Flipboard红板报

据BBC报道，英国王储查尔斯王子新冠病毒检测呈阳性。

来源：央视新闻

http://m.news.cctv.com/2020/03/25/ARTI3rBAGS5jtauGY3x4UyDZ200325.shtml

美国参议院就经济刺激计划达成协议，规模达2万亿美元。消息公布后美股股指期货跌幅收窄，道指期货转涨。但随后再度转跌，截至北京时间13:20标普500指数期货跌1.88%，纳指期货跌2.13%。

来源：腾讯新闻

https://view.inews.qq.com/a/FIN202003250246660S?uid=&devid=564FF66D-8796-49BA-A965-A96A40CA980E&qimei=

 

日本首相安倍晋三24日在记者会见中表示，与国际奥委会主席巴赫达成共识，不会取消东京奥运会，将将在2021年夏天举行。延期后的名称仍保留“2020东京奥运会”。

来源：央视新闻

http://m.news.cctv.com/2020/03/24/ARTIc6cjaty10EAK4Y3E2Nje200324.shtml

确诊新型冠状病毒后，武磊首次发声，他通过视频向大家报平安，表示目前自己精神状态非常好，症状已经基本完全消失。

武磊通过视频向大家报平安：“我确实感染了新冠病毒，我现在在家中隔离，精神状态非常好，症状基本完全消失了，对肺部也做了一系列检查，包括CT，结果非常好没有问题。”

“感谢大家的祝福，请你们放心，另外我也希望大家能够重视病毒，我们现在还没有完全战胜它，勤洗手、少出门，再次感谢大家的关心和祝福，我相信一定能战胜这次疫情，我已经迫不及待的想回到球场。”

来源：百度新闻体育

http://baijiahao.baidu.com/s?id=1661775460378652189

当地时间3月20日，美国总统特朗普宣布纽约州为新冠肺炎疫情“重大灾区”，为联邦提供救援物资铺平道路。

据美国媒体报道，这是美国总统首次因公共卫生安全事件宣布“重大灾区”。纽约州是美国疫情最严重的一个州，根据霍普金斯大学的统计，目前确诊病例为8398例，占全美确诊病例近一半。

来源：央视新闻

http://m.news.cctv.com/2020/03/21/ARTIlSdKqxoibxBJX5DK88PZ200321.shtml

One collector’s obsession with photographs of winter’s most ephemeral friend.

Written by Gideon Jacobs

Snowmen don’t last. Photos do. The camera’s ability to memorialize what is ephemeral is part of what makes the backyard sculptures of winter so photographable. We grow eager to freeze time when we realize a hard-earned creation won’t remain frozen for long.

The artist Eric Oglander suspects this has something to do with why there are so many old snapshots of snowmen. Oglander, who describes himself as a “collector of aesthetics,” remembers first encountering a snowman photo when doing a cursory eBay search for “antique photographs.” “The idea for the collection clicked immediately,” he said. “After I saw one, I started buying every snowman I found compelling.”

His collection now contains hundreds of photographs. Most depict seemingly middle and upper-middle-class families, many from North America and some from Europe: Ukraine, Bulgaria, Serbia, Hungary, France. All the images were made in the era of black and white processed film, the earliest dating back to the late 19th century. The identities of both the photographers and the subjects are unknown.

Even with its size and sweep, the collection as a whole conveys a sense of sincerity and sweetness. The human subjects, whether soldiers taking a break on a military base or a family smiling for a holiday card, are captured at a moment of respite from winter’s claustrophobia. During a season best spent inside, a big snowfall is the great universal excuse to leave the house — to ignore the elements for the sake of play.

This quality of innocence is only heightened by the fact that these images were made in times when the act of taking a photograph was an event itself, not the compulsive reflex it is today.

Oglander has long been interested in vernacular photography. He is the creator of the viral Instagram account Craigslist Mirrors, a collection that later became a book and a gallery show. His work with found photographs is part of a general fascination with objects that are aesthetically interesting but that were made primarily with utility in mind.

“Art made without the intention of being art has a certain ‘integrity,’” Oglander said. For him, the beauty of these objects emerges as a byproduct of another process, or are even completely accidental: the fortuitous overexposure of an image, a subject holding a delightfully awkward pose.

Photographs of snowmen, then, hold a kind of double appeal for Oglander. The images were made to capture a memory, but the snowmen were made just for fun — themselves a kind of “melting folk art.”

