As ever more sensors are deployed via the military internet of things (MIoT) and publicly available information (PAI) sources, commanders and their associated intelligence staffs will soon be overwhelmed with data. Failure to triage and operationally leverage this data deluge could ultimately have catastrophic results for the Air Force and its situational awareness. Current intelligence data review practices are as outdated as they are inefficient. Generally, two or more analysts must separately review and categorize potentially relevant data in order to limit systemic bias and subjectivity. However, todayâs artificial intelligence (AI) innovations are rapidly improving the information corroboration process. The modern analyst team is ideally human-controlled and machine-enabled, and machine learning (ML) is employed to better understand contextual relationships between data records. This symbiotic relationship allows the user to acquire deeper insights that support key organizational objectives. However, even todayâs âstate-of-the-artâ ML algorithms have limitations. While most commercially available qualitative data analysis tools can mine through numerous unique sources in nearly every major foreign language, their ML processes do not reliably deconflict newly discovered data records with known information. Polysentry, a provider of software solutions for both defense and commercial customers, gives decision makers the ability to quickly identify correlations and patterns in unstructured intelligence reports and data records that would have otherwise gone unidentified. With minor adaptations to Polysentryâs commercial technology, defense end-users will gain the ability to discover and extract quantifiable insights from large volumes of complex qualitative data, and then rapidly analyze those outputs to identify previously unseen strategic
