A.Recognizing one’s abilities. |
B.Analyzing one’s strengths and weaknesses. |
C.Matching one’s abilities to job vacancies. |
D.Presenting one’s abilities to future employers. |
A.Finding out what they can do about the employer. |
B.Avoiding asking unsure questions. |
C.Arriving as early as they can. |
D.Answering questions in a polite way. |
A.Graduates from famous universities. | B.Determined, skilled and able people. |
C.Capable and modest people. | D.People with much work experience. |
1. 目前火灾事故现状;
2.火灾逃生的自救建议措施;
3. 呼吁号召。
注意:1.写作词数应为120词左右;
2. 可以适当增加细节以使内容信息丰富;
参考词汇: 爬行 crawl; 电梯 elevator/lift; 消防演习 the fire drill
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3 . “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 |
4 . 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 |
A six-year-old Scottish girl has taken her first steps even though doctors said she would spend her life in a wheelchair. Arabella was born with arthrogryposis multiplex congenita (AMC, 先天性多发性关节挛缩症) affecting both her arms and legs, but when doctors in Scotland said there was nothing that could be done for Arabella, her mom Catherine refused to believe it and instead made it her mission to find a solution to help her daughter.
She reached out to Dr. David S Feldman, a famous doctor based in Florida who treats AMC with surgery (外科手术), and made an appointment with his team at the Paley European Institute in Warsaw, Poland. “We all agreed she is a great person for operation and can gain motion in her legs, which gave great hope to Arabella and her mom,” Dr, Feldman said. However, it needed a big amount of money for the operation, which the family couldn’t afford it.
When Catherine returned to Scotland to meet with some famous doctors, they said there was nothing that could be done and the surgery wasn’t suitable for Arabella. As a result, Catherine was a little disappointed. “They said we needed to be realistic and think about how we could improve Arabella’s life in a wheelchair,” Arabella’s father William said, “On hearing that, as any parents, it dashed (使破灭) our hopes so much...” For the next two months, the family was in a low spirit. William has been on a mission ever since to help his daughter walk again. He launched a crowd funding page to raise £125,000 for the surgery.
Arriving in October 2021 and remaining there over Christmas, Arabella underwent 12 weeks of careful rehabilitation and physiotherapy (康复理疗) with the dedicated nurses as she built up her muscles and learned to walk. Last August she returned to hospital for a second operation and a further six weeks of physiotherapy. They didn’t give up. On the contrary. they looked after Arabella carefully and encouraged her to feel confident of the operation.
注意:
1. 续写词数应为150左右;
2. 请按如下格式在答题卡的相应位置作答。
The parents tried their best to raise money for the second operation.
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Arabella had the second operation and physiotherapy.
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1.关键要素;
2.你的理由。
注意:标题、开头和结尾都已给,请完成中间部分,根据要素的多少,写两段、三段或四段,字数80词左右。
Key elements of a great film
Nowadays films play an important role in our life. And every great film must involve many key elements. Opinions about them vary among people. Below are my ideas about key elements of a great film.
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Of course, there are many other elements leading to a great film. Anyway an outstanding film must come from its inner shining elements. What about your idea?
In a small village called Brightville, there lived three friends: Kimberley, John and Alicia. They were all students at Brightville High School. Kimberley was a hardworking and determined girl; John was a curious and creative boy; and Alicia was a kind and helpful girl. They shared a common dream of achieving their goals through education.
On a sunny day, the three friends gathered at their favorite spot near the village river. They often met there to discuss their dreams and motivate each other. Kimberley always emphasized (强调) the importance of studying and setting goals. John loved exploring new things and finding creative ways to learn. Alicia, with her caring nature, always supported and encouraged her friends.
One day, they came across an old abandoned library in the heart of Brightville. The library was filled with dust-covered books. Kimberley saw this as an opportunity for their dreams to come true. She suggested turning the library into a learning center for the village.
The three friends spent months cleaning, organizing and renewing the library. They reached out to the villagers, and soon, volunteers joined them to set up the learning center. They created a friendly and welcome environment where students could come to study, read books, and seek help.
Word about the learning center spread throughout the village. Students from all ages and backgrounds started attending. Kimberley, John and Alicia devoted their after-school hours to tutoring (指导) the students. They understood that education was not just about memorizing facts but also about nurturing curiosity and passion for learning.
注意:1. 续写词数应为150左右;
2. 请按如下格式在答题卡的相应位置作答。
As days turned into months, they saw the effect of their hard work.
___________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________The success of the learning center in Brightville caught the attention of the local government.
___________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________☆询问Chris的近况并告知Chris这一好消息;
☆感谢Chris对你英语学习的帮助并征询在英国学习的建议(如选择课程,参观英国名胜簿);
☆请求对方回复。
注意:
(1)可适当加入细节,以使行文连贯,不少于100词;
(2)电子邮件的开头和结尾已为你写好,不计入总词数。
参考词汇:
中英交流协会 Sino-British Exchange Association
剑桥大学 Cambridge University
Dear Chris,
I am Li Jin.
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Yours,
Li Jin
9 . 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? |
A. amused B. common C. complete D. disconnecting E. means F. mission G. perform H. positive I. prepared J. spreading K. struggle |
The world of live action role-playing
For many people, the days of playing make-believe (假扮) ended in childhood. But for some, the game of make-believe lives on in Live Action Role-Playing, or LARP. This is a game where people act out characters in a(n)
Although pretty much anything goes in LARP, nearly every event involves players completing a(n)
The genius of LARP is that each event can be any kind of story. The most
One