Using snow to create a crude human facsimile is, Oglander points out, an age-old practice. “The Inuit would sculpt snowmen and snow-animals to use for target practice,” Oglander said. The fact that the structures had practical uses in the past, he said, deepens the meaning of the collection.

“Seeing one of these photographs is neat. Seeing 300 creates a thread through human history that ties us together,” he said.

“That might sound sappy, but that’s okay.”

Source link:https://www.nytimes.com/2020/03/19/arts/design/winter-photography-snowmen.html?action=click

 

The Long Read

With 850 million children worldwide shut out of schools, tech evangelists claim now is the time for AI education. But as the technology’s power grows, so too do the dangers that come with it. By Alex Beard

For a child prodigy, learning didn’t always come easily to Derek Haoyang Li. When he was three, his father – a famous educator and author – became so frustrated with his progress in Chinese that he vowed never to teach him again. “He kicked me from here to here,” Li told me, moving his arms wide.

Yet when Li began school, aged five, things began to click. Five years later, he was selected as one of only 10 students in his home province of Henan to learn to code. At 16, Li beat 15 million kids to first prize in the Chinese Mathematical Olympiad. Among the offers that came in from the country’s elite institutions, he decided on an experimental fast-track degree at Jiao Tong University in Shanghai. It would enable him to study maths, while also covering computer science, physics and psychology.

In his first year at university, Li was extremely shy. He came up with a personal algorithm for making friends in the canteen, weighing data on group size and conversation topic to optimise the chances of a positive encounter. The method helped him to make friends, so he developed others: how to master English, how to interpret dreams, how to find a girlfriend. While other students spent the long nights studying, Li started to think about how he could apply his algorithmic approach to business. When he graduated at the turn of the millennium, he decided that he would make his fortune in the field he knew best: education.

In person, Li, who is now 42, displays none of the awkwardness of his university days. A successful entrepreneur who helped create a billion-dollar tutoring company, Only Education, he is charismatic, and given to making bombastic statements. “Education is one of the industries that Chinese people can do much better than western people,” he told me when we met last year. The reason, he explained, is that “Chinese people are more sophisticated”, because they are raised in a society in which people rarely say what they mean.

Li is the founder of Squirrel AI, an education company that offers tutoring delivered in part by humans, but mostly by smart machines, which he says will transform education as we know it. All over the world, entrepreneurs are making similarly extravagant claims about the power of online learning – and more and more money is flowing their way. In Silicon Valley, companies like Knewton and Alt School have attempted to personalise learning via tablet computers. In India, Byju’s, a learning app valued at $6 billion, has secured backing from Facebook and the Chinese internet behemoth Tencent, and now sponsors the country’s cricket team. In Europe, the British company Century Tech has signed a deal to roll out an intelligent teaching and learning platform in 700 Belgian schools, and dozens more across the UK. Their promises are being put to the test by the coronavirus pandemic – with 849 million children worldwide, as of March 2020, shut out of school, we’re in the midst of an unprecedented experiment in the effectiveness of online learning.

But it’s in China, where President Xi Jinping has called for the nation to lead the world in AI innovation by 2030, that the fastest progress is being made. In 2018 alone, Li told me, 60 new AI companies entered China’s private education market. Squirrel AI is part of this new generation of education start-ups. The company has already enrolled 2 million student users, opened 2,600 learning centres in 700 cities across China, and raised $150m from investors. The company’s chief AI officer is Tom Mitchell, the former dean of computer science at Carnegie Mellon University, and its payroll also includes a roster of top Chinese talent, including dozens of “super-teachers” – an official designation given to the most expert teachers in the country. In January, during the worst of the outbreak, it partnered with the Shanghai education bureau to provide free products to students throughout the city.

Though the most ambitious features have yet to be built into Squirrel AI’s system, the company already claims to have achieved impressive results. At its HQ in Shanghai, I saw footage of downcast human teachers who had been defeated by computers in televised contests to see who could teach a class of students more maths in a single week. Experiments on the effectiveness of different types of teaching videos with test audiences have revealed that students learn more proficiently from a video presented by a good-looking young presenter than from an older expert teacher.

