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题型:阅读理解-阅读单选 难度:0.4 引用次数:101 题号:10395475

In a world where nearly 6 million fingerprint records of government employees are stolen in one computer hack, and where millions of people are victims of identity theft every year, the next step in cyber security may well be mapping your brain.

Researchers at Binghamton University, State University of New York are working on a biometric (生物特征 识别的)system that records how your brain reacts to certain images. With a little more polishing, the scientists' brainchild could become the way you get into a safe deposit box, your office or past scanners at the airport. It could replace the password for your online banking, your email or your social media accounts.

They started their project by measuring the brain waves of 30 subjects. The subjects were fitted with a cap that had 30 electrodes (电极)attached to it, and then shown various images and symbols — celebrity faces, words, pictures of food --- on a computer screen in 200-millisecond bursts. The brain\ reaction was recorded.

The idea is that every time a person needs to use a “password", he or she goes through the same procedure, and the results are matched with their first-time reaction. If the "brainprint" is compromised --- like what happened with the fingerprint records --- then the system is merely reset by running another set of images and collecting a different set of brain waves。. "Even if that was stolen, you could just cancel it and record one to something else", says professor Laszlo.

Laszlo and her team have shown that their system can be 100 percent accurate. So one of the more difficult parts of making the system practical already has been overcome. Now, they're spending much time recording accurate brainprints with as few as three electrodes, which could make recording in the future as easy as wearing a pair of special glasses. They're also working with cheaper materials and different methods to see if they can bring the cost down.

1. What does the underlined word "brainchild" in paragraph 2 refer to?
A.The brain function.B.The fingerprint record.
C.The online password.D.The biometric system.
2. Why were the images and symbols shown to the subjects?
A.To check the brain's reaction.
B.To create their brainprints.
C.To match brainprints with pictures.
D.To connect brain waves to electrodes.
3. What will happen when a brainprint fails?
A.A new one will be set
B.A spare one will be made ready beforehand.
C.The users' identity will be stolen.
D.The fingerprint record will replace it.
4. What are the researchers doing with the program now?
A.Improving its accuracy.
B.Recording more brainprints.
C.Making it user-friendly.
D.Increasing the number of electrodes.

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阅读理解-阅读单选(约720词) | 较难 (0.4)
文章大意:本文是一篇说明文。文章主要介绍了虚拟现实技术在课堂中的运用。

【推荐1】On a February afternoon in a Brooklyn classroom, 16-year-old Taylor came face to face with a cow — but it was all in her head. A virtual (虚拟的) reality headset had transported her and eight classmates to a New York farm 250 miles away and for students, the technology means field trips are no longer limited by the length of a bus ride. “I was not expecting it to be right in my face!” Taylor said after peeling off the purple headset and finding herself back in her classroom.

On any given day, students nationwide are deep-sea diving, observing medical operations, even swimming through the human circulatory system using gadgets (小装置) that are becoming increasingly accessible in both cost and content. At least it’s another way to engage the iPhone generation of students and at best, it can enhance their understanding and improve their grades.

“It instantly grabs the students,” said Colin Jones, who teaches science in the Plainview-Old Bethpage Central School District. He has used a system called zSpace to dissect (解剖) cells and has walked goggled students through the boreal forest with a Google app called Expeditions. “It’s something that can be done in a period or two,” he said, “it could take even a week sometimes when you’re doing in a lab.”

In Brooklyn, Taylor and classmates virtually walked through barns and fields in Watkins Glen, stretching arms toward videotaped pigs and cows. “It’s different from watching video because you can have more than one perspective; you can actually move,” Taylor said.

Students can not only move, but also feel. In the lab, the physical effects of virtual reality become clear as subjects standing on solid ground teeter (摇晃) on storeys-high virtual scaffolding or experience motion sickness without moving.

“Some of the research we’re doing has actually shown that what you experience in virtual reality has very similar, if not the same, physiological responses that you would get if you were doing the actual activity, like your heart rate, cognition, breathing and even everything,” said Richard Lamb, who studies how the brain processes information at the University at Buffalo Neurocognition Science Lab. “The effect on learning is to improve interest, understanding and recall.”

It’s unknown how many classrooms have or will adopt the technology, but experts say it’s still relatively rare largely due to the fact that, while individual headsets that require a user’s phone can cost as little as $20 or $30, systems and software for classes run into thousands of dollars. Early complaints about a lack of good software are fading as more companies enter the market, but the rules for use haven’t necessarily caught up to the technology. In New York, for example, simulated lab experiments don’t count toward the state’s hands-on lab time requirements. Even so, the science is the area where virtual reality, especially enhanced to let users manipulate their surroundings, holds particular promise for classrooms.

