The Laboratory for Analytic Sciences’ research themes focus on using data to improve intelligence analysis. Some of the questions arise from properties of the data—they might be large, streaming, or heterogeneous—while others arise from applications specific issues.

How can technology help intelligence analysts determine what’s important in order to effectively perform their analysis?

Our sensemaking research focuses on triaging and prioritizing content. Sensemaking application areas include:

• Video Sensemaking helps analysts understand vast amounts of video data by making video content searchable, summarizing it, and alerting users to moments of interest, all while incorporating user feedback to improve the systems.
• Audio Sensemaking involves the methods and technologies used by language analysts to extract valuable intelligence from complex, real-world foreign-language audio. This process addresses significant challenges such as unwanted background noise, a multitude of speaking styles, subtle cultural nuances, and the sheer volume of data, all while incorporating user feedback to improve the systems.

Sensemaking Project Examples

How can we decrease the costs of machine learning? Where can AI/ML give analysts a radical strategic advantage? 

Our research focuses on understanding and evaluating the capabilities and behavior of artificial intelligence/machine learning (AI/ML) technologies, to facilitate their appropriate integration into operational intelligence environments. Application areas include:

• AI Assessment and Evaluation involves testing and comparing AI solutions using standardized methods to evaluate their performance, capabilities, and reliability, ultimately informing decisions about which AI models to use in various intelligence analysis applications.

Recent Project Examples

How can technology help intelligence analysts lighten their cognitive load? 

Human-centered AI projects focus on improving how analysts partner effectively with automation, particularly by exploring novel user experience designs that integrate state-of-the-art AI and ML capabilities. Application areas include:

• AI-Enabled Workflows research examines efforts to build trust and collaboration between humans and AI by addressing the common pitfalls of over- or under-reliance on automated tools through enhanced credibility, strengthened evaluation approaches, and transparent communication of performance and uncertainty.
• Agentic AI research involves developing AI systems that can make decisions, perform tasks, and integrate with diverse information systems with minimal human input, aiming to enhance the efficiency of intelligence analysis while ensuring strict adherence to accuracy, objectivity, and compliance standards.

Human-Centered AI Project Examples

Human-centered AI projects demonstrate ways to address mission challenges related to enhancing the effectiveness of human analysts working with automated technology.