【推荐1】Rates of anti-dining syndrome in newborns surged in recent years, but a newer approach to caring for newborn babies exposed to drugs during pregnancy gets them out of the hospital sooner and with less medication. Newborns in drug withdrawal may experience upset stomach, miserable crying and extreme discomfort. Researchers looked at the impacts of the ESC (Eat, Sleep, Console care) approach on 1,300 infants at 26 US hospitals, and compared them with the current standard for caring for infants exposed to drugs.

ESC encourages involvement from parents, and prioritizes care that doesn’t involve medication, breastfeeding, for example. The usual approach involves a nurse measuring a baby’s withdrawal symptoms before providing treatment.

Compared to usual care, use of the ESC approach substantially decreased time until those infants were medically ready for discharge, without increasing specified harmful outcomes.

The infants assessed with the ESC method were discharged after eight days on average, compared with almost 15 days for the infants who were cared for by the standard approach. Additionally, infants in the ESC care group were 63% less likely to receive drug medication — 19.5% received medication compared with 52% in the group receiving usual care.

The current approach to usual care is a very comprehensive and nurse-led way of assessing the infant, whereas the ESC approach involves the mom in the way that you assess the infant, and allows the mom to try her best to comfort the infants and see if the infant is able to be consoled or is able to eat or is able to sleep.

“So, in that way, it’s a little bit more functional, like looking at the abilities of the infants versus how severely the infant is affected. Assessment results determine whether a baby should receive medication to control withdrawal symptoms,’’ said Baker, the director of the NIH HEAL Initiative, which provides funds to researchers studying ways to relieve the country’s drug health crisis.

【推荐2】Artificial Intelligence Develops an Ear for Birdsong

We can learn a lot from nature if we listen to it more — and scientists around the world are trying to do just that. From mountain peaks to ocean depths, biologists are planting audio recorders to eavesdrop (窃听) on the whistles and songs of whales, elephants, bats and especially birds. This summer, for example, over 2,000 electronic ears will record the sound scape of California’s Sierra Nevada mountain range.     1    

“Audio data is a real treasure because it contains vast amounts of information,” says ecologist Connor Wood, a Cornell University postdoctoral researcher, who is leading the Sierra Nevada project. “We just need to think creatively about how to share and access that information.”     2     Fortunately the latest generation of machine-learning AI systems can process thousands of hours of data in less than a day.

Stefan Kahl, a machine-learning expert at Cornell’s Center for Conservation Bioacoustics and Chemnitz University of Technology in Germany, built BirdNET, one of the most popular avian-sound-recognition systems used today. Wood’s team will rely on BirdNET to analyze the Sierra Nevada recordings.     3     The neural network then teaches itself which features can be used to associate an input (in this case, a bird’s call) with a label (the bird’s identity).

    4     Although they analyze audio much more quickly than humans, they still lag behind in filtering out overlapping sounds to home in on a signal of interest. Some researchers see this as the next problem for AI to tackle. Even the current imperfect versions, however, enable sweeping projects that would be far too time-consuming for humans to tackle alone. “As ecologists,” Wood says, “tools like BirdNET allow us to dream big.”

【推荐3】The US Institute of Electrical and Electronics Engineers (IEEE) recently asked scientists, lawyers, social scientists and other experts to consider some of these ethical dimension. To give two examples: on privacy, as we let more listening devices into our homes, how do we prevent the data they collect falling into the wrong hands through hacking (黑客) or simply being sold between companies without us receiving any money? Another example: mixed reality, including virtual reality, will become pervasive in the next few years. As we move from headsets to what the IEEE committee describes as “more delicate sensory enhancements” we will use technology to live in an illusory world in many aspects of our lives. How do we balance the rights of the individual, control over our virtual identity, and the need to live and interact on a face-to-face basis while being empowered to live rich lives in mixed reality?

There is, of course, always a tension between innovation and regulation. But it can often seem that giant steps are taken in technology with minimal public discussion. Take the self-driving car: although it may be safer than human drivers and is likely to save more than a million lives a year worldwide, it will also take jobs from drivers, traffic police, sign-makers, car-repair companies, carmakers and more. Is this a bargain we want to make? In taking that decision, have we given thought to a car that knows everywhere we go, decides routes, perhaps, based on paid advertisement from shops along the way—and listens and sees everything we do on board? What will happen to that data and can it be kept safe?

