1 . Aristotle thought the face was a window onto a person’s mind. Cicero agreed. Two thousand years passed, and facial expressions are still commonly thought to be a universally valid way to judge other people’s feelings, irrespective of age, sex and culture. A raised eyebrow suggests confusion. A smile indicates happiness.
Or do they? An analysis of hundreds of research papers that examined the relationship between facial expressions and underlying emotions has uncovered a surprising conclusion: there is no good scientific evidence to suggest that there are such things as recognizable facial expressions for basic emotions which are universal across cultures. Just because a person is not smiling, the researchers found, does not mean that person is unhappy.
This may raise questions about the efforts of information-technology companies to develop artificial-intelligence algorithms (算法) which can recognize facial expressions and work out a person’s underlying emotional state. Microsoft, for example, claims its “Emotion API” is able to detect what people are feeling by examining video footage of them. Another of the study’s authors, however, expressed scepticism. Aleix Martinez, a computer engineer at Ohio State University, said that companies attempting to obtain emotions from images of faces have failed to understand the importance of context.
For a start, facial expression is but one of a number of non-verbal ways，such as body posture, that people use to communicate with each other. Machine recognition of emotion needs to take account of these as well. But context can reach further than that. Dr Martinez mentioned an experiment in which participants were shown a close-up picture of a man’s face, which was bright red with his mouth open in a scream. Based on this alone, most participants said the man was extremely angry. Then the whole picture was shown. It was a football player with his arms outstretched, celebrating a goal. His angry-looking face was, in fact, a show of pure joy.
Given that people cannot guess each other’s emotional states most of the time, Dr Martinez sees no reason computers would be able to. “There are companies right now claiming to be able to do that and apply this to places I find really scary and dangerous, for example, in hiring people,” he says. “Some companies require you to present a video resume, which is analyzed by a machine-learning system. And depending on your facial expressions, they hire you or not, which I find really shocking.”
1. We can learn from the second paragraph that __________.| A．facial expressions are universal across cultures |
| B．it is hard to recognize some facial expressions |
| C．emotions and facial expressions may not be related |
| D．common facial expressions convey similar meanings |
| A．similar interest | B．fierce anger | C．strong support | D．great doubt |
| A．facial expression is an important way to communicate |
| B．machine recognition of emotion is not reliable at all |
| C．facial expression is not the only way to detect feelings |
| D．people may misread facial expressions for lack of context |
| A．Facial expressions are among the most universal forms of body language. |
| B．Computers can detect people’s mind by analyzing their facial expressions. |
| C．Facial expressions may not be the reliable reflection of a person’s emotions. |
| D．Companies can depend on machine recognition of emotion to hire people. |
2 . On August 29th, as Hurricane Dorian tracked towards America’s east coast, Elon Musk, the boss of Tesla, an electric-car maker, announced that some of his customers in the storm’s path would find that their cars had suddenly developed the ability to drive farther on a single battery charge. Like many modern vehicles, Mr. Musk’s products are best thought of as internet-connected computers on wheels. The cheaper models in Tesla’s line-up have parts of their batteries disabled by the car’s software in order to limit their range. At the tap of a keyboard in Palo Alto, the firm was able to remove those restrictions and give drivers temporary access to the full power of their batteries.
Mr. Musk’s computerized cars are just one example of a much broader trend. As computers and connectivity become cheaper, it makes sense to bake them into more and more things that are not, in themselves, computers, creating an “internet of things”.
Such a world will bring many benefits. Consumers will get convenience, and products that can do things non-computerized versions cannot. Businesses will get efficiency, as information about the physical world that used to be uncertain becomes concrete and analyzable.
In the long term, though, the most obvious effects will be in how the world works. Ever more companies will become tech companies; the internet will become everywhere. As a result, a series of unresolved arguments will spill over from the virtual world into the real one.
Start with ownership. As Mr Musk showed, the internet gives firms the ability to stay connected to their products even after they have been sold, transforming them into something closer to services than goods. That has already made the traditional ideas of ownership unclear. When Microsoft closed its ebook store in July, for instance, its customers lost the ability to read titles they had bought (the firm offered refunds). That shifts the balance of power from the customer to the seller.
