No, this isn't an April Fool's joke: A Russian defense contractor has patented a drone that uses a shotgun to blast other drones out of the sky. It comes from Almaz Antey, a Russian defense contractor that manufactures the S-400 Triumf surface-to-air missile that caused a rift between Turkey and the US. The tail-sitting drone takes off on the spot but flies like an airplane for greater efficiency, giving it a 40-minute range while packing a fully-automatic Vepr-12 shotgun with a 10-round magazine. The drone was built by the "Student Design Bureau of Aviation Modeling" at the Moscow Aviation Institute for Almaz Antey. It's of a similar type used by mining companies, farmers and others to survey pipelines and other installations. A visor-wearing operator uses a live video link to fly the drone and aim the weapon, which is tucked into the nose of the aircraft.

If you're inclined to puns, you might say medical samples are the lifeblood of hospital systems. But if you actually work with them, you know they're more of a headache. Because the same road traffic that keeps you from getting home keeps the couriers charged with moving these tissue and blood samples, collected by the millions daily and often in urgent need of analysis, from completing their missions. So it makes a lot of sense that when the FAA decided to sanction the first revenue-generating drone delivery scheme in the US, it went with one that promises to speed up that process, run by UPS and autonomous drone technology firm Matternet. It makes sense from the tech perspective, too: The cargo is extremely lightweight and compact, allowing the companies involved to focus on the delivery processes and mechanisms rather than trying to manage unwieldy payloads.

It's probably fair to say that in the history of politicking, few politicians have publicly declared what to do about America's crumbling malls, or how to provide free marriage counseling for all, or how to make filing taxes fun. But Andrew Yang, who's gunning to be the Democratic presidential candidate in 2020, certainly has--and those are the more minor concerns among a dizzying list of 80 policy positions on his campaign website. It's an indication that Yang is running a rather methodical, data-driven, science-happy campaign. He's applying that approach to more standard issue problems, like labor, climate change, and the economy, but giving them a decidedly tech-forward approach: how (and why) we should define robots, what use might geoengineering have in saving the planet, and should the government embrace universal basic income and give every American a $1,000 check. Yang talked with WIRED about all this and more in a recent interview.

Most of the time, machine learning does not touch on particularly sensitive social, moral, or ethical issues. Someone gives us a data set and asks us to predict house prices based on given attributes, classifying pictures into different categories, or teaching a computer the best way to play PAC-MAN -- what do we do when we are asked to base predictions of protected attributes according to anti-discrimination laws? How do we ensure that we do not embed racist, sexist, or other potential biases into our algorithms, be it explicitly or implicitly? It may not surprise you that there have been several important lawsuits in the United States on this topic, possibly the most notably one involving Northpointe's controversial COMPAS -- Correctional Offender Management Profiling for Alternative Sanctions -- software, which predicts the risk that a defendant will commit another crime. The proprietary algorithm considers some of the answers from a 137-item questionnaire to predict this risk.

Speaking at MIT Technology Review's EmTech Digital event, Microsoft's Vice President of AI & Research, Harry Shum, drew attention to the risks associated with AI as it becomes more creative in the future. In particular, Shum called on tech companies to "engineer responsibility into the very fabric of the technology. As MIT's Technology Review points out, we've already seen some of the fallout from the tech industry failing to anticipate flaws in AI. One such flaw is AI's difficulty thus far with identifying faces with dark skin tones, something Microsoft has been working to improve. But AI is also being used by China in alarming ways for surveillance, while, more recently, an Uber self-driving car killed a pedestrian in early 2018. According to Shum, AI's challenges will only ramp up as it becomes more complex, adding the ability to produce art, maintain near-human-like conversations, and accurately read human emotions. These abilities will pave the way for AI to more easily create propaganda or misinformation to be spread online, including fake audio and video. Microsoft is working to take these challenges into account. The company has created an AI ethics committee and is working with others in the industry to address problems posed by AI. Shum also told MIT Technology Review that Microsoft plans to add an ethics review step to its audit list before products hit the market "one day very soon," joining other steps such as privacy, security, and accessibility. "We are working hard to get ahead of the challenges posed by AI creation," Shum told MIT Technology Review. "But these are hard problems that can't be solved with technology alone, so we really need the cooperation across academia and industry.

IoT is already affecting most areas of our lives and transportation is no exception. Even before the age of autonomous cars IoT is improving road safety and I'm going to express this through my own personal experience and other uses. I am currently working in Dublin and I travel back home to Waterford every weekend to see my family. For the first few months, I would take the bus and with the frequent stops, distance and Dublin Traffic the journey would take 4 hours! This meant I was travelling on average for 8 hours over the weekend.

A team led by 22-year-old Anne Dattilo, an undergraduate student at the University of Texas, Austin, discovered two planets, officially named K2-293b and K2-294b. A team led by 22-year-old Anne Dattilo, an undergraduate student at the University of Texas, Austin, discovered two planets, officially named K2-293b and K2-294b. A team of astronomers led by an undergraduate student in Texas has discovered two planets orbiting stars more than 1,200 light-years from Earth. Astronomers already knew of about 4,000 exoplanets, so finding two more might not seem like huge news. But it's who found them and how that's getting attention.

Amir Kiani (from left), Chloe O'Connell and Nishit Asnani troubleshoot an algorithm to diagnose tuberculosis in computer lab at Stanford University. Amir Kiani (from left), Chloe O'Connell and Nishit Asnani troubleshoot an algorithm to diagnose tuberculosis in computer lab at Stanford University. Some computer scientists are enthralled by programs that can teach themselves how to perform tasks, such as reading X-rays. Many of these programs are called "black box" models because the scientists themselves don't know how they make their decisions. Already these black boxes are moving from the lab toward doctors' offices.

That wouldn't have been the case, though, if her teacher, Jason Whiting, had not opted to pioneer a coding course for middle school students this year. The course comes from Code.org, a national nonprofit focused on giving students access to computer science skills in schools for women and underrepresented minorities, according to the organization's website, the Argus Leader reported.

For unsupervised domain adaptation problems, the strategy of aligning the two domains in latent feature space through adversarial learning has achieved much progress in image classification, but usually fails in semantic segmentation tasks in which the latent representations are overcomplex. In this work, we equip the adversarial network with a "significance-aware information bottleneck (SIB)", to address the above problem. The new network structure, called SIBAN, enables a significance-aware feature purification before the adversarial adaptation, which eases the feature alignment and stabilizes the adversarial training course. In two domain adaptation tasks, i.e., GTA5 -> Cityscapes and SYNTHIA -> Cityscapes, we validate that the proposed method can yield leading results compared with other feature-space alternatives. Moreover, SIBAN can even match the state-of-the-art output-space methods in segmentation accuracy, while the latter are often considered to be better choices for domain adaptive segmentation task.