Defense Advanced Research Projects AgencyTagged Content List

Algorithms

A process or rule set used for calculations or other problem-solving operations

Showing 32 results for Algorithms + Programs RSS
The ultimate goal of the DARPA Accelerated Computation for Efficient Scientific Simulation (ACCESS) is to demonstrate new, specialized benchtop technology that can solve large problems in complex physical systems on the hour timescale, compared to existing methods that require full cluster-scale supercomputing resources and take weeks to months. The core principle of the program is to leverage advances in optics, MEMS, additive manufacturing, and other emerging technologies to develop new non-traditional hybrid analog and digital computational means.
The A-to-I Look-Through Program will fundamentally improve the operational bandwidth, linearity, and efficiency of electronic systems where the objective is to receive and transmit information using electromagnetic (radio) waves under extreme size/weight/power and environmental conditions required for DoD applications. This will be achieved by developing new electronic processing subsystems methods and architectures based on new understandings of mathematical principles and embedded signal processing. This program will develop ultra-wideband digital RF receivers based on A-to-I converter (AIC) technology.
The Artificial Intelligence Research Associate (AIRA) program is part of a broad DAPRA initiative to develop and apply “Third Wave” AI technologies that are robust to sparse data and adversarial spoofing, and that incorporate domain-relevant knowledge through generative contextual and explanatory models.
The Automating Scientific Knowledge Extraction (ASKE) program aims to develop technology to automate some of the manual processes of scientific knowledge discovery, curation and application. ASKE is part of DARPA's Artificial Intelligence Exploration (AIE) program, a key component of the agency’s broader AI investment strategy aimed at ensuring the United States maintains an advantage in this critical and rapidly accelerating technology area.
In order to transform machine learning systems from tools into partners, users need to trust their machine counterpart. One component to building a trusted relationship is knowledge of a partner’s competence (an accurate insight into a partner’s skills, experience, and reliability in dynamic environments). While state-of-the-art machine learning systems can perform well when their behaviors are applied in contexts similar to their learning experiences, they are unable to communicate their task strategies, the completeness of their training relative to a given task, the factors that may influence their actions, or their likelihood to succeed under specific conditions.