1 . Human speech contains more than 2,000 different sounds, from the common “m” and “a” to the rare clicks of some southern African languages. But why are certain sounds more common than others? A ground-breaking, five-year study shows that diet-related changes in human bite led to new speech sounds that are now found in half the world’s languages.
More than 30 years ago, the scholar Charles Hockett noted that speech sounds called labiodentals, such as “f” and “v”, were more common in the languages of societies that ate softer foods. Now a team of researchers led by Damián Blasi at the University of Zurich, Switzerland, has found how and why this trend arose.
They discovered that the upper and lower front teeth of ancient human adults were aligned (对齐), making it hard to produce labiodentals, which are formed by touching the lower lip to the upper teeth. Later, our jaws changed to an overbite structure (结构), making it easier to produce such sounds.
The team showed that this change in bite was connected with the development of agriculture in the Neolithic period. Food became easier to chew at this point. The jawbone didn’t have to do as much work and so didn’t grow to be so large.
Analyses of a language database also confirmed that there was a global change in the sound of world languages after the Neolithic age, with the use of “f” and “v” increasing remarkably during the last few thousand years. These sounds are still not found in the languages of many hunter-gatherer people today.
This research overturns the popular view that all human speech sounds were present when human beings evolved around 300,000 years ago. “The set of speech sounds we use has not necessarily remained stable since the appearance of human beings, but rather the huge variety of speech sounds that we find today is the product of a complex interplay of things like biological change and cultural evolution,” said Steven Moran, a member of the research team.
1. Which aspect of the human speech sound does Damián Blasi’s research focus on?| A．Its variety. | B．Its distribution. | C．Its quantity. | D．Its development. |
| A．They had fewer upper teeth than lower teeth. |
| B．They could not open and close their lips easily. |
| C．Their jaws were not conveniently structured. |
| D．Their lower front teeth were not large enough. |
| A．Supporting evidence for the research results. |
| B．Potential application of the research findings. |
| C．A further explanation of the research methods. |
| D．A reasonable doubt about the research process. |
| A．It is key to effective communication. | B．It contributes much to cultural diversity. |
| C．It is a complex and dynamic system. | D．It drives the evolution of human beings. |
2 . Get up at 6 am, arrive at the hospital one hour earlier to help patients check in, and accompany patients during consultations… In recent years, “patient escorts” has emerged as a new industry, and those who have taken on this career are known as “people who sell time”, 26-year-old Zhang Tian is one of them.
September 4 was a lucky day for Zhang Tian. On this day, Zhang Tian saw a video about patient escorts on a short video platform. The daily routine of patient escorts shown in the video fascinated her and gradually inspired her to take this on as a business. She browsed through many platforms and read multiple information and found there indeed exists a certain demand for patient escorts, especially for the elderly, children, and pregnant women. Since she had never engaged in this kind of work before, she spent two days in major hospitals in Wuhan, in order to familiarize herself with all the departments on different floors, as well as the processes of medical consultation and preparation for surgery.
After preliminary preparatory (预备的) work, Zhang Tian posted a video of myself-introduction on major social platforms, talking about the help and services a patient escort provides, as well as some tips for a quick and convenient medical consultation. At first, she was a little worried that her video would go unnoticed. However, after she uploaded the video, it got over 100 likes and she received her first ever offer as a patient escort.
The memory of her first task is still alive and fresh in her mind. She received a phone call on September 9 from a man whose father was seriously ill and might need surgery. He wanted Zhang Tian to accompany his father through his consultation and treatment.” Zhang Tian made full preparations before meeting her first client and did a very good job despite her nervousness.
“Later, the family expressed their gratitude to me over and over again, which warmed my heart and gave me a sense of achievement.” Zhang Tian said.