When we met, Li rhapsodised about a future in which technology will enable children to learn 10 or even 100 times more than they do today. Wild claims like these, typical of the hyperactive education technology sector, tend to prompt two different reactions. The first is: bullshit – teaching and learning is too complex, too human a craft to be taken over by robots. The second reaction is the one I had when I first met Li in London a year ago: oh no, the robot teachers are coming for education as we know it. There is some truth to both reactions, but the real story of AI education, it turns out, is a whole lot more complicated.

At a Squirrel AI learning centre high in an office building in Hangzhou, a city 70 miles west of Shanghai, a cursor jerked tentatively over the words “Modern technology has opened our eyes to many things”. Slouched at a hexagonal table in one of the centre’s dozen or so small classrooms, Huang Zerong, 14, was halfway through a 90-minute English tutoring session. As he worked through activities on his MacBook, a young woman with the kindly manner of an older sister sat next to him, observing his progress. Below, the trees of Xixi National Wetland Park barely stirred in the afternoon heat.

A question popped up on Huang’s screen, on which a virtual dashboard showed his current English level, unit score and learning focus – along with the sleek squirrel icon of Squirrel AI.

“India is famous for ________ industry.”

Huang read through the three possible answers, choosing to ignore “treasure” and “typical” and type “t-e-c-h-n-o-l-o-g-y” into the box.

“T____ is changing fast,” came the next prompt.

Huang looked towards the young woman, then he punched out “e-c-h-n-o-l-o-g-y” from memory. She clapped her hands together. “Good!” she said, as another prompt flashed up.

Huang had begun his English course, which would last for one term, a few months earlier with a diagnostic test. He had logged into the Squirrel AI platform on his laptop and answered a series of questions designed to evaluate his mastery of more than 10,000 “knowledge points” (such as the distinction between “belong to” and “belong in”). Based on his answers, Squirrel AI’s software had generated a precise “learning map” for him, which would determine which texts he would read, which videos he would see, which tests he would take.

As he worked his way through the course – with the occasional support of the human tutor by his side, or one of the hundreds accessible via video link from Squirrel AI’s headquarters in Shanghai – its contents were automatically updated, as the system perceived that Huang had mastered new knowledge.

Huang said he was less distracted at the learning centre than he was in school, and felt at home with the technology. “It’s fun,” he told me after class, eyes fixed on his lap. “It’s much easier to concentrate on the system because it’s a device.” His scores in English also seemed to be improving, which is why his mother had just paid the centre a further 91,000 RMB (about £11,000) for another year of sessions: two semesters and two holiday courses in each of four subjects, adding up to around 400 hours in total.

“Anyone can learn,” Li explained to me a few days later over dinner in Beijing. You just needed the right environment and the right method, he said.

Derek Haoyang Li, the founder of Squirrel AI, at a web summit in Lisbon. Photograph: Cody Glenn/Sportsfile via Getty Images

The idea for Squirrel AI had come to him five years earlier. A decade at his tutoring company, Only Education, had left him frustrated. He had found that if you really wanted to improve a student’s progress, by far the best way was to find them a good teacher. But good teachers were rare, and turnover was high, with the best much in demand. Having to find and train 8,000 new teachers each year was limiting the amount students learned – and the growth of his business.

The answer, Li decided, was adaptive learning, where an intelligent computer-based system adjusts itself automatically to the best method for an individual learner. The idea of adaptive learning was not new, but Li was confident that developments in AI research meant that huge advances were now within reach. Rather than seeking to recreate the general intelligence of a human mind, researchers were getting impressive results by putting AI to work on specialised tasks. AI doctors are now equal to or better than humans at analysing X-rays for certain pathologies, while AI lawyers are carrying out legal research that would once have been done by clerks.

Following such breakthroughs, Li resolved to augment the efforts of his human teachers with a tireless, perfectly replicable virtual teacher. “Imagine a tutor who knows everything,” he told me, “and who knows everything about you.”

In Hangzhou, Huang was struggling with the word “hurry”. On his screen, a video appeared of a neatly groomed young teacher presenting a three-minute masterclass about how to use the word “hurry” and related phrases (“in a hurry” etc). Huang watched along.

Moments like these, where a short teaching input results in a small learning output, are known as “nuggets”. Li’s dream, which is the dream of adaptive education in general, is that AI will one day provide the perfect learning experience by ensuring that each of us get just the right chunk of content, delivered in the right way, at the right moment for our individual needs.

