In August 2020, USNI News reported that the Navy had “initiated work to develop its first new carrier-based fighter in almost 20 years.” While the F-35C Lightning II will still be in production for many years, the Navy needs to have another fighter ready to replace the bulk of the F/A-18E/F/G Super Hornets and Growlers by the mid-2030s. This new program will design that aircraft. While this is an important development, it will be to the Navy’s detriment if the Next Generation Air Dominance (NGAD) program yields a manned fighter.
Designing a next-generation manned aircraft will be a critical mistake. Every year remotely piloted aircraft (RPAs) replace more and more manned aviation platforms, and artificial intelligence (AI) is becoming ever increasingly capable. By the mid-2030s, when the NGAD platform is expected to begin production, it will be obsolete on arrival if it is a manned platform. In order to make sure the Navy maintains a qualitative and technical edge in aviation, it needs to invest in an unmanned-capable aircraft today. Recent advances and long-term trends in automation and computing make it clear that such an investment is not only prudent but necessary to maintain capability overmatch and avoid falling behind.
This year, AI designed by a team from Heron Systems defeated an Air Force pilot, call sign “Banger,” 5-0 in a simulated dogfight run by DARPA. Though the dogfight was simulated and had numerous constraints, it was only the latest in a long string of AI successes in competitions against human masters and experts.
Since 1997, when IBM’s DeepBlue beat the reigning world chess champion Gary Kasparov over six games in Philadelphia, machines have been on a winning streak against humans. In 2011, IBM’s “Watson” won Jeopardy!. In 2017, DeepMind’s (Google) “AlphaGo” beat the world’s number one Go player at the complex Chinese board game. In 2019, DeepMind’s “AlphaStar” beat one of the world’s top-ranked Starcraft II players, a real-time computer strategy game, 5-0. Later that year an AI from Carnegie Mellon named “Pluribus” beat six professionals in a game of Texas Hold’em poker. On the lighter side, an AI writing algorithm nearly beat the writing team for the game Cards Against Humanity in a competition to see who could sell more card packs in a Black Friday write-off. After the contest the company’s statement read: “The writers sold 2% more packs, so their jobs will be replaced by automation later instead of right now. Happy Holidays.”
It’s a joke, but the company is right. AI is getting better and better every year and human abilities will continue to be bested by AI in increasingly complex and abstract tasks. History shows that human experts have been repeatedly surprised by AI’s rapid progress and their predictions on when AI will reach human parity in specific tasks often come true years or a decade early. We can’t make the same mistake with unmanned aviation.
Most of these competitive AIs use machine learning. A subset of machine learning is deep reinforcement learning which uses biologically inspired evolutionary techniques to pit a model against itself over and over. Models that that are more successful at accomplishing the specific goal – such as winning at Go or identifying pictures of tigers, continue on. It is like a giant bracket, except that the AI can compete against itself millions or even billions of times in preparation to compete against a human. Heron Systems’ AI, which defeated the human pilot, had run over four billion simulations before the contest. The creators called it “putting a baby in the cockpit.” The AI was given almost no instructions on how to fly, so even basic practices like not crashing into the ground were things it had to learn through trial and error.
This type of ‘training’ has advantages – algorithms can come up with moves that humans have never thought of, or use maneuvers humans would not choose to utilize. In the Go matches between Lee SeDol and AlphaGo, the AI made a move on turn 37, in game two, that shocked the audience and SeDol. Fan Hui, a three-time European Go champion and spectator of the match said, “It’s not a human move. I’ve never seen a human play this move.” It is possible that the move had never been played before in the history of the game. In the AlphaDogfight competition, the AI favored aggressive head-on gun attacks. This tactic is considered high-risk and prohibited in training. Most pilots wouldn’t attempt it in combat. But an AI could. AI algorithms can develop and employ maneuvers that human pilots wouldn’t think of or wouldn’t attempt. They can be especially unpredictable in combat against humans because they aren’t human.
