Airborne Neural Network

Neural networks are the basic machine learning architecture behind Deep Learning models. Deep Learning is at the forefront of Artificial Intelligence systems which is solving unsolved complex problems and providing path breaking innovation due to its ability to learn on its own, just like a human brain. More breakthroughs are expected with Neural networks as the computing infrastructural performance capacity further increases. For example, the recent breakthrough in Generative AI is based on Deep Learning models which have been trained on huge amounts of data on enormous infrastructure. However, if there is a need to train and run a Deep Learning system on huge compute infrastructure in Aerospace without tolerance for any delay and with lots of data being acquired continuously on the fly then currently there is no solution available. In future, having this capability to run large deep learning systems in Aerospace can help to create innovative solutions: 1. Increase the capacity of air traffic by establishing Airborne Air Traffic Control Systems which use Deep Learning models to direct each airborne vehicle 2. Process sensor data on the fly to do new findings and more accurate and fast weather predictions 3. Process imaging data on the fly to do new findings and more accurate geographical predictions 4. Process geospatial data on the fly to do new findings Many more kinds of innovative solutions can be built if the capacity to run large neural networks with large data can be achieved in Aerospace.