A joint research team recently have developed a new electronic skin that is similar to human skin in strength, durability(耐久性) and sensitivity. The skin or e-skin may play an important role in next-generation personalized medicine, soft robotics and artificial intelligence.

"The ideal e-skin will mimic(模仿) the many natural functions of human skin, such as sensing temperature and touch, accurately and in real time, " says leading researcher Yichen Cai. However, making suitably flexible electronics that can perform such delicate tasks while also used repeatedly is challenging, and each material involved must be carefully engineered.

Most e-skins are made by putting an active sensor on the surface that attaches to human skin. However, the connection between them is often too weak, which reduces the durability and sensitivity of the material; otherwise, if it is too strong, it won't be flexible enough, making it more likely to break the circuit.

"The landscape of skin electronics keeps shifting at a remarkable pace," says Cai. "The discovery of 2D sensors has accelerated efforts to turn these quite thin but strong materials into functional, durable artificial skins. "

The new man-made skin built by the researchers could sense objects from 20 centimeters away. It could further make a quick response when touched in less than one tenth of a second." It is a striking achievement for an e-skin to maintain toughness after repeated use, " said Shen, "which mimics the softness and rapid recovery of human skin."

This type of e-skin could monitor a range of biological information, such as changes in blood pressure, which can be detected from movements of arms and legs. This data can then be shared and stored on the cloud via Wi-Fi.

"One remaining problem to the widespread use of e-skins lies in mass production of high-resolution sensors," adds group leader Vincent Tung" however, the latest technology offers new promise."

Every morning at five o'clock, composer (作曲家) Walter Werzowa would sit down at his computer waiting for a particular daily e-mail. It came from a team that had been working all night to draft Beethoven's unfinished 10th Symphony. The e-mail contained hundreds of versions, and Werzowa listened to them all, looking for the perfect tune—a sound that was unmistakably Beethoven. But the phrases he was listening to weren't composed by Beethoven. They were created by artificial intelligence.

When Ludwig van Beethoven died in 1827, he left behind some musical drafts and notes. There was barely enough to make out a phrase, let alone a whole symphony. But that didn't stop people from trying.

Werzowa and a group of music experts and computer scientists teamed up to use machine learning to create the symphony. Ahmed Elgammal led the AI side of the team. The team's first task was to teach the AI to think like Beethoven. To do that, they gave it Beethoven's complete works, his drafts and notes. They taught it Beethoven's process—like how he went from four notes to his entire Fifth Symphony. Then they taught it to compose a bridge between two sections. With all that knowledge, the AI came as close to thinking like Beethoven as possible. But it still wasn't enough. The AI doesn't really produce something that can continue for a long time and be consistent. So the team had to put the selected pieces together to build a symphony.

Matthew Guzdial researches creativity (创造力) and machine learning at the University of Alberta. He didn't work on the Beethoven project, but he says, "Modern AI, modern machine learning, is all about just copying small local patterns. And it's up to a human to then take what the AI outputs and find the genius (天资). The genius wasn't in the AI. The genius was in the human who was doing the selection."

When researching New Year's resolutions in the 1980s, the psychologist John Norcross found over 50% of Americans made resolution. After 6 months, only 40% had stuck with it. Two years later, the number had dropped to 19%. Still, we keep telling ourselves we can lose weight, save money, and go to the gym.

It turns out that timing is important in determining whether we succeed. In May, 2012, the behavioral economist Katherine Milkman, at a social-science gathering, found herself in a discussion about "turning points". She said, "I had a strong instinct that turning points are effective - moments that feel like a new beginning."

So she teamed up with two colleagues, Jason Riis and Hengchen Dai, to see if the "turning points" idea held any merit. In follow-up studies, they found fresh starts do push us to change our behavior. With those, researchers suspect, comes a sense of optimism, the promise of "a new me," as Milkman put it. To test that theory, her team looked at daily Google searches for the term "diet" over 9 years, finding a predictable cycle: they topped at the start of any given week, month, or year, then gradually declined. The largest increase — 82% above the baseline — occurred immediately after New Year's.

The college gym attendance is no exception: shooting up in January and decreasing in the following months. Smaller spikes occurred at the beginning of each week, each month, and each term.

Finally, the researchers investigated commitments on the website stick, which allows you to set a goal and contractually determine the consequences for failing to attain it. Tracking 43,000 people over 2.5 years, they found the greatest number of contracts were signed at the start of the new year. Throughout the year, each week and each month had a mini-cycle of its own, with the beginning of the week corresponding to a 63% increase. "Every week brings a new opportunity," Riis says. "And people take advantage of that, whether or not they know it."