1 . 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 |
2 . 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? |
3 . 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 |
4 . Over millions of years humans have responded to certain situations without thinking too hard. If our ancestors spotted movement in the nearby forest, they would run first and question later. At the same time, the ability to analyze and to plan is part of what separates us from other animals. The question of when to trust your instinct (直觉)and when to think slow matters in the office as much as in the savannah(草原).
Slow thinking is the feature of a well-managed workplace. Yet instinct also has its place. Some decisions are more connected to emotional responses and less to analysis. In demanding customer-service or public-facing situations, instinct is often a better guide to how to behave.
Instinct can also be improved. Plenty of research has shown that instinct becomes more unerring with experience. In one well-known experiment, volunteers were asked to assess whether a selection of designer handbags were real or not. Some were instructed to operate on instinct and others to deliberate(深思熟虑)over their decision. Instinct worked better for those who owned at least three designer handbags; indeed, it outperformed analysis. The more expert you become, the better your instinct tends to be.
However, the real reason to embrace fast thinking is that it is, well, fast. It is often the only way to get through the day. To take one example, when your inbox floods with new emails at the start of a new day, there is absolutely no way to read them all carefully. Instinct is what helps you decide which ones to answer and which to delete or leave unopened. Fast thinking can also help the entire organization. The value of many managerial decisions lies in the simple fact that they have been made at all. Yet as data explodes, the temptation(诱惑)to ask for one more bit of analysis has become much harder to resist. Managers often suffer from overthinking, turning a simple problem into a complex one.
When to use instinct in the workplace rests on its own form of pattern recognition. Does the decision maker have real expertise in this area? Is this a field in which emotion matters more than reasoning? Above all, is it worth delaying the decision? Slow thinking is needed to get the big calls right. But fast thinking is the way to stop deliberation turning to a waste of time.
1. What does the underlined word “unerring” in Paragraph 3 probably mean?| A．Accurate. | B．Creative. | C．Controllable. | D．Obvious. |
| A．Managers can afford the cost of slow thinking. |
| B．Fast thinking can be a boost to work efficiency. |
| C．Slow thinking will hold us back in the long run. |
| D．Too much data is to blame for wrong decisions. |
| A．To explain how instinct works. |
| B．To compare instinct and slow thinking. |
| C．To highlight the value of instinct in the workplace. |
| D．To illustrate the development of different thinking patterns. |
5 . It won’t sound like a big surprise when I tell you that kindness plays an important role in a person’s wellbeing. It can lead to changes like higher self-esteem(自尊心) and lower blood pressure. Even just witnessing acts of kindness can make us happier.
Many of us don’t have a real sense of our value. It’s been estimated that as many as 85 percent of people struggle with low self-esteem.
Unlike a conscience(良心), this inner critic doesn’t motivate positive behavior.
One wonderful way to fight against our critical inner voice is through acts of being kind to others.
| A．However, the work doesn’t stop there. |
| B．People may lower the value of their own kind comments. |
| C．We all carry around a “critical inner voice” that tends to put us down. |
| D．Instead of seeing what we have to offer, we may think of ourselves as a burden. |
| E．This misunderstanding suggests that people devalue their own actions in relation to others. |
| F．Instead, it turns us against ourselves, making us underestimate our beneficial effect on others. |
| G．Yet, people may not truly know the impact that even the smallest of kind acts can have on another person. |
6 . It all started when I typed a perfectly reasonable prompt (提示词) into one of several apps on the market that can create an image based on text. “Skull space laser dinosaur starship explosion,” I wrote. The app processed for a few seconds, and returned four images, one of which was strangely accurate: a dinosaur-looking skull screamed out of an empty space, trailing fire. It looked like an illustration from the art magazine, and perhaps art from the magazine influenced its creation.
Text-to-image AIs identify images by looking at the text that people have used to describe those pictures online. When the app got my prompt, it studied images that random people had described as “dinosaur” or laser and soon then used what is called a diffusion model (扩散模型) to add a bunch of random chaos to those pictures. Once they were suitably completed, it “upscaled” them, removing noise and sharpening focus. Its work is so good that an artist using it recently won first place for digital images at the Colorado State Fair.
But there are major ethical (道德的) issues raised by the success of such AIs. The biggest has to do with those training data sets. Reporters recently discovered that the data set used by Text-to-image AIs contained images of violence. Some companies are working on ways to prevent the public from seeing images based on offensive and illegal pictures in the data set. A representative of the companies also noted that the images in its data set are “already available in the public internet on publicly available websites”.
But even if this problem is fixed there is still the question of all the other pictures online that are being transformed into AI-generated masterpieces. As many artists have pointed out, their works are being used without payment. The image-generating algorithm (算法) creates illustrations and even movies by using data sets stocked with art stolen from artists who post their works online.
Some AI researchers argue that their algorithms aren’t stealing from artists so much as learning from them just as human artists learn from each other. But a more ethical approach would be for companies to acknowledge their debt to artists and create a model of voluntary collective licensing, much like what radio stations first did in radio’s early days. Back then, musicians created groups like BMI to collectively license their music to radio stations — then BMI would pay artists based on how often their songs were played. Perhaps artists and art institutions today could form a “collecting society” that would allow companies to license their artwork for data sets.