One way in which Squirrel AI improves its results is by constantly hoovering up data about its users. During Huang’s lesson, the system continuously tracked and recorded every one of his key strokes, cursor movements, right or wrong answers, texts read and videos watched. This data was time-stamped, to show where Huang had skipped over or lingered on a particular task. Each “nugget” (the video to watch or text to read) was then recommended to him based on an analysis of his data, accrued over hundreds of hours of work on Squirrel’s platform, and the data of 2 million other students. “Computer tutors can collect more teaching experience than a human would ever be able to collect, even in a hundred years of teaching,” Tom Mitchell, Squirrel AI’s chief AI officer, told me over the phone a few weeks later.

The speed and accuracy of Squirrel AI’s platform will depend, above all, on the number of student users it manages to sign up. More students equals more data. As each student works their way through a set of knowledge points, they leave a rich trail of information behind them. This data is then used to train the algorithms of the “thinking” part of the Squirrel AI system.

This is one reason why Squirrel AI has integrated its online business with bricks-and-mortar learning centres. Most children in China do not have access to laptops and high-speed internet. The learning centres mean the company can reach kids they otherwise would not be able to. One of the reasons Mitchell says he is glad to be working with Squirrel AI is the sheer volume of data that the company is gathering. “We’re going to have millions of natural examples,” he told me with excitement.

The dream of a perfect education delivered by machine is not new. For at least a century, generations of visionaries have predicted that the latest inventions will transform learning. “Motion pictures,” wrote the American inventor Thomas Edison in 1922, “are destined to revolutionise our schools.” The immersive power of movies would supposedly turbo-charge the learning process. Others made similar predictions for radio, television, computers and the internet. But despite small successes – the Open University, TV universities in China in the 1980s, or Khan Academy today, which reaches millions of students with its YouTube lessons – teachers have continued to teach, and learners to learn, in much the same way as before.

There are two reasons why today’s techno-evangelists are confident that AI can succeed where other technologies failed. First, they view AI not as a simple innovation but as a “general purpose technology” – that is, an epochal invention, like the printing press, which will fundamentally change the way we learn. Second, they believe its powers will shed new light on the working of the human brain – how repetitive practice grows expertise, for instance, or how interleaving (leaving gaps between learning different bits of material) can help us achieve mastery. As a result, we will be able to design adaptive algorithms to optimise the learning process.

UCL Institute of Education professor and machine learning expert Rose Luckin believes that one day we might see an AI-enabled “Fitbit for the mind” that would allow us to perceive in real-time what an individual knows, and how fast they are learning. The device would use sensors to gather data that forms a precise and ever-evolving map of a person’s abilities, which could be cross-referenced with insights into their motivational and nutritional state, say. This information would then be relayed to our minds, in real time, via a computer-brain interface. Facebook is already carrying out research in this field. Other firms are trialling eye tracking and helmets that monitor kids’ brainwaves.

The supposed AI education revolution is not here yet, and it is likely that the majority of projects will collapse under the weight of their own hype. IBM’s adaptive tutor Knewton was pulled from US schools under pressure from parents concerned about their kids’ privacy, while Silicon Valley’s Alt School, launched to much fanfare in 2015 by a former Google executive, has burned through $174m of funding without landing on a workable business model. But global school closures owing to coronavirus may yet relax public attitudes to online learning – many online education companies are offering their products for free to all children out of school.

Daisy Christodoulou, a London-based education expert, suggests that too much time is spent speculating on what AI might one day do, rather than focusing on what it already can. It’s estimated that there are 900 million young people around the world today who aren’t currently on track to learn what they need to thrive. To help those kids, AI education doesn’t have to be perfect – it just needs to slightly improve on what they currently have.

In their book The Future of the Professions, Richard and Daniel Susskind argue that we tend to conceive of occupations as embodied in a person – a butcher or baker, doctor or teacher. As a result, we think of them as ‘too human’ to be taken over by machines. But to an algorithm, or someone designing one, a profession appears as something else: a long list of individual tasks, many of which may be mechanised. In education, that might be marking or motivating, lecturing or lesson planning. The Susskinds believe that where a machine can do any one of these tasks better and more cheaply than the average human, automation of that bit of the job is inevitable.

The point, in short, is that AI doesn’t have to match the general intelligence of humans to be useful – or indeed powerful. This is both the promise of AI, and the danger it poses. “People’s behaviour is already being manipulated,” Luckin cautioned. Devices that might one day enhance our minds are already proving useful in shaping them.

