Defense Advanced Research Projects AgencyTagged Content List

Artificial Intelligence and Human-Computer Symbiosis Technologies

Technology to facilitate more intuitive interactions between humans and machines

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The current wave of artificial intelligence, driven by machine learning (ML) techniques, is all the rage, and for good reason. With sufficient training on digitized writing, spoken words, images, video streams, and other digital content, ML has become the basis of voice recognition, self-driving cars, and other previously only-imagined capabilities.
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.
The director of the U.S. Defense Advanced Research Projects Agency (DARPA), Dr. Steven Walker, today announced the appointment of Dr. Peter Highnam as deputy director of the Agency. Highnam, a former DARPA program manager, is expected to help advance critical research in several fields including artificial intelligence, data analytics, communications, reconnaissance, electronic warfare, biosecurity, and warfighter health.
June 14, 2016,
DARPA Conference Center
DARPA’s Information Innovation Office (I2O) is hosting a Proposers Day to provide information to potential proposers on the objectives of the upcoming Data-Driven Discovery of Models program. The program aims to develop semi-automated model discovery systems that enable non-expert users (i.e., users with subject matter expertise but no data science background) to create empirical models of real, complex processes. DARPA believes such a capability would increase the productivity of data scientists, and enable many more users to make predictions from data.
| AI | Complexity | Data | Math |
Machine learning has shown remarkable success across many application areas in recent years, leveraging advances in computing power and the availability of large sets of training data. It provides a tremendous opportunity to deploy data-driven systems in more complex and interactive tasks including personalized autonomy, agile robotics, self-driving vehicles, and smart cities. Despite dramatic progress, the machine learning community still lacks an understanding of the trade-offs and mathematical limitations of related technologies for a given domain, problem, or dataset.