WASHINGTON (AP) — Two Air Force fighter jets recently clashed in aerial combat over California. One was flown by a pilot. The other was not.
This second jet was piloted by artificial intelligence, with the Air Force’s highest-ranking civilian in the front seat. It was the definitive demonstration of how far the Air Force has come in developing a technology with roots in the 1950s. But it’s just a hint of the technology that’s yet to come.
The United States is competing to stay ahead from China on AI and its use in weapons systems. The focus on AI has generated public concern that future wars will be fought by machines that select and attack targets without direct human intervention. Officials say that will never happen, at least not on the US side. But there are questions about what a potential adversary would allow, and the military sees no alternative but to quickly field U.S. capabilities.
“Whether you want to call it a race or not, it certainly is,” said Adm. Christopher Grady, vice chairman of the Joint Chiefs of Staff. “We both recognize that this will be a very critical element of the future battlefield. China is working on this as hard as we are.”
A look at the history of military AI development, what technologies are on the horizon, and how they will be kept in check:
FROM MACHINE LEARNING TO AUTONOMY
The roots of AI in the military are actually a hybrid of machine learning and autonomy. Machine learning occurs when a computer analyzes data and sets of rules to reach conclusions. Autonomy occurs when these conclusions are applied to act without additional human intervention.
This took shape in the 1960s and 1970s with the development of the Navy’s Aegis missile defense system. The Aegis was trained through a series of if/then rulesets programmed by humans to be able to detect and intercept missiles autonomously and more quickly than a human could. But the Aegis system was not designed to learn from its decisions and its reactions were limited to the set of rules it had.
“If a system uses ‘if/then’ it’s probably not machine learning, which is a field of AI that involves creating systems that learn from data,” said Air Force Lt. Col. Christopher Berardi, assigned to the Massachusetts Institute. of Technology to assist in the Air Force’s AI development.
AI took a huge step forward in 2012, when the combination of big data and advanced computing power allowed computers to begin analyzing information and writing sets of rules themselves. This is what AI experts call the “big bang” of AI.
The new data created by a computer that writes the rules is artificial intelligence. Systems can be programmed to act autonomously based on conclusions reached from rules written by machines, which is a form of autonomy made possible by AI.
TESTING AN AI ALTERNATIVE FOR GPS NAVIGATION
Air Force Secretary Frank Kendall got a taste of that advanced combat this month when he flew the Vista, the first F-16 fighter to be controlled by AIin a dogfighting exercise over Edwards Air Force Base, California.
While this jet is the most visible sign of ongoing AI work, there are hundreds of AI projects underway across the Pentagon.
At MIT, the military worked to scrub thousands of hours of recorded pilot conversations to create a dataset from the flood of messages exchanged between crews and air operations centers during flights, so that AI could learn the difference between messages. criticism, such as a lane being closed. and mundane conversations in the cabin. The goal was to have the AI learn which messages are essential to elevate to ensure controllers see them faster.
In another significant project, the military is working on an AI alternative to GPS satellite-dependent navigation.
In a future war, high-value GPS satellites would likely be hit or interfered with. The loss of GPS could blind US communications, navigation and banking systems and make the US military fleet of planes and warships less able to coordinate a response.
So last year, the Air Force used an AI program – loaded onto a portable computer strapped to the floor of a C-17 military cargo plane – to work on an alternative solution using Earth’s magnetic fields.
It is known that aircraft could navigate by following Earth’s magnetic fields, but until now this has not been practical because each aircraft generates so much of its own electromagnetic noise that there is no good way to filter out just Earth’s emissions.
“Magnetometers are very sensitive,” said Col. Garry Floyd, director of the Department of the Air Force-MIT Artificial Intelligence Accelerator program. “If you turn on the strobe lights on a C-17, we’ll see that.”
The AI learned through flights and piles of data which signals to ignore and which to follow, and the results “were very, very impressive,” Floyd said. “We’re talking tactical airdrop quality.”
“We think we may have added an arrow to the quiver of things we can do if we end up operating in a GPS-free environment. What will we do,” Floyd said.
The AI has so far only been tested on the C-17. Other aircraft will also be tested, and if it works, it could give the military another way to operate if the GPS goes down.
SAFETY RAILS AND PILOT TALK
The Vista, the AI-controlled F-16, has considerable safety barriers as the Air Force trains it. There are mechanical limits that prevent the AI, still learning, from executing maneuvers that would put the plane in danger. There is also a safety pilot who can take control of the AI with the press of a button.
The algorithm cannot learn during a flight, so each time it only has the data and rule sets it created on previous flights. When a new flight ends, the algorithm is transferred back to a simulator where it is fed new data collected during the flight to learn, create new sets of rules and improve its performance.
But AI is learning fast. Because of the super-speed computing that the AI uses to analyze data and then fly these new rulesets in the simulator, its pace in finding the most efficient way to fly and maneuver has already led it to beat some human pilots in flying exercises. aerial combat.
But safety is still a critical concern, and officials said the most important way to take safety into account is to control what data is fed back into the simulator for the AI to learn from. In the case of the jet, it is ensuring that the data reflects a safe flight. Ultimately, the Air Force hopes that a version of the AI under development could serve as the brains for a fleet of 1,000 unmanned warplanes under development by General Atomics and Anduril.
In the AI training experiment on how pilots communicate, military personnel assigned to MIT scrubbed recordings to remove sensitive information and pilots’ sometimes salty language.
Learning how pilots communicate is “a reflection of command and control, of how pilots think. Machines need to understand this too if they want to become really good,” said Grady, vice chairman of the Joint Chiefs. “They don’t need to learn to swear.”
