AI News, AI to Ensure Fewer UFOs

AI to Ensure Fewer UFOs

Photo: Black Sage Technologies Searching the Skies: Black Sage Technologies’ artificial-intelligence system spots flying objects and determines whether they’re a threat.

Human observers won’t have to guess—or keep their eyes glued to computer monitors—now that there’s superhuman artificial intelligence capable of distinguishing drones from those other flying objects.

Automated watchfulness, thanks to machine learning, has given police and other agencies tasked with maintaining security an important countermeasure to help them keep pace with swarms of new drones taking to the skies.

The security challenge has only grown over the past few years: Millions of people have bought consumer drones and sometimes flown them into off­limits areas where they pose a hazard to crowds on the ground or larger aircraft in the sky.

But because the stakes are high—mistakenly shooting down a small passenger plane or failing to take out an explosives-laden drone intruder could be equally disastrous—Black Sage puts its system through a rigorous training phase when it’s installed at a new site.

“In the past eight months, we’ve annotated 3 million drone images.” Though Black Sage’s and Dedrone’s automated detection systems are said to be capable of running without human assistance after their respective training phases, the companies’ clients may choose to put humans in the loop for engaging active defenses, such as jammers or lasers, to take down flying intruders.

Such caution is critical at sites like airports, where drone detection accuracy greater than 90 percent still means the occasional false alarm or case of mistaken identity.

This Brilliant Plan Could Stop Drone Terrorism. Too Bad It’s Illegal

TOO BAD IT’S ILLEGAL As the Black Sage cofounders heard the ominous buzzing overhead and watched the kids pretend to die, they felt a small measure of satisfaction—their drone-tracking system worked.ATELIER OLSCHINSKY IMAGINE YOU’RE PART of a great swelling crowd, one of 60,000 people who fill up the cauldron of noise and chaos that is a sold-out football stadium.

(The university asked that identifying details be withheld so as not to share its playbook with would-be attackers.) Campus officials launched a DJI quadcopter, a midsize, midpriced drone, and steered it toward the bleachers, pretending to spread nerve gas on the hundred students gathered below.

As they heard the ominous buzzing overhead and watched the college kids pretend to die, Romero and Lamm allowed themselves a small measure of satisfaction—Black Sage’s tracking system worked, and in the event of an actual attack it could give authorities a few crucial extra minutes to mobilize.

“We can do everything but stop this catastrophic incident from occurring.” Shaken and stirred, they returned to Black Sage’s headquarters in Boise, Idaho, and spent a year enhancing their system so that it can now not only track drones but also bring them safely to the ground using radio-frequency-jamming technology.

There is only one small hitch: Like almost every drone-­interdiction technology in development, frequency jammers run afoul of several US laws, most of which were passed when people hadn’t dreamed of owning their own unmanned aircraft.

Romero and Lamm’s solution to the mock terror in the stadium—a solution that they have shown can reliably counter the threats drones pose to targets as varied as prisons, airports, and ­arenas—is illegal here, which leaves the future of Black Sage’s technology, like the future of drones themselves, very much up in the air.

After graduating from college in 2007, he started a software company called Tsuvo that performed regression analysis—taking large data sets from disparate government agencies, some of which involved thousands of statistics, and distilling them into clean, color-coded graphics that even nonstatisticians could understand.

Where Romero is an adrenaline fiend—ask about the mountain bike perched in his office and he’ll show you a photo of himself on the bike, halfway through a backflip—Lamm, 45, likes nothing more than sailing with his two sons on a quiet lake.

While earning a PhD concentrated on machine vision in the late ’90s, he developed an algorithm that enabled a tractor-­­mounted camera to tell the difference between cotton plants and weeds, allowing farmers to spray herbicide more accurately.

In the aftermath of al Qaeda’s attack on the USS Cole in 2000 (an explosive-­laden speedboat crashed into the ship, killing 17 sailors), he helped a US Navy and Coast Guard contractor develop a robotic vision system that allowed ships to detect and quickly respond to speedboat attacks.

(With your own vessel rocking and an enemy boat closing in fast, it’s surprisingly difficult to track ships on the water.) He also took part in constructing the warning system in Washington, DC, that locks onto commercial airplanes that drift into restricted airspace and beams an unmistakable red-red-green, red-red-green laser signal into the cockpit to alert the plane’s pilots to fly elsewhere.

Lamm and Romero first crossed paths when their mutual friend asked for their help landing a government contract: The state of Idaho wanted to install a new warning system on a highway to prevent cars from crashing into animals after dark.

The existing warning system flashed a light whenever a deer or an elk crossed the road, but because the signal would also light up whenever the wind sent leaves and branches tumbling across the pavement—which was often—drivers came to ignore the warning lights altogether.

