1 . “Blame My Brain” by Nicola Morgan, reviewed by Rosalie Warren
As someone who constantly blames my brain for all sorts of things (not my fault — my brain did it!), I was
The subtitle is “The amazing teenage brain revealed” and amazing is, I soon
There are also brain-based explanations of why teenagers need so much sleep, why they don’t tidy their rooms, why they come
Nicola Morgan is not a neurologist or a
There’s plenty of humour and a good few well-deserved digs at the stupidity of parents and other well-meaning but misguided adults, which teenagers will
The illustrations by Andy Baker are great, too. And oh yes — there’s some interesting discussion on the differences between girls’ brains and boys’, if there are any. You’ll have to read it to find out...
1.A.attracted | B.interested | C.invested | D.introduced |
A.intended to | B.aimed at | C.targeted by | D.appealed to |
A.defended | B.dismissed | C.discovered | D.differed |
A.happens | B.projects | C.evolves | D.limits |
A.surprisingly | B.immediately | C.unfortunately | D.regularly |
A.expressing | B.explaining | C.declaring | D.exposing |
A.living | B.lively | C.alive | D.alone |
A.sympathetic | B.pessimistic | C.positive | D.negative |
A.laborious | B.humorous | C.productive | D.professional |
A.consulted | B.conducted | C.converted | D.suggested |
A.complicated | B.simplified | C.contrary | D.demanding |
A.denounce | B.distinguish | C.determine | D.depend |
A.appreciate | B.hate | C.respect | D.reflect |
A.confuse | B.combine | C.unite | D.associate |
A.mind | B.physical | C.mental | D.emotional |
2 . Last week, I sent the same request to ChatGPT, the latest artificial-intelligence chatbot from OpenAI. “Upon the Firth of Forth, a bridge doth stand,” it began. In less than a minute, the program had created in full a rhyming Shakespearean sonnet (莎士比亚十四行诗). Tools like ChatGPT seem poised to change the world of poetry — and so much else — but poets also have a lot to teach us about artificial intelligence. If algorithms (算法) are getting good at writing poetry, it’s partially because poetry was always an algorithmic business.
Even the most rebellious (叛逆的) poets follow more rules than they might like to admit. When schoolchildren are taught to imitate the structure of sonnet, they are effectively learning to follow algorithmic constraints. Should it surprise us that computers can do so, too?
But considering how ChatGPT works, its ability to follow the rules for sonnets seems a little more impressive. No one taught it these rules. It is based on a newer kind of AI known as a large language model (LLM). To put it simply, LLMs analyze large amounts of human writing and learn to predict what the next word in a string of text should be, based on context. One frequent criticism of LLMs is that they do not understand what they write; they just do a great job of guessing the next word.
When a private verse by Dickinson makes us feel like the poet speaks directly to us, we are experiencing the effects of a technology called language. Poems are made of paper and ink — or, these days, electricity and light. There is no one “inside” a Dickinson poem any more than one by ChatGPT. Of course, every Dickinson poem reflects her intention to create meaning. When ChatGPT puts words together, it does not intend anything. Some argue that writings by LLMs therefore have no meaning, only the appearance of it. If I see a cloud in the sky that looks like a giraffe, I recognize it as an accidental similarity. In the same way, this argument goes, we should regard the writings of ChatGPT as merely imitating real language, meaningless and random as cloud shapes.
When I showed my friends the sonnet by ChatGPT, they called it “soulless and barren.” Despite following all the rules for sonnets, the poem is predictable. But is the average sonnet by a human any better? If we now expect computers to write not just poems but good poems, then we have set a much higher bar.