“The biggest barrier, I think, is going to be the quality of that experience, how closely it mimics the physical world,” said David Evans, executive director of the National Science Teachers Association. “However, the ability to do dangerous things and to run many, many more common cases in a simulation (模拟) space as opposed to the real physical space represents a huge learning opportunity.”

Lamb, who taught chemistry, agreed. “Too often in schools, when we do things with labs, it’s... you mix this together, mix that together and get this outcome. And if you don’t get that outcome, you did something wrong, but we don’t have enough resources for you to redo it,” he said. In virtual reality, “all I do is hitting reset on the computer. I don’t have to actually use chemicals.”

Both Lamb and Evans stressed using the technology to have similar experience to their real world, where any number of subtle factors can affect an outcome. “ We have to remain anchored in the actual world,” Evans cautioned, “because that’s the one that we really need to explain.”

1. What’s Colin Jones’ attitude towards the application of virtual reality to teaching?
A.Positive.B.Critical.
C.Objective.D.Disapproving.
2. What does Richard Lamb really want to say in Paragraph 6?
A.Students can hardly experience everything in real life.
B.It’s beneficial for students to experience virtual reality.
C.Much exposure to virtual reality makes students focused.
D.Actual experiences are more important than virtual reality.
3. Virtual reality is rarely used in classrooms mainly because ____________.
A.students show little interest in it
B.rules for it haven’t been made so far
C.users will spend much money applying it
D.it isn’t good enough to be operated by students
4. What does David Evans think of virtual reality?
A.It imitates the real world perfectly.
B.It features many unpractical life skills.
C.It shouldn’t refer to dangerous things.
D.It offers guidance for users on real life.
5. What benefit does Lamb stress about virtual reality?
A.Saving lots of time.
B.Reducing resource waste.
C.Minimizing experimental errors.
D.Improving experimental success rate.
6. What does the underlined sentence in the last paragraph mean?
A.Virtual reality shouldn’t be divorced from reality.
B.There’re still many unsolved mysteries in real life.
C.People gain much inspiration from the actual world.
D.Everyone should have a chance to try virtual reality.
2018-12-09更新 | 64次组卷
阅读理解-阅读单选(约350词) | 较难 (0.4)
名校
文章大意:本文是一篇说明文。文章主要介绍了智能手机对人们生活的影响,以及如何借鉴智能手机的思维方法来提高能源效率。

【推荐2】Back in the early 2000s, lots of people couldn’t have imagined life without alarm clocks, CD players, calendars, cameras, or lots of other devices. But along came the iPhone and other smartphones, and they took over the functions of dozens of things we used to think were essential.

The smartphone story could even be a model for fighting climate change; not because smartphone use a small part of the energy of all the things they replace — although they do — but because they represent a different approach to design in general. And that approach is to focus on function rather than form. That requires focusing on understanding the underlying problem, and then engineering a wide range of potential solutions. This approach could revolutionize how we think about energy efficiency.

Traditionally, improvements in energy efficiency have mostly focused on individual devices, which can be quite fruitful. But focusing on individual devices is like if Apple had spent effort inventing a better alarm clock, a better CD player, a better calendar, and a better camera. Now with an iPhone, we don’t need the standalone devices at all, because it can function as all of them.

So when it comes to using energy efficiently, rather than just installing a more efficient heater, some people have focused instead on the desired function: staying warm. They designed and coated their house so well that they could get rid of their heater altogether, letting them heat their house with 99 % less energy.

In the same way, rather than just making cars more efficient, what if we focus on the desired function — getting where we want when we want — and create an efficient transportation system where we can drive less or get rid of our personal cars entirely? The most energy efficient car or heater is no car, or no heater, while still being able to get around and stay warm. In other words, it’s not thinking efficient, it’s thinking different.

1. What makes the iPhone a good example of environmental protection?
A.Perfecting individual devices.
B.Combining possible functions.
C.Adopting a simplest design.
D.Reducing the energy consumption.
2. What does the author think of traditional practices in energy improvements?
A.Conventional.B.Out-of-date.C.Adequate.D.Perfect.
3. What can we learn from the passage?
A.Think out of the box.B.Differences make it unique.
C.Be economical with energy.D.Step out of the comfort zone.
2023-11-19更新 | 56次组卷
阅读理解-阅读单选(约760词) | 较难 (0.4)
名校
文章大意:这是一篇说明文。本文主要讲述了人工智能越来越聪明,甚至可以自己编写代码,但要达到人类级别还要很长时间,所以软件工程师不用担心失去工作。

【推荐3】Computers are getting better at writing their own code but software engineers may not need to worry about losing their jobs just yet.

