Large Language Models Empowered Autonomous Edge AI for Connected Intelligence

• URL: http://arxiv.org/abs/2307.02779v3
• Date: Mon, 25 Dec 2023 06:25:38 GMT
• Title: Large Language Models Empowered Autonomous Edge AI for Connected Intelligence
• Authors: Yifei Shen, Jiawei Shao, Xinjie Zhang, Zehong Lin, Hao Pan, Dongsheng Li, Jun Zhang, Khaled B. Letaief
• Abstract summary: Edge artificial intelligence (Edge AI) is a promising solution to achieve connected intelligence. This article presents a vision of autonomous edge AI systems that automatically organize, adapt, and optimize themselves to meet users' diverse requirements.
• Score: 51.269276328087855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The evolution of wireless networks gravitates towards connected intelligence, a concept that envisions seamless interconnectivity among humans, objects, and intelligence in a hyper-connected cyber-physical world. Edge artificial intelligence (Edge AI) is a promising solution to achieve connected intelligence by delivering high-quality, low-latency, and privacy-preserving AI services at the network edge. This article presents a vision of autonomous edge AI systems that automatically organize, adapt, and optimize themselves to meet users' diverse requirements, leveraging the power of large language models (LLMs), i.e., Generative Pretrained Transformer (GPT). By exploiting the powerful abilities of GPT in language understanding, planning, and code generation, as well as incorporating classic wisdom such as task-oriented communication and edge federated learning, we present a versatile framework that efficiently coordinates edge AI models to cater to users' personal demands while automatically generating code to train new models in a privacy-preserving manner. Experimental results demonstrate the system's remarkable ability to accurately comprehend user demands, efficiently execute AI models with minimal cost, and effectively create high-performance AI models at edge servers.

Related papers

• Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G [58.440115433585824]
  Building future wireless systems that support services like digital twins (DTs) is challenging to achieve through advances to conventional technologies like meta-surfaces. While artificial intelligence (AI)-native networks promise to overcome some limitations of wireless technologies, developments still rely on AI tools like neural networks. This paper revisits the concept of AI-native wireless systems, equipping them with the common sense necessary to transform them into artificial general intelligence (AGI)-native systems.
  arXiv  Detail & Related papers  (2024-04-29T04:51:05Z)
• Federated Learning-Empowered AI-Generated Content in Wireless Networks [58.48381827268331]
  Federated learning (FL) can be leveraged to improve learning efficiency and achieve privacy protection for AIGC. We present FL-based techniques for empowering AIGC, and aim to enable users to generate diverse, personalized, and high-quality content.
  arXiv  Detail & Related papers  (2023-07-14T04:13:11Z)
• Personalized Resource Allocation in Wireless Networks: An AI-Enabled and Big Data-Driven Multi-Objective Optimization [22.77447144331876]
  The use of Artificial Intelligence (AI) is envisioned for wireless network design and optimization. One of the main future applications of AI is enabling user-level personalization for numerous use cases. The personalization technology advocated in this article is supported by an intelligent big data-driven layer designed to micro-manage the scarce network resources.
  arXiv  Detail & Related papers  (2023-07-08T00:26:36Z)
• HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face [85.25054021362232]
  Large language models (LLMs) have exhibited exceptional abilities in language understanding, generation, interaction, and reasoning. LLMs could act as a controller to manage existing AI models to solve complicated AI tasks. We present HuggingGPT, an LLM-powered agent that connects various AI models in machine learning communities.
  arXiv  Detail & Related papers  (2023-03-30T17:48:28Z)
• Optimization Design for Federated Learning in Heterogeneous 6G Networks [27.273745760946962]
  Federated learning (FL) is anticipated to be a key enabler for achieving ubiquitous AI in 6G networks. There are several system and statistical heterogeneity challenges for effective and efficient FL implementation in 6G networks. In this article, we investigate the optimization approaches that can effectively address the challenges.
  arXiv  Detail & Related papers  (2023-03-15T02:18:21Z)
• Enabling AI-Generated Content (AIGC) Services in Wireless Edge Networks [68.00382171900975]
  In wireless edge networks, the transmission of incorrectly generated content may unnecessarily consume network resources. We present the AIGC-as-a-service concept and discuss the challenges in deploying A at the edge networks. We propose a deep reinforcement learning-enabled algorithm for optimal ASP selection.
  arXiv  Detail & Related papers  (2023-01-09T09:30:23Z)
• Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications [39.223546118441476]
  6G will revolutionize the evolution of wireless from "connected things" to "connected intelligence" Deep learning and big data analytics based AI systems require tremendous computation and communication resources. edge AI stands out as a disruptive technology for 6G to seamlessly integrate sensing, communication, computation, and intelligence.
  arXiv  Detail & Related papers  (2021-11-24T11:47:16Z)
• Towards Self-learning Edge Intelligence in 6G [143.1821636135413]
  Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing. In this article, we identify the key requirements and challenges of edge-native AI in 6G.
  arXiv  Detail & Related papers  (2020-10-01T02:16:40Z)
• Communication-Efficient Edge AI Inference Over Wireless Networks [33.1306043471745]
  We present the principles of efficient deployment of model inference at network edge to provide low-latency and energy-efficient AI services. This includes the wireless distributed computing framework for low-latency device distributed model inference as well as the wireless cooperative transmission strategy for energy-efficient edge cooperative model inference.
  arXiv  Detail & Related papers  (2020-04-28T08:04:06Z)
• Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface [35.64178319119883]
  We develop an over-the-air based communication-efficient federated machine learning framework for intelligent IoT networks. We exploit the waveform superposition property of a multi-access channel to reduce the model aggregation error.
  arXiv  Detail & Related papers  (2020-04-13T09:48:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.