1 . Babies are surrounded by human language, always listening and processing. Eventually, they put sounds together to produce a “Daddy” or a “Mama”. But what still confuses neuroscientists is exactly how the brain works to put it all together.
To figure it out, a team of researchers turned to a frequent stand-in (代替) for babies when it comes to language learning: the song-learning zebra finch. “We’ve known songbirds learn their song by first forming a memory of their father’s song or another adult’s song. Then they use that memory to guide their song learning,” said Neuroscientist Todd Roberts. “It’s been a long-term goal of the field to figure out how or where in the brain this memory is. This type of imitative learning that birds do is very similar to the type of learning that we engage in regularly—particularly when we’re young, we use it to guide our speech learning.”
Roberts and his team had a feeling that the interface (交叉区域) between sensory areas and motor areas in the brain was critical for this process, and they focused on a group of brain cells called the NIf.
“In order to prove that we could identify these circuits, we thought if we could implant a false memory.” First, they used a virus to cause the neurons (神经元) in the birds’ NIf to become sensitive to light. Then, using a tiny electrode as a flashlight, they activated (激活) the neurons. The length of each pulse of light corresponded with the amount of time the neurons would fire. And the birds’ brains interpreted that time period as the length of each note.
Soon enough, the birds began to practice the notes they had learned, even though they never really heard the sounds. Amazingly, the birds produced them in the correct social situations. The researchers say this is the first time anybody has found exactly a part of the brain necessary for generating the sorts of memories needed to copy sounds.
“This line of research is going to help us identify where in the brain we encode memories of relevant social experiences that we use to guide learning. We know that there are several neurodevelopmental disorders in people that have really far-reaching effects on this type of learning.”
1. The zebra finch is researched because its song-learning mode ________.| A．decides whether it will sing songs |
| B．helps it to say “Daddy” or “Mama” |
| C．is like the way babies learn speech |
| D．reflects its talent for imitating its father’s song |
| A．The interface in the brain. |
| B．Guidance from adults. |
| C．Imitative learning type like birds’. |
| D．The way of regular learning. |
| A．Scientists activated some neurons by using an electrode. |
| B．A bird only sings what it heard before. |
| C．The brain produces tiny electrodes. |
| D．Birds are sensitive to light. |
| A．A change in our way of listening and processing. |
| B．A chance to have relevant social experiences. |
| C．A better knowledge of the secrets of learning. |
| D．Identification of neurodevelopmental disorders. |
2 . Like most robots, social robots use artificial intelligence to decide how to act on information received through cameras and other sensors. The ability to respond in ways that seem lifelike has been informed by research into such issues as how perceptions (知觉) form, what constitutes social and emotional intelligence, and how people can infer others’ thoughts and feelings. Advances in Al have enabled designers to translate such psychological and neuroscientific insights into algorithms that allow robots to recognize voices, feces and emotions; interpret speech and gestures; respond appropriately to complex verbal and nonverbal cues; make eye contact; speak conversationally; and adapt to people’s needs by learning from feedback, rewards and criticisms.
A 47-inch humanoid (类人物) called Pepper (from SoftBank Robotics) recognizes faces and basic human emotions and engages in conversations via a touch screen in its “chest,” About 15,000 Peppers worldwide perform such services as hotel check-ins, airport customer service, shopping assistance and fast-food checkout. Temi (from Temi USA) and Loomo (Segway Robotics) are the next generation of personal assistants—like Amazon Echo and Google Home but mobile, providing a new level of functionality. Loomo, for instance, is not only a companion but can also transform on command into a scooter (小型摩托车) for transport.