1. What do patient escorts do?| A．They assist doctors in hospitals. |
| B．They arrive at hospitals early to check in. |
| C．They take on this career to sell their time. |
| D．They help patients get treated in hospitals. |
| A．she enjoyed seeing an interesting video |
| B．she got inspiration for her own career |
| C．she found a demand for medical workers |
| D．she was well received on social platforms |
| A．She got familiar with the routine work in hospitals. |
| B．She spent two days in major hospitals to meet patients. |
| C．Her video on social platforms attracted her first client. |
| D．The man’s father was seriously ill and might need a surgery. |
| A．Hardworking and considerate. | B．Humorous and careful. |
| C．Ambitious and imaginative. | D．Talkative and positive. |
3 . Humans have long tried to conquer water. We’ve straightened once-winding rivers for shipping purposes. We’ve constructed levees (防洪堤) along rivers and lakes to protect people from flooding—We’ve erected entire cities on drained and filled-in wetlands. We’ve built dams on rivers to store water for later use.
Levees, which narrow channels causing water to flow higher and faster, nearly always break. Cities on former wetlands flood regularly—often disastrously. Dams starve downstream areas of sediment (沉积物) needed to protect coasts against rising seas. Straightened streams move faster than winding ones, giving water less time to flow downward. And they wash away riverbed ecosystems.
In addition to laying out this damage done by supposed water control, Gies takes readers on a hopeful global tour of solutions to these problems. Along the way, she introduces “water detectives” —scientists,engineers,urban planners, and many others.
These water detectives have found ways to give the slippery substance the time and space it needs to flow slowly underground. Around Seattle’s Thornton Creek, for instance, reclaimed land now allows for regular flooding, which has renewed riverbed habitat and created an urban oasis. In California’s Central Valley, scientists want to find ways to move unpolluted storm water into subsurface valleys that make ideal aquifers (含水层).
While some people are exploring new ways to manage water, others are leaning on ancient knowledge. Researchers in Peru are now studying old-style methods of water storage, which don’t require dams, in hopes of ensuring a steady flow of water to Lima—Peru’s populous capital that’s periodically affected by water shortage.
Understanding how to work with, not against, water will help humankind weather this age of drought and flood that’s being worsened by climate change.
| A．Controlling water, Gies convincingly argues, is a false belief. |
| B．Instead of trying to control water, they ask: What does water want? |
| C．It seems that water is cooperative and willing to flow where we direct it. |
| D．These old-style underwater concrete techniques pave the way for the construction of dams. |
| E．To further understand the whole ecosystem, they believe effective water control requires effort. |
| F．The study may help convince those who favor concrete-centric solutions to try something new. |
| G．Feeding groundwater supplies will in turn sustain rivers from below, which helps to maintain water levels and ecosystems. |
4 . Certain forms of AI are indeed becoming ubiquitous. For example, algorithms (算法) carry out huge volumes of trading on our financial markets, self-driving cars are appearing on city streets, and our smartphones are translating from one language into another. These systems are sometimes faster and more perceptive than we humans are. But so far that is only true for the specific tasks for which the systems have been designed. That is something that some AI developers are now eager to change.
Some of today’s AI pioneers want to move on from today’s world of “weak” or “narrow” AI, to create “strong” or “full” AI, or what is often called artificial general intelligence (AGI). In some respects, today’s powerful computing machines already make our brains look weak. AGI could, its advocates say, work for us around the clock, and drawing on all available data, could suggest solutions to many problems. DM, a company focused on the development of AGI, has an ambition to “solve intelligence”. “If we’re successful,” their mission statement reads, “we believe this will be one of the most important and widely beneficial scientific advances ever made.”
Since the early days of AI, imagination has outpaced what is possible or even probable. In 1965, an imaginative mathematician called Irving Good predicted the eventual creation of an “ultra-intelligent machine…that can far surpass all the intellectual (智力的) activities of any man, however clever.” Good went on to suggest that “the first ultra-intelligent machine” could be “the last invention that man need ever make.”