DeepMind, a U.K. artificial intelligence lab acquired by Google in 2014, announced Wednesday that it has created a piece of software called AlphaCode that can code just as well as an average human programmer. The London-headquartered firm tested AlphaCode’s abilities in a coding competition on Codeforces — a platform that allows human coders to compete against one another. “AlphaCode placed at about the level of the median competitor, marking the first time an AI code generation system has reached a competitive level of performance in programming competitions,” the DeepMind team behind the tool said in a blogpost.

But computer scientist Dzmitry Bahdanau wrote on Twitter that human-level coding is “still light years away.” “The system ranks behind 54.3% participants,” he said, adding that many of the participants are high school or college students who are just learning their problem-solving skills. Bahdanau said most people reading his tweet could “easily train to outperform AlphaCode.”

Researchers have been trying to teach computers to write code for decades but the concept has yet to go mainstream, partly because the AI tools that are meant to write new code have not been versatile enough.

An AI research scientist, who preferred to remain anonymous as they were not authorized to talk publicly on the subject, told CNBC that AlphaCode is an impressive technical achievement, but a careful analysis is required of the sort of coding tasks it does well on, versus the ones it doesn’t.The scientist said they believe AI coding tools like AlphaCode will likely change the nature of software engineering roles somewhat as they mature, but the complexity of human roles means machines won’t be able to do the jobs in their entirety for some time.“You should think of it as something that could be an assistant to a programmer in the way that a calculator might once have helped an accountant,” Gary Marcus, an AI professor at New York University, told CNBC. “It’s not one-stop shopping that would replace an actual human programmer. We are decades away from that.”

DeepMind is far from the only tech company developing AI tools that can write their own code.

Last June, Microsoft announced an AI system that can recommend code for software developers to use as they work. The system, called GitHub Copilot, draws on source code uploaded to code-sharing service GitHub, which Microsoft acquired in 2018, as well as other websites. Microsoft and GitHub developed it with help from OpenAI, an AI research start-up that Microsoft backed in 2019. The GitHub Copilot relies on a large volume of code in many programming languages and vast Azure cloud computing power.

Nat Friedman, CEO of GitHub, describes GitHub Copilot as a virtual version of what software creators call a pair programmer — that’s when two developers work side-by-side collaboratively on the same project. The tool looks at existing code and comments in the current file, and it offers up one or more lines to add. As programmers accept or reject suggestions, the model learns and becomes more sophisticated over time. The software makes coding faster, Friedman told CNBC. Hundreds of developers at GitHub have been using the Copilot feature all day while coding, and the majority of them are accepting suggestions and not turning the feature off, Friedman said.In a separate research paper published on Friday, DeepMind said it had tested its software against OpenAI’s technology and it had performed similarly. Samim Winiger, an AI researcher in Berlin, told CNBC that every good computer programmer knows that it is essentially impossible to create “perfect code.”“All programs are flawed and will eventually fail in unforeseeable ways, due to hacks, bugs or complexity,” he said.“Hence, computer programming in most critical contexts is fundamentally about building ‘fail safe’ systems that are ‘accountable’.”

In 1979, IBM said “computers can never be held accountable” and “therefore a computer must never make a management decision.”Winiger said the question of the accountability of code has been largely ignored despite the hype around AI coders outperforming humans.

“Do we really want hyper-complex, intransparent, non-introspectable, autonomous systems that are essentially incomprehensible to most and uncountable to all to run our critical infrastructure?” he asked, pointing to the finance system, food supply chain, nuclear power plants and weapons systems.

1. What do we learn about AlphaCode?
A.a U.K. artificial intelligence lab acquired by GitHub created it.
B.AlphaCode will likely change the nature of software engineering roles somewhat now.
C.It’s a one-stop shopping that would replace an actual human programmer.
D.It’s a piece of software that can code just as well as a plain human programmer.
2. What is the main point of IBM’s view in 1979 according to this passage?
A.The question of the accountability of code should be largely ignored.
B.A computer must never make a management decision because they can never be held accountable.
C.We would let systems that are essentially incomprehensible to most to run our critical infrastructure.
D.All programs are flawed and will eventually fail in unforeseeable ways.
3. According to the passage, GitHub Copilot couldn’t ________.
A.accept or reject suggestionsB.look at existing code in the current file
C.offer up one or more lines to addD.make coding faster
4. Which of the following can be the best title for the text?
A.Engineers may need to worry about losing their jobs.
B.Machines are getting better at writing their own code but human-level is ‘light years away’.
C.AlphaCode is an impressive technical achievement.
D.Microsoft announced an AI system that can recommend code for software developers.
2022-02-14更新 | 143次组卷
共计 平均难度:一般