Defense Advanced Research Projects AgencyTagged Content List

Data Analysis at Massive Scales

Extracting information and insights from massive datasets; "big data"; "data mining"

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May 1, 2018,
CENTRA Technology Incorporated Conference Center
DARPA’s Tactical Technology Office is hosting a Proposers Day to provide information to potential applicants on the structure and objectives of the new Urban Reconnaissance through Supervised Autonomy (URSA) program. URSA aims to develop technology to enable autonomous systems operated and supervised by U.S. ground forces to detect hostile forces and establish positive identification of combatants before U.S. troops encounter them. The URSA program seeks to overcome the inherent complexity of the urban environment by combining new knowledge about human behaviors, autonomy algorithms, integrated sensors, multiple sensor modalities, and measurable human responses to discriminate the subtle differences between hostile individuals and noncombatants.
Efficient discovery and production of new molecules is essential to realize capabilities across the DoD, from simulants and medicines essential to counter emerging threats, to coatings, dyes and specialty fuels needed for advanced performance.
The United States Government has an interest in developing and maintaining a strategic understanding of events, situations, and trends around the world, in a variety of domains. The information used in developing this understanding comes from many disparate sources, in a variety of genres, and data types, and as a mixture of structured and unstructured data. Unstructured data can include text or speech in English and a variety of other languages, as well as images, videos, and other sensor information.
The Anomaly Detection at Multiple Scales (ADAMS) program creates, adapts and applies technology to anomaly characterization and detection in massive data sets. Anomalies in data cue the collection of additional, actionable information in a wide variety of real world contexts. The initial application domain is insider threat detection in which malevolent (or possibly inadvertent) actions by a trusted individual are detected against a background of everyday network activity.
The Artificial Intelligence Research Associate (AIRA) program is part of a broad DAPRA initiative to develop and apply “Third Wave” AI technologies that are robust to sparse data and adversarial spoofing, and that incorporate domain-relevant knowledge through generative contextual and explanatory models.