Anthony JAcques

Key Findings Because of the growing demands and changing nature of the SDA mission, AI/ML tools have a high opportunity for impact if they can be force multipliers for SDA operators. AI/ML tools cannot address all the challenges of the SDA mission, but process changes can help these tools achieve greater impact. Realizing significant impacts from AI/ML requires moving to an architecture that enables more AI/ML development and fielding.

Key Findings Neural networks are capable approximators for complex nonlinear functions. Prediction and classification capabilities can be applied to SDA mission processes with standard and Bayesian neural networks. AI/ML tool developers should focus on tools that are compatible with the operational SDA architecture, data infrastructure, and processes.

This research was prepared for the United States Army and conducted within RAND Arroyo Center’s Forces and Logistics Program. This report is part of the RAND research report series. RAND reports present research findings and objective analysis that address the challenges facing the public and private sectors. All RAND reports undergo rigorous peer review to ensure high standards for research quality and objectivity. This document and trademark(s) contained herein are protected by law.