Defense Advanced Research Projects AgencyTagged Content List

Data Analysis at Massive Scales

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

Showing 19 results for Data + Automation RSS
Dramatic success in machine learning has led to a torrent of Artificial Intelligence (AI) applications. Continued advances promise to produce autonomous systems that will perceive, learn, decide, and act on their own. However, the effectiveness of these systems is limited by the machine’s current inability to explain their decisions and actions to human users (Figure 1). The Department of Defense (DoD) is facing challenges that demand more intelligent, autonomous, and symbiotic systems. Explainable AI—especially explainable machine learning—will be essential if future warfighters are to understand, appropriately trust, and effectively manage an emerging generation of artificially intelligent machine partners.
The U.S. Government operates globally and frequently encounters so-called “low-resource” languages for which no automated human language technology capability exists. Historically, development of technology for automated exploitation of foreign language materials has required protracted effort and a large data investment. Current methods can require multiple years and tens of millions of dollars per language—mostly to construct translated or transcribed corpora.
The purpose of the Multi-Domain Analytics (MDA) program is to enable automated data analysis across networks at different security levels, without manually moving impracticably large amounts of data. Each network contains different sets of data, which must be correlated in order to create a comprehensive context.
Automation and artificial intelligence are revolutionizing discovery and production of functional molecules by enabling fast, reproducible experimentation and efficient property optimization. These capabilities have already made a significant impact on prevalent molecular classes, such as pharmaceuticals, but niche areas characterized by unique chemical space, limited literature precedence, and requirements for specialized experimental hardware have experienced relatively slow improvement. One such area, critical to national security, is energetics.
SDR and software development kits (SDK) such as GNU Radio exist as free and open source technologies that are widely used in research, industry, academia, government, and hobbyist environments to support both wireless communications research and real-world radio systems. However, even with high end multi-core x86 central processing units (CPU) there are adaptive radar, electronic warfare (EW), and communications applications that cannot be implemented onto SDR with a purely homogeneous CPU due to high latency and power consumption.