In May 2018, a group of students at Hangzhou’s Middle School No 11 returned to their classroom to find three cameras newly installed above the blackboard; they would now be under full-time surveillance in their lessons. “Previously when I had classes that I didn’t like very much, I would be lazy and maybe take naps,” a student told the local news, “but I don’t dare be distracted after the cameras were installed.” The head teacher explained that the system could read seven states of emotion on students’ faces: neutral, disgust, surprise, anger, fear, happiness and sadness. If the kids slacked, the teacher was alerted. “It’s like a pair of mystery eyes are constantly watching me,” the student told reporters.

The previous year, China’s state council had launched a plan for the role AI could play in the future of the country. Underpinning it were a set of beliefs: that AI can “harmonise” Chinese society; that for it to do so, the government should store data on every citizen; that companies, not the state, were best positioned to innovate; that no company should refuse access to the government to its data. In education, the paper called for new adaptive online learning systems powered by big data, and “all-encompassing ubiquitous intelligent environments” – or smart schools.

At AIAED, a conference in Beijing hosted by Squirrel AI, which I attended in May 2019, classroom surveillance was one of the most discussed topics – but the speakers tended to be more concerned about the technical question of how to optimise the effectiveness of facial and bodily monitoring technologies in the classroom, rather than the darker implications of collecting unprecedented amounts of data about children. These ethical questions are becoming increasingly important, with schools from India to the US currently trialling facial monitoring. In the UK, AI is being used today for things like monitoring student wellbeing, automating assessment and even in inspecting schools. Ben Williamson of the Centre for Research in Digital Education explains that this risks encoding biases or errors into the system and raises obvious privacy issues. “Now the school and university might be said to be studying their students too,” he told me.

While cameras in the classroom might outrage many parents in the UK or US, Lenora Chu, author of an influential book about the Chinese education system, argues that in China anything that improves a child’s learning tends to be viewed positively by parents. Squirrel AI even offers them the chance to watch footage of their child’s tutoring sessions. “There’s not that idea here that technology is bad,” said Chu, who moved from the US to Shanghai 10 years ago.

Rose Luckin suggested to me that a platform like Squirrel AI’s could one day mean an end to China’s notoriously punishing gaokao college entrance exam, which takes place for two days every June and largely determines a student’s education and employment prospects. If technology tracked a student throughout their school days, logging every keystroke, knowledge point and facial twitch, then the perfect record of their abilities on file could make such testing obsolete. Yet a system like this could also furnish the Chinese state – or a US tech company – with an eternal ledger of every step in a child’s development. It is not hard to imagine the grim uses to which this information could be put – for instance, if your behaviour in school was used to judge, or predict, your trustworthiness as an adult.

Students leaving a gaokao college entrance exam in Hangzhou, China. Photograph: Imaginechina/Rex/Shutterstock

On the one hand, said Chu, the CCP wants to use AI to better prepare young people for the future economy, and to close the achievement gap between rural and urban schools. To this end, companies like Squirrel AI receive government support, such as access to prime office space in top business districts. At the same time, the CCP, as the state council put it, sees AI as “opportunity of the millennium” for “social construction”. That is, social control. The ability of AI to “grasp group cognition and psychological changes in a timely manner” through the surveillance of people’s movements, spending and other behaviours means it can play “an irreplaceable role in effectively maintaining social stability”.

The surveillance state is already penetrating deep into people’s lives. In 2019, there was a significant spike in China in the registration of patents for facial recognition and surveillance technology. All new mobile phones in China must now be registered via a facial scan. At the hotels I stayed in, Chinese citizens handed over their ID cards and checked in using face scanners. On the high-speed train to Beijing, the announcer repeatedly warned travellers to abide by the rules in order to maintain their personal credit. The notorious social credit system, which has been under trial in a handful of Chinese cities ahead of an expected nationwide roll out this year, awards or detracts points from an individual’s trustworthiness score, which affects their ability to travel and borrow money, among other things.

The result, explained Chu, is that all these interventions exert a subtle control over what people think and say. “You sense how the wind is blowing,” she told me. For the 12 million Muslim Uighurs in Xinjiang, however, that control is anything but subtle. Police checkpoints, complete with facial scanners, are ubiquitous. All mobile phones must have Jingwang (“clean net”) app installed, allowing the government to monitor your movements and browsing. Iris and fingerprint scans are required to access health services. As many as 1.5 million Uighurs, including children, have been interned at some point in a re-education camp in the interests of “harmony”.

