How Technology Improves Pilot Safety

How AI Is Revolutionizing Aviation Safety - A Founder's Perspective After taking a company from zero to billion-dollar valuation, I'm now applying that same innovation mindset to aviation through AI regulatory technology. Our product FAR/BOT is trained on federal aviation regulations and helps pilots like myself: ✅ Verify currency requirements after weather downtime ✅ Confirm aircraft airworthiness ✅ Navigate complex airspace regulations ✅ Streamline pre-flight preparation After two months grounded during Midwest winters, I used our AI solution to refresh my knowledge of FAA requirements and my Carbon Cub FX3's operating specs before taking to the skies again. This practical application of AI is just one example of how regulatory technology creates real safety benefits while reducing cognitive load for professionals across regulated industries. What other regulated fields do you think are prime for AI assistance? How might similar tools transform your industry? #AIinAviation #RegulatoryTech #PilotSafety #AviationTech #FutureOfFlight

5 years ago in safety history (24 JUN 2020) the plane saved the pilot. More often, the pilot saves the plane. A US Air Force F-22 Raptor flying out of Joint Base Elmendorf Richardson in Alaska (see stock photo inset below) was saved when AGCAS activated when pilot / plane were in a tight spot. This was the third save by AGCAS (according the Air Force safety center). Replacement cost for an F-22 is $143 - 350 million, and replacement cost for an F-22 pilot is $10 million. Automated ground collision avoidance system (AGCAS) is a software system that watches the ground, airspeed, and direction to anticipate controlled flight into terrain (CFIT). The system takes control from the pilot to return the aircraft to a safe flight trajectory. This F-22 had overbanked while flying in bad weather and was accelerating with nose down. The system activated at 13,520 ft and 600 mph. The plane recovered at 2,600 ft. This save was from spatial disorientation (Spatial-D). The system can also save from g-force induced loss of consciousness (GLOC). All work systems are socio-technical systems - there are people, methods, machines, and information working together to provide functions. The F-22 Raptor is just a more clear example of people and machine than most examples from power, transportation, manufacturing, and healthcare. Automation does not replace people in complex systems. Automation augments people. Use automation to perform repetitive ongoing tasks (like cruise control on a car), use people in the middle where expertise and judgment give best system reliability, and use these automated control systems as guardrails to crosscheck the people for the save (like automatic braking on a car). My ask this week of safety leaders in power, transportation, manufacturing, and healthcare is to practice Deference to Expertise. Let's ask our operators. Do our work systems treat people like they are the problem in the complex system? Or, do our work systems treat people like they are the solution to the complex system? Said differently, does our automation help or get in the way? And if the work systems are not helping - steer those ideas into local learning systems. Perhaps Mica Endsley, who worked on these systems for the Air Force and on autonomous vehicles, or Nancy Leveson, who worked on traffic alert and collision avoidance system (TCAS) in commercial aviation, could lend some insight? #safety #hro #derisking #safetyleadership

“MIT scientists have developed a deep learning system, Air-Guardian, designed to work in tandem with airplane pilots to enhance flight safety. This artificial intelligence (AI) copilot can detect when a human pilot overlooks a critical situation and intervene to prevent potential incidents. The backbone of Air-Guardian is a novel deep learning system known as Liquid Neural Networks (LNN), developed by the MIT Computer Science and Artificial Intelligence Lab (CSAIL). LNNs have already demonstrated their effectiveness in various fields. Their potential impact is significant, particularly in areas that require compute-efficient and explainable AI systems, where they might be a viable alternative to current popular deep learning models. Air-Guardian employs a unique method to enhance flight safety. It monitors both the human pilot’s attention and the AI’s focus, identifying instances where the two do not align. If the human pilot overlooks a critical aspect, the AI system steps in and takes control of that particular flight element. This human-in-the-loop system is designed to maintain the pilot’s control while allowing the AI to fill in gaps. “The idea is to design systems that can collaborate with humans. In cases when humans face challenges in order to take control of something, the AI can help. And for things that humans are good at, the humans can keep doing it,” said Ramin Hasani, AI scientist at MIT CSAIL and co-author of the Air-Guardian paper. Paper is here: https://lnkd.in/gu_kR-BR https://lnkd.in/gGaRr_kJ