With the advance of artificial intelligence (AI), the concept of interactive AI (IAI) has been introduced, which can interactively understand and respond not only to human user input but also to dynamic system and network conditions. In this article, we explore an integration and enhancement of IAI in networking.We first comprehensively review recent developments and future perspectives of AI and then introduce the technology and components of IAI. We then explore the integration of IAI into next-generation networks, focusing on how implicit and explicit interactions can enhance network functionality, improve user experience, and promote efficient network management. Subsequently, we propose an IAI-enabled network management and optimization framework, which consists of environment, perception, action, and brain units. We also design a pluggable large language model (LLM) module and retrieval augmented generation (RAG) module to build the knowledge base and contextual memory for decision-making in the brain unit. We demonstrate through case studies that our IAI framework can effectively perform optimization problem design. Finally, we discuss potential research directions for IAI-based networks.

中文翻译：

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交互式人工智能与下一代网络的检索增强一代


随着人工智能（AI）的进步，引入了交互式人工智能（IAI）的概念，它不仅可以交互地理解和响应人类用户输入，还可以交互地理解和响应动态系统和网络条件。在本文中，我们探讨了IAI在网络中的集成和增强。我们首先全面回顾了AI的最新发展和未来前景，然后介绍了IAI的技术和组件。然后，我们探索将 IAI 集成到下一代网络中，重点关注隐式和显式交互如何增强网络功能、改善用户体验并促进高效的网络管理。随后，我们提出了一个基于 IAI 的网络管理和优化框架，该框架由环境、感知、行动和大脑单元组成。我们还设计了一个可插拔的大语言模型（LLM ）模块和检索增强生成（RAG）模块，为大脑单元中的决策构建知识库和情境记忆。我们通过案例研究证明我们的 IAI 框架可以有效地执行优化问题设计。最后，我们讨论基于 IAI 的网络的潜在研究方向。