IARPA's mission is to promote high-risk, high-payoff research that has the potential to enhance the performance of IC activities. IARPA’s use of a challenge to stimulate breakthroughs in science and technology also supports the White House’s Strategy for American Innovation, as well as government transparency and efficiency.
Can you build algorithms to classify facility, building, and land use from satellite imagery? The functional Map of the World (fMoW) Challenge invites solvers from around the world to develop deep learning and other automated techniques to classify points of interest from satellite imagery. The goal of the challenge is to promote research in object identification and classification to automatically identify facility, building, and land use.
Imagine if there was a way to identify an unknown substance in real-time, by simply pointing a scanner at the surface in which the substance is located. Chemicals, or areas containing potential chemical residues, could be scanned at standoff range and the substance identified on the spot. Such a development would enable expedited results in time sensitive situations and “one hundred percent” screening for security or process control. The Modeling of Reflectance Given Only Transmission of High-concentration Spectra for Chemical Recognition Over Widely-varying eNvironments (MORGOTH’S CROWN) Challenge seeks development of algorithms that would help accomplish just that! This summer, the MORGOTH’S CROWN Challenge invites participants from around the world to develop algorithms to predict changes in a chemical’s infrared (IR) spectrum caused by changes in its molecular environment. Participants will join a global community working to benchmark research and foster innovation in IR spectral prediction through crowdsourcing.
Have you developed software to identity faces in general web photographs? Can your software verify that a face in one photograph is the same as in another? The Intelligence Advanced Research Projects Activity (IARPA), within the Office of the Director of National Intelligence (ODNI), announces the launch of the Face Recognition Prize Challenge. The challenge aims to improve biometric face recognition by improving core face recognition accuracy.
Can you build the best autonomous nail to nail fingerprint capture device? The Intelligence Advanced Research Projects Activity (IARPA), within the Office of the Director of National Intelligence (ODNI), announced the launch of the Nail to Nail (N2N) Fingerprint Grand Challenge. The challenge aims to improve live and forensic biometric fingerprint recognition by improving biometric fingerprint collection and recognition systems by eliminating plain fingerprint captures.
Can you develop the Multi-View Stereo (MVS) 3D mapping algorithm for commercial satellite imagery with the best accuracy and completeness? The 3D Mapping Challenge in July 2016 invites Solvers from around the world to generate an algorithm to convert high-resolution satellite images to 3D point clouds. Participants will join a global community working to benchmark research and foster innovation of 3D point clouds through crowdsourcing.
The winners of the ASpIRE Challenge have been announced. For more information, click here.
IARPA’s Automatic Speech recognition In Reverberant Environments (ASpIRE) Challenge is seeking that grail.
WASHINGTON – The Intelligence Advanced Research Projects Activity (IARPA), within the Office of the Director of National Intelligence (ODNI), announced today the winner of its first public challenge contest, Investigating Novel Statistical Techniques to Identify Neurophysiological Correlates of Trustworthiness (INSTINCT).
The winners of the INSTINCT Challenge have been announced! For more information, click here.
How do you know if you can trust someone? Answering this question accurately is essential for society in general—but particularly so in the Intelligence Community (IC), where knowing whom to trust is often vital.
Who We Are: The Intelligence Advanced Research Projects Activity (IARPA) TRUST program attempts to learn whether one’s own neural, psychological, physiological, and behavioral signals can reflect, and predict, a partner’s trustworthiness.