With the advance of artificial intelligence (AI), the emergence of Google Gemini and OpenAI Q* marks the direction towards artificial general intelligence (AGI). To implement AGI, 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 the 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 the 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 the effectiveness of the framework through case studies. Finally, we discuss potential research directions for IAI-based networks.

翻译：随着人工智能（AI）的进步，Google Gemini和OpenAI Q*的出现标志着通向通用人工智能（AGI）的方向。为实现AGI，交互式人工智能（IAI）的概念被引入，其不仅能交互式理解并响应人类用户的输入，还能动态适应系统与环境变化。本文探讨了IAI在网络领域的集成与增强。我们首先全面回顾了人工智能的最新进展与未来展望，继而介绍了IAI的技术与组件。随后，我们探索了IAI与下一代网络的集成，重点分析了隐式与显式交互如何增强网络功能、提升用户体验并促进高效网络管理。在此基础上，我们提出了一种IAI驱动的网络管理与优化框架，该框架包含环境单元、感知单元、动作单元与大脑单元。我们同时设计了可插拔的大语言模型（LLM）模块与检索增强生成（RAG）模块，用于在大脑单元中构建知识库与上下文记忆以支持决策。通过案例研究验证了该框架的有效性。最后，我们讨论了基于IAI网络的潜在研究方向。