1 . Several dozen graduate students in London were recently tasked with outwitting a large language model (LLM), a type of AI designed to hold useful conversations. LLMs are often programmed with guardrails designed to stop them giving harmful replies: instructions on making bombs in a bathtub, say, or the confident statement of “facts” that are not actually true.
The aim of the task was to break those guardrails. Some results were merely stupid. For example, one participant got the chatbot to claim ducks could be used as indicators of air quality. But the most successful efforts were those that made the machine produce the titles, publication dates and host journals of non-existent academic articles.
AI has the potential to be a big benefit to science. Optimists talk of machines producing readable summaries of complicated areas of research; tirelessly analysing oceans of data to suggest new drugs and even, one day, coming up with hypotheses of their own. But AI comes with downsides, too.
Start with the simplest problem: academic misconduct.Some journals allow researchers to use LLMs to help write papers. But not everybody is willing to admit to it. Sometimes, the fact that LLMs have been used is obvious. Guillaume Cabanac, a computer scientist, has uncovered dozens of papers that contain phrases such as “regenerate response” — the text of a button in some versions of ChatGPT that commands the program to rewrite its most recent answer, probably copied into the manuscript (原稿) by mistake.
Another problem arises when AI models are trained on AI-generated data. LLMs are trained on text from the Internet. As they churn out (大量炮制) more such text, the risk of LLMs taking in their own outputs grows. That can cause “model collapse”. In 2023 llia Shumailov, a computer scientist, co-authored a paper in which a model was fed handwritten digits and asked to generate digits of its own, which were fed back to it in turn. After a few cycles, the computer’s numbers became more or less illegible.After 20iterations (迭代), it could produce only rough circles or blurry lines.
Some worry that computer-generated insights might come from models whose inner workings are not understood. Inexplainable models are not useless, says David Leslie at an AI-research outfit in London, but their outputs will need rigorous testing in the real world. That is perhaps less unnerving than it sounds. Checking models against reality is what science is supposed to be about, after all.
For now, at least, questions outnumber answers. The threats that machines pose to the scientific method are, at the end of the day, the same ones posed by humans. AI could accelerate the production of nonsense just as much as it accelerates good science. As the Royal Society has it,nullius in verba: take nobody’s word for it. No thing’s, either.
1. The result of the task conducted in London shows that ________.| A．LLMs give away useful information | B．the guardrails turn out to be ineffective |
| C．AI’s influence will potentially be decreased | D．the effort put into the study of AI hardly pays off |
| A．The readability of the models’output is underestimated. |
| B．The diverse sources of information confuse the models. |
| C．Training on regenerated data stops models working well. |
| D．The data will become reliable after continuous iterations. |
| A．impractical | B．unjustified | C．groundless | D．unsettling |
| A．Faster Nonsense: AI Could Also Go Wrong |
| B．Imperfect Models: How Will AI Make Advances? |
| C．The Rise of LLMs: AI Could Still Be Promising |
| D．Bigger Threats: AI Will Be Uncontrollable |
2 . “Assume you are wrong.” The advice came from Brian Nosek, a psychology professor, who was offering a strategy for pursuing better science.
To understand the context for Nosek’s advice, we need to take a step back to the nature of science itself. You see despite what many of us learned in elementary school, there is no single scientific method. Just as scientific theories become elaborated and change, so do scientific methods.
But methodological reform hasn’t come without some fretting and friction. Nasty things have been said by and about methodological reformers. Few people like having the value of their life’s work called into question. On the other side, few people are good at voicing criticisms in kind and constructive ways. So, part of the challenge is figuring out how to bake critical self-reflection into the culture of science itself, so it unfolds as a welcome and integrated part of the process, and not an embarrassing sideshow.
What Nosek recommended was a strategy for changing the way we offer and respond to critique. Assuming you are right might be a motivating force, sustaining the enormous effort that conducting scientific work requires. But it also makes it easy to interpret criticisms as personal attacks. Beginning, instead, from the assumption you are wrong, a criticism is easier to interpret as a constructive suggestion for how to be less wrong — a goal that your critic presumably shares.
One worry about this approach is that it could be demoralizing for scientists. Striving to be less wrong might be a less effective motivation than the promise of being right. Another concern is that a strategy that works well within science could backfire when it comes to communicating science with the public. Without an appreciation for how science works, it’s easy to take uncertainty or disagreements as marks against science, when in fact they reflect some of the very features of science that make it our best approach to reaching reliable conclusions about the world. Science is reliable because it responds to evidence: as the quantity and quality of our evidence improves, our theories can and should change, too.
