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 45 results for Artificial Intelligence + Programs RSS
The Digital RF Battlespace Emulator (DRBE) program aims to create the world’s first, large-scale, virtual RF environment for developing, training, and testing advanced radio frequency (RF) systems. The DRBE system will seek to enable numerous RF systems such as radar and electronic warfare (EW) systems to interact with each other in a fully closed-loop RF environment.
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.
The Von Neumann architecture has significantly aided the rapid advancement of computing over the past seven decades. However, moving data between the processors and memory components of this architecture requires significant time and high-energy consumption, which constrains the computing performance and workload. Overcoming this bottleneck requires new computing architectures and devices that can significantly advance the computing performance beyond the traditional practice of transistor scaling (i.e., Moore’s Law).
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.
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.