【推荐1】Have you ever wondered what goes through your mind when you choose where to sit in a new classroom? Or in a waiting room full of strangers? Or on a bus? Researchers have found out some interesting facts.

Girls sit by girls and boys sit by boys. Adults sit together and young people choose another young people to sit near. But it goes further than this. We even choose to sit near someone who looks like us. People with glasses are more likely to sit near other people with glasses. People with long hair sit closer to other people with long hair.

We seem to believe that people with similar habits or hobbies will share similar experiences and we are more likely to be accepted by people like ourselves, or even, we think we may be safer with people who look like us. Sometimes that’s true but it’s a pity if we always stay with the same people, the same group. The danger in always staying in our comfort zone is that we just recycle the same opinions, the same tastes and the same ideas. We lose the chance to learn something new, find out interesting things, hear funny stories and discover differences.

When we always stay with the same people, how can we break down the barriers which prevent us from getting to know people with different ideas? And how can we avoid the ignorance? If you want to live in a society that opens to changes, new things and different opinions, be the cat among the pigeons.

Move out of your comfort zone. Go and sit next to someone different. And don‘t just sit there in silence. Say hello. Ask a question. Start a conversation. That’s how we make friends. That’s how we learn about people. That’s how we open our minds to new ideas. That’s how we live an exciting life.

【推荐3】Young people spending hours staring at screens means half the world may need glasses within 30 years.

Phone devices and computer screens are to blame for the rising risk of short-sightedness among children and young adults, say scientists. Researchers have found high levels of looking at a phone or tablet is linked with around a 30 percent higher risk of shortsightedness, also known as myopia (近视). But when it is combined with excessive computer use, that risk rose to around 80 percent. Researchers fear that this could mean that by 2050, half the world or five billion people could be short-sighted.

The authors examined more than 3, 000 studies investigating smart device exposure and myopia in children and young adults aged between three months old and 33 years old.

In 2019, the WHO recommended children under two should not have any screen time and children aged two to five should have no more than one hour a day of sedentary screen time.

But in the same year, a CensusWide survey of 2, 000 British families found children were spending an average of 23 hours a week staring at screens. Numerous studies have also suggested that number has vastly increased during the COVID-19 pandemic.

Professor Bourne at Anglia Ruskin University said, “Around half the global population is expected to have myopia by 2050, so it is a health concern that is escalating quickly.”

“Our study is the most comprehensive yet on this issue and shows a potential link between screen time and myopia in young people. This research comes at a time when our children have been spending more time than ever looking at screens for long periods, due to school closures, and it is clear that urgent research is needed to further understand how exposure to digital devices can affect our eyes and vision,” added he.

【推荐1】The World Health Organization (WHO) reports Parkinson’s disease (帕金森病) is currently the fastest growing neurological condition worldwide. It harms the brain and often causes patients to lose the ability to control their movements. Researchers note that what is challenging to cure such patients is that early signs of Parkinson’s disease are not easy to identify. Many signs only appear years after the disease sets in. Researchers have investigated existing methods for detecting the disease before it has a chance to develop. These included examinations of brain images. But these methods are costly and require specialized medical centers.

Dina Katabi is a professor of electrical engineering and computer science at MIT. She helped lead the research. She said the act of breathing has always been connected to Parkinson’s disease. This connection led the research team to consider the potential of detecting the disease from one’s breathing without looking at movements. Previous studies have shown that breathing patterns linked to Parkinson’s disease can appear years before other signs.

Based on the above, American researchers have developed an artificial intelligence (AI) method to identify Parkinson’s disease. This means the AI system can be an early predictor of the disease. The system works by measuring a person’s breathing patterns during sleep. It can also measure the severity of disease and record its progression. The collected data is then processed through what the researchers call a neural (神经的) network, which is a computer system built to act like the human brain.

Experiments showed that with just one night of sleep, the AI system was able to detect Parkinson’s disease with up to 86 percent accuracy. With 12 nights of data, the rate went up to 95 percent.

The researchers say the simplicity of the tool will enable many more people to be tested for the disease in their homes. They said this can especially help people in traditionally underserved communities, as well as individuals living in rural areas or with limited abilities to visit hospitals.

【推荐2】For some people, the term “black box” brings to mind the recording devices in airplanes that are valuable for later analyses if the unthinkable happens. But black box is also an important term in the world of artificial intelligence. AI black boxes refer to Al systems with internal workings that are invisible to the user. You can feed them input and get output, but you cannot examine the system’s logic that produced the output.

Machine learning is the main aspect of artificial intelligence. It is the basic of AI systems like ChatGPT. There are three parts to machine learning: an algorithm (算法) or a set of algorithms, training data and a model. An algorithm is a set of procedures. In machine learning, an algorithm learns to identify patterns after being trained on a large set of examples — the training data. Once a machine-learning algorithm has been trained, the result is a machine-learning model. The model is what people use. Any of the three parts of a machine-learning system can be hidden, or in a black box. So to protect their intellectual property, Al developers often put the model in a black box.

The opposite of a black box is sometimes referred to as a glass box. An Al glass box is a system whose algorithms, training data and model are all available for anyone to see.

In many cases, there is a good reason to be careful of black box machine-learning algorithms and models. Suppose a machine-learning model has made a diagnosis about your health. Would you want the model to be in a black box or a glass box?

For years, many people in the computing field thought that keeping software in a black box would prevent hackers (黑客) from examining it and therefore it would be secure. This assumption has largely been proved wrong because hackers can reverse (逆向)-engineer software and discover vulnerabilities to make use of. If software is in a glass box, then software testers and well-intentioned hackers can examine it and inform the creators of disadvantages, thereby reducing cyberattacks.

【推荐3】As Geoffrey Hinton, widely seen as the godfather of artificial intelligence, warns about growing dangers from how it is developing, businesses are scrambling to see how they can use the technology to their advantage.

Recently, the hot topic is how to employ ChatGPT style technology across their businesses as quickly as possible. A boss looked at his computer, entered the transcript of a customer complaint call, and asked ChatGPT to respond to it based on set of rules. In about a minute it came up with a very credible answer, which was 85% accurate but only cost 1% of the cost of employing staff. The good news for all is the pure enhancement to productivity.

Technology has improved the quality of life. A lot of innovation has made our entertainment time more enjoyable. But the real shock has been that these technologies are usable in a commercial context, not just for “low-cognitive, repetitive” tasks, i. e. robotic-tasks likely to be automated.

The surprise has been how employable the technology is to highly creative, high-value work. Specifically, creating an image, or music, or coding a programme can be achieved in seconds rather than weeks of briefing and refining. So the good news is that rapid employment of this technology could solve the longstanding productivity crisis. The bad news is that it could occur so rapidly as to overtake workers’ ability to adapt in time, creating social crisis. Could we face in call centers and creative studios in the 2020s, the equivalent of what happened in the coal mines in the 1980s?

Some individuals have started to suggest that states should provide a basic income due to the possibility of AI leading to job loss. The response of tech enthusiasts is that “You won’t be replaced by an AI, but you might be replaced by someone who knows how to use AI”. But they used to say that’s why everybody should learn how to code. That might not be such wise career advice any more.