Defense Advanced Research Projects AgencyTagged Content List

Algorithms

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

Showing 85 results for Algorithms RSS
11/01/2018
First announced in June 2017, DARPA’s Electronics Resurgence Initiative (ERI) – a five-year, upwards of $1.5B investment in the future of domestic electronic systems – is rolling out the second phase of its research priorities. Comprised of several ongoing DARPA programs – including the six recently awarded ERI “Page 3” programs –ERI addresses long-foreseen obstacles to Moore’s Law and the challenges impeding 50 years of rapid progress in electronics advancement. The next phase of ERI will focus on further enmeshing the technology needs and capabilities of the defense enterprise with the commercial and manufacturing realities of the electronics industry.
11/16/2018
Throughout DARPA’s history, artificial intelligence (AI) has been an important area of groundbreaking research and development (R&D). In the 1960s, DARPA researchers completed some of the foundational work in the field, leading to the creation of expert systems, or the first wave of AI technologies. Since then, DARPA has funded developments in the second wave of AI – machine learning – which has significantly impacted defense and commercial capabilities in areas such as speech understanding, self-driving cars, and image recognition.
12/10/2018
Today’s critical Department of Defense (DOD) systems and platforms rely on advanced electronics to address national security objectives. To help tackle obstacles facing a half-century of electronics advancement, DARPA launched the Electronics Resurgence Initiative (ERI) – a five-year, upwards of $1.5 billion investment in the future of domestic electronic systems. In November, DARPA expanded ERI with the announcement of ERI Phase II, which seeks to further enmesh the technology needs and capabilities of the defense enterprise with the commercial and manufacturing realities of the electronics industry.
01/31/2019
A key ingredient in effective teams – whether athletic, business, or military – is trust, which is based in part on mutual understanding of team members’ competence to fulfill assigned roles. When it comes to forming effective teams of humans and autonomous systems, humans need timely and accurate insights about their machine partners’ skills, experience, and reliability to trust them in dynamic environments. At present, autonomous systems cannot provide real-time feedback when changing conditions such as weather or lighting cause their competency to fluctuate. The machines’ lack of awareness of their own competence and their inability to communicate it to their human partners reduces trust and undermines team effectiveness.
02/06/2019
Today, machine learning (ML) is coming into its own, ready to serve mankind in a diverse array of applications – from highly efficient manufacturing, medicine and massive information analysis to self-driving transportation, and beyond. However, if misapplied, misused or subverted, ML holds the potential for great harm – this is the double-edged sword of machine learning.