Fears about the appearance of bad, powerful, man-made intelligent machines have been reinforced (强化) by many works of fiction — Mary Shelley’s Frankenstein and the Terminator film series, for example. But if AI does eventually prove to be our downfall, it is unlikely to be at the hands of human-shaped forms like these, with recognisably human motivations such as aggression (敌对行为). Instead, I agree with Oxford University philosopher Nick Bostrom, who believes that the heaviest risks from AGI do not come from a decision to turn against mankind but rather from a dogged pursuit of set objectives at the expense of everything else.
The promise and danger of true AGI are great. But all of today’s excited discussion about these possibilities presupposes the fact that we will be able to build these systems. And, having spoken to many of the world’s foremost AI researchers, I believe there is good reason to doubt that we will see AGI any time soon, if ever.
1. What does the underlined word “ubiquitous” in Paragraph I probably mean?| A．Enormous in quantity. | B．Changeable daily. |
| C．Stable in quality. | D．Present everywhere. |
| A．Help to tackle problems. | B．Make brains more active. |
| C．Benefit ambitious people. | D．Set up powerful databases. |
| A．supportive | B．disapproving |
| C．fearful | D．uncertain |
| A．It may be only a dream. |
| B．It will come into being soon. |
| C．It will be controlled by humans. |
| D．It may be more dangerous than ever. |
5 . When Elinor Lobel was 16, a “smart” insulin (胰岛素) pump was attached to her body. Powered by AI, it tracks her glucose levels and administers the right dose of insulin at the right time to keep her healthy. It is one of the new ways that data and AI can help improve lives.
Books that criticize the dark side of data are plentiful. They generally suggest there is much more to fear than fete in the algorithmic(算法的)age.
But the intellectual tide may be turning. One of the most persuasive supporters of a more balanced view is Elinor Lobel’s mother, Orly, a law professor. In The Equality Machine she acknowledges AI’s capacity to produce harmful results. But she shows how, in the right hands, it can also be used to fight inequality and discrimination.
A principle of privacy rules is “minimization”: collect and keep as little information as possible, especially in areas such as race and gender. Ms Lobel flips the script, showing how in hiring, pay and the legal system, knowing such characteristics leads to fairer outcomes.
Ms Lobel’s call to use more, not less, personal information challenges data-privacy orthodoxy(正统观念). But she insists that “tracking differences is key to detecting unfairness.” She advocates g loosening of privacy rules to provide more transparency(透明)over algorithmic decisions.
The problems with algorithmic formulae(公式) are tackled in depth in Escape from Model Land by Erica Thompson of the School of Economics. These statistical models are the backbone of big data and AL. Yet a perfect model will always be beyond reach. “All models are wrong,” runs a wise saying. “Some are useful.”
Ms Thompson focuses on a challenge she calls the Hawkmoth Effect. In the better known Butterfly Effect, a serviceable model, Vin the prediction of climate change, becomes less reliable over time because of the complexity of what it is simulating(模拟), or because of inaccuracies in the original data. In the Hawkmoth Effect, by contrast, the model itself is flawed; it might fail to take full account of the interplay between humidity, wind and temperature.
The author calls on data geeks to improve their solutions to real-world issues, not merely refine their formulae—in other words, to escape from model land. “We do not need to have the best possible answer,” she writes, “only a reasonable one.”
Both these books exhibit a healthy realism about data, algorithms and their limitations. Both recognize that making progress involves accepting limitations, whether in law or coding. As Ms Lobel puts it: “It’s always better to light a candle than to curse the darkness.”
