Your system fuses ATC speech recognition, NLP, and ADS-B signals to detect and mitigate human error in air traffic control. Given the rapid advancements in multimodal AI, have you explored integrating visual data sources (e.g., satellite imagery, radar feeds, or airport surveillance cameras) to further improve situational awareness and error detection? What challenges do you foresee in making Yeager more contextually…
I believe scaling laws will hold as we start to feed all of this context data into an integrated model. You could imagine a deep-q style reinforcement learning model that ingests layers of structured and visual data and outputs alerts and eventually commands. The main challenge I foresee here will be observability... it's easy enough to shove a ton of data into a black box and get a good answer 98% of the time. But regulation is likely to require such a system to be highly observable/explainable so the human can keep up with what's going on and step in as needed.
Looking further into the future, it's plausible the concrete structures of today with humans looking out windows will be replaced with sensor packages atop a long flagpole that stream high-res optical/ir camera data, surface radar, weather information, etc into a control room with VR layers that help controllers stay on top of busier and busier airspace.