1. What is the main idea of paragraph 1 and paragraph 2?A.ChatGPT will make a difference to poetry based on algorithms. |
B.There is no doubt that AI can copy the grammatical rules of poetry. |
C.Poetry guidelines provide a possibility for AI’s poetry writing. |
D.There is a similarity between algorithms and poetry. |
A.ChatGPT is trained to follow the rules by LLMs. |
B.ChatGPT can analyze and predict human languages. |
C.ChatGPT is technologically supported by LLMs. |
D.ChatGPT itself learn to follow the rules. |
A.He talks about cloud to describe the meaninglessness of AI’s poetry. |
B.He tells of Dickinson to describe the meaninglessness AI’s poetry. |
C.He mentions cloud to suggest its close relationship with AI’s poetry. |
D.He refers to Dickinson to suggest her close relationship with AI’s poetry. |
A.Acceptable and favorable | B.Amazed and admiring |
C.Indifferent and uncaring | D.Doubtful and uneasy |
SATISFACTION GUARANTEED
(Adapted)
Larry Belmont worked for a company that made robots. Recently it had begun experimenting with a household robot. It was going to be tested out by Larry’s wife, Claire.
Claire didn’t want the robot in her house, especially as her husband would be away on a business trip for three weeks, but Larry persuaded her that the robot wouldn’t harm her or allow her to be harmed. It would be a bonus. However, when she first saw the robot, she felt alarmed. His name was Tony. He seemed more like a human than a machine. He was tall and handsome with smooth hair and a deep voice, although his facial expression never changed.
On the second morning, Tony brought her breakfast and then asked her whether she needed help dressing. She felt embarrassed and quickly told him to go. Now she was being looked after by a robot that looked so human, and it was disturbing.
One day, Claire mentioned that she didn’t think she was clever. Tony said that she must feel very unhappy to say that. Claire thought it was ridiculous that she was being offered sympathy by a robot, but she gradually admired his wisdom and integrity and began to trust him. He always treated her with dignity. She told him how she was unhappy that her home wasn’t elegant enough for Larry, who wanted to improve his social position with a bigger salary. She wasn’t like Gladys Claffern, one of the richest and most powerful women around.
As a favour, Tony promised to help Claire make herself more beautiful and her home more elegant. So Claire borrowed some library books for him to read, or rather, scan. She looked at his fingers with wonder as they turned each page. How absurd, she thought. He was just a machine.
Tony gave Claire a new hairstyle and improved her makeup. As he was not allowed to accompany her to the shops, he wrote out a list of things that he would need for his work on the house. Claire went downtown and bought these things. She had an appointment to paint her nails, then she went into an expensive clothes shop. The saleswoman there was rude to her, so she rang Tony and told him she was being treated badly. He spoke to the woman, who immediately changed her attitude. Claire thanked Tony, telling him that he was a “dear”. As she turned around, there stood Gladys Claffern. How awful to be discovered by her, Claire thought. By the look on her face, Claire knew that Gladys thought they were in a relationship. After all, she knew Claire’s husband’s name was Larry, not Tony. Although it was completely innocent, Claire felt guilty.
When Claire got home, she wept. Gladys was everything Claire wished to be. Tony told her she was being sensitive and was just as good as Gladys. He suggested that she invite Gladys and her friends to the house the night before he was to leave and Larry was to return. By that time, Tony expected that the house, which was being completely transformed, would be ready.
Tony worked steadily on the improvements. Claire tried to help by working on a light suspended from the ceiling, but she fell off the ladder. Even though Tony had been in the next room, he managed to catch her in time. As he held her, she felt the warmth of his body. She screamed, pushed him away, and ran to her room.
The night of the party arrived. The clock struck eight. The guests would be arriving soon, so Claire dismissed Tony for the rest of the night. At that moment, Tony took her in his arms, bringing his face close to hers. She heard him declare that he did not want to leave her the next day, and that he felt more than just the desire to please her. Then the front door bell rang.
1. What’s the text mainly about?A.How to make a robot. |
B.How a robot helps people. |
C.What a robot can do. |
D.A test on a household robot. |
A. The night of the party. B. Claire’s attitude to the robot and her feeling at the sight of the robot. C. What Tony did for Claire. |
Part 2 (Paras.3-8)
Part 3 (Para.9)
3. What does Larry Belmont think of testing out the robot in his house?
A.It is an extra benefit. | B.It is his responsibility. |
C.It helps improve his house. | D.It can make Claire happy. |
A.Proud. | B.Happy. |
C.Guilty. | D.Embarrassed. |
A.Because Claire wants to hold a party in her house. |
B.Because Claire plans to give Larry a surprise. |
C.Because Claire doesn’t think it good enough for Larry. |
D.Because Claire intends to make the best of Tony. |
A.Tony falls in love with Claire. |
B.Tony will have a rest that night. |
C.Tony will stay with Claire forever. |
D.Tony,the robot needs to be improved. |
A household robot called Tony was to be tested out in Larry’s house. Though Claire, Larry’s wife, didn’t like
However, Tony gradually won Claire’s trust. He took good care of Claire and even managed to rescue her
(1)Claire didn’t want the robot in her house, especially as her husband would be away on a business trip for three weeks, but Larry persuaded her that the robot wouldn’t harm her or allow her to be harmed.