Despite these worries, I like Nosek’s suggestion because it builds in cognitive humility along with a sense that we can do better. It also builds in a sense of community — we’re all in the same boat when it comes to falling short of getting things right.
Unfortunately, this still leaves us with an untested hypothesis (假说): that assuming one is wrong can change community norms for the better, and ultimately support better science and even, perhaps, better decisions in life. I don’t know if that’s true. In fact, I should probably assume that it’s wrong. But with the benefit of the scientific community and our best methodological tools, I hope we can get it less wrong, together.
1. What can we learn from Paragraph 3?| A．Reformers tend to devalue researchers’ work. |
| B．Scientists are unwilling to express kind criticisms. |
| C．People hold wrong assumptions about the culture of science. |
| D．The scientific community should practice critical self-reflection. |
| A．the enormous efforts of scientists at work | B．the reliability of potential research results |
| C．the public’s passion for scientific findings | D．the improvement in the quality of evidence |
| A．discouraging | B．ineffective | C．unfair | D．misleading |
| A．doubtful but sincere | B．disapproving but soft |
| C．authoritative and direct | D．reflective and humorous |
3 . 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? |
4 . 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? |
5 . It seems rather obvious that facial characteristics are determined by our genes. But until recently geneticists(遗传学家) had very little understanding of which parts of our DNA were linked to our facial appearance.
An international team of researchers identified more than 130 chromosomal(染色体的) regions associated with specific aspects of facial shape. This is a critical first step toward understanding how genetics impact our faces, Live Science noted.
Researchers scanned the DNA of more than 8,000 people and analyzed dozens of shape measurements from their 3D facial images to look at the statistical relationships between about 7 million genetic markers—known locations in the genetic code where humans vary—and the facial features.
“When we find a statistical relation between a facial feature and one or more genetic markers, it points us to a very precise region of DNA on a chromosome. The genes located around that region then become our prime candidates for facial features like nose or lip shape,” Seth Weinberg, co-author of the study, wrote on Live Science.
Researchers discovered some interesting patterns after looking at the implicated(牵涉其中) genes at these DNA regions. Your nose is the part that is most influenced by your genes. Areas like the cheeks, which are highly influenced by lifestyle factors like diet, showed the fewest genetic associations.
There is also a high degree of overlap(重合) between the genes involved in facial and limb development. This provides an important clue as to why many genetic syndromes(综合症) are characterized by both hand and facial malformations(畸形). Some genes involved in facial shape may be involved in cancer, too. It explains why people treated for pediatric(小儿科的) cancer show some distinctive facial features.
So, can someone take your DNA and construct an accurate image of your face? It’s unlikely. The 130-plus genetic regions that were identified explain less than 10 percent of the variation in facial shape. But even if we understood all of the genes impacting facial appearance, prediction would still be a big challenge. That’s because facial features are affected by other factors as well, such as age, diet, climate and sun exposure.
Still, the knowledge of patients’ genetic information can be an invaluable tool in creating personalized treatment plans in fields like orthodontics(畸齿矫正学) or reconstructive surgery. For example, if someday doctors can use genetics to predict when a child’s jaw will hit its growth peak, it will help them decide the best time to intervene.
1. What’s the main purpose of the research?| A．To explain why humans vary based on statistics. |
| B．To identify the factors impacting facial appearance. |
| C．To discover the link between genes and facial features. |
| D．To study the relationship between facial features and genetic markers. |
| A．The nose is most influenced by genes. |
| B．Facial malformations affect limb development. |
| C．The cheeks are most closely associated with age. |
| D．Facial shape and cancer are impacted by exactly the same genes. |
| A．It could improve orthodontic treatments. |
| B．It could be useful for changing facial shapes. |
| C．It could help recreate one’s jaw at an early age. |
| D．It could help predict facial appearance with ease. |
6 . The environmental practices of big businesses are shaped by a fundamental fact that offends our sense of justice. A business may maximize the amount of money it makes by damaging the environment and hurting people. When government regulation is effective, and the public is environmentally aware, environmentally clean big businesses may out-compete dirty ones, but the reverse is likely to be true if government regulation is ineffective and the public doesn’t care.
It is easy to blame a business for helping itself by hurting other people. But blaming alone is unlikely to produce change. It ignores the fact that businesses are not charities but profit-making companies, and they are under obligation to maximize profits for shareholders by legal means.
Our blaming of businesses also ignores the ultimate responsibility of the public for creating the conditions that let a business profit through destructive environmental policies. In the long run, it is the public, either directly or through its politicians, that has the power to make such destructive policies unprofitable and illegal, and to make sustainable environmental policies profitable.
