Why do children learn so quickly?  Is it simply a necessity, or is a child' s brain more capable of taking in new information than an adult' s brain? 

" It is a common way of thinking that ‘children are like sponges' and have the magical ability to learn new skills faster than an adult, but there are some misconceptions here, " said Debbie Raven, a researcher at the University of Chester. " A child' s cognitive (认知的) development is age-related and, naturally, children perform worse than their older peers in most areas. However, there are times when being young brings an advantage, and this is especially true around their earliest years. 

This advantage is largely due to neuroplasticity, meaning the brain' s ability to form and change its connections, pathways and wiring based on experiences. Neuroplasticity is what gives children the capacity to learn-and, if necessary, unlearn-habits, routines, approaches and actions very quickly. This ability is most constant and rapid before a child' s fifth birthday, when much of what they encounter or experience is novel. " This ability to learn quickly is connected to several areas, including plasticity, their experiences with adults, their environment, and their biological drive to explore, " Raven said. " Childhood is a place where children spend their time catching up with adults' more sophisticated abilities. " 

Language learning, in particular, is an area where children often have a huge advantage over adults. This is largely because " babies are able to tune in to the rhythm and sounds used in their native language, and can therefore become fluent speakers by the age of four. " This ability can help young children learn a second or third language with apparent ease, Raven said. 

In a research paper, the authors suggest that " human babies are born seeing and hearing linguistic information that older children and adults miss, although they lose this ability with more experience in their environments. Moreover, babies can " discriminate speech sounds and tones used in al of the world' s languages, making them open to al input, regardless of the linguistic environment they are born into. 

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."

We all know that unpleasant feeling when we're talking about something interesting and halfway through our sentence we're interrupted. But was that really an interruption? The answer depends on whom you ask, according to new research led by Katherine Hilton from Stanford University.

Using a set of controlled audio clips (录音片段), Hilton surveyed 5,000 American English speakers to better understand what affects people's perceptions of interruptions. She had participants listen to audio clips and then answer questions about whether the speakers seemed to be friendly and engaged, listening to one another, or trying to interrupt.

Hilton found that American English speakers have different conversational styles. She identified two distinct groups: high and low intensity speakers. High intensity speakers are generally uncomfortable with moments of silence in conversation and consider talking at the same time a sign of engagement. Low intensity speakers find it rude to talk at the same time and prefer people speak one after another in conversation.

The differences in conversational styles became evident when participants listened to audio clips in which two people spoke at the same time but were agreeing with each other and stayed on topic, Hilton said. The high intensity group reported that conversations where people spoke at the same time when expressing agreement were not interruptive but engaged and friendlier than the conversations with moments of silence in between speaking turns. In contrast, the low intensity group perceived any amount of simultaneous (同时) chat as a rude interruption, regardless of what the speakers were saying.

"People care about being interrupted, and those small interruptions can have a massive effect on the overall communication," Hilton said. "Breaking apart what an interruption means is essential if we want to understand how humans interact with each other."