Defense Advanced Research Projects AgencyTagged Content List

Algorithms

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

Showing 34 results for Algorithms + AI RSS
May 13, 2016,
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 TRAnsformative DESign (TRADES) program. The Proposers Day will be held on Friday, May 13, 2016 from 8:30 AM to 12:30 PM (EDT) at the Executive Conference Center (4075 Wilson Blvd. Suite 350 Arlington, VA 22203).
August 29, 2017,
Webcast
The Defense Advanced Research Projects Agency (DARPA) Defense Sciences Office (DSO) is sponsoring a Proposers Day webcast to provide information to potential proposers on the objectives of an anticipated Research Announcement (RA) for the Young Faculty Award (YFA) program.
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.