Virtual business models will jar in the physical world. Tech firms are generally happy to move fast and break things. But you cannot release the beta version (测试版) of a fridge. Apple, a smartphonemaker, provides updates for its phones for only five years or so after their release; users of Android smartphones are lucky to get two. But goods such as washing machines or industrial machinery can have lifespans of a decade or more. Firms will need to work out how to support complicated computerised devices long after their original programmers have moved on.
Data will be another flashpoint. For much of the internet the business model is to offer “free” services that are paid for with valuable user data, collected with consent (同意) that is half-informed at best. In the virtual world, arguments about what should be tracked, and who owns the resulting data, can seem airy and theoretical. In the real one, they will feel more urgent.
Predicting the consequences of any technology is hard — especially one as universal as computing. The emergence of the consumer internet, 25 years ago, was met with starry-eyed optimism. These days the internet’s faults dominate the headlines. But the people have the advantage of having lived through the first internet revolution — which should give them some idea of what to expect.
1. From the passage we can tell that Tesla can ______.| A．drive faster than usual in extreme weather | B．adjust the range of its battery power |
| C．charge the battery at the tap of a keyboard | D．operate when the battery is fully drained |
| A．Early adopters of certain apps find that they ceased to work after the firm lost interest. |
| B．The insurance company uses data from fitness trackers to adjust customers’ premiums(保费). |
| C．Computerized machinery can’t predict its breakdowns or schedule preventive maintenance. |
| D．A high-tech fridge company restricts its customers from repairing their fridges themselves. |
| A．boom | B．conflict | C．vanish | D．expand |
| A．how the world will change as computers spread into everyday objects |
| B．the adoption of electric vehicles and the possible problems to expect |
| C．what should be done to prevent the breakdown of computerized devices |
| D．different views on the current application of Internet Technology |
3 . The close relationship between speakers and their speech has led some scholars to suggest that language determines the view we have of the world around us. Different languages segment natural phenomena differently. We name seven colors in the rainbow: violet, indigo, blue, green, yellow, orange and red. Speakers of other languages may see only four, as did Turkish before our system was introduced, or even as few as two, roughly the lighter shades versus the darker. There is nothing in nature to demonstrate how we should chop up the spectrum of the rainbow, but when we have learned a given language, we distinguish the shades it designates, both in the rainbow and elsewhere students of language assume from such a situation that language determines much of the and patterns we see in the world around us, and that it directs our concepts and actions
Changes in the choice of language, then, might modify behavior. Today gasoline trucks are generally labeled" flammable(易燃的)". The in-prefix was taken as equivalent to that of words like "inactive", where in- means not". It is actually the in- of words like "intense", where it strengthens the meaning. The word "inflammable", then, means "highly flammable" The faulty interpretation of language, however, determined the attitudes of many speakers, who then adjusted their behavior in relation to the language. Prudent truck owners have taken notice and changed the warning to“ flammable"
Such observations led Whorf to a concept with deeper patterns of language, such as the use of tenses in the language of Europe. Tense is the linguistic expression or time. English and other European languages generally require their speakers to identify the time of an event, whether present: It is raining; past: It rained; or future: It will rain. By contrast, many languages, such as the Hopi language of New Mexico, lack expression for tense. Nor do such languages objectify time. In Hopi one cannot count days, minutes, years as though they were objects like stones. Everyday expressions like "Three years went by" are impossible in Hopi.
Comparing such languages, Whorf proposed that "our use of tense or our objectified view of time is favorable to historicity se t)and to everything connected with the keeping of records." That is to say, because of the patterns for referring to time in English and other languages, their speakers maintain records and emphasize bookkeeping, accounting, and the like. In accordance with it, ones conception of the world is relative to the language one learns
While the relativity hypothesis(假设) has attracted considerable attention, it has never been experimentally demonstrated to the valid. a large scale attempt to test the outlook of Hopi-speaking children versus English speaking children turned out to be inconclusive. It remains a task of future scholars to determine whether the hypothesis is valid and also whether one should assume a weak or strong position with regard to it. Clearly we are deeply tied to our native language. But whether it regulates our perceptions or our view of the world Is still an open question
1. The case of the label "flammable" is mentioned to prove that_________.| A．languages can affect our choices of action |
| B．prefixes can lead to disasters if used improperly |
| C．some truck drivers can adjust their behavior |
| D．misunderstanding can happen among speakers |
| A．reflects deeper patterns of European languages |
| B．transforms abstract ideas into objects |
| C．helps avoid certain ambiguity in concepts or ideas |
| D．makes it possible to modern e the Hopi language |
| A．Different languages often have different methods of keeping historical records |
| B．We need more studies to find out if we are closely related to our native tongues |
| C．Our mother tongues have a great influence on our world views and behavior |
| D．It's no easy task to confirm the link between mother tongues and our concepts |
4 . One of the most inspiring quotes I ever heard was by Brian Tracy. He said: “The difference between successful people and unsuccessful people is that successful people fail many more times than unsuccessful people.” I personally experienced the wisdom of that understanding right after my first book was published. Like many authors，I expected hundreds of bookstore customers lining up for me to sign copies for them. I’m afraid to say, it didn’t quite happen like that.
