Defense Advanced Research Projects AgencyTagged Content List

Analytics for Data at Massive Scales

Extracting information from large data sets

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Warfighters encounter foreign language images in many forms, including captured paper documents and computer files. Given the quantity of foreign-language material and the scarcity of linguists, military personnel and analysts can find it difficult to identify, translate and interpret important information in a timely fashion. What these personnel and analysts have lacked to date is the capability to automatically and rapidly convert foreign-language text images into English transcripts that provide relevant, distilled and actionable information.
From phony news on Web sites to terrorist propaganda on social media to recruitment videos posted by extremists, conflict in the information domain is becoming a ubiquitous addition to traditional battlespaces. Given the pace of growth in social media and other networked communications, this bustling domain of words and images—once relegated to the sidelines of strategic planning—is poised to become ever more critical to national security and military success around the globe.
The goal of the RADICS program is to develop innovative technologies for detecting and responding to cyber-attacks on U.S. critical infrastructure, especially those parts essential to DoD mission effectiveness. DARPA is interested specifically in early warning of impending attacks, situation awareness, network isolation and threat characterization in response to a widespread and persistent cyber-attack on the power grid and its dependent systems.
With the spread of blogs, social networking sites and media-sharing technology, and the rapid propagation of ideas enabled by these advances, the conditions under which the nation’s military forces conduct operations are changing nearly as fast as the speed of thought. DARPA has an interest in addressing this new dynamic and understanding how social network communication affects events on the ground as part of its mission of preventing strategic surprise.
The Synergistic Discovery and Design (SD2) program aims to develop data-driven methods to accelerate scientific discovery and robust design in domains that lack complete models. Engineers regularly use high-fidelity simulations to create robust designs in complex domains such as aeronautics, automobiles, and integrated circuits. In contrast, robust design remains elusive in domains such as synthetic biology, neuro-computation, and polymer chemistry due to the lack of high-fidelity models. SD2 seeks to develop tools to enable robust design despite the lack of complete scientific models.