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 Artificial Intelligence + Tech-Foundations 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.
The goal of the Fundamental Design (FUN Design) program is to determine whether we can develop or discover a new set of building blocks to describe conceptual designs. The design building blocks will capture the components’ underlying physics allowing a family of nonintuitive solutions to be generated.
Artificial intelligence (AI) and machine learning (ML) systems have advanced significantly in recent years. Despite a wide range of impressive results, current AI is not intelligent in the biological sense. These systems are limited to performing only those tasks for which they have been specifically programmed and trained, and are inherently subject to safety hazards when encountering situations outside them.
Driven by the rapidly evolving national security threat landscape, future defense systems will need access to low size, weight, and power (SWaP) artificial intelligence (AI) solutions that can rapidly transition from idea to practice. In recent years, the ability to learn from large datasets has advanced significantly due to increases in hardware performance, advances in machine learning (ML) algorithms, and the availability of high quality open datasets.
Serial Interactions in Imperfect Information Games Applied to Complex Military Decision Making (SI3-CMD) builds on recent developments in artificial intelligence and game theory to enable more effective decisions in adversarial domains. SI3-CMD will explore several military decision making applications at strategic, tactical, and operational levels and develop AI/game theory techniques appropriate for their problem characteristics.