1 . We Need to Think about Conservation on a Different Timescale
Time, perceived by humans in days, months, and years, contrasts with nature’s grander scales of centuries and millennia, referred to as “deep time.” While paleontologists (古生物学者) are trained to think in deep time, conservationists are realizing the challenges it poses. Shortsightedness about time limits modern conservation, with efforts often overlooking past healthy conditions of ecosystems in the context of climate and biodiversity crises.
The shifting baseline syndrome (综合症), where standards in a place change gradually, makes conservation more complex. It involves evaluating ecosystems primarily on their recent past, often with negative consequences.
Recent shifts in California’s forest management practices, from stopping fires to embracing Indigenous knowledge of controlled burns, exemplify the importance of understanding historical ecosystem dynamics. To enhance conservation, adopting a deep-time approach is crucial.
Modern mathematical modeling, combined with long-term data, offers a pathway for preserving ecosystems. In California’s kelp (海带、海藻) forest, researchers identified an overlooked keystone species — the extinct Seller’s Sea Cow (大海牛). By examining past kelp forests, a deeper story impacting regeneration was revealed. The sea cow, a massive plant-cater, contributed to a diverse, vital undergrowth by trimming kelp and letting light reach the area.
The researchers put forward a novel approach to kelp forest restoration: selectively harvesting kelp, imitating the sea cow’s impact. This strategy, considering historical dynamics, challenges assumptions about recent ecosystems and offers new conservation methods.
Rather than only focusing on removing urchins (海胆) or reintroducing sea otters, the researchers suggest employing teams of humans to selectively harvest kelp, as the Steller’s sea cow once did, to encourage fresh growth. This sustainable harvest could benefit both the ecosystem and human consumption.
In short, assumptions based on the recent past may impede the understanding and protection of ecosystems. On the other hand, the application of controlled burns, similar modeling studies, and a deep-time perspective (视角) could significantly transform conservation efforts. Recognizing our role in an ongoing narrative spanning millions of years is essential, urging a comprehensive understanding of ecosystems through time. Embracing this role is crucial for shaping the future and establishing vital connections from the past to the future.
1. What is the “shifting baseline syndrome,” mentioned in the passage?A.A syndrome that affects human beings’ perception of time. |
B.A phenomenon where ecological standards shift in a place. |
C.A psychological disorder common among conservationists. |
D.A condition where ecosystems change gradually over time. |
A.It promotes the prevention of wildfires. | B.It aids in mathematical modeling efforts. |
C.It helps reveal historical ecosystem dynamics. | D.It enhances human consumption of ecosystems. |
A.Reform. | B.Disrupt. | C.Quicken. | D.Deepen. |
A.Shifting baseline syndrome has positive ecological changes. |
B.Mathematical modeling with the latest data can be effective. |
C.Deep-time perspective and historical dynamics are crucial. |
D.Recent history is more preferred in ecosystem restoration. |
2 . Often people receive a guitar, mandolin, or some other musical instrument as a birthday or Christmas gift. There’s joy everywhere. The giver of the gift knows how much the receiver wants to learn this instrument and the receiver is actually holding it in his hands instead of longing for it through the shop window.
Finding an instructor that fits into a busy work schedule is hard enough, but once you decide on a lesson plan, then you must consider the practice time, how to practice, what to practice — and let’s face it — not all people learn something the same way. So in order to learn a musical instrument, how much practice time is enough and what kind of practice is right for you?
There is no set amount of time that anyone should practice a musical instrument. When I was in programming classes, I could have studied nightly for 5 hours each night. It would have taken me years to learn the art of computer programming. Though I’m attracted by the systematic logic of it, my talent is towards another thing. However, on the other hand, if I spent an hour every couple days with a passionate (充满热情的) hobby like playing the violin, not only would the time fly quickly, I’d also be learning at a much greater speed since the built-in passion is the motivation for advancement.
So as much as it’s important to practice, a step back is to first find the harmonious instrument that fits you as a person as development of your personality. If you’re learning the guitar because it’s cool — obviously that’s the modern-day mindset, however, you might not be actually linking your talent for musical satisfaction with your most creative advantages you have to offer.
