题目内容
【题目】
【1】落日的美丽就是使我印象最深刻的东西。
The___________ of the sunset is _________ _________me most.
【2】云朵漂浮在空中时，遮住了月亮，天色暗了下来。
____________in the sky, the clouds _________ _______the moon and it was dark.
【3】你又胖了， 你本该咨询医生的。
You are ______ ______ weight again. You ________ ________ ________the doctor
【4】他决定通过努力工作来赚船费而不想冒险。
Rather than _________a chance, he decided to________ his_______ by working hard.
【5】我坚信你会遵守诺言的
I have stong__________ in you _______you will keep your ___________。
【6】他在乡村长大，这就是他喜爱乡村生活的原因。
He was________________ in the country, which ___________ for his love for the country life.
【答案】
【1】beautywhatimpresses
【2】Floatingblockedout
【3】puttingonshouldhaveconsulted
【4】takeearnpassage
【5】beliefthatword/promise
【6】broughtupaccounted
【解析】根据所给汉语完成句子。
【1】此处是what引导的表语从句，根据所给汉语，故答案为beauty what impresses。
【2】现在分词做时间状语，再根据所给汉语，故答案为beauty what impresses。
【3】固定句式：should have done本应该做而没有做，根据所给汉语可知答案为 putting on should have consulted。
【4】 固定词组：take a chance冒险。再根据所给汉语，可知答案为take earn passage。
【5】固定词组：keep one’s word/promise 遵守诺言。再根据所给汉语可知答案为word/promise。
【6】固定词组：bring up养育；account for对---作出解释，说明---的原因。再根据所给汉语可知答案为brought up accounted。
情态动词+have done结构
1.“must + have + 过去分词”表示对过去发生的事情或状态进行推测,语气比较坚定,通常只用于肯定句.如：It must have rained last night,for the ground is wet.
2.“can / could + have + 过去分词”表示对过去某种情况的怀疑或不确定.can和could一般用于否定句和疑问句,could的语气较can弱.如：He can't have finished the work so soon.
3.“may / might + have + 过去分词”表示对已发生的动作或存在的状态进行不肯定的推测,might的语气比may弱一点.这种结构主要用于肯定句和否定句,疑问句改用can或could.如：They may not have known it beforehand.
4.“need + have + 过去分词”表示过去做了不必做或不需要做的事情,或过去做某事纯属多余.如：I needn't have bought so much wine—only five people came.
5.“should / ought to + have + 过去分词”表示过去本该做某事但没做,其否定式表示过去不该做某事但做了,这种句式含有不满或责备之意,ought to的语气比should强一些.如：You ought to / should have studied harder.你本应该更努力学习的.（但没有）
6.“would ＋ have ＋ 过去分词”表示对过去的某种情况进行猜测,或本来要做某事却因某种原因未做成,通常用来说明某一情况,但不像用should或ought to那样含有责备之意.如：I guess the poet would have been about twenty when she wrote her first poem.
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Artificial intelligence (AI) is rushing into business. Firms of all types are using AI to forecast demand, hire workers and deal with customers. The McKinsey Global Institute, a think-tank within a consultancy, forecasts that just applying AI to marketing, sales and supply chains could create economic value of $2.7trn over the next 20 years.
Such grand forecasts fuel anxiety as well as hope. Less familiar, but just as important, is how AI will transform the workplace.
Start with the benefits.AI ought to improve productivity. Humanyze, a people analytics software provider, combines data from its badges（工牌）with employees’ calendars and e-mails to work out, say, whether office layouts favour teamwork .Slack, a workplace messaging app, helps managers assess how quickly employees accomplish tasks. Companies will see when workers are not just dozing off but also misbehaving.
Employees will gain, too. Thanks to advance in computer vision, AI can check that workers are wearing safety equipment and that no one has been harmed on the factory floor. Some will appreciate more feedback on their work and welcome a sense of how to do better.
Machines can help ensure that pay rises and promotions go to those who deserve them. That starts with hiring. People often have biases but algorithms（算法）, if designed correctly, can be more unprejudiced. Software can flag patterns that people might miss.
Yet AI’s benefits will come with many potential drawbacks. Algorithms may not be free of the biases of their programmers, which can have unintended consequences. The length of a travel may predict whether an employee will quit a job, but this focus may harm poorer applicants. Older staff might work more slowly than younger ones and could risk losing their positions if all AI looks for is productivity. And surveillance（监控）may feel Orwellian—a sensitive matter now that people have begun to question how much Facebook and other tech giants know about their private lives.
As regulators and employers weigh the pros and cons of AI in the workplace, three principles ought to guide its spread. First, data should be anonymized where possible. Microsoft, for example, has a product that shows individuals how they manage their time in the office, but gives managers information only in aggregated（整合）form. Second, the use of AI ought to be transparent. Employees should be told what technologies are being used in their workplaces and which data are being gathered. As a matter of routine, algorithms used by firms to hire, fire and promote should be tested for bias and unintended consequences. Last, countries should let individuals request their own data, whether they are ex-workers wishing to contest a dismissal or jobseekers hoping to demonstrate their ability to prospective employers.
The march of Al into the workplace calls for trade-offs between privacy and performance. A fairer, more productive workforce is a prize worth having, but not if it chains employees. Striking a balance will require thought, a willingness for both employers and employees to adapt and a strong dose of humanity.
AI Spy | |
Passage outline | Supporting details |
Introduction | While its future in business is full of 【1】, AI affects the workplace negatively. |
Advantages of AI | ·AI makes business more productive by analyzing the office layout, assessing the employees’ working efficiency and 【2】 their behavior. ·AI can 【3】 employees’ safety and provide feedback for them to better themselves. ·AI helps businesses hire more suitable employees and develop a better 【4】 of promotion and pay rise. |
Potential drawbacks of AI | ·Undesirable results may arise due to the biases of the programmers. ·Poorer applicants and older staff are at a 【5】 ·Employees’ privacy is 【6】 in the age of AI. |
Principles 【7】 AI’s spread | ·Keep the data anonymous when they are gathered and used. 【8】 employees of technologies used in the workplace and test the algorithms to avoid undesirable results. ·【9】 employees to access data for their own sake. |
Summary | Only when employees and employers are 【10】 to adapt and respect each other, can AI make workplace fairer and more productive. |