(2)Claire thought it was ridiculous that she was being offered sympathy by a robot, but she gradually admired his wisdom and integrity and began to trust him.
(3)She told him how she was unhappy that her home wasn’t elegant enough for Larry, who wanted to improve his social position with a bigger salary.
4 . Atomic shapes are so simple that they can’t be broken down any further. Mathematicians are trying to turn to artificial intelligence (AI) for help to build a periodic table of these shapes, hoping it will assist in finding yet-unknown atomic shapes.
Tom Coates at Imperial College London and his colleagues are working to classify atomic shapes known as Fano varieties, which are so simple that they can’t be broken down into smaller components. Just as chemists arranged element s in the periodic table by their atomic weight and group to reveal new insights, the researchers hope that organizing these atomic shapes by their various properties will help in understanding them.
The team has given each atomic shape a sequence of numbers based on its features such as the number of holes it has or the extent to which it bends around itself. This acts as a bar code (条形码) to identify it. Coates and his colleagues have now created an AI that can predict certain properties of these shapes from their bar code numbers alone, with an accuracy of 98 percent.
The team member Alexander Kasprzyk at the University of Nottingham, UK, says that the AI has let the team organize atomic shapes in a way that begins to follow the periodic table, so that when you read from left to right, or up and down, there seem to be general patterns in the geometry (几何) of the shapes.
Graham Nib lo at the University of Southampton, UK, stresses that humans will still need to understand the results provided by AI and create proofs of these ideas. “AI has definitely got unbelievable abilities. But in the same way that telescopes (望远镜) don’t put astronomers out of work, AI doesn’t put mathematicians out of work,” he says. “It just gives us new backing that allows us to explore parts of the mathematical landscape that are out of reach.”
The team hopes to improve the model to the point where missing spaces in its periodic table could point to the existence of unknown shapes.
1. What is the purpose of building a periodic table of shapes?A.To gain deeper insights into the atomic shapes. |
B.To create an AI to predict the unknown shapes. |
C.To break down atomic shapes into smaller parts. |
D.To arrange chemical elements in the periodic table. |
A.Its holes. | B.Its bends. |
C.Its atomic weight. | D.Its properties. |
A.Design. | B.Help. | C.Duty. | D.Threat. |
A.Thanks to AI, new atomic shapes have been discovered. |
B.Mathematicians turn to AI to create more atomic shapes. |
C.AI helps build a relationship between chemistry and maths. |
D.A periodic table of shapes can be built with the help of AI. |
5 . In US emergency rooms (ER), the average wait time to see a doctor is more than two hours. There are more patients in need than there are doctors, nurses and other staff to help them. Many parents have suffered through hours in the ER with a sick, upset child, only to get sent home because their case is not considered urgent. What if there was another choice—like a house call from an intelligent machine?
Now, a new study shows that AI systems can assess (评估) a child’s medical chart and come up with a diagnosis (诊断), a determination of what is wrong with that patient.
The study took place at Guangzhou Women and Children’s Medical Center in southern China. First, a team of doctors reviewed 6, 183 medical charts. They summarized the information in these charts into a list of keywords linked to disease-related symptoms or signs, such as “fever”. Researchers then taught these keywords to the AI system. Once trained, the system scanned children’s charts for the key terms, checking if they were present or not in order to come to a conclusion. Finally, it offered diagnoses based on the charts, narrowing down from among 55 illness categories.
Dongxiao Zhu, an assistant professor of computer science at Wayne State University who did not take part in the study, however, sees this as “augmented intelligence (增强智能)” rather than “artificial intelligence”, because the system handled only 55 illness categories. Compare that to thousands of possibilities in the real world. The machine cannot yet get into the more complex aspects of a medical decision.
