Defense Advanced Research Projects AgencyTagged Content List

Analytics for Data at Massive Scales

Extracting information from large data sets

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The U.S. government has always had an interest in developing and maintaining a strategic understanding of events, situations, and trends around the world. In recent years, however, information complexity has exceeded the capacity of analysts to glean meaningful or actionable insights as data pours in from disparate sources, across a variety of genres, and a mixture of structured and unstructured forms, from military intelligence to social media to accurate and inaccurate news.
The social sciences can play important roles in assisting military planners and decision-makers who are trying to understand complex human social behaviors and systems, potentially facilitating a wide range of missions including humanitarian, stability, and counter-insurgency operations. Current social science approaches to studying behavior rely on a variety of modeling methods—both qualitative and quantitative—which seek to make inferences about the causes of social phenomena on the basis of observations in the real-world.
Advances in artificial intelligence (AI) are making virtual and robotic assistants increasingly capable in performing complex tasks. For these “smart” machines to be considered safe and trustworthy collaborators with human partners, however, robots must be able to quickly assess a given situation and apply human social norms. Such norms are intuitively obvious to most people—for example, the result of growing up in a society where subtle or not-so-subtle cues are provided from childhood about how to appropriately behave in a group setting or respond to interpersonal situations. But teaching those rules to robots is a novel challenge.
A mantra of these data-rife times is that within the vast and growing volumes of diverse data types, such as sensor feeds, economic indicators, and scientific and environmental measurements, are dots of significance that can tell important stories, if only those dots could be identified and connected in authentically meaningful ways. Getting good at that exercise of data synthesis and interpretation ought to open new, quicker routes to identifying threats, tracking disease outbreaks, and otherwise answering questions and solving problems that previously were intractable.
DARPA published its Young Faculty Award (YFA) 2018 Research Announcement today, seeking proposals in 26 different topic areas—the largest number of YFA research areas ever solicited.