To train his machine-learning algorithms to distinguish between animals and clutter, he would spend 45 minutes of his lunchtime each day (perfect for nocturnal sightings in Idaho) watching the infrared images and signaling yes or no as to whether they were wildlife.

The system accumulated thousands of data points on the moving objects that crossed the camera’s field of view—speed, acceleration, direction—and once that data was correlated with Romero’s yes/no designations, the algorithm learned to recognize what probably was an animal and what probably wasn’t.

Once the province of military developers, then of rich folks who could afford the technology, drones soared into the mainstream in 2013 when Chinese drone maker DJI introduced the Phantom, the first consumer-­priced unmanned aircraft system.

It jump-started what Marke Gibson, the FAA’s drone expert and a former Air Force general, calls “the most fundamental change in aviation in our lifetime.” With hundreds of thousands of new aircraft navigating increasingly crowded airspace, Lamm and Romero noticed there were alarmingly few ways to keep track of the errant ones.

The adaptation wasn’t as simple as taking their existing radar and camera equipment and pointing it skyward, though: Romero and Lamm had to write new software to process the ever-­changing latitude, longitude, and altitude of an incoming target, all while taking into account the curvature of the Earth.

“The AI figured it out.” By the summer of 2015 they had a system that could reliably detect an incoming drone about half a kilometer away, identify it, and stay locked on it regardless of evasive maneuvers.

The government has taken steps to prevent people from doing dumb things with their drones: Last summer the FAA released licensing and registration rules to compel drone buyers to learn how to fly responsibly.

“Every prison, every airport, every facility with sensitive equipment outdoors, stadiums, amusement parks, racetracks … everybody is now worried about drones,” says James Williams, an aviation specialist at the international law firm Dentons.

When a drone approached, radar would detect it, cameras would track it, and with the touch of a button, 12 million candlepower of light would blind the drone and disable its video and espionage capabilities.

Shortly after the high-­wattage experiment, Romero went to an international security conference in Dubai in early 2016, where he met the owner of a company that makes radio jammers to protect armored vehicles in war zones.

Still, Romero and Lamm thought that if they could jam only those frequency bands most commonly used in drone communication—and if they could limit their jamming to objects at which they have aimed their system—they could minimize the disruption to surrounding radio and GPS communications.

“We’ve got a $100 million customer in a hot, sandy place who doesn’t care about the FCC, and we have a solution they’ll love—so let’s do it.” Lamm and Romero are understandably vague about where they test and sell their equipment overseas.

“At that point, it was handshakes, smiles, and a happy customer.” Though the Black Sage jammer includes a narrow-beam antenna to minimize frequency disruptions in the surrounding area, Romero and Lamm concede that using the latest version of their system in a crowded urban area could cause hundreds of businesses to lose their Wi-Fi for up to 30 seconds.

(The FCC wouldn’t comment on Black Sage or the issue of frequency jamming.) Meanwhile, the FAA is hosting biweekly meetings with the FCC and other three-letter agencies to work out standards for what kind of antidrone systems can be developed and under what conditions they can be safely deployed.

AI to spot UFOs

Photo: Black Sage Technologies 'Black Sage Technologies' develops an AI (artificial-intelligence) system to spots flying objects and determines whether they’re a threat.

It is also impossible for humans to sit in front of the monitors to identify whether the flying object is a bird, simply drone toy, small passenger plane or a drone carrying a deadly bomb.

Automated watchfulness, thanks to machine learning, has given police and other agencies tasked with maintaining security an important countermeasure to help them keep pace with swarms of new drones taking to the skies.

It will compromise the safety of flight passengers as millions of people have bought consumer drones and sometimes flown them into off­-limits areas where they pose a hazard to larger aircraft in the sky.

But because the stakes are high—mistakenly shooting down a small passenger plane or failing to take out an explosive-laden drone intruder could be equally disastrous—Black Sage puts its system through a rigorous training phase when it’s installed at a new site.

Then a human operator helps train the machine-learning­ algorithms by positively identifying certain classes of drones (rotor or fixed-wing) as well as other objects such as birds or manned aircraft.

In the long run, we can't say that it will be fully automated in future or will be operated by an operator to apply jammers, laser system or guns to take down flying intruders.


Amazon does not yet have regulatory approval to blot out the sun, but citizens have picked up the slack by making drones the latest gizmo craze, with hundreds of thousands bought during the 2015 holiday season alone.

Perhaps most tellingly, Stanford’s 10-week skills-and-thrills course in designing, building and flying a drone—popular enough to often go simply by its catalog number, AA241X—is rooted in the kind of interdisciplinary collaboration that has become a touchstone of university activities.

“Unmanned aircraft systems,” notes Juan Alonso, an aero-astro professor who is one of the AA241X instructors, “are first and foremost systems—a combination of multiple physical and functional elements that must work together in order to accomplish a complex task.

