We live in the age of the algorithm (算法). Increasingly, the decisions that affect our lives— where we go to school, whether we get a car loan, how much we pay for health insurance— are being made not by humans, but by mathematical models.
One application that has become particularly common is the use of algorithms to evaluate job performance. Sarah, a teacher who, despite being widely respected by her students, their parents and her colleagues, was fired because she performed poorly according to an algorithm. When an algorithm rates you poorly, you are immediately branded as an underperformer and there is rarely an opportunity to appeal against those judgments. In many cases, methods are considered secrets and no details are shared. And data often seems convincing.
As a matter of fact, the belief that school performance in America is declining is based on a data mistake. A Nation at Risk is the report that rang the initial alarm bells about declining SAT (Scholastic Assessment Test) scores. Yet if they had taken a closer look, they would have noticed that the scores in each smaller group were increasing. The reason for the decline in the average score was that more disadvantaged kids were taking the test. However, due to the data mistake, teachers as a whole were judged to be failing.
Wall Street is famous for its mathematicians who build complex models to predict market movements and develop business plans. These are really smart people. Even so, it is not at all uncommon for their models to fail. The key difference between those models and many of the ones being used these days is that Wall Street traders lose money when their data models go wrong. However, as CV Neil points out in her book, the effects of widely —used machine — driven judgments are often not borne by those who design the algorithms, but by everyone else.
As we increasingly rely on machines to make decisions, we need to ask these questions: What assumptions are there in your model? What hasn’t been taken into account? How are we going to test the effectiveness of the conclusions? Clearly, something has gone terribly wrong. When machines replace humans to make a judgment, we should hold them to a high standard. We should know how the data was collected. And when numbers lie, we should stop listening to them.
1. Why school performance in America is believed to be declining?| A．Teachers perform poorly. |
| B．Big data is popular. |
| C．The data is wrong. |
| D．There is misunderstanding about algorithms. |
| A．Follow the machines. | B．Make a judgment by tests. |
| C．Stop listening to machines. | D．Make the data convincing. |
A． | B． |
C． | D． |
| A．The drawbacks of algorithm. |
| B．The application of algorithm in business. |
| C．The popularity of algorithm to employers. |
| D．The advantages and disadvantages of algorithm. |
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【推荐1】Today’s artificial intelligence may not be that clever, but it just got much quicker in understanding. A learning program designed by three researchers can now recognize and draw handwritten characters after seeing them only a few times, just as a human can. And the program can do it so well that people can’t tell the difference.
The findings, published in the journal Science, represent a major step forward in developing more powerful computer programs that learn in the ways that humans do.
Although computers are excellent at storing and processing data, they’re less-than-stellar students. Your average 3-year-olds could pick up basic concepts faster than the most advanced program.
In short, “You can generalize,” said coauthor Joshua Tenenbaum. But there’s something else humans can do with just a little exposure—they can break an object down into its key parts and dream up something new. “To scientists like me who study the mind, the gap between machine-learning and human-learning capacities remains vast,” Tenenbaum said. “We want to close that gap, and that’s our long-term goal.”
Now, Tenenbaum and his colleagues have managed to build a different kind of machine learning algorithm ( 算 法 )—one that, like humans, can learn a simple concept from very few examples and can even apply it in new ways. The researchers tested the model on human handwriting, which can vary sharply from person to person, even when each produces the exact same character.
The scientists built an algorithm with an approach called Bayesian program learning, or BPL, a probability-based program. This algorithm is actually able to build concepts as it goes.
In a set of experiments, the scientists tested the program using many examples of 1,623 handwritten characters from 50 different writing systems from around the world. In a one-shot classification challenge, people were quite good at it, with an average error rate of 4.5 percent. But BPL, slightly edged them out, with a comparable error rate of 3.3 percent. The scientists also challenged the program and some human participants to draw new versions of various characters they presented. They then had human judges determine which ones were made by man and which were made by machine. As it turned out, the humans were barely as good as chance at figuring out which set of characters was machine-produced and which was created by humans.
The findings could be used to improve a variety of technologies in the near term, including for other symbol-based systems such as gestures, dance moves and spoken and signed language. But the research could also shed fresh light on how learning happens in young humans, the scientists pointed out.
1. What is the passage mainly about?| A．An advance in artificial intelligence. | B．A special learning program for students. |
| C．The application of artificial intelligence. | D．A new approach of developing programs. |
| A．students are better at processing data | B．computers are incomparable to students |
| C．students are less smart than computers | D．computers are less clever in some aspects |
| A．Humans were slow at recognizing characters. |
| B．BPL wrote characters in a quite different manner. |
| C．BPL could identify and write characters as humans. |
| D．Humans could create more characters than computers. |
| A．Computers learn in the same way as humans. |
| B．The findings may help improve human-learning. |
| C．Machine-learning is superior to human-learning. |
| D．Young humans can understand algorithms quickly. |
【推荐2】After dinner, my family sat in the living room, chatting about a hit. Thousands of people spent Thursday—a Valentine’s Day of sorts in China—searching not only for Mr. or Mrs. Right, but also for Mr. and Mrs. Left.
