English

Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges

Artificial Intelligence 2023-09-15 v1 Networking and Internet Architecture

Abstract

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas. This fascination extends particularly to the Internet of Things (IoT), a landscape characterized by the interconnection of countless devices, sensors, and systems, collectively gathering and sharing data to enable intelligent decision-making and automation. This research embarks on an exploration of the opportunities and challenges towards achieving AGI in the context of the IoT. Specifically, it starts by outlining the fundamental principles of IoT and the critical role of Artificial Intelligence (AI) in IoT systems. Subsequently, it delves into AGI fundamentals, culminating in the formulation of a conceptual framework for AGI's seamless integration within IoT. The application spectrum for AGI-infused IoT is broad, encompassing domains ranging from smart grids, residential environments, manufacturing, and transportation to environmental monitoring, agriculture, healthcare, and education. However, adapting AGI to resource-constrained IoT settings necessitates dedicated research efforts. Furthermore, the paper addresses constraints imposed by limited computing resources, intricacies associated with large-scale IoT communication, as well as the critical concerns pertaining to security and privacy.

Keywords

Cite

@article{arxiv.2309.07438,
  title  = {Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges},
  author = {Fei Dou and Jin Ye and Geng Yuan and Qin Lu and Wei Niu and Haijian Sun and Le Guan and Guoyu Lu and Gengchen Mai and Ninghao Liu and Jin Lu and Zhengliang Liu and Zihao Wu and Chenjiao Tan and Shaochen Xu and Xianqiao Wang and Guoming Li and Lilong Chai and Sheng Li and Jin Sun and Hongyue Sun and Yunli Shao and Changying Li and Tianming Liu and Wenzhan Song},
  journal= {arXiv preprint arXiv:2309.07438},
  year   = {2023}
}