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    模型互联网:现状和未来挑战

    AI-Modelnet: Current State and Future Challenges

    • 摘要: 计算机的主要功能在于计算处理,互联网的价值在于共享与协作。计算机催生互联网,互联网放大计算 机的价值。互联网、大数据与云计算的快速发展共同推动了人工智能迈入大模型(Large Models, LMs)应用时代。目前,大模型在实际应用中正面临训练成本高、泛化能力受限、服务连续性差及缺乏高效分布式更新等挑战,促使模型间的高效协同与交互成为有潜力的解决方案。然而,随着模型轻量化、私有化及垂直化部署的加速推进,模型互联互通正成为大模型发展急需突破的瓶颈难题。借鉴互联网的发展路径,本文提出了一类称之为“模型互联网”(AI-Modelnet)的开放式互联架构,旨在构建由分布式AI模型驱动的自组织智能网络,通过标准化协作与路由机制实现互联互通、能力共享与协同推理。首先,本文简要介绍了当前单一LM与多模型协同应用的发展现状。其次,系统性地阐述了模型互联网的总体构想,指出模型互联的独特性。然后,结合已构建的原型实验系统与相关应用实例,验证了模型互联网架构的可行性与实用性;最后,展望了模型互联网在更大规模智能协作中的发展潜力,并讨论了未来研究的关键方向。

       

      Abstract: The main feature of computers is computational processing, and the value of the Internet lies in sharing and collaboration. Computers give birth to the Internet, and the Internet amplifies the value of computers. The rapid development of the Internet, big data, and cloud computing is pushing artificial intelligence into the era of large models (LMs). At present, the wide application of single LM is facing challenges such as high training cost, low precision and trust, discontinuous service, and the inability to be updated in a distributed manner. However, with the popularity of lightweight, private, and vertical applications, model interconnection is becoming a bottleneck problem that needs to be broken urgently in the development of large models. Drawing inspiration from the development trajectory of the Internet, this paper proposes an open interconnection architecture termed the "AI Model Network" (AI-Modelnet), which aims to establish a self-organizing intelligent network driven by distributed AI models. Through standardized collaboration and routing mechanisms, it enables interconnection, capability sharing, and collaborative reasoning. Firstly, this paper briefly reviews the current research of single LM and multi-model collaboration. Then, the overall framework of AI-Modelnet is articulated systematically and its distinctive value is pointed out. Finally, the development potential of the model internet in large-scale intelligent collaboration was outlined, and key directions for future research were discussed.

       

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