The Defense Threat Reduction Agency wants information on companies, universities and other organizations working on artificial intelligence, machine learning and data science technologies that could help counter weapons of mass destruction and other emerging threats. DTRA intends to use AI, ML and data science tools to improve decision-making and situational awareness for countering WMD and supporting deterrence missions, automate the identification of CWMD and deterrence objects and activities and facilitate information delivery to meet warfighter operational needs, according to a request for information posted Friday. The technology interest areas outlined in the RFI include AI-enhanced modeling and simulation, natural language processing, computer vision, high performance computing and multiagent systems. The agency is seeking information on data analytics, cloud platforms for data transfer and harmonization, data storage and accessibility, automated data labeling and other data-related capabilities. DTRA has asked interested stakeholders to share information on other specific interest areas, including the detection of spectral emissions, sensor data integration, human/computer interface and extraction of actionable information from noisy data.

The project lead says that future troops may be deployed with watches or chest straps that could predict when they will get sick and how long it would take to recover. When U.S. Service members get ill at the last minute, it could cause serious consequences in regards to executing critical duties. To get ahead of the issues, the Defense Threat Reduction Agency (DTRA), leading health technology company Royal Phillips and the Defense Innovation Unit (DIU), launched a project to develop a technology that could predict whether a service member is getting sick 48 hours in advance. The project was launched 18 months ago and announced its completion on Oct 22. "By coupling large-scale data, with our experience in AI and remote patient monitoring with DTRA's drive for innovation, we were able to develop a highly predictive early-warning algorithm based on non-invasively collected biomarkers," Joe Frassica, chief medical officer and head of research for Philips North America, said in the release. Using 165 distinct biomarkers across 41,000 cases, the Phillips team created the Rapid Analysis of Threat Exposure (RATE) algorithm which is the "first large-scale empirical exploration of prediction of pre-symptomatic infection in humans."