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The Intelligence Advanced Research Projects Activity (IARPA) is an organization within the Office of the Director of National Intelligence responsible for leading research to overcome difficult challenges relevant to the United States Intelligence Community. IARPA characterizes its mission as follows: "To envision and lead high-risk, high-payoff research that delivers innovative technology for future overwhelming intelligence advantage."
IARPA funds academic and industry research across a broad range of technical areas, including mathematics, computer science, physics, chemistry, biology, neuroscience, linguistics, political science, and cognitive psychology. Most IARPA research is unclassified and openly published. IARPA transfers successful research results and technologies to other government agencies. Notable IARPA investments include quantum computing, superconducting computing, machine learning, and forecasting tournaments.
IARPA characterizes its mission as follows:
To envision and lead high-risk, high-payoff research that delivers innovative technology for future overwhelming intelligence advantage.
In 1958, the first Advanced Research Projects Agency, or ARPA, was created in response to an unanticipated surprise--the Soviet Union's successful launch of Sputnik on October 4, 1957. The ARPA model was designed to anticipate and pre-empt technological surprise. As then-Secretary of Defense Neil McElroy said, "I want an agency that makes sure no important thing remains undone because it doesn't fit somebody's mission." The ARPA model has been characterized by ambitious technical goals, competitively awarded research led by term-limited staff, and independent testing and evaluation.
IARPA invests in multi-year research programs, in which academic and industry teams compete to solve a well-defined set of technical problems, regularly scored on a shared set of metrics and milestones. Each program is led by an IARPA Program Manager (PM) who is a term-limited Government employee. IARPA programs are meant to enable researchers to pursue ideas that are potentially disruptive to the status quo.
Most IARPA research is unclassified and openly published. Former director Jason Matheny has stated the agency's goals of openness and external engagement to draw in expertise from academia and industry, or even individuals who "might be working in their basement on some data-science project and might have an idea for how to solve an important problem". IARPA transfers successful research results and technologies to other government agencies.
IARPA is known for its programs to fund research into anticipatory intelligence, using data science to make predictions about future events ranging from political elections to disease outbreaks to cyberattacks, some of which focus on open-source intelligence. IARPA has pursued these objectives not only through traditional funding programs but also through tournaments and prizes. c is an example of one such program. Other projects involve analysis of images or video that lacks metadata by directly analyzing the media's content itself. Examples given by IARPA include determining the location of an image by analyzing features such as placement of trees or a mountain skyline, or determining whether a video is of a baseball game or a traffic jam. Another program focuses on developing speech recognition tools that can transcribe arbitrary languages.
Below are some of the past and current research programs of IARPA.
Aggregative Contingent Estimation (ACE) Program aimed "to dramatically enhance the accuracy, precision, and timeliness of intelligence forecasts for a broad range of event types, through the development of advanced techniques that elicit, weight, and combine the judgments of many intelligence analysts."
ATHENA Program was a research program about cybersecurity. It aimed "to provide an early warning system for detecting precursors to cyberattacks".
Babel Program developed "agile and robust speech recognition technology that can be rapidly applied to any human language in order to provide effective search capability for analysts to efficiently process massive amounts of real-world recorded speech". The program tries to develop a software that can transcribe and search among all the languages.
Biometrics Exploitation Science & Technology (BEST) Program focused on "significantly advance the state-of-the-science for biometrics technologies". It was to discover techniques on utilising biometric from a subject in a less controlled environment which could produce a similar result to that in a controlled environment.
Synthetic Holographic Observation (SHO) Program's stated goal was "to enable full-parallax, full-color, high-resolution display of dynamic 3D data without head-gear, and possessing visually continuous perspectives without artifacts over wide viewing angles."
Creation of Operationally Realistic 3-D Environments (CORE3D) aims to "develop rapid automated systems for 3-D models which are designed with complex physical properties and automated methods that will pull commercial, satellite, and airborne imagery."
Crowdsourcing Evidence, Argumentation, Thinking and Evaluation (CREATE) Program is about "to develop, and experimentally test, systems that use crowdsourcing and structured analytic techniques to improve analytic reasoning". It hopes to improve the intelligence community's ability on better understanding evidence and sources in order to produce accurate information.
Deep Intermodal Video Analytics (DIVA) aims to "advance state-of-the-art artificial visual perception, and automate video monitoring."
Functional Genomic and Computational Assessment of Threats (Fun GCAT) aims to "develop next-generation biological data tools to improve DNA sequence screening, augment biodefense capabilities through the characterization of threats, and advance our understanding of the relative risks posed by unknown sequences."
Hybrid Forecasting Competition (HFC) aims to "aims to improve accuracy in predicting worldwide geopolitical issues, including foreign political elections, interstate conflict, disease outbreaks, and economic indicators by leveraging the relative strengths of humans and machines."
Machine Translation for English Retrieval of Information in Any Language (MATERIAL) aims to "develop and deploy fully automatic systems that will allow English-only speakers to accurately and efficiently identify foreign language documents of interest."
Molecular Analyzer for Efficient Gas-phase Low-power Interrogation (MAEGLIN) aims to "develop a compact system capable of unattended environmental sampling and chemical identification with minimal (preferably no) consumables."
Multimodal Objective Sensing to Assess Individuals with Context (MOSAIC) Program aims to develop "unobtrusive, passive, and persistent measurement to predict an individual's job performance". It designs and tests sensors which can collect data about monitoring employees' work performance.
Rapid Analysis of Various Emerging Nano-electronics (RAVEN) aims to "develop tools to rapidly image current and future integrated circuit chips."
^ abO'Connell, A. D.; Hofheinz, M.; Ansmann, M.; Bialczak, Radoslaw C.; Lenander, M.; Lucero, Erik; Neeley, M.; Sank, D.; Wang, H. (April 2010). "Quantum ground state and single-phonon control of a mechanical resonator". Nature. 464 (7289): 697-703. Bibcode:2010Natur.464..697O. doi:10.1038/nature08967. PMID20237473.