Drones are experiencing something of an arms race for aerial domination. As fast as the technology develops to make the unmanned vehicles better in the air, new ways are being developed to take them down. Video footage has emerged of a soldier in the US using a'cyber rifle' to take down a drone, causing it to lose power instantly in flight before falling out of the air. Video footage has emerged of a US soldier using a'cyber rifle' to take down a drone, causing it to lose power instantly in flight and fall out of the air (still pictured). The cyber-rifle is built by the Army Cyber Institute at West Point in the US, and was tested in front of the US Secretary for Defense, Ash Carter.

Secretary of Defense Ash Carter is just out of frame on the right side of the screen. As soon as it was airborne, the drone flying inside West Point crashed to the ground at the feet of Secretary of Defense Ash Carter. The soldier responsible for the drone's demise gently lowered the weapon, no smoke wafting from its barrel, not even a sound made with the shot. Built by the Army Cyber Institute at West Point, the rifle was demonstrated last fall at the Association of the United States Army exposition in Washington, DC. Unlike pretty much every other variety of gun, this rifle doesn't shoot any projectiles.

Drone maker DJI's latest release, the Phantom 4, makes it easier than ever to capture beautiful aerial photos and videos. But the Chinese firm is working on ways to make the process simpler still. The company is currently building a set of automatic camera maneuvers that could allow Phantom 4 owners to nail complex shots by just pushing a button. "A lot of people, what they'll do is they'll fly around and their first shots aren't that great because they're getting used to it," says Pan. The feature would allow Phantom 4 owners to choose from a selection of camera maneuvers that the drone would be able to execute on its own.

It's a weird feeling, cruising around Silicon Valley in a car driven by no one. I am in the back seat of one of Google's self-driving cars – a converted Lexus SUV with lasers, radar and low-res cameras strapped to the roof and fenders – as it maneuvers the streets of Mountain View, California, not far from Google's headquarters. I grew up about five miles from here and remember riding around on these same streets on a Schwinn Sting-Ray. Now, I am riding an algorithm, you might say – a mathematical equation, which, written as computer code, controls the Lexus. The car does not feel dangerous, nor does it feel like it is being driven by a human. It rolls to a full stop at stop signs (something no Californian ever does), veers too far away from a delivery van, taps the brakes for no apparent reason as we pass a line of parked cars. I wonder if the flaw is in me, not the car: Is it reacting to something I can't see? The car is capable of detecting the motion of a cat, or a car crossing the street hundreds of yards away in any direction, day or night (snow and fog can be another matter). "It sees much better than a human being," Dmitri Dolgov, the lead software engineer for Google's self-driving-car project, says proudly. He is sitting behind the wheel, his hands on his lap. As we stop at the intersection, waiting for a left turn, I glance over at a laptop in the passenger seat that provides a real-time look at how the car interprets its surroundings. On it, I see a gridlike world of colorful objects – cars, trucks, bicyclists, pedestrians – drifting by in a video-game-like tableau. Each sensor offers a different view – the lasers provide three-dimensional depth, the cameras identify road signs, turn signals, colors and lights. The computer in the back processes all this information in real time, gauging the speed of oncoming traffic, making a judgment about when it is OK to make a left turn.

Look at this blurry drone. That's as good a glimpse as we're likely to get for a while. Tossed by the wind, controlled by inexperienced pilots, or just flying at the wrong angle at the wrong time, quadcopters crash into their surroundings. While these crashes aren't a major risk to life and limb, they're certainly an obstacle to better drone use: No one wants to rely on a flying machine that crashes into obstacles. We've seen many proposed drone systems with some form of "sense and avoid" before, most recently DJI's Phantom 4, but none as elegant as the Skydio.

New drone footage has captured the growing pile of lifejackets abandoned by migrants after reaching the Greek island of Lesbos. It is now more than three metres deep in some places, with boats being added to the pile.

Drone images accumulate much faster than they can be analyzed. Researchers have developed a new approach that combines crowdsourcing and machine learning to speed up the process. Who would win in a real-life game of "Where's Waldo," humans or computers? A recent study suggests that when speed and accuracy are critical, an approach combing both human and machine intelligence would take the prize. With drones being used to monitor everything natural disaster sites, pollution, or wildlife populations, analyzing drone images in real-time has become a critically important big data challenge. Publishing in the journal Big Data, researchers, including Stéphane Joost from EPFL, present a new approach to rapidly interpret aerial images taken by camera drones that combines human crowdsourcing and machine learning.

Every time we post about autonomous delivery drones, we have to point out that despite the promises implied by overproduced and optimistic videos, the drones are simply not capable of autonomous navigation in complex environments. Same goes for those camera drones that promise to follow you: the videos inevitably show them following skiers on wide open slopes, surfers on the wide open sea, or other people doing things very far away from inconvenient obstacles like trees. So far, we've only seen a tiny handful of drones capable of dynamically detecting and avoiding obstacles at a useful speed. Qualcomm and UPenn have been working on some, and MIT has that speedy tree-avoiding fixed-wing drone. A Silicon Valley company called Skydio, founded by a team of researchers from MIT and Google X's Project Wing, have posted a video that shows a drone following people jogging and biking while autonomously avoiding tree trunks and branches.

An anti-tank missile fires by an Iraqi CH-4 drone destroys an ISIS artillery piece, seen in footage captured by the drone's onboard, retractable sensor turret. China's armed CH-3 and CH-4 drones have recently made international news, being used by nations that range from Iraq to Nigeria. And now NORINCO, one of China's leading defense contractors, is getting in on the international drone export game, showing off a new armed drone helicopter. The Sky Saker H300, seen here in Dubai, is China's first helicopter UCAV. Being cheaper and easier to use then larger UCAVs like the Reaper and CH-4, it could become a battalion and company level UCAV for on demand air strikes.

A near miss between a drone and a Lufthansa aircraft landing at Los Angeles International Airport may give impetus to moves to regulate consumer drones more closely, including by using technologies like geofencing. "This is one more incident that could have brought down an airliner, and it's completely unacceptable. A near-miss of 200 feet should serve as a stark reminder of the dangers posed by reckless drone use," said Senator Dianne Feinstein, a Democrat from California, in a statement. The senator said the Senate must pass legislation she introduced last year, called the Consumer Drone Safety Act, which would require safety features for consumer drones and strengthen the federal laws that govern their operation. The bill would, among other things, direct the Federal Aviation Administration to require safety features for newly manufactured consumer drones, such as geofencing to govern the altitude and location of flights and collision-avoidance software.