As we shape the use of AI in education, it’s likely that AI will shape us too. Jiang Xueqin, an education researcher from Chengdu, is sceptical that it will be as revolutionary as proponents claim. “Parents are paying for a drug,” he told me over the phone. He thought tutoring companies such as New Oriental, TAL and Squirrel AI were simply preying on parents’ anxieties about their kids’ performance in exams, and only succeeding because test preparation was the easiest element of education to automate – a closed system with limited variables that allowed for optimisation. Jiang wasn’t impressed with the progress made, or the way that it engaged every child in a desperate race to conform to the measures of success imposed by the system.

One student I met at the learning centre in Hangzhou, Zhang Hen, seemed to have a deep desire to learn about the world – she told me how she loved Qu Yuan, a Tang dynasty romantic poet, and how she was a fan of Harry Potter – but that wasn’t the reason she was here. Her goal was much simpler: she had come to the centre to boost her test scores. That may seem disappointing to idealists who want education to offer so much more, but Zhang was realistic about the demands of the Chinese education system. She had tough exams that she needed to pass. A scripted system that helped her efficiently master the content of the high school entrance exam was exactly what she wanted.

On stage at AIAED, Tom Mitchell had presented a more ambitious vision for adaptive learning that went far beyond helping students cram for mindless tests. Much of what he was most excited by was possible only in theory, but his enthusiasm was palpable. As appealing as his optimism was, though, I felt unconvinced. It was clear that adaptive technologies might improve certain types of learning, but it was equally obvious that they might narrow the aims of education and provide new tools to restrict our freedom.

Li insists that one day his system will help all young people to flourish creatively. Though he allows that for now an expert human teacher still holds an edge over a robot, he is confident that AI will soon be good enough to evaluate and reply to students’ oral responses. In less than five years, Li imagines training Squirrel AI’s platform with a list of every conceivable question and every possible response, weighting an algorithm to favour those labelled “creative”. “That thing is very easy to do,” he said, “like tagging cats.”

For Li, learning has always been like that – like tagging cats. But there’s a growing consensus that our brains don’t work like computers. Whereas a machine must crunch through millions of images to be able to identify a cat as the collection of “features” that are present only in those images labelled “cat” (two triangular ears, four legs, two eyes, fur, etc), a human child can grasp the concept of “cat” from just a few real life examples, thanks to our innate ability to understand things symbolically. Where machines can’t compute meaning, our minds thrive on it. The adaptive advantage of our brains is that they learn continually through all of our senses by interacting with the environment, our culture and, above all, other people.

Li told me that even if AI fulfilled all of its promise, human teachers would still play a crucial role helping kids learn social skills. At Squirrel AI’s HQ, which occupies three floors of a gleaming tower next door to Microsoft and Mobike in Shanghai, I met some of the company’s young teachers. Each sat at a work console in a vast office space, headphones on, eyes focused on a laptop screen, their desks decorated with plastic pot plants and waving cats. As they monitored the dashboards of up to six students simultaneously, the face of a young learner would appear on the screen, asking for help, either via a chat box or through a video link. The teachers reminded me of workers in the gig economy, the Uber drivers of education. When I logged on to try out a Squirrel English lesson for myself, the experience was good, but my tutor seemed to be teaching to a script.

Squirrel AI’s head of communications, Joleen Liang, showed me photos from a recent trip she had taken to the remote mountains of Henan, to deliver laptops to disadvantaged students. Without access to the adaptive technology, their education would be a little worse. It was a reminder that Squirrel AI’s platform, like those of its competitors worldwide, doesn’t have to be better than the best human teachers – to improve people’s lives, it just needs to be good enough, at the right price, to supplement what we’ve got. The problem is that it is hard to see technology companies stopping there. For better and worse, their ambitions are bigger. “We could make a lot of geniuses,” Li told me.

Source link:https://www.theguardian.com/technology/2020/mar/19/can-computers-ever-replace-the-classroom

ALAN TAYLOR    MARCH 18, 2020

Closures, travel restrictions, and self-isolation measures due to the new coronavirus have cleared public squares, roads, and travel destinations across the globe. Places built to accommodate thousands have become virtual ghost towns, as tourists and locals remain at home. Photographers in France, India, the U.S., Italy, China, Australia, and more have been capturing the eerie stillness of these public spaces, recently emptied of the crowds they were built for.

