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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The rapid pace of new commercial satellite constellation launches has led to a significant increase in the amount and availability of geospatial imagery. Unfortunately, no straightforward way currently exists for analysts to access and analyze all of that imagery. The current ad hoc, time-intensive approach requires gathering and curating data from a large number of available sources, downloading it to specific locations, and running it through separate suites of analytics tools.
The growing complexity of space operations coupled with an increased need for timely decisions demands innovative approaches to battle management command and control (BMC2) technologies. To help ensure future U.S. technological and strategic superiority, DARPA’s Hallmark program seeks to develop revolutionary tools and technologies to plan, assess, and execute U.S. military operations in space. The program has completed initial research and awarded Phase 1 contracts to 11 organizations, which both augment existing commercial technologies and pursue entirely new capabilities. Hallmark has released a Broad Agency Announcement seeking additional technologies for potential inclusion.
Chemical innovation plays a key role in developing cutting-edge technologies for the military. Research chemists design and synthesize new molecules that could enable a slew of next-generation military products, such as novel propellants for spacecraft engines; new pharmaceuticals and medicines for troops in the field; lighter and longer-lasting batteries and fuel cells; advanced adhesives, coatings and paints; and less expensive explosives that are safer to handle. The problem, however, is that existing molecule design and production methods rely primarily on experts’ intuition in a laborious, trial-and-error research process.
Machine learning (ML) systems today learn by example, ingesting tons of data that has been individually labeled by human analysts to generate a desired output. As these systems have progressed, deep neural networks (DNN) have emerged as the state of the art in ML models. DNN are capable of powering tasks like machine translation and speech or object recognition with a much higher degree of accuracy. However, training DNN requires massive amounts of labeled data–typically 109 or 1010 training examples. The process of amassing and labeling this mountain of information is costly and time consuming.
Over its 60-year history, DARPA has played a leading role in the creation and advancement of artificial intelligence (AI) technologies that have produced game-changing capabilities for the Department of Defense. Starting in the 1960s, DARPA research shaped the first wave of AI technologies, which focused on handcrafted knowledge, or rule-based systems capable of narrowly defined tasks. While a critical step forward for the field, these systems were fragile and limited.