Defense Advanced Research Projects AgencyTagged Content List


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

Showing 33 results for Algorithms + News RSS
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.
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.
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.
Current AI systems excel at tasks defined by rigid rules – such as mastering the board games Go and chess with proficiency surpassing world-class human players. However, AI systems aren’t very good at adapting to constantly changing conditions commonly faced by troops in the real world – from reacting to an adversary’s surprise actions, to fluctuating weather, to operating in unfamiliar terrain. For AI systems to effectively partner with humans across a spectrum of military applications, intelligent machines need to graduate from closed-world problem solving within confined boundaries to open-world challenges characterized by fluid and novel situations.
Universal quantum computers with millions of quantum bits, or qubits – which can represent a one, a zero, or a coherent linear combination of one and zero – would revolutionize information processing for commercial and military applications. Realizing that vision, however, is still decades away. The problem is the performance and reliability of quantum devices depend on the length of time the underlying quantum states can remain coherent. If you wait long enough, interactions with the environment will make the state behave like a conventional classical system, removing any quantum advantage. Often, this coherence time is significantly short, which makes it difficult to perform any meaningful computations.