Zhu is also concerned about the amount of human work that went into the study—namely, the time and energy spent by human doctors. They spent hours grading the machine’s assessments and comparing them to their own. It’s no wonder that the process took four years. Considering that, it may be a while before you can skip the ER and see a robot-doctor instead.
1. What can we infer from Paragraph 1?A.Patients pay too much for the ER. |
B.American doctors aren’t responsible. |
C.Children are treated urgently in the ER. |
D.The emergency rooms are crowded with patients. |
A.AI systems still have a long way to go. |
B.AI systems diagnose disease like doctors. |
C.AI systems will take over from doctors someday. |
D.AI systems get into complex medical decisions. |
A.By examining a patient first. |
B.By reviewing many medical charts. |
C.By scanning keywords about a disease. |
D.By observing disease-related symptoms. |
A.They need to be improved a lot. |
B.They will replace real doctors soon. |
C.They are suitable for complex disease. |
D.They help doctors make a quick analysis. |
6 . The Great PowerPoint Panic of 2003.
Sixteen minutes before touchdown on the morning of February 1, 2003, the space shuttle Columbia (“哥伦比亚”号航天飞机)
The immediate
By the start of 2003, the phrase “death by PowerPoint” had well and truly entered the
Wired ran an excerpt (节选) from Tufte’s booklet in September 2003 under the headline “PowerPoint Is Evil.” A few months later, The New York Times Magazine included his assessment — summarized as “PowerPoint Makes You Dumb” — in its
Despite the backlash it inspired in the
On its face at least, the idea that PowerPoint makes us stupid looks like a textbook case of misguided technological doomsaying. Today’s concerns about social media somehow resemble the PowerPoint critique. Both boil down to a worry that new media technologies
A.disappeared | B.disintegrated | C.distributed | D.disappointed |
A.side | B.cause | C.feature | D.issue |
A.collected | B.unified | C.dropped | D.single |
A.discounted | B.viewed | C.accessed | D.founded |
A.muted | B.absorbed | C.buried | D.sunk |
A.technical | B.popular | C.negative | D.special |
A.possibly | B.reasonably | C.ordinarily | D.necessarily |
A.accommodated | B.combined | C.distinguished | D.enhanced |
A.abstract | B.repetition | C.review | D.brief |
A.press | B.publication | C.media | D.criticism |
A.opened | B.created | C.threw | D.jumped |
A.rules | B.harmonizes | C.impacts | D.roars |
A.feature | B.encourage | C.value | D.defend |
A.Therefore | B.However | C.Certainly | D.Surprisingly |
A.difference | B.truth | C.time | D.concern |
7 . Armed with a toolkit of techniques and tricks to calm the mind and bring focus back to your body, you can stop stressful situations from sabotaging your day, says Katy Georgiou.
GROUND YOURSELF
Making contact with the ground is your baseline go-to response for stress. This technique can be especially helpful if you find your stress regularly turns into panic. Wherever you are, whatever you’re doing, place your feet flat on the ground so that you feel stable, and then close your eyes. If you’re able to sit on the floor cross-legged or to lie down flat, then even better.
Think of this as earthing: really connect with the ground beneath your body. Some studies suggest that this simple act can help reduce or relieve symptoms of stress such as pain and fatigue, reduce blood pressure, and improve sleep. If you’re feeling disconnected from the world, it can also remind you that you belong to it and are a crucial part of it — the ground will always be there for you.
LOVE THYSELF
Adopting regular, daily or weekly routines for self-care can be very containing, creating consistency amid all sorts of stressful life events happening around you. Looking in the mirror each day can actually remind you that you exist, so feel free to factor some reflective gazing into your daily routine, whether it’s while applying moisturiser, shaving, or brushing your hair. Studies have shown that being confronted with your reflection can have powerful effects, taking us out of our heads and into the immediate present. For added effect, pay attention to the way your products interact with your hair and skin as you apply them.
Playing around with smells, colours and textures in your hands will also engage your senses. Using a scented shampoo or smoothing on body lotion after a warm bath can be easy ways to do this.
