Added: Oct 19, 2018 11:50 am DARPA is soliciting innovative research proposals in the area of machine common sense to enable Artificial Intelligence (AI) applications to understand new situations, monitor the reasonableness of their actions, communicate more effectively with people, and transfer learning to new domains.

The Defense Department is looking for suggestions for its drone program. The Defense Advanced Research Projects Agency (DARPA) is welcoming ideas for new technologies that can enhance drones and allow them to operate more smoothly. The theme for the innovation drive is autonomy, a statement from Federal Business Opportunities explained. The project is called OFFSET, an acronym for OFFensive Swarm Enabled Tactics, and it is the second drive for such technologies, according to Tech Crunch. DARPA's goal is to collect a variety of ideas on new technologies that inform the development of sensors, propeller blades, software, etc.

The goal of Explainable AI (XAI) is to create a suite of new or modified machine learning techniques that produce explainable models that, when combined with effective explanation techniques, enable end users to understand, appropriately trust, and effectively manage the emerging generation of AI systems.

DARPA is soliciting innovative research proposals in the area of semi-automated discovery of machine learning and statistical models and processing pipelines. Proposed research should investigate innovative approaches that enable revolutionary advances in science, devices, or systems. Specifically excluded is research that primarily results in evolutionary improvements to the existing state of practice.

Bookmark this page by right-clicking here and choosing "Add to Favorites" The Defense Advanced Research Projects Agency (DARPA) Defense Sciences Office (DSO) is requesting information on research related to the investigation and characterization of fundamental limits of machine learning with supportive theoretical foundations. Although the main focus is on machine learning, extensions and implications for human-machine systems are also of interest. The notion of fundamental limits here means that the conclusion about achievable performance limits should hold independent of specific learning methods or algorithms.