Artificial Intelligence/Machine Learning

The objective of this research topic is to explore Boyd’s Observe, Orient, Decide and Act Loop with the goal of finding disruptive courses of action in a multi-domain environment that allow warfighters to impact both the rate of engagement with a competitor, but also the rhythm of engagement that allow our commanders and warfighters to leverage both the complexity and dynamism inherent in a multi-domain operation to create decisive wins through strategic surprise.

The proposed SBIR topic aims to advance the capabilities of large language models by addressing critical challenges and enhancing functionalities relevant to military applications, particularly within the U.S. Army.

This Army SBIR solicitation is for artificial intelligence and machine learning-driven methodologies that can control the production processes for Nano-Nitramines. The Army seeks to produce the most efficient, effective formulations currently known in an agile and flexible manner. AI/ML-driven manufacturing and formulation science can apply “cradle to grave” for Nano-Nitramines, and can enable their widespread adoption by making research and production automated.

With the increasing availability of digital imagery, including satellite data for electro-optical/infrared, synthetic aperture radar and full-motion video, there is a growing need for automated methods to efficiently process and analyze vast amounts of multi-modal data.

Starting with how the U.S. Army develops Acquisition Requirements Packages, small businesses must develop and deliver a state-of-art, artificial intelligence and machine learning-enabled system to modernize and automate Army acquisition processes.