IARPA in the News 2014

The Hill

A top spy agency is launching a challenge contest “to advance understanding of human interactions that involve trust and trustworthiness.”

The effort announced by the Office of the Director of National Intelligence on Thursday is meant to help the agency determine who can be trusted for intelligence missions.

Defense One

There’s a new website in town that looks to crowdsource predictions about everything from drone delivery to the future price of BitCoins. The SciCast site, which researchers at George Mason University launched in December, allows users from around the world to make predictions and pose questions in order to forecast possible future events and technological breakthroughs. And it’s funded by the Director of National Intelligence’s Intelligence Advanced Research Projects Activity, or IARPA.

USA Today

Researchers tracking social media and Web searches have detected outbreaks of the flu and rare diseases in Latin America by up to two weeks before they were reported by local news media or government health agencies, a U.S. intelligence official told USA TODAY.

Working at a series of universities and companies around the country, the researchers are part of a program led by the Intelligence Advanced Research Projects Agency (IARPA) that is aimed at anticipating critical societal events, such as disease outbreaks, violent uprisings or economic crises before they appear in the news.

Geospatial Intelligence Forum Magazine

In the commercial world, retailers apply predictive analytics to customer behavior in order to maximize sales and profits, crunching data to find the right time to make the right offer to the right customer. Retailers have such a rich trove of historical customer data that, with the use of the latest technology, consumer behavior can be predicted with considerable accuracy.

C4ISR & Networks

The Intelligence Advanced Research Projects Activity (IARPA) wants to correct drift in its intelligence forecasting models.

In a new request for information, IARPA seeks methods to automatically detect and correct drift in forecasting models. Suitable methods would update models to take into account more historical data or current events, or when the assumptions underpinning the model change, such as contagion assumptions for an epidemiological model.