1. The reflecting pool and National Mall, normally filled with tourists, shown nearly empty due to the impacts of the coronavirus, on March 17, 2020, in Washington, D.C.

Win McNamee / Getty

2. California Street, usually filled with cable cars, is seen mostly empty in San Francisco, California, on March 17, 2020. On Monday, millions of residents in the Bay Area, including Silicon Valley, were ordered to stay home to slow the spread of the deadly coronavirus.       

Josh Edelson / AFP / Getty

3. Cinderella Castle is seen at the end of a deserted Main Street at Disney’s Magic Kingdom theme park, after it closed in an effort to combat the spread of the coronavirus, in an aerial view over Orlando, Florida, on March 16, 2020.

Gregg Newton / Reuters

4. An aerial view from a drone shows a few people walking along the ocean in South Beach on March 16, 2020, in Miami Beach, Florida. City officials closed an area of the beach that is popular with college spring breakers and asked them to refrain from large gatherings, where COVID-19 could spread.

Joe Raedle / Getty

5. The Trocadero, empty, with the Eiffel Tower in the distance, on the first day of a government-enforced quarantine on March 17, 2020, in Paris, France

Veronique de Viguerie / Getty

6. The mostly-deserted steps of the Sydney Opera House, where scheduled performances have been canceled due to the coronavirus, seen on a quiet morning in Sydney, Australia, on March 18, 2020       

Loren Elliott / Reuters

7. A view of a deserted Safdarjung Tomb, closed to tourists on March 17, 2020, in New Delhi, India. The Indian Council of Medical Research on Tuesday said that India is in stage two of the coronavirus outbreak. The third stage is community transmission, which India hopes to avoid. On Monday, the provincial government in the capital, New Delhi, barred the assembly of more than 50 people and ordered the closure of all the public monuments and museums, including the iconic Taj Mahal and Red Fort until the end of this month.       

Yawar Nazir / Getty

8. A view of Piazza dei Miracoli in Pisa, Italy, after sanitation operations for the coronavirus emergency took place, on March 17, 2020

Laura Lezza / Getty

9. A view of the empty Zoloti Vorota station in central Kyiv, Ukraine, after a metro shutdown amid coronavirus concerns on March 17, 2020       

Valentyn Ogirenko / Reuters

10. The Brooklyn Bridge, with significantly less traffic due to coronavirus cases and fears, photographed on March 17, 2020, in New York City

Justin Heiman / Getty

11. A pedestrian crosses a nearly-empty Kiener Plaza Park in St. Louis, Missouri, on March 17, 2020. Activity in downtown St. Louis was light as businesses curtailed operations and encouraged employees to work from home if possible in an effort to slow the spread of the coronavirus.

Jeff Roberson / AP

12. A person walks through the Fulton Center station in Lower Manhattan on March 15, 2020, in New York City.

Stephanie Keith / Getty

13. The parking lot at The Shops at Hilltop sits empty on March 17, 2020, in Richmond, California.

Justin Sullivan / Getty

14. A view shows the deserted Rue de Rivoli in Paris during lockdown, on March 18, 2020.

Christian Hartmann / Reuters

15. A young man uses his smartphone as he sits on a platform at a deserted railway station in the Ukrainian city of Lviv on March 18, 2020.

Yuri Dyachyshyn / AFP / Getty

16. A view of an empty ski lift at Squaw Valley Resort in Olympic Valley, California, on March 14, 2020. Alterra Mountain Company, which owns Squaw Valley, suspended operations at all 15 of its North American resorts on March 15, until further notice.

Ezra Shaw / Getty North America

17. The rotunda of the U.S. Capitol stands empty as a precaution due to the novel coronavirus, in Washington, D.C., on March 17, 2020.

Saul Loeb / AFP / Getty

18. A nearly-empty street is pictured after Honduras’s government imposed a nationwide curfew for a seven-day period as part of the measures to contain the spread of the coronavirus, in Tegucigalpa, Honduras, on March 17, 2020.

Jorge Cabrera / Reuters

19. A closed and empty Jardin des Tuileries, photographed in Paris, France, on March 17, 2020

Veronique de Viguerie / Getty

20. An empty Westminster Bridge in London, England, photographed on March 18, 2020       

Kirsty Wigglesworth / AP

21. A man takes a selfie in an empty Catalunya Square in Barcelona, Spain, on March 15, 2020. As part of the measures to prevent spread of the coronavirus, the Spanish government declared a 15-day state of emergency.       

