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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.

DARPA has selected five teams of researchers to support PREventing EMerging Pathogenic Threats (PREEMPT), a 3.5-year program first announced in January 2018 to reinforce traditional medical preparedness by containing viral infectious diseases in animal reservoirs and insect vectors before they can threaten humans. Through studies in secure laboratories and simulated natural environments, the PREEMPT researchers will model how viruses might evolve within animal populations, and assess the safety and efficacy of potential interventions.

Goliath grouper, black sea bass, and snapping shrimp, along with bioluminescent plankton and other microorganisms, are set to be the unlikely heroes of DARPA’s Persistent Aquatic Living Sensors (PALS) program. Five teams of researchers are developing new types of sensor systems that detect and record the behaviors of these marine organisms and interpret them to identify, characterize, and report on the presence of manned and unmanned underwater vehicles operating in strategic waters.

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.

The testing, evaluation and training of future military systems will increasingly take place in virtual environments due to rising costs and system complexity as well as the limited availability of military ranges. Virtual simulators are already used to augment real-world training for modern fighter aircraft pilots, and they hold significant promise for addressing the rigorous demands of testing and training AI-enabled technologies. Current simulated environments, however, rely on conventional computing that is incapable of generating the computational throughput and speed to accurately replicate real-world interactions, model the scale of physical test ranges or meet the technical requirements of more complex systems.

Blast injuries, burns, and other wounds experienced by warfighters often catastrophically damage their bones, skin, and nerves, resulting in months to years of recovery for the most severe injuries and often returning imperfect results. This long and limited healing process means prolonged pain and hardship for the patient, and a drop in readiness for the military. However, DARPA believes that recent advances in biosensors, actuators, and artificial intelligence could be extended and integrated to dramatically improve tissue regeneration. To achieve this, the new Bioelectronics for Tissue Regeneration (BETR) program asks researchers to develop bioelectronics that closely track the progress of the wound and then stimulate healing processes in real time to optimize tissue repair and regeneration.

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.

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.