My father informed us that second goods App Xianyu, owned by e-commerce giant Alibaba, organized “blind dates” pairing users who had misplaced one of their wireless earphones with others who had lost the opposite earbud. “Your cousin, an overseas student, participated in the activity,” he added.
Afterwards, my dad surfed the Internet over the iPhone and showed a video in the App Xianyu to us. “Dear friends, it’s a special day today,” read the event’s announcement, referring to May 20, a date associated with romance in China because its pronunciation sounds similar to “I love you” in Mandarin. In a twinkling, Bullet screen (弹幕) appeared in the video. With the rising popularity of wireless earphones such as Apple’s AirPods, over 130,000 earpieces went missing in 2020, resulting in their owners having no choice but to sell the remaining one. This year, the figure of abandoned earbuds stood at a whopping 100,000 by mid-May. On the Internet I browsed some information. Right-side earbuds are apparently more faithful to their owners: In 56% of cases, sellers had lost their left Carphone.
Dad told us that the idea came from the lighthearted way Xianyu users have described their single earbuds, saying they were looking for an “in-law” for their “son” or “daughter”. Those, particularly with a charging case, said they already owned an “apartment” for the future couple.
“The young generation are good at making full use of their possessions and refuse to be wasteful,” my mother commented to us. “Besides single earphones, there’s also huge demand and supply for reuniting single gaming controllers, earrings, and gloves into a pair.”
With the passage of time, younger generations no longer see secondhand items as shabby.
1. Why did the author’s cousin take part in the activity?| A．To attend blind dates. | B．To pair the earbud. |
| C．To make some friends. | D．To collect big data. |
| A．Right earbuds. | B．Left earbuds. | C．AirPods. | D．Applications. |
| A．A son-in-law can be provided with accommodation. |
| B．A daughter-in-law can be provided with accommodation. |
| C．The pairing earbuds can be charged fully in the apartment. |
| D．Xianyu users can learn about some useful in formation. |
| A．Mean. | B．Curious. | C．Committed. | D．Economical. |
【推荐3】Being able to take advantage of truly unlimited data is a smartphone user's dream, but everyone I've talked to about 5G is more excited about the usage unlocked by next - generation wireless devices. From smart home security to self - driving cars, all the Internet - connected equipment in your life will be able to talk to each other at lightning - fast speed with reduced delay.
"5 G is one of those forerunners, along with artificial intelligence, of this coming data age, ” said Steve Koenig, senior director of market research for the Consumer Technology Association. "Self - driving vehicles are emblematic in this data age - they show application of data completely. With one single task, driving, you have large amounts of data coming from the vehicle itself, and a variety of sensors (传感器)are collecting a lot of information to model its environment as it moves. It's pulling in data from other vehicles about conditions down the road. There's lots of data behind that task, which is why we need the speed and lower latency ((延迟).
AR glasses and virtual (虚拟的)reality headphones haven't yet been inside the mainstream, but tech companies are joyfully saying that such equipment will eventually replace our smartphones. With 5G, that could actually happen. This is notable because companies such as Apple are reportedly developing AR glasses to improve - or even replace - smartphones.
Ericsson showed at February's Mobile World Congress in 2019 how smart glasses could become faster and lighter with a 5G connection, because instead of being weighed down with components, the glasses could rely on outside equipment for processing power.
But don't get too excited. There's still a lot of work to be done in the meantime, including some necessary testing to make sure the radio plays nicely with basic systems and service construction so that 5G isn, t concentrated only in big cities.
1. What does the first paragraph focus on?| A．Potential of 5G. | B．The super speed of 5G. |
| C．Usage of smart equipment. | D．The future of smart equipment. |
| A．available | B．productive | C．representative | D．popular |
| A．Uncertain. | B．Optimistic. | C．Cautious. | D．Disapproving. |
| A．To make radio play nicely. | B．To construct big 5G cities. |
| C．To do 5G trials effectively. | D．To expand 5G coverage fully. |
【推荐1】Plastics have been found from the top of Mount Qomolangma to newly-formed beaches in Hawaii. The amount of plastic in our environment is shocking. What can we do about it? I’m inspired by Kate Nelson, who has lived without using single-use plastic for over ten years. Kate is also the founder of Sea the Mermaids, an organization focusing on stopping human-sourced ocean pollution through education and community action.
She recently wrote an inspiring and practical guidebook I Quit Plastics: and you can too, which is full of information and tips on how to cook, clean, shop, wear and live plastic-free. Upon first opening, you will see a recipe for Cashew Cheese that looks amazing! Not only does this book provide many delicious recipes, but also explores interesting problems about plastic pollution.
For example, Kate explores the problems about plastics and social justice, including plastic privilege(特权). She points out that wealthier countries, such as the USA, export their plastic waste to Southeast Asia, but many of these countries cannot process their own waste. In addition, most of the affordable food, though processed and unhealthy, is heavily packaged in plastic. People that live in “food deserts” in cities and depend on corner stores have no choice when it comes to avoiding plastics. Kate writes in the book, “Not everyone will have bulk (散装) food stores or farmers’ markets near where they live.”
