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 18 results for AI + Automation RSS
April 2, 2019,
DARPA Conference Center
The Microsystems Technology Office is holding a Proposers Day meeting to provide information to potential proposers on the objectives of the new Real Time Machine Learning (RTML) program and to facilitate teaming. The principal objective of RTML is to reduce the design costs associated with developing Application-Specific Integrated Circuits (ASICs) tailored for emerging machine learning (ML) applications. Researchers on the program will develop a software platform capable of automatically generating novel chip designs based on ML frameworks.
March 5, 2019,
Executive Conference Center
The Defense Advanced Research Projects Agency (DARPA) Defense Sciences Office (DSO) is sponsoring a Proposers Day to provide information to potential proposers on the objectives of an anticipated Broad Agency Announcement (BAA) for the Science of Artificial Intelligence and Learning for Open-world Novelty (SAIL-ON) program.
Efficient discovery and production of new molecules is essential to realize capabilities across the DoD, from simulants and medicines essential to counter emerging threats, to coatings, dyes and specialty fuels needed for advanced performance.
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 (Figure 1). The Department of Defense (DoD) 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.