1 . Singapore uses about two billion liters of water every day-a number it expects could double in the next four decades.

That kind of consumption is adding pressure to the Asian city state to address growing concerns about global water shortage. So it's building new technology to prepare itself for a future where obtaining clean water will be even more difficult. "Singapore truly has become a global water center,"said Shane Snyder,executive director of the Nanyang Environment & Water Research Institute at Singapore's Nanyang Technological University. “But as it stands, it imports approximately 40% of its water today. And with climate change,that water has become far less dependable.”

Singapore, meanwhile, is home to more than five million people and is covered in fountains,reservoirs and other water features-including the world's tallest indoor waterfall, The Rain Vortex that pumps about 45,000 liters of water per minute. But it has no natural water sources of its own,instead relying heavily on recycled water and imports from its neighbors.

Snyder's research facility is one of several places developing solutions for Singapore's water dependency. The hope is to create projects that could be used across the city. "What we have become used to as reliable water may quickly change-so we have to be prepared,and we have to be thinking about the basic facilities in advance. " Snyder said. " There's a big drive to become water independent-to control our own future -and that is largely dependent on the technologies we're developing. "

Another company,Wateroam, is already taking innovation from Singapore to the rest of the region. Founded in 2014,Wateroam says it has developed a lightweight,portable filtration（过滤）device that has already provided clean drinking water to more than 75,000 people across Southeast Asia. Wateroam CEO David Pong said one of the most innovative aspects of the product is its simplicity. The water filtration device is no bigger than a bicycle pump,yet it can provide clean water to villages of 100 people for up to two years.

"We've been very blessed to have access to clean drinking water," Pong said. It's necessary to bring the privilege to the rest of the region, and advocate that clean water is an essential aspect for life on Earth,according to him.

3 . Who is smarter? A human being or artificial intelligence (人工智能)?

The question swept the world last week when a Google-developed program called AlphaGo defeated the world top player, South Korean Lee Se-del, 4-1.

So, what comes next?

Some people have been arguing that artificial intelligence, or AI in short, will be a bad thing for humans. In an interview with the BBC in 2014, UK scientist Stephen Hawking warned that “the development of full artificial intelligence could mean the end of the human race.”

So are we really about to live in the world shown in the Terminator movies?

“Not quite,” answered The Economist. After all, it’s not hard to get a computer program to remember and produce facts. What is hard is getting computers to use their knowledge in everyday situations.

“We think that, for the human being, things like sight and balance (视觉平衡), are natural and ordinary in our life.” Thomas Edison, founder of Motion Figures, a company that is bringing AI to boys, told the newspaper. “But for a robot, to walk up and down just like human beings requires various decisions to be made every second, and it’s really difficult to do.”

As The Economist put it, “We have a long way to go before AI can truly begin to be similar to the human brain, even though the technology can be great.”

Meanwhile, John Markoff of The New York Times said that researchers should build artificial intelligence to make people more effective.

“Our fate is in our own hands,” he wrote. “Since technology depends on the values of its creators, we can make human choices that use technology to improve the world.”

4 . Too often we accuse others of not listening, pretending that we ourselves are faultless, yet in our hearts we know that many of the mistakes we make come about because we haven’t listened carefully enough. We get things wrong because we haven’t listened carefully enough. We get things wrong because we haven’t quite understand what someone meant when they were talking to us. Anyone who has ever taken the minutes of a long meeting will know how hard it is to remember — despite the benefit of notes — exactly what everyone says. But success depends on getting right — and that means listening.

Listening is not the same thing as hearing; it is not an effort actively. It demands attention and concentration. It may mean quizzing the speaker for additional information or for clarification — it is always better to ask than to continue regardless and get things wrong. However, if you allow your mind to wander onto something else, even for a few minutes, you’ll miss what the speaker is saying — probably at the very moment when he or she is saying something critical. And not having heard, you won’t know you’ve missed anything until it’s too late.

The most common bad habit we have is to start thinking of what we are going to say about the subject long before the other speaker has finished. We then stop listening. Even worse, this often adds rudeness to inattentiveness, as once you have decided what to say there is a fair chance you will interrupt to say it. Good listeners don’t interrupt. In fact it is often worth explaining the main idea of what you have listened well.

Above all, be patient and accept that many people are not very good communicators. It’s helpful to remember that the ways people move and position themselves while they are speaking can reveal a great deal about what they are saying. Equally important you should put yourself in the other person’s place, both intellectually and emotionally; it will help you to understand what they are getting at and form a response. But don’t be too clever. Faced with a know-all, many people keep quiet because they see no point in continuing.

5 . A new UBC Okanagan study finds children not only gain the benefits of working with therapy (治疗) dogs – they enjoy it too.

“While we do frequently see children improve in therapy dog programs, we didn’t have data to support that they enjoyed the time as well,” says Nicole Harris, who conducted this research while a master’s student in the School of Education.

The research saw 22 children from the Okanagan Boys and Girls Club take part in a series of sessions to help them build their social skills. Over six weeks, the children were accompanied by therapy dogs as they completed lessons. Each week the children were taught a new skill, such as introducing themselves or giving directions to others. The children would first practice with their assigned therapy dog before running through the exercise with the rest of the group. In the final phase, the children, accompanied by their new furry friend, would practice their new skills with university students located in the building.

“Therapy dogs are often able to reach children and facilitate their growth in surprising ways. We saw evidence of this in the social skills of children when they were paired with a therapy dog,” says Dr. John-Tyler Binfet, associate professor in the School of Education and director of BARK. “The dogs helped create a non-threatening climate while the children were learning these new skills. By interacting with them, the children’s moods improved and their engagement in their lessons increased.” While the children were learning and practising their new skills, the research team collected data.

At the end of the six weeks, Harris interviewed eight children, aged 5 to 11 years old, who regularly attended the sessions. Each child indicated the social skill-training program with therapy dogs was an enjoyable and positive experience and the dogs were a meaningful and essential part of the program.

As a full-time elementary school teacher, Harris notes that schools have become increasingly important in helping students develop social and emotional skills, and this research could contribute to the development of future school-based or after-school programs.

9 . 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.

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