Defense Advanced Research Projects AgencyTagged Content List

Artificial Intelligence and Human-Computer Symbiosis Technologies

Technology to facilitate more intuitive interactions between humans and machines

Showing 47 results for Artificial Intelligence + Data RSS
The Understanding Group Biases (UGB) program seeks to develop and prove out capabilities that can radically enhance the scale, speed, and scope of automated, ethnographic-like methods for capturing group biases and cultural models from increasingly available large digital datasets.
Currently, understanding and assessing the readiness of the warfighter involves medical intervention with the help of advanced equipment, such as electrocardiographs (EKGs) and other specialized medical devices, that are too expensive and cumbersome to employ continuously or without supervision in non-controlled environments. On the other hand, currently 92 percent of adults in the United States own a cell phone, which could be used as the basis for continuous, passive health, and readiness assessment.
The World Modelers program aims to develop technology that integrates qualitative causal analyses with quantitative models and relevant data to provide a comprehensive understanding of complicated, dynamic national security questions. The goal is to develop approaches that can accommodate and integrate dozens of contributing models connected by thousands of pathways—orders of magnitude beyond what is possible today.
Program Manager
Dr. Odom joined DARPA in late 2019 as a program manager in the Adaptive Capabilities Office focused on technology development to drive new warfighting architectures.
Office Director
Dr. William Scherlis assumed the role of office director for DARPA’s Information Innovation Office (I2O) in September 2019. In this role he leads program managers in the development of programs, technologies, and capabilities to ensure information advantage for the United States and its allies, and coordinates this work across the Department of Defense and U.S. government.