计工作进行审核，并从法律合同中读取标准的法律语言。

Of course, accountants and lawyers are still needed,but they're going to be needed for complex tax structuring, for path breaking lawsuits.

当然，仍然需要会计师和律师，但复杂的税收结构和突破性的诉讼将需要他们。

But machines will shrink their ranks and make these jobs harder to come by.

但机器会缩小它们的队伍，使这些工作更难找到。

Now, as I mentioned just now, machines are not making progress on novel situations.

现在，正如我刚才提到的那样，机器在新的情况上没有取得进展。

Let me give you another example. An advertising copy behind a marketing campaign needs to grab consumers' attention.

我再举一个例子。营销活动背后的广告文案需要抓住消费者的注意力。

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.

拷贝必须从人群中脱颖而出，因为商业战略意味着发现市场的空白，其他人没有做的事情，也就是未知的事情。

And it will be humans that are creating the copy behind our marketing campaigns.

而我们营销活动背后的复制品将是人类创造的。

And it will also be humans that are developing our business strategy. Machines can't fulfill such tasks.

人类也将在制定我们的商业战略，机器无法完成这样的任务。

Okay, today we've looked at machine learning, what machines can do, what they can't do and the future of work.

好了，今天我们研究了机器学习，机器能做什么，不能做什么，以及未来的工作。

Now I'd like to leave you a question: To what extent will machines change the way we study in the future? Thank you.

现在我想留给大家一个问题:未来机器会在多大程度上改变我们的学习方式？谢谢。

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