It’s been my experience that every person has a certain level of musical talent. My enjoyable challenge has been to assist them in this adventure and actually locate their best abilities as quickly as possible. Then and only then can we match learners with instruments and truly begin a fun and exciting walk down the road of happiness and contentment, where music, ability, personality and soul all meet. Once this piece of the mystery puzzle is in place, I’ve never had to work at motivating a learner to practice.
1. In the author’s opinion, which of the following is the most important when learning a music instrument?A.The amount of time for practice. | B.A scientific learning method. |
C.A good music instructor. | D.The strong fondness for music. |
A.is received as a birthday or Christmas gift |
B.follows a modern fashion in music training |
C.is easy to learn and fits the learner very much |
D.contributes to developing the learner’s character |
A.She writes pop music. | B.She’s a music instructor. |
C.She advertises for music lesson. | D.She’s a music instrument collector. |
A.Does practice make perfect? | B.Does talent make a difference? |
C.Does a lesson plan really fit you? | D.Does hard work make up for lack of talent? |
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 . Wikipedia (维基百科), one of the last remaining pillars of the open and free web, is in existential crisis.
The trend towards rationality (理性) was endangered long before the birth of the World Wide Web. As Neil Postman noted in his 1985 book Amusing Ourselves to Death, the rise of television introduced not just a new medium but a new atmosphere: a gradual shift from a typographic (印刷的) culture to a photographic one, which in turn meant a shift from rationality to emotions, opinions to entertainment.
In an image-centered and pleasure-driven world, Postman noted, there is no place for thinking, because you simply cannot think with images. It is text that enables us to “uncover lies and confusions, and to detect abuses of logic and common sense. It also means to weigh ideas, to compare and contrast statements, to connect one generalization to another.”
The dominance of television was not contained to our living rooms. It overturned all of those habits of mind, fundamentally changing our experience of the world, affecting politics, religion, business, and culture. It reduced many aspects of modern life to entertainment and commerce. “Americans don’t talk to each other; we entertain each other,” Postman wrote. “They don’t exchange ideas; they exchange images. They do not argue with propositions; they argue with good looks, celebrities and commercials.”
At first, the web seemed to push against this trend. When it emerged towards the late 1980s as a purely text-based medium, it was seen as a tool to pursue knowledge, not pleasure. Reason and thought were most valued in this garden. Universities were among the first to connect to this new medium, hosting discussion groups, informative blogs, electronic magazines, and academic forums. It was an intellectual project, not about commerce or control, created in a scientific research center in Switzerland.
Wikipedia was a fruit of this garden. So was Google search and its text-based advertising model. And so were blogs, which valued text, hyperlinks, knowledge, and literature. And for more than a decade, the web created an alternative space that threatened television’s power over society.
Social networks, though, have since colonized the web for television’s values. From Facebook to Instagram, the medium refocuses our attention on videos and images, rewarding emotional appeals — “like” buttons over rational ones. Instead of searching for knowledge, it engages us in an endless passion for instant approval from an audience, for which we are constantly but unconsciously performing. It reduces our curiosity by showing us exactly what we already want and think, based on our profiles and preferences. The Enlightenment’s motto(座右铭)of “Dare to know” has become “Dare not to care to know.”
Now the challenge is to save Wikipedia and its promise of a free and open collection of all human knowledge among the conquest of social media — how to collect and preserve knowledge when nobody cares to know. We need to understand that the decline of the web and thereby of the Wikipedia is part of a much larger civilizational shift which has just started to unfold.
1. According to Neil Postman, which of the following statements is TRUE?A.Television started a revolution in photographic technology. |
B.Texts help people think critically to make judgements. |
C.Images give people more chances to communicate deeper. |
D.The web was meant to serve as an entertainment platform. |
A.experiences | B.appearances | C.opinions | D.consequences |
A.We are constantly distracted and can’t focus for long. |
B.We are well kept updated with the latest information. |
C.We have become more curious about the outside world. |
D.We have become uninterested in intellectual pursuits. |
A.The Decline of Television |
B.The Power of Social Media |
C.Why We Amuse Ourselves to Death |
D.How Social Media Endangers Knowledge |
5 . 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.