Social robots have particular appeal for assisting the world’s growing elderly population. The PARO Therapeutic Robot (developed by Japan’s National Institute of Advanced Industrial Science and Technology), which looks like a seal, soft and cute, is meant to stimulate and reduce stress for those with Alzheimer’s disease and other patients in care facilities: it responds to its name by moving its head, and it cries for petting. Mabu (Catalia Health) engages patients, particularly the elderly, as a wellness aide, reminding them to take walks and medication and to call family members. Social robots are also gaining popularity with consumers as toys. Early attempts to include social behavior in toys, such as Hasbro’s Baby Alive and Sony’s AIBO robotic dog, had limited success. But both are resurging (复活), and the most recent version of AIBO has advanced voice and gesture recognition, can be taught tricks and develops new behaviors based on previous interactions.
Worldwide sales of consumer robots reached an estimated $5.6 billion in 2018, and the market is expected to grow to $19 billion by the end of 2025, with more than 65 million robots sold a year. This trend may seem surprising given that multiple well-funded consumer robot companies, such as Jibo and Anki, have failed. But a wave of robots is lining up to take the place of old robots, including BUDDY (Blue Frog Robotics), a big-eyed mobile device that plays games in addition to acting as a personal assistant and providing home automation and security.
1. What does the first paragraph mainly talk about?| A．How social robots receive information. |
| B．What research has been conducted about social robots. |
| C．Why social robots can respond in lifelike ways. |
| D．How designers translate insights into social robots. |
| A．filling an expanding variety of roles | B．getting higher intelligence |
| C．interacting with people | D．learning to respond in lifelike ways |
| A．social robots can have various forms and appearances |
| B．PARO can interact with people by moving its head like a dog |
| C．the most recent version of AIBO has achieved as great success as before |
| D．the sales of consumer robots have been increasing as ever expected |
| A．More companies will invest on social robots. |
| B．Social robots play nicely with human beings. |
| C．Social robots have great effects on elder people’s life. |
| D．Artificial intelligence enables social robots to make decisions. |
3 . Microsoft announced this week that its facial-recognition system is now more accurate in identifying people of color, touting (吹嘘）its progress at tackling one of the technology’s biggest biases (偏见).
But critics, citing Microsoft’s work with Immigration and Customs Enforcement, quickly seized on how that improved technology might be used. The agency contracts with Microsoft for cloud-computing tools that the tech giant says is largely limited to office work but can also include face recognition.
Columbia University professor Alondra Nelson tweeted, “We must stop confusing ‘inclusion’ in more ‘diverse’ surveillance (监管）systems with justice and equality.”
Facial-recognition systems more often misidentify people of color because of a long-running data problem： The massive sets of facial images they train on skew heavily toward white men. A Massachusetts Institute of Technology study this year of the face-recognition systems designed by Microsoft, IBM and the China-based Face++ found that facial-recognition systems consistently giving the wrong gender for famous women of color including Oprah Winfrey, Serena Williams, Michelle Obama and Shirley Chisholm, the first black female member of Congress.
The companies have responded in recent months by pouring many more photos into the mix, hoping to train the systems to better tell the differences among more than just white faces. IBM said Wednesday it used 1 million facial images, taken from the photo-sharing site Flickr, to build the “world’s largest facial data-set” which it will release publicly for other companies to use.
IBM and Microsoft say that allowed its systems to recognize gender and skin tone with much more precision. Microsoft said its improved system reduced the error rates for darker-skinned men and women by “up to 20 times,” and reduced error rates for all women by nine times.
Those improvements were heralded（宣布）by some for taking aim at the prejudices in a rapidly spreading technology, including potentially reducing the kinds of false positives that could lead police officers misidentify a criminal suspect.
But others suggested that the technology's increasing accuracy could also make it more marketable. The system should be accurate, “but that’s just the beginning, not the end, of their ethical obligation,” said David Robinson, managing director of the think tank Upturn.
At the center of that debate is Microsoft, whose multimillion-dollar contracts with ICE came under fire amid the agency’s separation of migrant parents and children at the Mexican border.
