In an unmanned supermarket, a customer waits to pay. With the use of advanced digital payment technology, including biological recognition and in-depth learning, the man leaves in a minute. There's no cashier in the store. At a large university, students use a hand-scanning machine that authorizes their entry into the gym. Those people are using "smart machines" which identify people by their physical characteristics. These new devices use fingers, hands, faces, eyes and voices. Some machines may even use smells. This new technology, called biometrics, gets information from parts of the body.
In the past, biometric machines were used mainly in government agencies or in prisons. But now that the cost of the technology is lower, these machines are starting to be used everywhere, from border services to schools.
Some people, however, are concerned that these machines will mean the destruction of personal privacy. They worry that the machines will get personal information about them. know it seems like these machines are invading our privacy, but actually, biometric machines help to protect it," says Jay Tarkett, who works at a company that develops the machines, "They can be used instead of passwords on a computer, for example. They can also identify criminals at airports. So, really, they help to promote public safety, and all the information stored is kept confidential by the machine itself."
Some people don't like the idea of using fingerprints because they associate them with criminals, and feel like they are being accused of something, In addition, they don't work for some people, such as bricklayers, who wear down their fingerprints. Yet, face recognition does work well because the subject doesn't really have to do anything, To cash a check at a bank, for example, the customer has to do nothing more than look at a machine similar to an automatic teller. If the face matches the picture kept on file, the customer gets the money with no problems, The need to carry identification with you from place to place, then, would all but cease. It has been found that the hand scan works well ill the college gym. Before this machine was used, students at the college entered the gym using cards similar to credit cards. The problem was that students often lost or forgot their cards. With the hand-scanning machine, however, the problem was solved right away.
But the machines are still new, and there can be problems. For example, voice recognition works on the phone, but it is not precise, and can be tricked. Another constraint(限制)we notice；, with machines that use face recognition in particular, is that they can be fooled if people color their hair or gain a lot of weight. However, this particular problem may be solved by a new type of technology that scans a person's iris, the colored part of the eye. It can even identify the person from a few feet away, recognizing a customer as he or she approaches the ATM.
1. What's the author's intention by presenting two examples in Paragraph 1?| A．To bring in the topic of the passage. |
| B．To stress how smart machines work. |
| C．To show the popularity of smart machines. |
| D．To introduce the convenience smart machines offer. |
| A．Bitter. | B．Worried, |
| C．Satisfied. | D．Optimistic. |
| A．An introduction to biometric machines. |
| B．An advertisement for biometric machines. |
| C．A brief description of hand scanning machines. |
| D．An analysis of biometric machines' value in economy. |
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【推荐1】As reporters and editors find themselves the victims of layoffs at digital publishers and traditional newspaper chains alike, journalism generated by machine is on the rise. Roughly a third of the content published by Bloomberg News uses some form of automated technology. The system used by the company, Cyborg, is able to assist reporters in creating thousands of articles on company earnings reports each quarter. The program can analyze a financial report the moment it appears and spit out an immediate news story that includes the most important facts and figures.
In addition to covering company earnings for Bloomberg,robot reporters have been productive producers of articles on Minor League Baseball for The Associated Press (AP),high school football for The Washington Post and earthquakes for Los Angeles Times. Last week, The Guardian's Australia edition published its first machine-assisted article. And Forbes recently announced that it was testing a tool called Bertie to provide reporters with rough drafts and story templates. The New York Times has experimented with using AI to personalize newsletters.
The AP was an early adopter when it struck a deal in 2014 with Automated Insights, a technology company specializing in language generation software that produces billions of machine-generated stories a year. In addition to leaning on the software to generate minor league and college game stories, The AP has also used it to strengthen its coverage of company earnings reports. It has gone from producing 300 articles on earnings reports per quarter to 3,700.
As the use of AI has become a part of the industry's toolbox,journalism executives say it is not a threat to humans. Rather, the idea is to allow journalists to spend more time on substantive work."The work of journalism is creative, it' s about curiosity, it' s about storytelling, iť' s about digging, it' s critical thinking, it's judgment - and that is where we want our journalists to spend their energy,"says Lisa Gibbs, the director of news partnerships for The AP.
“AI was once a new shiny technology used by high tech companies,but now it' s actually becoming a necessity," says Francesco Marconi,the head of research and development at The Journal. He likened the addition of AI in newsrooms to the introduction of the telephone. “It gives you more access, and you get more information quicker," he says. “I think a lot of the tools in journalism will soon be powered by AI.”
