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Defense Advanced Research Projects AgencyProgram Information

Active Interpretation of Disparate Alternatives (AIDA)

Dr. Boyan Onyshkevych

The United States Government has an interest in developing and maintaining a strategic understanding of events, situations, and trends around the world, in a variety of domains. The information used in developing this understanding comes from many disparate sources, in a variety of genres, and data types, and as a mixture of structured and unstructured data. Unstructured data can include text or speech in English and a variety of other languages, as well as images, videos, and other sensor information. Even structured sources can vary in the expressiveness, semantics, and specificity of their representations. Moreover, analysis is complicated by the need to overcome the noisy, conflicting, and potentially intentionally deceptive nature of the data.

It is a challenge for those who strive to achieve and maintain an understanding of these events, situations, and trends that information from each medium is often analyzed independently, without the context provided by information from other media. Often, each independent analysis results in only one interpretation, with alternatives being eliminated due to lack of evidence, even in the absence of contradictory evidence. When these independent, impoverished analyses are combined, generally late in the analysis process, the result can be a single apparent consensus view that does not reflect a true consensus.

The goal of Active Interpretation of Disparate Alternatives (AIDA) is to develop a multihypothesis semantic engine that generates explicit alternative interpretations of events, situations, and trends from a variety of unstructured sources, for use in noisy, conflicting, and potentially deceptive information environments. This engine must be capable of mapping knowledge elements automatically derived from multiple media sources into a common semantic representation, aggregating information derived from those sources, and generating and exploring multiple hypotheses about the events, situations, and trends of interest. This engine must establish confidence measures for the derived knowledge and hypotheses, based on the accuracy of the analysis and the coherence of the semantic representation of each hypothesis. This engine must also be capable of utilizing knowledge in the common semantic representation and the generated hypotheses as alternate contexts for the media analysis algorithms by altering their models or prior probabilities to enhance accuracy and resolve ambiguities in line with expectations from the context. In addition, the engine must be able to communicate with its user to reveal the generated hypotheses and to allow the user to alter the hypotheses or to suggest new ones.

Tags

| AI | Analytics | Autonomy | Data | Imagery | Language |

 

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Selected DARPA Achievements

DARPA collaborated with industry on stealth technology.
DARPA’s Stealth Revolution
In the early days of DARPA’s work on stealth technology, Have Blue, a prototype of what would become the F-117A, first flew successfully in 1977. The success of the F-117A program marked the beginning of the stealth revolution, which has had enormous benefits for national security.
DARPA microelectronics gave rise to today's GPS devices.
Navigation in the Palm of Your Hand
Early GPS receivers were bulky, heavy devices. In 1983, DARPA set out to miniaturize them, leading to a much broader adoption of GPS capability.
First rough conceptual design of the ARPANET.
Paving the Way to the Modern Internet
ARPA research played a central role in launching the Information Revolution. The agency developed and furthered much of the conceptual basis for the ARPANET—prototypical communications network launched nearly half a century ago—and invented the digital protocols that gave birth to the Internet.
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