DoD collects loads of data from satellites, drones and Internet-of-things devices. But it needs help making sense of the intelligence and analyzing it quickly enough so it can be used in combat operations. But, the sheer volume of video content produced makes identifying, assembling and delivering actionable intelligence — from multiple sources and across thousands of hours of footage — a habitually long, laborious process.
The DoD is now developing an AI-driven algorithm to work in conjunction with its drone footage to spot, tag and bookmark potential threat targets. Military analysts are using Google-developed AI algorithms to mine live video feeds from drones. With machine learning techniques, software is taught to find particular objects or individuals at speeds that would be impossible for any human analyst. This AI technology can differentiate between people, objects and buildings, much like Google’s driverless cars. Undersecretary of Defense for Intelligence Joseph Kernan said Project Maven only started a year ago and so far has been “extraordinarily” useful in overseas operations.
Project Maven, also known as the Algorithmic Warfare Cross-Function Team, was launched in April 2017 by then-Deputy Defense Secretary Bob Work to accelerate the department’s integration of big data, artificial intelligence and machine learning into DoD programs. Project Maven is a Pentagon project using machine learning to sort through masses of intelligence, surveillance and reconnaissance data – unmanned systems video, paper, computer hard drives, thumb drives and more – collected by the department and intelligence agencies for operational use across the services. “As numerous studies have made clear, the department of defense must integrate artificial intelligence and machine learning more effectively across operations to maintain advantages over increasingly capable adversaries and competitors,” Work wrote.
Among its objectives, the project aims to develop and integrate computer-vision algorithms needed to help military and civilian analysts encumbered by the sheer volume of full-motion video data that DoD collects every day in support of counterinsurgency and counterterrorism operations. Project Maven focuses on computer vision — an aspect of machine learning and deep learning — that autonomously extracts objects of interest from moving or still imagery, Cukor said. Biologically inspired neural networks are used in this process, and deep learning is defined as applying such neural networks to learning tasks.
Drew Cukor, chief of the DoD’s Algorithmic Warfare Cross-Function Team, said in July: “People and computers will work symbiotically to increase the ability of weapon systems to detect objects. Eventually we hope that one analyst will be able to do twice as much work, potentially three times as much, as they’re doing now. That’s our goal.” It has reportedly already been put into use against Islamic State. He said the immediate focus is 38 classes of objects that represent the kinds of things the department needs to detect, especially in the fight against the Islamic State of Iraq and Syria.
SOCOM Commander Army Gen. Richard Clarke said that, in recent conversations he has had with commanders in Afghanistan, between 2001 and 2018 “70 percent of their time was putting bomb on target.” But now, through better use of AI for speedy and precise targeting and a greater understanding of the effectiveness of information operations, they have reversed that ratio. In a combat situation, Clark said, “you’re not trying to kill every bad guy out there” but rather are targeting a leader or a group of leaders. AI has already gained a strong foothold in logistics and maintenance in Pentagon thinking and is now making its way to commanders. “Maven has made some inroads [because] it is actively giving them courses of actions” and even parallel courses of actions to take simultaneously to further confuse an enemy.
Mohsen Fakhrizadeh, who founded Iran’s nuclear program in the 2000s, had a security detail of 11 guards while traveling with his wife on Nov. 27 in a car on a highway outside Tehran when an automatic machine gun outfitted with AI and an advanced camera zoomed in on his face and fired 13 times, an Islamic Revolutionary Guards Corps deputy commander told local media Sunday. The assassination of Iran’s top nuclear scientist in Nov 2019 was carried out remotely with artificial intelligence and a machine gun equipped with a “satellite-controlled smart system”, Iranian news agencies quoted a senior Iranian commander as saying.
Ali Fadavi, the deputy commander of Iran’s Revolutionary Guards Corps, told Iranian news agencies that Mohsen Fakhrizadeh was driving when a weapon opened fire on his car on a highway near Tehran. The weapon “zoomed in on Fakhrizadeh” using an “advanced camera”, Fadavi said. “No terrorists were present on the ground.” Fadavi said the gun used to kill Fakhrizadeh had been placed on a pickup truck and controlled by a satellite, and had fired 13 shots. “During the operation artificial intelligence and face recognition were used,” Fadavi said. “His wife, sitting 25cm away from him in the same car, was not injured.”
This incident points to many advanced technologies like Maven algorithms that might have been applied on images captured by advanced camera, precision fire of automatic machine gun and remote control operation through satellite.
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