We place particular emphasis on making sure the students tackle all of the elements in the system and gain experience at all stages of the design, prototyping and operation.” STANFORD’S INFLUENCE on how drones are made, used and perceived may become one of the university’s signature contributions to social change.

Stanford faculty and alums from all the scientific and mathematical disciplines, not to mention those who will help shape the legislative and regulatory territory, anticipate a world in which drones can help us find parking, examine bridge corrosion and obey virtual barriers—“geofences” that program boundaries—when nearing forbidden locations.

Perhaps the most intriguing factor is one emphasized by aero-astro professor Marco Pavone, who co-authored a recent article with its headline touting “Flying Smartphones.” Pavone, aero-astro professor Mac Schwager, ’00, and Ross Allen, PhD ’16, described aerial drones as on the way to changing consumer electronic technology with as much everyday impact as smartphones had on personal computing.

Pavone, Schwager and Allen envision aerial possibilities that range from the relatively mundane, such as monitoring freeway congestion, to the flamboyantly avant-garde, such as artistic or advertising displays formed by groups of drones radiating colored light.

For drones that largely means “endowing the robot with flexibility in its decision-making capabilities.” Figuring out how to make drones “smarter” is crucial, says Pavone, because it’s assumed they will encounter far more impediments, safety cues and shifting conditions than engineers can anticipate.

video filmed in Pavone’s Autonomous Systems Laboratory demonstrates the issue about as entertainingly as possible: Allen, Pavone’s former PhD student, recorded himself “fencing” with a small drone in an indoor space that confined its range of motion.

After working as director of flight operations and client services for the San Francisco start-up Skycatch, he relocated to Denver and joined Aeryon U.S., a provider of small unmanned aerial systems, as director of worldwide client solutions (flight demonstrations, training and product implementation).

Stanford, he says, “is in a great position to influence how the industry is shaped from a technological standpoint.” There are a number of academic and commercial hot spots for drones around the United States—Stanford faculty are quick to point out the nationally dispersed momentum in the field—but there may be an extra cachet to the theme of “Silicon Valley meets aviation.” That’s because, as Alonso explains, “It’s not just about some people building the airplanes or building the vehicles.

Kochenderfer, ’03, MS ’03, is director of the Stanford Intelligent Systems Laboratory, whose core focus is on “decision-making under uncertainty.” Before returning to the university as a professor in 2013, he was at the Massachusetts Institute of Technology’s Lincoln Laboratory, where he instigated the ongoing development of a major advancement in an international collision avoidance system for manned aircraft.

“Drones Will Change Everything” is a prominent slogan on the company’s website, but business and policy vice president Gabriel Dobbs, JD ’14, MBA ’14, supplies the caveat: “Drones can do remarkable things but not everything people imagine they can do.” Photos: Ved Chirayath, '12, MS '14, PhD '16 EYE IN THE SKY: Aeronautics and astronautics professor Juan Alonso calls Ved Chirayath, '12, MS '14, PhD '16, “probably the most creative student I’ve ever had.” Stanford featured Chirayath’s eye-popping work in the story “Wonder World” (May/June).

A set of radiant photographs documented the results of Chirayath’s computer algorithm for fluid lensing: technology for taking photographs through water that can negate distortions and gauge (among other things) the health of coral reefs centimeter by centimeter with high resolution in three dimensions.

It might be hard to find a better example of multiple disciplines—aviation, computer science, marine biology and photography—addressing such a vital area of scientific research in the emerging era of small unmanned aerial systems.

Kespry’s day-to-day operations, which as of late August include a 2.0 version of the firm’s drone system, are headlined by proficiencies in flight time, wind resilience and data gathering.

It’s also the kind of enterprise that provokes many of the questions about how drones will affect jobs, and in this case highlights the argument that aerial examination of, say, a sprawling quarry is a significant safety enhancement.

Details are copious, but the essential provisions are these: The unmanned aircraft must weigh less than 55 pounds, observe an altitude ceiling of 400 feet and a maximum speed of 100 miles per hour, not fly over anyone not participating in the operation, not fly at night, and stay within eyesight of the operator (precluding for now the beginning of grand-scale package bombardment).

Mechanical engineering and computer science grad Hao Yi Ong, ’15, MS ’15, worked under the supervision of Kochenderfer to devise a conflict avoidance system that’s among the concepts being evaluated by NASA, which partners with the FAA on air traffic management research.

“Does it fly backwards as it talks?” BEFORE YOU BUY If you’re among the holiday season shoppers who expect to join the legion of new drone owners, you should probably do some prep work about what to buy and how to operate your very own unmanned aerial system.

“Too many people lose their new toys because they fly them too far away, or run into electronic interference or angry seagulls or gun-toting neighbors.” Aero-astro professor Juan Alonso says caution trumps all: “Never, never, never fly if you even think that a safety issue might come up.” Comments (0)

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