Summary of Conversational Crowdsensing: a Parallel Intelligence Powered Novel Sensing Approach, by Zhengqiu Zhu et al.
Conversational Crowdsensing: A Parallel Intelligence Powered Novel Sensing Approach
by Zhengqiu Zhu, Yong Zhao, Bin Chen, Sihang Qiu, Kai Xu, Quanjun Yin, Jincai Huang, Zhong Liu, Fei-Yue Wang
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel sensing paradigm, conversational crowdsensing, aims to facilitate faster response and wider popularization of crowdsensing systems in Industry 5.0. This approach alleviates workload and professional requirements by organizing diverse workforce participants (biological, robotic, and digital) through effective conversation across three levels: inter-human, human-AI, and inter-AI. The architecture is designed to accomplish various tasks across three sensing phases: requesting, scheduling, and executing. To realize conversational crowdsensing, the paper explores foundational technologies such as LLM-based multi-agent systems, scenarios engineering, and conversational human-AI cooperation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Conversational crowdsensing is a new way of collecting information by having conversations between people, robots, and artificial intelligence (AI). This helps to get more work done faster and makes it easier for different workers to collaborate. The approach organizes three types of participants: humans, robots, and digital AI systems. It also involves three levels of conversation: talking between humans, humans and AI, and AI systems only. This new way of crowdsensing has many potential applications in industries and can help people solve complex problems together. |
