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Sensemaking Research Roadmap.
| 题名： | Sensemaking Research Roadmap. |
| 作者： | Subbalakshmi, K. P.; Galstyan, A.; Chellappa, R.; Clancy, C. |
| 关键词： | Artificial intelligence, Human machine systems, Information science, Network science, Multimode, Algorithms, Scientific research, Intelligence community (united states), Hs2(hybrid systems for sensemaking), Research roadmap |
| 摘要： | On 2-3 May 2018, the Systems Engineering Research Center (SERC) convened a workshop of experts to examine current research trends, challenges, and open science questions in artificial intelligence (AI)-enabled sensemaking technologies. Consensus from the workshop was that two most pertinent research thrusts that are needed for developing a higher form of sensemaking are (1) multi-modal analysis for sensemaking and (2) hybrid systems for sensemaking. Within these thrust areas exist several research tracks. We discuss these in Sections 3 and 4, respectively. Currently, four elements influence multi-modal sensemaking systems: (1) volume of information generated by both conventional and social media sensors; (2) intentional corruption of information in these modalities; (3) different speeds at which information propagates over the internet, (4) the often contradictory information about the same topic from different sources. These tend to be more on intra-modal or cross-modal modalities, using a few subsets of data. Significant research is required to progress from these analytics to true multi-modal sensemaking systems. We propose the following research tracks: interoperability issues, reliability, and trustworthiness, next-generation AI and sensemaking algorithms, and assessing and closing the loop for multi-modal sensemaking systems. In Section 5, we provide a projected timeline for the research. We believe these tracks are crucial to developing the science of holistic sensemaking. Hybrid sensemaking faces its own set of challenges. The goal of HS2 is to explore the possibilities for human-machine hybrid systems. We seek to eventually develop a networked ecosystem of human analysts and machines. The HS2 research tracks are: HS2 taxonomy and performance measures, interactive and continuous sensemaking, HS2 autonomy and trust, HS2 as networks: organizational sciences perspective, and HS2 interfaces. We project much of the HS2 research to be 5-10 years out or more. |
| 报告类型： | 科技报告 |
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