Kate’s writing is easy to read without sounding preachy (说教的). Her explanations and reasoning are clear. From food to beauty to cleaning, Kate’s practical recipes and tips make it easy for everyone to reduce their chances of using single-use plastic. She develops effective strategies that others can easily adopt and offers clear steps to help people improve on the plastic quifting journey.
1. Why does the author talk about plastic in the first paragraph?| A．To lead to the topic. | B．To share an experience. |
| C．To doubt serious pollution. | D．To explain plastic pollution. |
| A．Famous persons. | B．The latest news. |
| C．Classic music. | D．Reusable shopping bags. |
| A．They eat too much unhealthy food. |
| B．They can’t afford to buy enough food. |
| C．They can’t avoid plastic-packaged food. |
| D．They prefer bulk food to packaged food. |
| A．An environment report. | B．A book review. |
| C．An author’s introduction. | D．A scientific research. |
【推荐2】Stop those negative thoughts! When it comes to brain power, it appears your thoughts matter. That was the eye-opening conclusion of a study published in the journal Alzheimer’s and Dementia.
For this study, scientists carefully measured the cognitive(认知)function of 292 middle-aged to older people over a four-year period. The cognitive assessments included measures of memory, attention and language.
The study subjects(实验对象)had their thinking patterns regularly monitored by responding to a series of questions over two of the four years. The thought-pattem questionnaires were designed to identify repetitive negative thinking (RNT for short). RNT includes often thinking about negative past events as well as future sources of anxiety.
About a third of the study subjects had PET scans(正电子发射计算机断层扫描)of their brains tomeasure levels of the abnormal brain protcins, tau and amyloid. Tau and amyloid build up in people affected with Alzheimer’s disease.
The findings? Study subjects with greater RNT-these repetitive negative thought patterns-exhibited a clear decrease in cognitive function and memory over the four-year period. What’s more, they had more tau and amyloid built up in their brains. It is well-documented that our thoughts have powerful, direct effects on our bodies, so these results aren’t surprising.
Thankfully, studies show that we can change our thought patterns through mental-training practices, with meditation(冥想)documented to be one of the very best.
As someone who often got trapped in negative memories of the remote past, I can speak personally to the remarkable power of meditation to relieve this destructive thinking pattern, and I encourage everyone to explore this practice. I meditate every day and gratefully achieve that goal about 90% of the time.
Feel free to share this post with friends and loved ones because one of the greatest gifts we can give is the gift of better health. Enjoy!
1. How is the subjects’ RNT determined?| A．By measuring their blood level. |
| B．By analyzing their questionnaires. |
| C．By monitoring their behaviors. |
| D．By examining their signs of diseases. |
| A．Forgetting the negative past. |
| B．Changeable thinking patterns. |
| C．Worse body shape. |
| D．Poorer brain function. |
| A．Having brain scanned regularly. |
| B．Buiding up our strength. |
| C．Changing our study pattern. |
| D．Practicing mental training. |
| A．Favorable. | B．Doubtful. | C．Reserved. | D．Unclear. |
【推荐3】It seems like giant pandas might not be the best at hide-and-seek. With their noticeable black and white coats, they would appear to have a hard time blending (融合) into so many environments. But a new study finds that the symbolic markings help them disappear into their surroundings.
For their study, researchers analysed photos of giant pandas in their natural habitat. They found that the animals are good at visually (视觉上地) hiding in their environments because they use habitats with dark and lighting conditions, and also snow during some of the year.
They found that the black fur blends mainly into shade and dark tree trunks (树干). But it also matches the ground, rocks, and leaves. The white fur matches snow, rocks, and bright leaves. Sometimes pandas also have pale brown fur that blends into rocks, ground, leaves, and shady background areas.
As a last step, the researchers used a color map technique to compare how giant pandas resemble their background with other species that are considered able to visually hide in their environments. They found that pandas fell in the middle of this list.
It might seem a bit confusing because giant pandas are very easy to discover at a zoo. But the viewer and environment make a difference. “We modeled their coloration through predators’(捕食者的) eyes as well as how humans see them so we are sure of the results,“ study author Tim Caro of the University of Bristol says. ”It seems that giant pandas appear eye-catching to us because of short viewing distances and specific backgrounds: when we see them, either in photographs or at the zoo, it is almost always from close up, and often against a background that doesn’t reflect their natural habitat,“ says author Nick Scott—Samuel of the University of Bristol.
1. What did the researchers find out about giant pandas?| A．Their coloring helps them hide in nature. |
| B．They are well protected in nature reserves. |
| C．They can always find their favourite surroundings. |
| D．They have difficulty adapting to new environments. |
| A．Pandas take cover in trees. | B．Brown pandas are very unique. |
| C．There’re many types of giant pandas. | D．Different colors serve different functions. |
| A．Stay in. | B．Look like. | C．Come from. | D．Get familiar with. |
| A．To tell us the importance of pandas’ living in natural habitats. |
| B．To show the living conditions of giant pandas in a zoo. |
| C．To give a further explanation to clear people’s doubts. |
| D．To list people’s different points of view on their study. |
