As the demand for autonomous capabilities surges, the USAF spearheads the development of next-generation unmanned aerial vehicles infused with AI intelligence to further the goals of the Collaborative Combat Aircraft program.
Enter the X-62 Variable In-Flight Stability Test Aircraft, a bespoke F-16 fighter jet originally used to test what would become the precursor to the F-22 Raptor’s thrust vectoring capability. The aircraft is providing the test bed necessary to make significant leaps toward integrating AI in kinetic systems.
The goal is to meld the expertise and unmatched skill of U.S. Air Force pilots with the computational reasoning and speed offered by AI, merging a human pilot’s intuition with algorithmic precision to change the face of air combat as we know it.
“Machine learning is different from more traditional, rules-based coding because rather than using “if-then” statements to make decisions, the machine learning algorithms are using robust statistical methods to discern patterns within massive data sets,” said Col. James Valpiani, the commandant of United States Air Force Test Pilot School at Edwards Air Force Base.
“The resulting patterns are not easy for humans to read, understand or predict how they'll perform once they're implemented in a real-world environment, and that leads to really hard questions about trust and responsibility, especially in the realm of combat autonomy. But these aren’t just issues that are specific to the Air Force, they apply in everyday life. Autonomous vehicles are using these same machine learning algorithms.”