1. What do Bloomberg and The AP have in common?| A．Both are developing language generation software. |
| B．Both use AI to produce company earnings reports. |
| C．Both work with the same AI technology company. |
| D．Both have laid off a large number of their employees. |
| A．AI is being widely applied in journalism. |
| B．Robot reporters are likely to cause problems. |
| C．Reporters and editors are facing unemployment. |
| D．Traditional media resist machine-assisted articles. |
| A．It is a well-matched substitute for journalists. |
| B．It challenges reporters to think more critically. |
| C．It liberates journalists from time-consuming tasks. |
| D．It guarantees reporters focus more on important work. |
| A．To present the weakness of telephones. |
| B．To predict journalism' s bright future. |
| C．To discuss changes in AI technology. |
| D．To show how big a step forward AI is. |
【推荐2】Ancient documents are an important part of ancient civilization. Unfortunately, such documents are often vulnerable (易受……伤害的) to aging and related damage, which greatly reduce their legibility (易读性).
An international group of computer scientists have teamed up to strike up the conversation with ancient people through their writing that have been broken down by time. They have developed a computer-assisted method to help us have a conversation with people from the distant past.
Appreciating ancient documents, even when in good condition, can require a reader to dig into the role of a cryptologist (密码学家). That’s because the combination of a writer’s handwriting legibility, lettering styles, old-fashioned spelling and grammar conventions or abbreviations (缩写) can make even a native language seem unfamiliar. Besides, the same document’s legibility can be reduced even more by faded ink, widened lettering due to exposure to wetness, and so on.
Typical digital reconstruction changes a document into black text on a white background in an attempt to reveal the text through noise filtering (过滤) and contrast improvement. However, in this current method, the image is much more of a restoration effort, keeping much of the look and virtual feel of the document.
Researchers applied a complex integration of several digital color interpretation techniques and Gaussian mixture models to identify and separate features of text, paper, and historical objects. This allows for the specific selection of different layers of information based on slight spectral (光谱的) differences. So a user could decide whether a notation (符号), page decoration, coffee mug ring stain or another feature was critical to the understanding by adding or removing layers.
In some cases, the technique could preserve features that would otherwise be lost, such as the vague watermarks of a paper manufacturer, which could offer vital insights to historians. “For example, Leonardo da Vinci’s famous works were written on pages with watermarks from various paper suppliers that allowed document historians to group them into likely writing periods,” said Usman Habib, an expert on digital restoration.
According to the paper, the processing can be accomplished on a standard desktop computer, and it takes just a few minutes. This would make it a great tool for historical document researchers and people attempting to figure out old family recipes.
1. What does the international team mainly aim to do?| A．Categorize documents of the past. |
| B．Share developments in computer science. |
| C．Analyze causes of ancient works’ breakdown. |
| D．Restore historical writing digitally. |
| A．Risky but rewarding. | B．Complicated and challenging. |
| C．Repetitive but fruitless. | D．Straightforward and accessible. |
| A．It helps remove the trouble of doing repair work. |
| B．It saves time by applying contrasting backgrounds. |
| C．It preserves the original appearance of the documents. |
| D．It raises the quality of images through noise filtering. |
| A．Translating the text into different languages. |
| B．Generating alternative copies of documents. |
| C．Identifying the dates of the related historical objects. |
| D．Getting rid of unnecessary layers of information. |
| A．Critical. | B．Unclear. | C．Favorable. | D．Intolerant. |
【推荐3】AI agents are prediction engines using the web as their memory. They do no more than predict which words are more likely to follow any other word or group of words in a given language. When you ask ChatGPT a question, it analyzes it into words and their sequence, returning answers that match those sequences opposite. It might sound like a simple trick, and it is, yet the secret sauce is the size of the database the AIs use to perform it.
Of the very various mix of content used to train ChatGPT, 60 percent was information collected from websites, blogs or social media. Another 20 percent was content shared on Reddit and evaluated relatively highly by the users. The rest was books typically found in the public field (mostly older and general purpose), with a bit of Wikipedia (3 percent) mixed in for good measure.
AI’s store for each word the probability that any other word will follow it. The quality and value of these predictions depend very much on how often and on how many circum- stances the software encounters any two (or more words) in the neighborhood, how long a sentence goes, and which sentence might follow another. When put together, these predictions favour the most influential texts of a given culture, which shaped generations upon generations of English language teachers and the students they educated.
ChatGPT speaks like a parrot because its delivery is not automatically adjusted. More re- search and engineering are needed to adjust the tool to each request’s real-life intentions and consequences. In academic learning, these situations should be the pre- and post-stages of the research process: finding arguments and packaging them for public consumption.
In their current forms, ChatGPT and its siblings (姐弟) are like those three-year-olds who can recite entire stories read to them only once. But turning a three-year-old into a learned Person takes 20 years of labour—some, structured education. It is time to stop reading Al agents stories and send them to a real school.
1. Which determines the accuracy of AI predictions?| A．Words. | B．Network. | C．Database. | D．Questions. |
| A．By listing data. | B．By giving examples. |
| C．By making comparisons. | D．By quoting experts’ arguments. |
| A．Users of AI. | B．Words’ frequency. |
| C．AI’s cultural nature. | D．The length of a sentence. |
| A．How a ChatGPT works | B．Where a ChatGPT is found |
| C．A ChatGPT needs packaging | D．A ChatGPT has a long way to go |