In an open letter to Microsoft chief executive Satya Nadella urging the company to cancel that contract, Microsoft workers pointed to a company blog post in January that said Azure Government would help ICE “accelerate recognition and identification.” “We believe that Microsoft must take an ethical stand, and put children and families above profits,” the letter said.
A Microsoft spokesman, pointing to a statement last week from Nadella, said the company’s “current cloud engagement” with ICE supports relatively anodyne（温和的）office work such as “mail, calendar, massaging and document management workloads.” The company said in a statement that its facial-recognition improvements are “part of our going work to address the industry-wide and societal issues on bias.”
Criticism of face recognition will probably expand as the technology finds its way into more arenas, including airports, stores and schools. The Orlando police department said this week that it would not renew its use of Amazon. com’s Rekognition system.
Companies ”have to acknowledge their moral involvement in the downstream use of their technology,”
Robinson said. “The impulse is that they’re going to put a product out there and wash their hands of the consequences. That’s unacceptable.”
1. What is “one of the technology’s biggest biases” in Paragraph 1?| A．Class bias. | B．Regional difference. |
| C．Professional prejudice. | D．Racial discrimination. |
| A．Justice and equality have been truly achieved. |
| B．It is due to the expansion of the photo database. |
| C．It has already solved all the social issues on biases. |
| D．The separation of immigrant parents from their children can be avoided. |
| A．Data problems. | B．The market value. |
| C．The application field. | D．A moral issue |
| A．Skeptical. | B．Approval. |
| C．Optimistic. | D．Neutral. |
| A．companies had better hide from responsibilities |
| B．companies deny problems with its technical process |
| C．companies should not launch new products on impulse |
| D．companies should be responsible for the new product and the consequences |
| A．The wide use of Microsoft system | B．Fears of facial-recognition technology |
| C．The improvement of Microsoft system | D．Failure of recognizing black women |
4 . Regrets are often painful. A study suggests that some people can overcome them, said Jia Wei Zhang, a psychology graduate at the University of California. But this isn’t the case for everyone, he said.
The researchers wondered why some people report feeling improvement from regrets but others don’t. Does it lie in how people approach their regrets?
In the study, the researchers focused on self-compassion (自我同情) as a potential factor in why some people have an easier lime leaving their regrets behind them.
400 people attended an experiment. First, they were asked to write about their biggest regret. Half wrote something they did but wish they hadn’t done; the other half wrote something they didn’t do but wish they had. Then, the participants were randomly assigned to one of three groups: self compassion, self-esteem and a control group. The self-esteem group was asked to respond by “talking to yourself about this regret from the aspect of confirming your positive qualities”.
The control group was asked to write about their favorite hobby rather than their regret. Then, they were asked about their feelings of forgiveness, acceptance and personal improvement following the exercise.
They found that the self-compassion group reported greater feelings of acceptance, forgiveness and personal improvement, compared with the control group and the self esteem group. In other words, focusing on your best qualities is not what helps you feel better about a regret. Rather, being compassionate toward yourself is what may make a difference, the researchers found.
It’s possible that people who practice self-compassion are able to confront their regrets and see what went wrong, so they can make a better choice in the future, Zhang told Live Science. Self-compassion pushes people to accept their regret instead of running away from it.
The researchers used an example of this from a previous study on breast cancer patients who were asked to try thinking about their treatment in a positive light before it began. The women who did so reported greater feelings of personal growth later on.
1. What does the underlined word “confront” in Paragraph 7 mean?| A．Handle | B．Forget. |
| C．Ignore | D．Show. |
| A．Think of a way out. | B．Write about their regrets. |
| C．Record their favorite hobbies. | D．Treat their regrets in positive ways. |
| A．To cover up their regrets. |
| B．To see if they have weaknesses. |
| C．To see if they have self compassion. |
| D．To help them feel better about their regrets. |
| A．To show the method of the research. |
| B．To show the value of the acceptance. |
| C．To show the truth of people’s feeling. |
| D．To show the difficulty of the research. |