To create ethical AI systems, we need to acknowledge the people whose work makes those systems so magical. We can’t simply snarf up every image online — we need humans to manage those data sets and we need to pay them to do it.
1. What can we learn about Text-to-image AIs from the first two paragraphs?| A．They are developed to process pictures. |
| B．They are used to describe online pictures. |
| C．They use a diffusion model to combine pictures. |
| D．They create their works based on online pictures. |
| A．the influence upon art creation. | B．the availability of online pictures. |
| C．the neglect of the artists’ copyright. | D．the prospect of artists being replaced. |
| A．To introduce the role that BMI played in AI history. |
| B．To present a way to regulate the use of online pictures. |
| C．To prove the necessity of licensing music to radio stations. |
| D．To demonstrate the urgency of forming a collecting society. |
| A．It is not practical to improve the image-generating algorithm. |
| B．The function of Text-to-image AIs shouldn’t be underestimated. |
| C．Human efforts should be valued in the application of Text-to-image AIs. |
| D．Companies should be held responsible for the illegal pictures on public websites. |
7 . The Power of ”Like“
Like it or love it, social media is a major part of life. Teens spend more than half of their waking hours online. And most of what they do is read and respond to posts by friends and family. Clicking on a thumbs-up or a heart icon is an easy way to stay in touch.
Clicking ”like“ on a post can increase the number of people who see it. If other people have liked a post, new viewers will be more likely to like it too. Many social media sites share more of the higher-ranked posts.
According to recent studies, viewing one’s own posts with a lot of likes activates the reward system in their brains, especially for teens. Positive responses to teens’ own photos (in the form of many likes)tell them that their friends appreciate the material they’re posting.
A like is a social cue. Teens use it to learn how to navigate their social world. Clicking”like“ is a simple act that can have complex results. All tech users will be thoughtful about social media.
| A．As a result, that popularity can feed on itself. |
| B．It’s no surprise that feedback from peers affects how teens behave. |
| C．Joining social media can give people a sense of being in the know. |
| D．Their brains respond to those likes by turning on the reward center. |
| E．For example, images related to alcohol may encourage teens to drink. |
| F．And that can, inappropriately, make us feel less successful than them. |
| G．But those ”likes“ can have power that goes beyond a simple connection. |
Schloss and her partner set out to find out
Gary is a children’s book author. He first became
10 . Too Much Information
Computer hackers, in order to get more secret information, constantly improve at breaking into cyberdefenses (网络防御系统) to steal valuable documents. So some researchers propose using an artificial-intelligence algorithm (算法) to hopelessly confuse them, once they break in, by hiding the real deal in a mountain of misleading documents and information.
The algorithm, called Word Embedding-based Fake Online Repository Generation Engine (WE-FORGE), creates decoys of patents under development. If hackers were after, say, the recipe for a new drug, they would have to find the relevant needle in a sea of false documents. This could mean checking each recipe in detail-and perhaps investing in a few dead-end ones. “The name of the game here is, ‘Make it harder, ” explains V. S Subrahmanian, its developer, Dartmouth College Cyber Security researcher. “Pain those stealing from you. ”
Subrahmanian says he tackled this project after reading that companies are unaware of new kinds of cyberattacks for an average of 312 days after they begin. “Hackers have almost a year to decamp with all our documents, patents and intellectual property, ”he says. “They have stolen almost everything. It’s not just the crown jewels-it’s the crown jewels, and the jewels of the cleaning lady, and the watch of the secretary!”
The documents produced by WE-FORGE could also act as hidden traps to confuse hackers, says Rachel Tobac, CEO of SocialProof Security. These documents might alert security when accessed. Companies have typically used human-created false copies for this strategy. But now the algorithm is able to do that for us.
The system produces convincing traps by searching through a document for key words. For each one it finds, it calculates a list of related concepts and replaces the original term with one chosen at random. The process can produce dozens of documents that contain no patent information but still look credible. Subrahmanian and his team asked computer science and chemistry graduates to evaluate real and false patents from their respective fields. And the humans found the WE-FORGE-created documents highly believable.
WE-FORGE might eventually expand its boundary. Both Subrahmanian and Tobac think this research will attract commercial interest. “I could definitely see an organization investing in this type of product, ” Tobac says. “If this creates believable decoys without releasing sensitive details within those traps, then I think you’ve got a huge with there. ”
1. What does the underlined word “decoys” in Paragraph 2 refer to?| A．Misleading documents. |
| B．Original terms. |
| C．Computer operating systems. |
| D．Cyber securities. |
| A．the greediness of hackers |
| B．the wealth of the companies |
| C．the unreliability of the network |
| D．the variety of intellectual property |
| A．It will sound alarm upon being operated. |
| B．It is profitable for the users by avoiding their loss. |
| C．It has attracted investors for its application in many fields. |
| D．It can compose irrelevant concepts in the false documents. |