David Ramos / Getty

22. A science teacher live-streams to her students from a flowering field in Hangzhou, in China’s Zhejiang province, on March 16, 2020. Campuses in China are still not allowed to take in students, nearly two months after the COVID-19 outbreak.

Feature China / Barcroft Media / Getty

23. People descend the Bethesda metro-station escalator at rush hour on March 16, 2020, after Maryland’s Governor, Larry Hogan, ordered the shutdown of all bars and eateries in the state.

Tom Brenner / Reuters

24. The old town of Annecy, France, deserted on March 17, 2020

Richard Bord / Getty

25. A woman stands at her balcony as she joins others in applauding doctors and nurses fighting the coronavirus disease, part of a nationwide initiative to show unity and support in Sofia, Bulgaria, on March 16, 2020.

Dimitar Kyosemarliev / Reuters

26. The square in front of the Notre-Dame de Reims cathedral in Reims, France, photographed on March 17, 2020

Francois Nascimbeni / AFP / Getty

27. A view of the international bridge between Peru and Bolivia, after Peru’s government announced the border’s closure in a bid to slow the spread of the new coronavirus, in Desaguadero, Bolivia, on March 17, 2020.

David Mercado / Reuters

28. A musician plays a violin as he waits for the last train before the metro is shut down, at Zoloti Vorota station in Kyiv, Ukraine, on March 17, 2020.     

Valentyn Ogirenko / Reuters

Source link:
https://www.theatlantic.com/photo/2020/03/photos-quiet-emptiness-under-coronavirus/608272/

国家监委调查组今天发布《关于群众反映的涉及李文亮医生有关情况调查的通报》。

通报说，2020年2月7日，国家监委成立调查组，就群众反映的涉及李文亮医生的有关情况依法开展调查。现将调查情况通报如下：

2019年12月，湖北省部分医院陆续收治了一些不明原因的肺炎病人。12月27日，湖北省中西医结合医院张继先医生向医院报告了其接诊3例不明原因肺炎患者情况。当天，武汉市疾控中心安排进行了流行病学调查和检测。12月29日，湖北省中西医结合医院又报告发现4例来自于华南海鲜市场的不明原因肺炎病例。武汉市卫健委组织专家团队进行调查，并于12月30日15时10分、18时50分在系统内下发《关于报送不明原因肺炎救治情况的紧急通知》等两份部门文件，要求做好不明原因肺炎救治工作，全面开展华南海鲜市场相关肺炎病例搜索和回顾性调查。该两份通知分别于当天15时22分和19时许被人上传到互联网上。

2019年12月30日17时30分左右，李文亮医生收到同事发给他的信息，17时43分，李文亮医生在微信群中转发、发布“华南水果海鲜市场确诊了7例SARS”“在我们医院后湖院区急诊科隔离”等信息。18时42分，又在该群发布“最新消息是，冠状病毒感染确定了，正在进行病毒分型”“大家不要外传，让家人亲人注意防范”。

按照武汉市关于不明原因肺炎疫情防控工作安排，武汉市公安机关依据传染病防治、治安管理等法律法规，以及市卫健委的情况通报，对在网上出现的转发、发布SARS等传染病信息情况进行了调查处置。2020年1月3日13时30分左右，武汉市公安局武昌分局中南路派出所与李文亮医生联系后，李文亮医生在同事陪同下来到该派出所。经谈话核实后，谈话人员现场制作了笔录并对李文亮医生制作了训诫书。

调查通报详细公布了李文亮医生的发病、治疗、抢救以及抚恤、善后等情况。

组织抢救的医生说，李文亮医生是我们的同事，又很年轻，我们不希望他走，只要有一点希望我们就不愿放弃，当时没有想其他任何因素，就是想救活他，所以抢救了很长时间。据为李文亮医生会诊的院外专家讲，从李文亮医生病案看，医院治疗比较规范，能够根据病情调整药物和措施。医院建议采取和实际采取的重要医疗措施，均征求了李文亮医生或其家属意见。

调查组的工作建议是：由于中南路派出所出具训诫书不当，执法程序不规范，调查组已建议湖北省武汉市监察机关对此事进行监督纠正，督促公安机关撤销训诫书并追究有关人员责任，及时向社会公布处理结果。

国家监委调查组

2020年3月19日