7 . History has not yet
Whatever we
Historian Neil Howe sees
A.remarked | B.convinced | C.guaranteed | D.revealed |
A.numbers | B.houses | C.accommodates | D.contains |
A.peers | B.adolescents | C.folks | D.guys |
A.over | B.without | C.besides | D.beyond |
A.diagnosed | B.dismissed | C.labeled | D.coined |
A.end up | B.consider about | C.appeal for | D.approve of |
A.distribution force | B.purchasing power | C.global view | D.unique outlooks |
A.vivid | B.instructive | C.instant | D.profitable |
A.feed up with | B.put up with | C.make up for | D.identify with |
A.faking | B.revising | C.illustrating | D.maintaining |
A.supervising | B.forming | C.representing | D.promoting |
A.parallels | B.contrasts | C.comparisons | D.reservations |
A.because | B.although | C.while | D.when |
A.emphasis | B.generation | C.intensity | D.cultivation |
A.routes | B.schemes | C.names | D.definitions |
8 . 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? |
9 . The high-tech revolution has inspired a pleasure endless stream of new and exciting electronic products that we just can’t live without. In fact, the speed of technological innovation can make last year’s must-have this year’s junk. And that’s the problem.
The average life span of a personal computer has been shortened to around 18 months and this has nothing to do with worn-mice or damaged disk drives. Simply put, electronic products can become out of date before you’ve even figured out how they work.
So what happens to all those old keyboards, monitors, organizers and CPUs? Most are stored away in a warehouse (仓库), taking up valuable space. But many end up in landfills, and that is where the trouble really begins. Computer monitors can contain up to 3.5 kg of dangerous waste once they are no longer in use.
Unfortunately, this problem is not going to disappear anytime soon. In fact, it is growing by the minute. In Japan alone, people throw away some 20 million TVs, washing machines, refrigerators and air conditioners each year. What is to be done with all this techno-trash?
One way to reduce waste is to avoid throwing away in the first place. Many companies reuse parts from old products in new models. This is not cheating-it makes both environmental and economic sense. Cannon, for example, has adopted a philosophy known as “kyosei”, meaning “living and working together for the common good?” — a goal of achieving balance between the environment and the corporate (公司的) activities. The company has even gone so far as to say that environmental assurance should come before all business activities, and that companies unable to achieve such assurance do not deserve to remain in business.
As part of that effort, the company has started a global recycling program with a goal to reduce, reuse and recycle more than 90% of its used products. In 1999, for example, Cannon collected 128, 000 copying machines and 12, 175 tons of toner cartridges (色粉盒) in Japan, Europe and the United States.
Some argue that electronic garbage can also be controlled during the design phase. This concept, called “design for the environment”. Not only does this make environmental sense, but it saves the customer money. IBM, meanwhile, recently planned programs in Canada and the US that, for a small fee, will take back not just an IBM but also any manufacturer’s computer. Depending on the age and condition, the equipment will then be either donated to charity, or broken down for reusable parts and recyclable materials.
1. With the rapid development of science and technology high-tech products can ____.A.last for many years |
B.become worn out soon |
C.become old and useless soon |
D.be used forever |
A.Too much room is needed for their probable storage. |
B.People do not know how to deal with them at all. |
C.The amount of this techno-trash is increasing everyday without stop. |
D.Harmful substances contained within may pollute the environment. |
A.Business must be achieved at the cost of environment. |
B.Environment holds great importance than business. |
C.Business and environment has little impact on each other. |
D.Recycling makes only environment sense instead of economic benefits. |
A.while designing products, we must make something to contain garbage |
B.while designing products, don’t throw away garbage away |
C.while designing, we must work out how much garbage the new product will bring about |
D.while designing, we must take environment into consideration. |
A.The problem caused by high-tech products can’t be solved in short time |
B.The techno-trash problem can easily be solved in big countries |
C.The problem can be solved to some degree if enough attention is paid |
D.It is still hard to say whether this problem can be solved or not |
10 . 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. |