CLEAR YOUR MIND
Abandon all your thoughts and try to focus only on your surroundings. What can you see, hear, smell, taste and touch? Identify three things you can hear, one thing you can taste, four things you can see and two things you can feel on your skin. Pick out colours in the room you are sitting in, notice textures and different kinds of light. If somebody is with you, tell them what you are experiencing. The point here is that your senses are your best and easiest route back to feeling calm, by coming out of your head and rooting yourself back in the present. This is incredibly helpful if you’re having a panic attack or flop response.
1. If your friend Jane always feels worn out and suffers from sleep deprivation, which of the following techniques will you especially recommend to her?A.Connect her body to the ground beneath her. |
B.Adopt a daily gaze at her reflection in the mirror. |
C.Exchange her scentless shampoo for an aromatic one. |
D.Focus on what she can see, hear, smell, taste and touch. |
A.Lying down flat can better relieve your stress. |
B.Grounding yourself can give you a sense of belonging to the world. |
C.Brushing your hair while looking in the mirror can remind you of your existence. |
D.Those having a panic attack should shut their senses down. |
A.help people understand themselves better |
B.introduce some practical methods for stress management |
C.emphasize the significance of exploiting multiple senses |
D.promote a mindset of living in the moment |
8 . How Did You Get Five Fingers?
Your arms and toes began as tiny buds that sprouted from your sides when you were just a four-week-old embryo (胚胎). By six weeks, these limb buds had grown longer and five rods of cartilage 软骨) had appeared in their flattened tips. By week seven, the cells between the rods had died away, forming five small fingers or toes from once-solid masses of flesh.
Now, a team of scientists led by James Sharpe from the Centre for Genomic Regulation in Barcelona has discovered that these events are carefully orchestrated by three molecules. They mark out zones in the embryonic hand where fingers will grow, and the spaces in between that are destined to die. Without such molecules, pianos and keyboards wouldn’t exist, and jazz hands would be jazz palms.
These three molecules work in a way first envisioned by Alan Turing, a legendary English mathematician and code-breaker. Back in 1952, Turing proposed a simple mathematical model in which two molecules could create patterns by spreading through tissues and interacting with each other. For example, the first molecule might activate the second, while the second blocks the first. Neither receives any guidance about where to go; through their dance, they spontaneously organize themselves into spots or stripes.
Since then, many scientists have found that these Turing mechanisms exist. They’re responsible for a cheetah’s spots and a zebrafish’s stripes. For 30 years, people have also suggested that they could sculpt our hands and feet, but no one had found the exact molecules involved.
Sharpe knew that these molecules would need to show a striped pattern. Sox9 seemed like the most promising candidate. It is activated in a striped pattern from a very early stage of development. By comparing cells where Sox9 is active or inactive, Jelena Raspopovic and Luciano Marcon found two other groups of genes—Bmp and Wnt—also formed striped patterns. Bmp rises and falls in step with Sox9 and both are active in the digits. Wnt is out of phase; it’s active in the gaps. The three molecules also affect each other: Bmp activates Sox9 while Wnt blocks it; and Sox9 blocks both of its partners. It looked like these were the molecules the team was searching for not a pair, as Turing suggested, but a trinity. To confirm this, they created a simulation of a growing limb bud and showed that Sox9, Bmp and Wnt could organize themselves into a pattern of five stripes, by activating and blocking each other.
There’s still a lot to discover, though. For example, I’ve used Bmp and Wnt as shorthands here—in reality, each represents a class of several molecules, and the team still needs to work out which specific member is part of the Turing’s proposal.
1. The underlined sentence in the second paragraph means that ________.A.some certain molecules are necessary for the growth of human fingers |
B.the development of embryos is dependent on some certain molecules |
C.without some certain molecules, music won’t exist in this world |
D.the molecules work in a way that Alan Turing once offered |
A.Molecules interact by following a strict mathematical model. |
B.Molecules have a strong will to form patterns in nature. |
C.The formation of patterns in nature may be dominated by molecules. |
D.Alan Turing was able to track down the movement of molecules. |
A.A protein that determines humans’ development in childhood. |
B.A gene especially important for the development of our limbs. |
C.A striped pattern that always interacts with Bmp and Wnt. |
D.A simulation of growing limbs that activate and block each other. |
A.How human limbs are developed may well be similar to how animal spots are shaped. |
B.The way Sox9 interacts with Bmp and Wnt is still a mystery that needs further studying. |
C.Sox9 can activate both Bmp and Wnt to form our limbs, according to scientific research. |
D.Sox9, Bmp and Wnt are three specific molecules that determine the growth of fingers. |
9 . On March 7, 1907, the English statistician Francis Galton published a paper which illustrated what has come to be known as the “wisdom of crowds” effect. The experiment of estimation he conducted showed that in some cases, the average of a large number of independent estimates could be quite accurate.