The public can do that by accusing businesses of harming them. The public may also make their opinion felt by choosing to buy sustainably harvested products; by preferring their governments to award valuable contracts to businesses with a good environmental track record; and by pressing their governments to pass and enforce laws and regulations requiring good environmental practices.
In turn, big businesses can exert powerful pressure on any suppliers that might ignore public or government pressure. For instance, after the US public became concerned about the spread of a disease, transmitted to humans through infected meat, the US government introduced rules demanding that the meat industry abandon practices associated with the risk of the disease spreading. But the meat packers refused to follow these, claiming that they would be too expensive to obey. However, when a fast-food company made the same demands after customer purchases of its hamburgers dropped, the meat industry followed immediately. The public’s task is therefore to identify which links in the supply chain are sensitive to public pressure.
Some readers may be disappointed or outraged that I place the ultimate responsibility for business practices harming the public on the public itself. I also believe that the public must accept the necessity for higher prices for products to cover the added costs of sound environmental practices. My views may seem to ignore the belief that businesses should act in accordance with moral principles even if this leads to a reduction in their profits. But I think we have to recognize that, throughout human history, government regulation has arisen precisely because it was found that not only did moral principles need to be made explicit, they also needed to be enforced.
My conclusion is not a moralistic one about who is right or wrong, admirable or selfish. I believe that changes in public attitudes are essential for changes in businesses’ environmental practices.
1. The main idea of Paragraph 3 is that environmental damage__________.| A．is the result of ignorance of the public |
| B．requires political action if it is to be stopped |
| C．can be prevented by the action of ordinary people |
| D．can only be stopped by educating business leaders |
| A．reduce their own individual impact on the environment |
| B．learn more about the impact of business on the environment |
| C．raise awareness of the effects of specific environmental disasters |
| D．influence the environmental policies of businesses and governments |
| A．Meat packers stopped supplying hamburgers to fast-food chains. |
| B．Meat packers persuaded the government to reduce their expenses. |
| C．A fast-food company forced their meat suppliers to follow the law. |
| D．A fast-food company encouraged the government to introduce regulations. |
| A．Will the world survive the threat caused by big businesses? |
| B．How can big businesses be encouraged to be less driven by profit? |
| C．What environmental dangers are caused by the greed of businesses? |
| D．Are big businesses to blame for the damage they cause to the environment? |
What may well be the oldest metal coins in the world have been identified at an ancient abandoned city known as Guanzhuang in China. Like many Bronze Age (青铜时代) coins from the region, they were cast in the shape of spades with finely carved handles. These ancient coins existed during an in-between period between barter (以物易物) and money, when coins were a novel concept, but everybody knew that agricultural tools were valuable.
Reading about this incredible discovery, I kept thinking about the way modern people represent computer networks by describing machines as having “addresses”, like a house. We also talk about one computer using a “port” to send information to another computer, as if the data were a floating boat with destination. It’s as if we are in the Bronze Age of information technology, grasping desperately for real-world reference to transform our civilization.
Now consider what happened to spade coins. Over centuries, metalworkers made these coins into more abstract shapes. Some became almost human figures. Others’ handles were reduced to small half-circles. As spade coins grew more abstract, people carved them with number values and the locations where they were made. They became more like modern coins, flat and covered in writing. Looking at one of these later pieces, you would have no idea that they were once intended to look like a spade.
This makes me wonder if we will develop an entirely new set of symbols that allow us to interact with our digital information more smoothly.
Taking spade coins as our guide, we can guess that far-future computer networks will no longer contain any recognizable references to houses. But they still might bring some of the ideas we associate with home to our mind. In fact, computer networks — if they still exist at all — are likely to be almost the indispensable part of our houses and cities, their sensors inset with walls and roads. Our network addresses might actually be the same as our street addresses. If climate change leads to floods, our mobile devices might look more like boats than phones, assisting us to land.
My point is that the metaphors of the information age aren’t random. Mobile devices do offer us comfort after a long day at work. In some sense, our desire to settle on the shores of data lakes could change the way we understand home, as well as how we build computers. So as we cast our minds forward, we have to think about what new abstractions will go along with our information technology. Perhaps the one thing we count on is that humans will still appreciate the comforts of home.
