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 Communicating with Computers (CwC) program aims to enable symmetric communication between people and computers in which machines are not merely receivers of instructions but collaborators, able to harness a full range of natural modes including language, gesture and facial or other expressions. For the purposes of the CwC program, communication is understood to be the sharing of complex ideas in collaborative contexts. Complex ideas are assumed to be built from a relatively small set of elementary ideas, and language is thought to specify such complex ideas—but not completely, because language is ambiguous and depends in part on context, which can augment language and improve the specification of complex ideas.
| AI | Autonomy | Data |
Department of Defense (DoD) operators and analysts collect and process copious amounts of data from a wide range of sources to create and assess plans and execute missions. However, depending on context, much of the information that could support DoD missions may be implicit rather than explicitly expressed. Having the capability to automatically extract operationally relevant information that is only referenced indirectly would greatly assist analysts in efficiently processing data.
Deep Purple aims to advance the modeling of complex dynamic systems using new information-efficient approaches that make optimal use of data and known physics at multiple scales. The program is investigating next-generation deep learning approaches that use not only high throughput multimodal scientific data from observations and controlled experiments (including behaviors such as phase transitions and chaos), but also of the known science of such systems at whatever scales it exists.
Dramatic success in machine learning has led to a torrent of Artificial Intelligence (AI) applications. Continued advances promise to produce autonomous systems that will perceive, learn, decide, and act on their own. However, the effectiveness of these systems is limited by the machine’s current inability to explain their decisions and actions to human users (Figure 1). The Department of Defense (DoD) is facing challenges that demand more intelligent, autonomous, and symbiotic systems. Explainable AI—especially explainable machine learning—will be essential if future warfighters are to understand, appropriately trust, and effectively manage an emerging generation of artificially intelligent machine partners.
The goal of the Fundamental Design (FUN Design) program is to determine whether we can develop or discover a new set of building blocks to describe conceptual designs. The design building blocks will capture the components’ underlying physics allowing a family of nonintuitive solutions to be generated.