Content Triage, as used by LAS, refers to the process of finding information of intelligence value within potentially very large data sets. The projects under Content Triage will address data initiatives that aim to improve analysts’ ability to efficiently and effectively search, explore, prioritize, retain and extract value from an ever-increasing amount of data in various formats.
Human-Machine Teaming helps analysts gather data and produce intelligence on adversaries’ actions, intentions, and capabilities so decision-makers can make plans, decisions, and execute policies and operations. Projects in this space focus on building models of what analysts do, identification of analyst tasks that are appropriate for enhancements, and the development of interventions, or identification of pre-existing analytics that could be used in new ways to enhance analyst tradecraft.
The Machine Learning research theme is all about the LAS strategic goal of advancing the science of machine learning (ML) and operationalizing it for intelligence analysts. How can practical applications of ML be used to address challenges that mission analysts face now, or will face in the future?
Academic and Enterprise Engagement
The projects under the Academic and Enterprise Engagement (AEE) theme aim to address the LAS strategic goals of building and sustaining community as well as addressing operational needs through direct collaboration with mission and academic partners.