1. Ms Lobel intends to convey that| A．minimisation is a good privacy rule to go by |
| B．algorithms are currently challenged by data privacy |
| C．employing more personal data should be encouraged |
| D．identifying algorithms’ problems leads to better outcomes |
| A．It develops from Butterfly Effect. |
| B．It emphasizes accuracy of original data. |
| C．It enjoys popularity in climate research field. |
| D．It is mentioned to show the model can be faulty. |
| A．Using algorithms to detect differences is hard. |
| B．The application of data and algorithms is limited. |
| C．The reliability of data should be attached importance to. |
| D．Improving algorithms involves accepting its imperfection. |
| A．The Algorithm’s Prospect | B．The Algorithm’s Mercy |
| C．The Algorithm’s Complexity | D．The Algorithm’s Recognition |
6 . I was lucky enough to test into the best high school in the city. But then came my
I knew I had to work to ground myself. My earliest strategy involved keeping quiet and trying to
Fortunately, my first round of grades turned out to be
I loved any subject that involved writing and labored through math. I had classmates who were always a step or two ahead of me, whose achievements seemed effortless, but I tried not to let that get to me. I was beginning to understand that if I put in extra hours of studying, I could often
| A．excitement | B．satisfaction | C．anger | D．worry |
| A．freed | B．dogged | C．warmed | D．guided |
| A．observe | B．admire | C．support | D．calm |
| A．Or | B．So | C．But | D．For |
| A．guess | B．doubt | C．interest | D．risk |
| A．excellent | B．average | C．different | D．unique |
| A．responsibility | B．friendship | C．confidence | D．teamwork |
| A．suddenly | B．frequently | C．accidentally | D．slowly |
| A．close | B．notice | C．locate | D．create |
| A．expecting | B．trying | C．wondering | D．suffering |
7 . Debate about artificial intelligence (AI) tends to focus on its potential dangers: algorithmic bias (算法偏见) and discrimination, the mass destruction of jobs and even, some say, the extinction of humanity. However, others are focusing on the potential rewards. Luminaries in the field such as Demis Hassabis and Yann LeCun believe that AI can turbocharge scientific progress and lead to a golden age of discovery. Could they be right?
Such claims are worth examining, and may provide a useful counterbalance to fears about large-scale unemployment and killer robots. Many previous technologies have, of course, been falsely hailed as panaceas (万灵药). But the mechanism by which AI will supposedly solve the world’s problems has a stronger historical basis.
In the 17th century microscopes and telescopes opened up new vistas of discovery and encouraged researchers to favor their own observations over the received wisdom of antiquity (古代), while the introduction of scientific journals gave them new ways to share and publicize their findings. Then, starting in the late 19th century, the establishment of research laboratories, which brought together ideas, people and materials on an industrial scale, gave rise to further innovations. From the mid-20th century, computers in turn enabled new forms of science based on simulation and modelling.
All this is to be welcomed. But the journal and the laboratory went further still: they altered scientific practice itself and unlocked more powerful means of making discoveries, by allowing people and ideas to mingle in new ways and on a larger scale. AI, too, has the potential to set off such a transformation.
Two areas in particular look promising. The first is “literature-based discovery” (LBD), which involves analyzing existing scientific literature, using ChatGPT-style language analysis, to look for new hypotheses, connections or ideas that humans may have missed. The second area is “robot scientists”. These are robotic systems that use AI to form new hypotheses, based on analysis of existing data and literature, and then test those hypotheses by performing hundreds or thousands of experiments, in fields including systems biology and materials science. Unlike human scientists, robots are less attached to previous results, less driven by bias—and, crucially, easy to replicate. They could scale up experimental research, develop unexpected theories and explore avenues that human investigators might not have considered.
The idea is therefore feasible. But the main barrier is sociological: it can happen only if human scientists are willing and able to use such tools. Governments could help by pressing for greater use of common standards to allow AI systems to exchange and interpret laboratory results and other data. They could also fun d more research into the integration of AI smarts with laboratory robotics, and into forms of AI beyond those being pursued in the private sector. Less fashionable forms of AI, such as model-based machine learning, may be better suited to scientific tasks such as forming hypotheses.
