Defense Advanced Research Projects AgencyTagged Content List

Supervised Autonomy

Automated capabilities with human supervision; "human in the loop"

Showing 6 results for Autonomy + Analytics RSS
05/31/2017
Advances in artificial intelligence (AI) are making virtual and robotic assistants increasingly capable in performing complex tasks. For these “smart” machines to be considered safe and trustworthy collaborators with human partners, however, robots must be able to quickly assess a given situation and apply human social norms. Such norms are intuitively obvious to most people—for example, the result of growing up in a society where subtle or not-so-subtle cues are provided from childhood about how to appropriately behave in a group setting or respond to interpersonal situations. But teaching those rules to robots is a novel challenge.
09/07/2017
DARPA published its Young Faculty Award (YFA) 2018 Research Announcement today, seeking proposals in 26 different topic areas—the largest number of YFA research areas ever solicited.
The United States Government has an interest in developing and maintaining a strategic understanding of events, situations, and trends around the world, in a variety of domains. The information used in developing this understanding comes from many disparate sources, in a variety of genres, and data types, and as a mixture of structured and unstructured data. Unstructured data can include text or speech in English and a variety of other languages, as well as images, videos, and other sensor information.
Expanded global access to diverse means of communication is resulting in more information being produced in more languages more quickly than ever before. The volume of information encountered by DoD, the speed at which it arrives, and the diversity of languages and media through which it is communicated make identifying and acting on relevant information a serious challenge. At the same time, there is a need to communicate with non-English-speaking local populations of foreign countries, but it is at present costly and difficult for DoD to do so.
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.