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Artificial Intelligence and Human-Computer Symbiosis Technologies

Technology to facilitate more intuitive interactions between humans and machines

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During the 1854 cholera epidemic in London, Dr. John Snow plotted cholera deaths on a map, and in the corner of a particularly hard-hit quadrangle of buildings was a water pump. Snow's maps, a 19th-century version of big data, suggested an association between cholera and the pump, but the germ theory of disease had not yet been invented and it took human ingenuity to realize that the pump was a causal mechanism of disease transmission.
| AI | Automation | Data |
Today is the grand opening of the Colosseum. We are not referring here to the storied concrete Colosseum in Rome, which was completed in 80 A.D. and remains famous for its ancient gladiatorial spectacles. We are talking here about DARPA’s Colosseum, a next-generation electronic emulator of the invisible electromagnetic world. Though it resides in a mere 30-foot by 20-foot server room on the campus of the Johns Hopkins University Applied Physics Laboratory (APL) in Laurel, MD, the Colosseum is capable of creating a much larger, and critically important wireless world.
Competitors from around the world came together this month for the preliminary round of DARPA’s Spectrum Collaboration Challenge (SC2) at The Johns Hopkins University Applied Physics Laboratory (APL) in Laurel, MD. This was the first event of the three-year long tournament designed to generate new wireless paradigms and access strategies in which radio networks enhanced with artificial intelligence (AI) will autonomously collaborate and reason about how to share the increasingly congested electromagnetic (EM) spectrum.
Some of the systems that matter most to the Defense Department are very complicated. Ecosystems, brains and economic and social systems have many parts and processes, but they are studied piecewise, and their literatures and data are fragmented, distributed and inconsistent. It is difficult to build complete, explanatory models of complicated systems, and so effects in these systems that are brought about by many interacting factors are poorly understood.
| AI | Automation | Data |
Dramatic success in machine learning has led to a torrent of Artificial Intelligence (AI) applications. Continued advances promise to produce autonomous systems that will perceive, learn, decide, and act on their own. However, the effectiveness of these systems is limited by the machine’s current inability to explain their decisions and actions to human users. The Department of Defense is facing challenges that demand more intelligent, autonomous, and symbiotic systems. Explainable AI—especially explainable machine learning—will be essential if future warfighters are to understand, appropriately trust, and effectively manage an emerging generation of artificially intelligent machine partners.