Machine Learning and Gene Editing at the Helm of a Societal Evolution.

The integration of artificial intelligence (AI) and biotechnology, whilst in its infancy, presents significant opportunities and risks, and proactive policy is needed to manage these emerging technologies. Whilst AI continues to have significant and broad impact, its relevance and complexity magnify when integrated with other emerging technologies. The confluence of Machine Learning (ML), a subset of AI, with gene editing (GE) in particular can foster substantial benefits as well as daunting risks that range from ethics to national security.

Improving human-in-the-loop simulation to optimize soldier-systems integration.

Objective: To identify requirements for human-in-the-loop simulation capabilities and improve their utility in predicting and optimizing soldier-systems integration.

Background: Technological development rates within the military are rapidly increasing. Emergent technologies often exclude in-depth consideration of human-system interactions until the physical prototyping phase.

Neural network for regression problems with reduced training sets.

Although they are powerful and successful in many applications, artificial neural networks (ANNs) typically do not perform well with complex problems that have a limited number of training cases. Often, collecting additional training data may not be feasible or may be costly. Thus, this work presents a new radial-basis network (RBN) design that overcomes the limitations of using ANNs to accurately model regression problems with minimal training data.