1. Many Bronze Age coins were made into the shape of a spade because ___________.| A．a lot of emphasis was put on agriculture |
| B．this stylish design made the coins valuable |
| C．these coins also served as agricultural tools |
| D．the handles made the coins easily exchanged |
| A．To show they both used to be new concepts when first invented. |
| B．To explain abstract digital worlds are different from concrete coins. |
| C．To suggest computers will experience dramatic changes as coins did. |
| D．To highlight their same importance in our civilizational transformation. |
| A．Flexible. | B．Essential. | C．Wasteful. | D．Alternative. |
| A．What Coins and Computers Bring Us |
| B．How Agriculture Loses to Digital Industry |
| C．How Bronze Age Develops to Information Age |
| D．What Ancient Money Tells Us About the Future |
8 . If you’ re reaching for the last piece of pizza at a party, and meanwhile see another hand going for it, your next move probably depends on how you feel and whom the hand belongs to. Your little sister — you might just grab the pizza. Your boss — you probably will give up. But if you’re hungry and feeling particularly confident, you might go for it.
Now researchers have made progress in understanding how mammals’ brain encodes social rank and uses this information to shape behaviours — such as whether to fight for that last pizza slice. They discovered that an area of the brain called the medial prefrontal cortex (mPFC) was responsible for representing social rank in mammals; changes to a mouse’s mPFC affect its dominance (支配) behaviour. But it was unknown how the mPFC represented this information and which neurons (神经元) were involved in changing dominance behaviour.
In the new study, Professor Kay Tye let groups of four mice share a cage, allowing a social hierarchy (等级) to naturally develop — some mice became more dominant and others more subordinate. As soon as the mice were paired up, he discovered, the activity of their mPFC neurons could predict — with 90 percent certainty — the rank of their opponent.
“We expected animals might only signal rank when they are in a competition,” says co-researcher Nancy. “But it turns out animals walk around with this representation of social rank all the time.”
When the researchers next asked whether the activity of the mPFC neurons was associated with behaviour, they found something surprising. The brain activity patterns were linked with slight changes in behaviour, such as how fast a mouse moved, and they also could predict — a full 30 seconds before the competition started — which mouse would win the food reward.
The winner was not always the more dominant, but the one engaged in a “winning mindset”. Just as you might sometimes be in a more competitive mood and be more likely to snatch that pizza slice before your boss, a subordinate mouse might be in a more “winning mindset” than a more dominant mouse and end up winning.
The areas of the mPFC associated with social rank and “winning mindset” are next to one another and highly connected. Signals on social rank impact the state of the brain involved in “winning mindset”. In other words, a subordinate mouse’s confidence and “winning mindset” may partially decrease when faced with a dominant one.
“This is further evidence to suggest that we are in different brain states when we are with others compared to when we’re alone,” says Tye. “Regardless of who you’re with, if you’re aware of other people around you, your brain is using different neurons.”
1. The author writes Paragraph 1 in order to ________.| A．tell an interesting story | B．present a typical example |
| C．introduce a major topic | D．provide a convincing proof |
| A．The mPFC neurons. | B．The researchers. |
| C．The brain activity patterns. | D．The changes in behaviour. |
| A．mPFC neurons change dominance behaviour. |
| B．Brain activities can influence social hierarchy. |
| C．Dominant opponents boost “winning mindset”. |
| D．Social rank and “winning mindset” affect behaviour. |
| A．Those eager to win may succeed. | B．When alone, we are more confident. |
| C．Social rank guides competitive behaviour. | D．“Winning mindset” establishes dominance. |
9 . Lia Thomas, a student at the University of Pennsylvania, is an excellent swimmer. She often beats her rivals by tens of seconds, breaking records. Her success is based on three things. One is natural talent. Another is persistent training. And the third is biology.
For although she identifies as a woman,Ms Thomas was born male.Since humans cannot change their sex (unlike their self-identified gender),she remains that way.On the eve of her biggest competition, Ms Thomas finds herself at the centre of the bad-tempered debate about whether trans women-males who identify as women-should compete in women’s sports.That,in turn,is part of a broader argument: should brute (纯粹的) biological facts sometimes override people’s deeply held feelings about their identities?
This newspaper believes it is almost always unfair to allow transgender women to compete in women’s sports.The advantages bestowed by male puberty (青春期) are so big that no amount of training or talent can enable female athletes to overcome them.Florence Griffith Joyner’s 100-metres world sprinting record has stood for three decades.A male matching it would not even make it to the Olympics, let alone the final.In 2016,at an American event for high-schoolers, four of the eight boys in the 100-metres final ran faster.
