AI发展需从做大模型转向小型化与分布式协同。
Position: AI Scaling: From Up to Down and Out
- 提出规模扩展的三大方向：向上、向下、向外
- 强调小型化与分布式能降低能耗、提升可及性
- 适合关注AI可持续性与落地应用的研究者
AI规模化传统上指模型变大变强，但日益增长的效率、适应性与跨领域协作需求，要求更全面视角。本文提出涵盖‘向上’‘向下’‘向外’的全景式AI规模化框架。指出模型持续放大存在固有瓶颈，未来应聚焦‘向下’（小型化）与‘向外’（分布式协同）。这些范式可应对碳足迹、公平获取、跨域协作等技术和社会挑战。在医疗、智能制造、内容生成等领域展示其在效率、个性化与全球连接方面的突破潜力。同时指出关键挑战：模型复杂性与可解释性的平衡、资源约束管理、伦理发展推动。通过整合三者，提出统一路线图，重定义AI研究与应用未来，助力人工通用智能（AGI）进展。
原文摘要 · Abstract (English)
AI Scaling has traditionally been synonymous with Scaling Up, which builds larger and more powerful models. However, the growing demand for efficiency, adaptability, and collaboration across diverse applications necessitates a broader perspective. This position paper presents a holistic framework for AI scaling, encompassing Scaling Up, Scaling Down, and Scaling Out. It argues that while Scaling Up of models faces inherent bottlenecks, the future trajectory of AI scaling lies in Scaling Down and Scaling Out. These paradigms address critical technical and societal challenges, such as reducing carbon footprint, ensuring equitable access, and enhancing cross-domain collaboration. We explore transformative applications in healthcare, smart manufacturing, and content creation, demonstrating how AI Scaling can enable breakthroughs in efficiency, personalization, and global connectivity. Additionally, we highlight key challenges, including balancing model complexity with interpretability, managing resource constraints, and fostering ethical development. By synthesizing these approaches, we propose a unified roadmap that redefines the future of AI research and application, paving the way for advancements toward Artificial General Intelligence (AGI).
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