Examining Redundancy in the Context of Safe Machine Learning

This paper describes a set of experiments with neural network classifiers on the MNIST database of digits. The purpose is to investigate na\"ive implementations of redundant architectures as a first step towards safe and dependable machine learning. We report on a set of measurements using the MNIST database which ultimately serve to underline the expected difficulties in using NN classifiers in safe and dependable systems.

Conceptual Design of Human-Drone Communication in Collaborative Environments

Autonomous robots and drones will work collaboratively and cooperatively in tomorrow's industry and agriculture. Before this becomes a reality, some form of standardised communication between man and machine must be established that specifically facilitates communication between autonomous machines and both trained and untrained human actors in the working environment. We present preliminary results on a human-drone and a drone-human language situated in the agricultural industry where interactions with trained and untrained workers and visitors can be expected. We present basic visual indicators enhanced with flight patterns for drone-human interaction and human signaling based on aircraft marshaling for humane-drone interaction. We discuss preliminary results on image recognition and future work.