This effect capitalizes on the fact that when people make errors, those errors aren’t always the same. Some people will tend to overestimate, and some to underestimate. When enough of these errors are averaged together, they cancel each other out, resulting in a more accurate estimate. If people are similar and tend to make the same errors, then their errors won’t cancel each other out. In more technical terms, the wisdom of crowds requires that people’s estimates be independent. If for whatever reasons, people’s errors become correlated or dependent, the accuracy of the estimate will go down.
But a new study led by Joaquin Navajas offered an interesting twist (转折) on this classic phenomenon. The key finding of the study was that when crowds were further divided into smaller groups that were allowed to have a discussion, the averages from these groups were more accurate than those from an equal number of independent individuals. For instance, the average obtained from the estimates of four discussion groups of five was significantly more accurate than the average obtained from 20 independent individuals.
In a follow-up study with 100 university students, the researchers tried to get a better sense of what the group members actually did in their discussion. Did they tend to go with those most confident about their estimates? Did they follow those least willing to change their minds? This happened some of the time, but it wasn’t the dominant response. Most frequently, the groups reported that they “shared arguments and reasoned together”. Somehow, these arguments and reasoning resulted in a global reduction in error. Although the studies led by Navajas have limitations and many questions remain, the potential implications for group discussion and decision-making are enormous.
1. What is paragraph 2 of the text mainly about?A.The methods of estimation. | B.The underlying logic of the effect. |
C.The causes of people’s errors. | D.The design of Galton’s experiment. |
A.the crowds were relatively small | B.there were occasional underestimates |
C.individuals did not communicate | D.estimates were not fully independent |
A.The size of the groups. | B.The dominant members. |
C.The discussion process. | D.The individual estimates. |
A.Unclear. | B.Dismissive. | C.Doubtful. | D.Approving. |
10 . Transition. It’s a pleasant word and a calming concept. It means going surely and sweetly from somewhere present to somewhere future. Unless, that is, it is newspapers’ ‘transition’ to the
Just look at the latest print circulation figures. The Daily Telegraph, The Guardian and many of the rest are down overall between 8% and 10% year-on-year, but their websites go ever higher.
All of that may well be true, depending on timing, geography and more.
One is the magazine world, both in the UK and in the US. It ought to be
As for news and current affairs magazines — which you’d expect to find in the eye of the digital storm — they had a 8.4% increase to report. In short, on both sides of the Atlantic, although some magazine areas went down, many showed rapid growth.
You can discover a
So if sales in that area have fallen so little, perhaps the
Already 360 US papers—including most of the biggest and best — have built paywalls around their products. However, the best way of attracting a paying readership appears to be a deal that offers the print copy and digital access as some kind of
Of course this huge difference isn’t
A.publishing | B.online | C.ideal | D.unknown |
A.On the other hand | B.After all | C.To begin with | D.For instance |
A.stop | B.exist | C.emerge | D.fit |
A.regulated | B.advancing | C.collapsing | D.minimized |
A.solid | B.simple | C.creative | D.changeable |
A.cultural | B.common | C.scientific | D.similar |
A.later | B.harder | C.clearer | D.slower |
A.all | B.neither | C.both | D.either |
A.service | B.system | C.crisis | D.figure |
A.right | B.vague | C.designed | D.mixed |
A.made up | B.told apart | C.took over | D.held on |
A.joint | B.mysterious | C.modern | D.complex |
A.In other words | B.On the contrary | C.What’s more | D.Even so |
A.new | B.sad | C.big | D.good |
A.spared | B.updated | C.noticed | D.edited |