Defense Advanced Research Projects AgencyTagged Content List

Analytics for Data at Massive Scales

Extracting information from large data sets

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Bonnie Dorr (left), program manager in DARPA’s Information Innovation Office (I2O), shakes hands with Henry Kautz, past president of the Association for the Advancement of Artificial Intelligence (AAAI), upon her recent induction as an AAAI Fellow. Each year, AAAI bestows the lifetime honor of Fellow on only a handful of researchers for their exceptional leadership, research and service contributions to the field of artificial intelligence.
Today's web searches use a centralized, one-size-fits-all approach that searches the Internet with the same set of tools for all queries. While that model has been wildly successful commercially, it does not work well for many government use cases. For example, it still remains a largely manual process that does not save sessions, requires nearly exact input with one-at-a-time entry, and doesn't organize or aggregate results beyond a list of links. Moreover, common search practices miss information in the deep web—the parts of the web not indexed by standard commercial search engines—and ignore shared content across pages.
In every population that encounters an infectious organism, a few individuals prove to be resilient—unfazed by that pathogen because they are either resistant to it (their immune systems keep the pathogen from multiplying to dangerous levels) or tolerant (they don’t get as sick as they otherwise might despite carrying high pathogen loads). Conventional disease treatments such as antibiotics have almost exclusively sought to emulate natural resistance by keeping patients’ pathogen levels as low as possible.
The chikungunya virus (CHIKV) is quickly spreading through the Western Hemisphere; as of May 15, 2015, the Pan-American Health Organization (PAHO) had tallied close to 1.4 million suspected cases and more than 33,000 confirmed cases since the virus’ first appearance in the Americas in December 2013. Spread by mosquitoes, chikungunya is rarely fatal but can cause debilitating joint and muscle pain, fever, nausea, fatigue and rash, and poses a growing public health and national security risk. Governments and health organizations could take more effective proactive steps to limit the spread of CHIKV if they had accurate forecasts of where and when it would appear. But such predictions for CHIKV and other emerging infectious diseases remain beyond the reach of current modeling capabilities.
Popular search engines are great at finding answers for point-of-fact questions like the elevation of Mount Everest or current movies running at local theaters. They are not, however, very good at answering what-if or predictive questions—questions that depend on multiple variables, such as “What influences the stock market?” or “What are the major drivers of environmental stability?” In many cases that shortcoming is not for lack of relevant data. Rather, what’s missing are empirical models of complex processes that influence the behavior and impact of those data elements.