AI progress is creating a growing range of risks and opportunities, but it is often unclear how
they should be navigated. In many cases, the barriers and uncertainties faced are at least …

The sigmoid function is a widely used nonlinear activation function in neural networks. In this
article, we present a modular approximation methodology for efficient fixed-point hardware …

Neural network approximations have become attractive to compress data for automation and
autonomy algorithms for use on storage-limited and processing-limited aerospace …

Advanced capabilities planned for the next generation of autonomous and increasingly
autonomous air vehicles will include non-traditional components based on artificial …

The design of aircraft collision avoidance algorithms is a subtle but important challenge that
merits the need for provable safety guarantees. Obtaining such guarantees is nontrivial …

In this case study, we have explored the use of a neural network model checker to analyze
the safety characteristics of a neural network trained using reinforcement learning to …

Neural networks are vulnerable to adversarial attacks, ie, small input perturbations can
result in substantially different outputs of a neural network. Safety-critical environments …

While Artificial Intelligence (AI) has become an important asset in many areas of science and
technology, safety is often not treated as important as required for aviation. Neglecting safety …

Abstract The integration of Unmanned Aircraft Systems (UAS) in the National Airspace
System (NAS) for Urban Air Mobility (UAM) operations will create the need to develop …

Every successful space mission begins with a successful launch. As space access costs
lower and launch efficiencies progress, launch tempos are increased creating more access …