Defense Advanced Research Projects AgencyTagged Content List

Artificial Intelligence and Human-Computer Symbiosis Technologies

Technology to facilitate more intuitive interactions between humans and machines

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The efficient discovery and production of new molecules is essential for a range of military capabilities—from developing safe chemical warfare agent simulants and medicines to counter emerging threats, to coatings, dyes, and specialty fuels for advanced performance. Current approaches to develop molecules for specific applications, however, are intuition-driven, mired in slow iterative design and test cycles, and ultimately limited by the specific molecular expertise of the chemist who has to test each candidate molecule by hand.
Today’s machine learning systems are more advanced than ever, capable of automating increasingly complex tasks and serving as a critical tool for human operators. Despite recent advances, however, a critical component of Artificial Intelligence (AI) remains just out of reach – machine common sense. Defined as “the basic ability to perceive, understand, and judge things that are shared by nearly all people and can be reasonably expected of nearly all people without need for debate,” common sense forms a critical foundation for how humans interact with the world around them.
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.
Rapid comprehension of world events is critical to informing national security efforts. These noteworthy changes in the natural world or human society can create significant impact on their own, or may form part of a causal chain that produces broader impact. Many events are not simple occurrences but complex phenomena composed of a web of numerous subsidiary elements – from actors to timelines. The growing volume of unstructured, multimedia information available, however, hampers uncovering and understanding these events and their underlying elements.
| AI | Analytics | Data |
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.