Additionally, while some worry about the uncommon “trolley problem” of whom the car should choose to hit in a strange accident—an old lady or a mother and baby—perhaps the more frequent issue will be how we find out what the algorithm (运算程序) was thinking at the time of an accident, because AIs (Artificial Intelligence) are self-learning and devise their own strategies.     

Similar concerns are emerging over the internet of things. Robot vacuum-cleaners already plot cleaning cycles using computer-aided vision that, for some models, is relayed to their manufacturers. As more things at home become connected, they will be hackable and the data they collect saleable.

It’s time for some messy, democratic discussions about the future of AI.

【推荐1】Students in Washington, D.C., will return to classrooms this month to begin a new school year. But at least five percent of their teachers will not return with them. Last month, the District of Columbia public schools told two hundred six teachers that they are not good enough to stay.

The school system dismissed seventy-five teachers last year. It was the first year of a new teacher rating system. Experts say such large numbers of dismissals are rare in American schools. But in Washington the rating system is not governed by the labor contract (劳务合同) with teachers union. So school officials have more freedom. The system is called IMPACT. Teachers are observed in the classrooms five times a year for at least thirty minutes each time. They are also judged by student test scores.

Emily Cohen, a policy director, says the National Council supports the IMPACT system She says, “This is an evaluation method that is finally able to find who is highly effective and who is ineffective.” Michelle Rhee created the IMPACT system. She gained national attention for her aggressive reform efforts when she led the troubled school system in the nation’s capital.

Some Washington teachers say their rating depend too heavily on test scores. For some teachers, half of their rating is based on how well their students do. But Emily Cohen says test scores are important. “Testing is probably one of the-it’s the most objective data that we have on teacher performance. So it’s not just looking at student test performance, it is using other sources of data.”

Almost sixty percent of the teachers who were rated “minimally effective” last year stayed in the school system and improved. School officials say these teachers received help to become better.

【推荐3】Digital assistants such as Siri are increasingly common on phones and computers. Developing an emotional relationship with a piece of software can, however, bring discomfort to some people.

As a study published in Psychological Science by Park Daeun, of Chungbuk National University in South Korea, and her colleagues, shows, one emotion sometimes involved in machine-human interaction is embarrassment.

Dr Park and her team recruited 187 participants into their study. To start with, each was presented with a series of statements like “you have a certain amount of intelligence, and you can’t really do much to change it”, and “you can always substantially change how intelligent you are”. Participants rated their responses to these statements on a six-point scale, on which one meant “strongly disagree” and six meant “strongly agree”. The reason for this initial test was that Dr. Park knew from previous work that, in academic settings, those who believe intelligence to be changeable are comfortable asking for assistance while those who believe it to be fixed often feel ashamed to do so.

The initial test done, the researchers presented their volunteers with a second test, which involved looking at 16 sets of three words and trying to think of a fourth word that linked them. For example, when offered “room, blood, salts” , a correct answer would be “bath”. Sometimes the first three words were accompanied by an unrequested hint (in the example given, this was “tub”). Sometimes they were not.

Hints appeared as the written form of the word in question,accompanied bya computer-shaped icon (图形). For half of participants this icon had a humanlike face, and the hint was placed inside a speech bubble originating from that face, thus anthropomorphising (赋予人性) the presentation to some degree. For the other half the icon lacked a face and there was no speech bubble. After the final set of words had been displayed, participants were asked to agree or disagree with follow-up statements about their experience, such as “it was embarrassing to receive help during the task”, and “others might think I am incompetent (无能的) because I received help during the task.” This time, they quantified their feelings on a seven-point scale, with high scores representing greater feelings of unease.

The researchers found that participants who believed intelligence to be unchangeable (the top sixth of the sample) felt more embarrassed and more incompetent after the tests. They averaged 3.2 when measured for feelings of shame and embarrassment if the computer icons they had seen giving the hints had had faces and speech bubbles. But only 2.7 if not. In contrast, people who strongly believed that intelligence could be changed over time (the bottom sixth of the sample) felt the same level of discomfort (around 2.5) whether or not the icons had been anthropomorphised.