构建全球AI模型互联网络,实现模型间协作与能力共享。
AI-Model Network: Concept, Current State and Future
- 提出跨模型互联的AI-ModelNet架构,类比互联网打通模型壁垒。
- 通过原型系统验证框架可行性,支持多场景协同推理应用。
- 适合关注大模型协作、分布式AI的开发者与研究者。
尽管计算机的核心功能是计算与处理,互联网的价值则源于共享与协作。计算机创造了互联网,而互联网又提升了计算机的价值。随着互联网、云计算和大数据的快速发展,人工智能正迈入大模型(LMs)时代。然而,大模型的实际应用受困于高昂的训练成本和部署复杂性,促使向轻量化、私有化和领域特定模型转变。随着异构模型的快速普及与广泛分布,实现模型间的有效互动与协作已成为大模型发展的关键瓶颈。本文借鉴互联网的发展历程,提出全球人工智能模型网络(AI-ModelNet)的概念、愿景与系统架构,通过建立模型间通路,实现互联、能力共享与协同推理。首先简要回顾单模型与多模型研究现状;随后阐述AI-ModelNet的系统愿景与分层架构,并通过原型系统与多样化应用案例验证其可行性;最后初步探讨未来研究的关键方向。
原文摘要 · Abstract (English)
While the primary function of computers lies in computation and processing, the core value of the Internet is rooted in sharing and collaboration. Computers create the Internet, and the Internet empowers the value of computers. The rapid development of the Internet, cloud computing, and big data is pushing artificial intelligence into the era of large models (LMs). However, the practical application of LMs is currently hindered by high training costs and deployment complexities, driving a shift toward lightweight, private, and domain-specific models. With the rapid proliferation and wide distribution of heterogeneous models, enabling effective interaction and collaboration among them has emerged as a critical bottleneck that urgently needs to be addressed in LM development. Drawing inspiration from the development of the Internet, this paper proposes the concept, vision, and system architecture of world wide AI-model network (AI-ModelNet). It is a novel paradigm that achieves interconnection, capability sharing, and collaborative reasoning by establishing pathways between models. We first briefly review the current state of single-model and multi-model research. Subsequently, the systemic vision and hierarchical architecture of AI-ModelNet are articulated, followed by validation of the framework's feasibility through a prototype system and diverse application cases. Finally, key directions for future research are discussed preliminarily.
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