Much of the male advantage is granted by testosterone (睾丸素), a potent anabolic steroid whose levels rise sharply in male puberty.For many years,many sporting bodies, following the lead of the International Olympic Committee, hoped to deal with the issue by allowing trans women to compete in women’s events provided they took testosterone-suppressing drugs.But the science suggests this does not level the playing field.Suppressing testosterone in adults, it seems, does little to undo the advantages granted by a male adolescence.
Sports must therefore choose between inclusion and fairness; and they should choose fair play. That does not mean, as is sometimes claimed, that trans women would be barred from all sport.One way to make that clear would be to replace the “men’s” and “women’s” categories with “open” and “female” ones.The first would be open to all comers.The second would be restricted on the basis of biology.
Sport is public, and results can be measured objectively. That means the argument that the material facts of biology should sometimes outrank a person’s subjective sense of identity is easier to make. But bias exists, as a Republican bill in Florida to restrict “instruction” in schools about gender identity or sexual orientation makes plain.
That should be resisted. Most of the time,it costs little or nothing to respect people’s choices about how they wish to present themselves.In the rare cases where rights clash (不相容), society must weigh the balance sensitively and with open eyes.
1. The author mentions Joyner’s 100-metres world sprinting record to show that ________.| A．most female athletes can’t rival trans women athletes |
| B．male puberty is the best time for sports competition |
| C．it is unfair for Ms Thomas to compete in women’s sports |
| D．this record can’t make a male reach the threshold of the Olympics |
| A．bill | B．bias | C．instruction | D．identity |
| A．disagreeable | B．open-minded | C．sympathetic | D．conservative |
| A．Inclusion and Fairness | B．Respect People’s Choices |
| C．“Open” and “Female” in Sports | D．Biology Matters A Great Deal |
10 . HANDSTITCHED WORLDS: THE CARTOGRAPHY OF QUILTS
Quilts (床罩) are a narrative art; with themes that are political, spiritual, communal, or commemorative, they are infused with history and memory, mapping out intimate stories and legacies through a handcrafted language of design. Handstitched Worlds: The Cartography of Quilts is an invitation to read quilts as maps, tracing the paths of individual histories that illuminate larger historic events and cultural trends.
Spanning the nineteenth to twenty-first centuries, this insightful and engaging exhibition brings together 18 quilts from the collection of the American Folk Art Museum, New York, representing a range of materials, motifs, and techniques from traditional early-American quilts to more contemporary sculptural assemblages. The quilts in Handstitched Worlds show us how this too-often overlooked medium balances creativity with tradition, individuality with collective zeitgeist. Like a road map, these unique works offer a path to a deeper understanding of the American cultural fabric.
Number of Works:18 quilts
Organized by: American Folk Art Museum, New York
Approximate size:175-200 linear feet
Security: Moderate security
Participation Fee: Please inquire
Shipping: IA&A makes all arrangements; exhibitors pay outgoing shipping costs within the contiguous U.S.
Booking Period:12 weeks
Tour: June 2021—August2024
Contact: TravelingExhibitions@ArtsandArtists.org
Leigh Yawkey Woodson Art Museum, Wausau, WI
June 12, 2021—August 29, 2021
Washington State Historical Society, Tacoma, WA
September 17, 2021—January 23, 2022
Utah Museum of Fine Arts, Salt Lake City, UT
February 19, 2022—May 14, 2022
Fort Wayne Muesum of Art, Fort Wayne, IN
June 18, 2022—September 11, 2022
AVAILABLE
October 2022—January 2023
Dane G. Hansen Memorial Museum, Logan, KS
February 17,2023—May 14, 2023
AVAILABLE
June 2023—December 2023
Lauren Rogers Museum of Art, Laurel, MS
January 30, 2024—April 21, 2024
AVAILABLE
May 2024—August 2024
All tour dates can be customized to meet your scheduling needs. Please contact Traveling Exhibitions @ Artsand Artists.org for more information.
1. What is the purpose of the exhibition of Handstitched Worlds: The Cartography of Quilts?| A．To promote creativity and individuality thorough the engaging exhibition. |
| B．To provide an opportunity for visitors to learn to make quilts stitch by stitch. |
| C．To give visitors an insight into the history and culture of America in specific periods. |
| D．To enrich the understanding of the American culture by a tour visit to museums across America. |
| A．The exhibition is free both for the exhibitors and for the visitors. |
| B．Exhibitors that are interested can choose whatever dates they want. |
| C．The artistic and historic value of handstitched quilts used to be neglected. |
| D．Exhibitors that are interested can book the exhibition 12 weeks in advance. |
| A．exhibitors | B．visitors | C．artists | D．historians |