1. Regarding Demis and Yann’s viewpoint, the author is likely to be ______.| A．supportive | B．puzzled | C．unconcerned | D．doubtful |
| A．LBD focuses on testing the reliability of ever-made hypotheses. |
| B．Resistance to AI prevents the transformation of scientific practice. |
| C．Robot scientists form hypotheses without considering previous studies. |
| D．Both journals and labs need adjustments in promoting scientific findings. |
| A．Official standards have facilitated the exchange of data. |
| B．Performing scientific tasks relies on government funding. |
| C．Less popular AI forms might be worth paying attention to. |
| D．The application of AI in public sector hasn’t been launched. |
| A．Transforming Science. How Can AI Help? |
| B．Making Breakthroughs. What Is AI’s Strength? |
| C．Reshaping History. How May AI Develop Further? |
| D．Redefining Discovery. How Can AI Overcome Its Weakness? |
8 . When a chunk of ice fell from a collapsing glacier(冰川)on the Swiss Alps’ Mount Eiger in 2017, part of the long deep sound it produced was too low for human ears to detect. But these vibrations held a key to calculating the ice avalanche’s(崩塌)critical characteristics.
Low-frequency sound waves called infrasound that travel great distances through the atmosphere are already used to monitor active volcanoes from afar. Now some researchers in this field have switched focus from fire to ice: dangerous blocks snapping off glaciers. Previous work has analyzed infrasound from snow avalanches but never ice, says Boise State University geophysicist Jeffrey Johnson. “This was different,” Johnson says. “A signature of a new material has been detected with infrasound.”
Usually glaciers move far too slowly to generate an infrasound signal, which researchers pick up using detectors that track slight changes in air pressure. But a collapse—a sudden, rapid breaking of ice from the glacier’s main body—is a prolific infrasound producer. Glacial collapses drive ice avalanches, which pose an increasing threat to people in mountainous regions as rising temperatures weaken large fields of ice. A glacier “can become detached from the ground due to melting, causing bigger break— offs,” says University of Florence geologist Emanuele Marchetti, lead author of the new study. As the threat grows, scientists seek new ways to monitor and detect such collapses.
Researchers often use radar to track ice avalanches, which is precise but expensive and can monitor only one specific location and neighboring avalanche paths. Infrasound, Marchetti says, is cheaper and can detect break—off events around a much broader area as well as multiple avalanches across a mountain. It is challenging, however, to separate a signal into its components (such as traffic noises, individual avalanches and nearby earthquakes) without additional measurements, says ETH Zurich glaciologist Malgorzata Chmiel. “The model used by Marchetti is a first approximation for this,” she says. Isolating the relevant signal helps the researchers monitor an ice avalanche’s speed, path and volume from afar using infrasound.
Marchetti and his colleagues are now working to improve their detectors to pick up more signals across at-risk regions in Europe, and they have set up collaborations around the continent to better understand signals that collapsing glaciers produce. They are also refining their mathematical analysis to figure out each ice cascade’s physical details.
1. What can we learn from Paragraph 2 and Paragraph 3?| A．Infrasound has a major role to play in discovering new materials. |
| B．Ice avalanches are a bigger threat to people than volcanic eruptions. |
| C．Researchers are trying to use infrasound in detecting ice avalanches. |
| D．Scientists employ infrasound more in mountain areas than in other places. |
| A．The combination with other relevant signals. |
| B．The accuracy in locating a certain avalanche. |
| C．The ability in picking up signals in wider areas. |
| D．The sensitivity in tracking air pressure changes. |
| A．distinguishing different components of a signal |
| B．detecting multiple avalanches at the same time |
| C．calculating the speed and path of ice avalanches |
| D．monitoring the specific location of ice break—offs |
| A．From Fire to Ice | B．Glacier Whispers |
| C．Nature is Warning | D．Secret of Ice Avalanches |
9 . It was a week after my mom had passed away and I didn’t know how to go on with life. So when I received an email from a friend about a race benefiting cancer research, I ignored it. It seemed to prick my heart, as cancer was the disease that had taken my mother away from me.
But something about my friend’s words—“I can help organize the whole thing”—stuck with me. I felt obliged（有义务的）to agree. In the weeks to come, I managed to re-enter the world of the living. I checked our team’s website daily, feeling proud each time a donation ticked up our total. I knew my mom would have wanted it that way. She was the type who never got defeated. It was this very spirit that helped me get by.
When the race ended, I noticed the runners all had one thing in common: There were big smiles on their faces. They made it look so rewarding and effortless. I wanted in.
