Human-Machine Inference Networks For Smart Decision Making: Opportunities and Challenges
It addresses the need for novel solutions in signal processing and machine learning for HuMaINs, which is an emerging but incremental area.
The paper tackles the problem of optimizing inference tasks by combining human and machine cognitive strengths in Human-Machine Inference Networks (HuMaINs), achieving higher performance than either alone, though no concrete numbers are provided.
The emerging paradigm of Human-Machine Inference Networks (HuMaINs) combines complementary cognitive strengths of humans and machines in an intelligent manner to tackle various inference tasks and achieves higher performance than either humans or machines by themselves. While inference performance optimization techniques for human-only or sensor-only networks are quite mature, HuMaINs require novel signal processing and machine learning solutions. In this paper, we present an overview of the HuMaINs architecture with a focus on three main issues that include architecture design, inference algorithms including security/privacy challenges, and application areas/use cases.