An AI also offers significant advantages over humans in piloting an aircraft because it is not limited by biology. An AI can make decisions in fractions of a second and simultaneously receive input from any number of sensors. It never has to move its eyes or turn its head to get a better look. In high-speed combat where margins are measured in seconds or less, this speed matters. An AI also never gets tired – it is immune to the human factors of being a pilot. It is impervious to emotion, mental stress, and arguably the most critical inhibitor, the biological stresses of high-G maneuvers. Human pilots have a limit to their continuous high-G maneuver endurance. In the AlphaDogfight, both the AI and “Banger,” the human pilot, spent several minutes in continuous high-G maneuvers. While high G-maneuvers would be fine for an AI, real combat would likely induce loss of consciousness or G-LOC for human pilots.
Design and Mission Profiles
Aircraft, apart from remotely piloted aircraft (RPAs), are designed with a human pilot in mind. It is inherent to the platform that it will have to carry a human pilot and devote space and systems to all the necessary life support functions. Many of the maximum tolerances the aircraft can withstand are bottlenecked not by the aircraft itself, but to its pilot. An unmanned aircraft do not have to worry about protecting a human pilot or carrying one. It can be designed solely for the mission.
Aviation missions are also limited to the endurance of human pilots, where there is a finite number of hours a human can remain combat effective in a cockpit. Using unmanned aircraft changes that equation so that the limit is the capabilities of the aircraft and systems itself. Like surveillance drones, AI-piloted aircraft could remain on station for much longer than human piloted aircraft and (with air-to-air refueling) possibly for days.
The future operating environment will be less and less forgiving for human pilots. Decisions will be made at computational speed which outpaces a human OODA loop. Missiles will fly at hypersonic speeds and directed energy weapons will strike targets at the speed of light. Lockheed Martin has set a goal for mounting lasers on fighter jets by 2025. Autonomous aircraft piloted by AI will have distinct advantages in the future operating environment because of the quickness of its ability to react and the indefinite sustainment of that reaction speed. The Navy designed the Phalanx system to be autonomous in the 1970s and embedded doctrine statements into the Aegis combat system because it did not believe that humans could react fast enough in the missile age threat environment. The future will be even more unforgiving with a hypersonic threat environment and decisions made at the speed of AI that will often trump those made at human speeds in combat.
Unmanned aircraft are also inherently more “risk worthy” than manned aircraft. Commanders with unmanned aircraft can take greater risks and plan more aggressive missions that would have featured an unacceptably low probability of return for manned missions. This increased flexibility will be essential in rolling back and dismantling modern air defenses and anti-access, area-denial networks.
Unmanned is Already Here
The U.S. military already flies hundreds of large RPAs like the MQ-9 Predator and thousands of smaller RPAs like the RQ-11 Raven. It uses these aircraft for reconnaissance, surveillance, targeting, and strike. The Marine Corps has flown unmanned cargo helicopters in Afghanistan and other cargo-carrying RPAs and autonomous aircraft have proliferated in the private sector. These aircraft have been displacing human pilots in the cockpit for decades with human pilots now operating from the ground. The dramatic proliferation of unmanned aircraft over the last two decades has touched every major military and conflict zone. Even terrorists and non-state actors are leveraging unmanned aircraft for both surveillance and strike.
Apart from NGAD, the Navy is going full speed ahead on unmanned and autonomous vehicles. Last year it awarded a $330 million dollar contract for a medium-sized autonomous vessel. In early 2021, the Navy plans to run a large Fleet Battle Problem exercise centered on unmanned vessels. The Navy has also begun to supplement its MH-60S squadrons with the unmanned MQ-8B. Chief among its advantages over the manned helicopter is the long on-station time. The Navy continues to invest in its unmanned MQ-4C maritime surveillance drones and has now flight-tested the unmanned MQ-25 Stingray aerial tanker. In fact, the Navy has so aggressively pursued unmanned and autonomous vehicles that Congress has tried to slow down its speed of adoption and restrict some funding.