My first signing was arranged at the largest bookstore in the city. Filled with anticipation，I was put into a private signing room in the beautiful store. Despite a nice sign placed outside the room exhibiting images of both me and my book, not a single customer entered the room. As each minute passed, I became increasingly anxious.
Do they not like the title? I wondered. Do they not like the book cover?
After 90 minutes of this torture, I was absolutely distraught.
For the four years writing the book, I had felt a sense of mission and purpose like never before in my life. Working a full 8-hour day in my clinic, I had to get into bed by 9:30 pm every day, so I could wake up at 5:30 in the morning and have two hours of writing before heading into my clinic. Before I ever began each writing session, I would close my eyes for 10 minutes and then whisper, “Please grant me the words to touch just one person’s life”
Now, sitting there alone at my first book signing，I wondered if my entire life wasn’t just a big joke. At that moment, just when I couldn’t feel any worse, a middle-aged couple walked into the room. I managed to hide my emotions and introduced myself and my book. There was something different about the way they were looking at me that I couldn’t quite identify. But I didn’t know what else to say. The couple turned to each other, and the husband nodded to his wife. She then told me, “I think we’ll get the book.” My heart began to pound. But I realized the woman was trying to say something else.
“The reason we’re buying it，” she said hesitantly, “is because our son committed suicide two years ago. Maybe your story will help us get over it.”
At that moment, I knew if I never sold another copy of the book, my four years of writing it had served its purpose. Although I would have many more challenging years until my book caught on and sold well, this couple’s story was all the motivation I needed at that point to keep me moving ahead. Thanks to them, I would come to the realization that the greatest of lives are made all in the same way: One challenge... one hurdle... one step... and one small victory at a time.
1. The writer quotes Brian Tracy to emphasize the importance of _________.| A．conscience | B．success |
| C．confidence | D．perseverance |
| A．bored and impatient | B．cheerful and proud |
| C．upset and disappointed | D．miserable and ashamed |
| A．The writer had to quit his job to make time for his writing. |
| B．The writer was not sure about the purpose of his writing at first. |
| C．The writing was completed with great self-discipline and efforts. |
| D．The process of writing the book was full of pains and frustration. |
| A．he had succeeded in selling his first book to the couple |
| B．he had managed to touch someone’s life with his book |
| C．he was quite satisfied with the feedback of his readers |
| D．he had found someone who appreciated his writing |
5 . Every few years, there’s a hot new management strategy that promises to make employees happier, healthier and more productive. To that end we’ve seen the rise of positions like ‘chief happiness officer’ as well as workplace dogs and on-site meditation. But while the employers may have improved the office itself, they have not solved the stress itself: the crushing tide of emails and IMs, which—thanks to the rise of smartphones-can pull us back to work, anytime, anywhere.
Now, in an effort to prevent burnout, a growing number of employers have started to suggest ways in which employees should unplug their connected devices. The automaker, Volkswagens, in collaboration with its union, sets its serves to stop mobile email service for some works from 30mnutes after quitting time until 30 minutes before starting time.
These measures may sound dramatic and possibly impractical, but there is a data to suggest they are needed. A recent research suggested that limiting the number of times a day that we check email or work-chat services—from say 10 or 20 to three or four –can not only reduce stress levels but also increase the overall productivity..
