The Pentagon's New Artificial Intelligence Is Already Hunting Terrorists

["Dave Farber" <dave () farber net>]

---------- Forwarded message ---------
From: Dewayne Hendricks <dewayne () warpspeed com>
Date: Wed, Jan 17, 2018 at 1:56 PM
Subject: [Dewayne-Net] The Pentagon's New Artificial Intelligence Is
Already Hunting Terrorists
To: Multiple recipients of Dewayne-Net <dewayne-net () warpspeed com>


The Pentagon’s New Artificial Intelligence Is Already Hunting Terrorists
After less than eight months of development, the algorithms are helping
intel analysts exploit drone video over the battlefield.
By MARCUS WEISGERBER
Dec 21 2017
<
http://www.defenseone.com/technology/2017/12/pentagons-new-artificial-intelligence-already-hunting-terrorists/144742/
>

Earlier this month at an undisclosed location in the Middle East, computers
using special algorithms helped intelligence analysts identify objects in a
video feed from a small ScanEagle drone over the battlefield.

A few days into the trials, the computer identified objects — people, cars,
types of building — correctly about 60 percent of the time. Just over a
week on the job — and a handful of on-the-fly software updates later — the
machine’s accuracy improved to around 80 percent. Next month, when its
creators send the technology back to war with more software and hardware
updates, they believe it will become even more accurate.

It’s an early win for a small team of just 12 people who started working on
the project in April. Over the next year, they plan to expand the project
to help automate the analysis of video feeds coming from large drones — and
that’s just the beginning.

“What we’re setting the stage for is a future of human-machine teaming,”
said Air Force Lt. Gen. John N.T.“Jack” Shanahan, director for defense
intelligence for warfighter support, the Pentagon general who is overseeing
the effort. Shanahan believes the concept will revolutionize the way the
military fights.

“This is not machines taking over,” he said. “This is not a technological
solution to a technological problem. It’s an operational solution to an
operational problem.”

Called Project Maven, the effort right now is focusing on helping U.S.
Special Operations Command intelligence analysts identify objects in video
from small ScanEagle drones.

In coming months, the team plans to put the algorithms in the hands of more
units with smaller tactical drones, before expanding the project to larger,
medium-altitude Predator and Reaper drones by next summer.

Shanahan characterized the initial deployment this month as “prototype
warfare” — meaning that officials had tempered expectations. Over the
course of about eight days, the team refined the algorithm, six times.

“This is maybe one of our most impressive achievements is the idea of
refinement to the algorithm,” Shanahan said.

Think of it as getting a new update to a smartphone application every day,
each time improving its performance.

Before it deployed the technology, the team trained the algorithms using
thousands of hours of archived battlefield video captured by drones in the
Middle East. As it turned out, the data was different from the region where
the Project Maven team deployed.

“Once you deploy it to a real location, it’s flying against a different
environment than it was trained on,” Shanahan said. “Still works of course
… but it’s just different enough in this location, say that there’s more
scrub brush or there’s fewer buildings or there’s animals running around
that we hadn’t seen in certain videos. That is why it’s so important in the
first five days of a real-world deployment to optimize or refine the
algorithm.”

While the algorithm is trained to identify people, vehicles and
installations, it occasionally mischaracterizes an object. It’s then up to
the intel analyst to correct the machine, thus helping it learning.

The team has paired the Maven algorithm with a system called Minotaur, a
Navy and Marine Corps “correlation and georegistration application.” As
Shanahan describes it, Maven has the algorithm, which puts boxes on the
video screen, classifying an object and then tracking it. Then using
Minotaur, it gets a georegistration of the coordinates, essentially
displaying the location of the object on a map.

“That’s new, it’s different and it’s much needed for an analyst because
this was all being done manually in the past,” the general said.

“Having those things together is really increasing situational awareness
and starts the process of giving analysts a little bit of time back — which
we hope will become a lot of time back over time — rather than just having
to stay glued to the video screen,” Shanahan said.

[snip]

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