So I enrolled in another race two months later. Considering I could barely run a mile, it was ambitious. But my friend and I made a training plan so I wouldn’t come in last. I followed it religiously and didn’t let anything get in my way.
Running up and down the city’s hills, I was flooded with memories. I had lived there after college and my mother had visited often. I passed Bloomingdale’s, recalling the time she and I had gotten into a screaming argument there.
I was about to beat myself up when I remembered what Mom had said after her diagnosis of cancer. “I don’t want you to feel guilty about anything.” Her paper-thin hands had held me tightly. A weight lifted from my shoulders.
When the race day arrived, I gave it my all for my mom and for all she had taught me and continued to teach me. As I ran, whenever I felt like slowing down, I pictured her cheering me on.
Crossing the finish line, I was filled with her love and a sense of peace.1. Why did the author ignore the email in the beginning?
| A．She felt it hard to finish the race. |
| B．She had no time to join in the event. |
| C．She thought the research meaningless. |
| D．She was reminded of her mother’s death. |
| A．The company of her friends. | B．The inspiration from her mom. |
| C．The pleasure in going for a run. | D．The success in organizing an event. |
| A．Considerate and polite. | B．Brave and humorous. |
| C．Strong-willed and caring. | D．Outgoing and patient. |
| A．How I Got Healed in Running | B．The Loss of Sweet Memories |
| C．What Matters Most in Running | D．The Rewards of Great Friendship |
10 . Many people would answer the question of what makes us human by insisting that we are cultural beings. There is no doubt that we are. But one definition of culture is the totality of traditions acquired in a community by social learning from other individuals, and many animal species have traditions. Can we then say that some animals are cultural beings too?
One approach to study culture in animals is the so-called Method of Exclusion (排除), in which scientists investigate behavioral variations across populations of one species. In a famous study, scientists learned that chimpanzee (黑猩猩) behaviors were socially passed on as they were present at some sites but not at others, despite having same ecological settings. For example, chimpanzees in Tai National Park in Ivory Coast are well-known for their nut-cracking skills. Chimpanzees in Gombe national part in Tanzania, on the other hand, do not crack nuts, although nuts exist in their environment too.
However, when applying the Method of Exclusion, one has to be very careful. There are other factors that could also explain the pattern of behavioral evaluation. For example, some of the chimpanzee techniques scientists evaluated occur in only one of the three subspecies. So it’s quite possible that these behaviors also have an innate component. This would mean that one chimpanzee subspecies uses a new technique not out of cultural tradition, but because the behavior is fixed to specific genes. Another factor that has to be excluded is of course the environment Chimpanzees in Mahale do not fish algae (水藻), simply because algae does not exist there.
But when we exclude all the variations that can be explained by genes or environment, we still find that animals do show cultural variations. Does that mean there is no real difference between them and us after all? Not exactly: There is a fundamental difference between human and animal culture. Only humans can build culturally on what generations before us have learned. This is called “cumulative culture”. We don’t have to keep reinventing the wheel. This is called the “ratchet (棘轮) effect”. Like a ratchet that can be turned forward but not back, people’s cultural techniques evolve.
It is likely that behaviors we see today in chimpanzee cultures could be invented over and over again by individual animals themselves. In contrast, a child born today would not be able to invent a computer without the knowledge of many past generations.
1. Why does the author mention the example of the chimpanzees in two parks in Paragraph 2?| A．To prove that culture does exist in animals. |
| B．To justify the uniqueness of the research method. |
| C．To compare how chimpanzees behave in different parks. |
| D．To stress the importance of environment in studying culture. |
| A．Advanced. | B．Inborn. | C．Adaptive. | D．Intelligent. |
| A．Cumulative culture is what sets humans apart from animals. |
| B．Culure in animals is as worthy to be valued as human culture. |
| C．Animals don’t have the ability to invent behaviors in a community. |
| D．The “ratchet effect” decides if humans can build on past experiences. |