But in order for any solution to succeed, works have to be willing to regulate their own habits. And that is especially tough in a country in the U.S, where being superbusy, or at least appearing to be superbusy, is a point of pride. Even if more U.S employers were to implement the kinds of limits that Volkswagen do, experts are skeptical that they’d work. ‘If the social norm is to be on the time, you don’t want to be the odd one out,’ says Angela Leaney, a New Jersey based marketing consultant, adding that some bosses will think less of employees for not answering emails after work hours, even if they say they won’t.
Moreover, dictating when and how employees should use their connected devices will inevitably hamper many workers. There are plenty of people who do their best work at 3 a.m. In fact, a majority of working adults say that being able to check work email at home makes it easier to get more done; many also said it improved their relationships with their colleagues.
For now, it seems, the best way for employers to foster a fulfilled, productive workforce is to be flexible , both inside and outside the office. One example, although Andy Monfried, the CEO and founder of Lotame, a New York-based data management company, say those kinds of time limits wouldn’t work for his business—it’s too global –he does give his employees flexibility on when and where they can work. He’s also vigilant about burnout. ‘I vowed that I would not crest company where people had the Sunday-night blues—the kind where you go to bed sick to your stomach,’ he says. ‘I tell people if that’s happening repeatedly, it’s a sign of work-life imbalance and they should come talk to me.’
1. From stopping employees getting exhausted, employers have tried to _________| A．promise to make their staff happier and more productive |
| B．allow pet dogs in the office |
| C．encourage meditation in the work place |
| D．suggest ways to disconnect their mobile devices |
| A．employers will find ways to regulate workers’ working habit. |
| B．U.S experts cast doubt on the feasibility of limiting connected time. |
| C．U.S employers won’t think of contacting employees after work. |
| D．Volkswagen’s policies will also apply to U.S. companies. |
| A．facilitate | B．handicap |
| C．relieve | D．toughen |
| A．There seems to be no right way to unplug from work |
| B．Flexibility on when and where to work is the best way to avoid burnout |
| C．Employees applaud the way to cue down their connected time. |
| D．Impractical as the measures sound, they will work well for employers. |
6 . With the coming of big data age, data science is supposed to be starved for, of which the adaption can point a profound change in corporate competitiveness. Companies, both born-in the digital era and traditional world are showing off their skills in data science. Therefore, it seems to have been creating a great demand for the experts of this type.
Mr Carlos Guestrin, machine learning professor from University of Washington argues that all software applications will need inbuilt intelligence within five years, making data scientists—people trained to analyze large bodies of information — key workers in this emerging “cognitive” technology economy. There are already critical applications that depend on machine learning, a subfield of data science, led by recommendation programs, fraud detection system, forecasting tools and applications for predicting customer behavior.
Many companies that are born digital—particularly internet companies that have a great number of real-time customer interactions to handle—are all-in when it comes to data science. Pinterest, for instance, maintains more than 100 machine learning models that could be applied to different classes of problems, and it constantly fields request from managers eager to use this resource to deal with their business problem.
The most important factor weighing on many traditional companies will be the high cost of launching a serious machine-learning operation. Netflix is estimated to spend ＄150m a year on a single application and the total bills is probably four times that once all its uses of the technology are taken into account.
Another problem for many non-technology companies is talent.Of the computer science experts who use Kaggle, only about 1000 have deep learning skills, compared to 100,000 who can apply other machine learning techniques, says Mr Goldbloom. He adds that even some big companies of this type are often reluctant to expend their pay scales to hire the top talent in this field.
A third barrier to adapting to the coming era of “smart” applications, however, is likely to be cultural. Some companies, such as General Electric, have been building their own Silicon Valley presence to attract and develop the digital skills they will need.
Despite the obstacles, some many master this difficult transition.But companies that were built, from the beginning, with data science at their center, are likely to represent serious competition.
1. What cannot be inferred from the passage about the machine learning ?| A．Machine learning operations are costly in Netflix. |
| B．Machine learning plays an important role in existent applications. |
| C．Machine learning experts are not highly paid in some non-technology companies. |
| D．Machine learning models are not sufficient to solve business problems in Pinterest. |
| A．avcids |
| B．creates |
| C．solves |
| D．classifies |
| A．High cost |
| B．Expert crisis |
| C．Technological problem |
| D．Customer interactions |
