提出可持续人工智能框架，让AI像人一样持续学习、低耗运行。
Toward Carbon-Neutral Human AI: Rethinking Data, Computation, and Learning Paradigms for Sustainable Intelligence
- 构建人机协同的增量学习系统，动态适应新任务
- 实现碳足迹感知优化，降低训练能耗与人工标注成本
- 适合关注绿色AI、可持续发展的研究者与工程师
人工智能的迅猛发展带来巨大算力需求，引发环境与伦理担忧。本文批判现有大规模静态数据集和单一训练范式，提出新型人类智能（Human AI, HAI）框架，强调增量学习、碳感知优化与人机协同，提升AI的适应性、效率与可问责性。通过借鉴生物认知机制与动态架构设计，HAI在持续情境化学习中实现性能与生态责任的平衡。文章阐述了理论基础、系统设计与运行原则，解决主动学习、持续适应与能效部署等关键挑战，为负责任的人类中心型智能提供可行路径。
原文摘要 · Abstract (English)
The rapid advancement of Artificial Intelligence (AI) has led to unprecedented computational demands, raising significant environmental and ethical concerns. This paper critiques the prevailing reliance on large-scale, static datasets and monolithic training paradigms, advocating for a shift toward human-inspired, sustainable AI solutions. We introduce a novel framework, Human AI (HAI), which emphasizes incremental learning, carbon-aware optimization, and human-in-the-loop collaboration to enhance adaptability, efficiency, and accountability. By drawing parallels with biological cognition and leveraging dynamic architectures, HAI seeks to balance performance with ecological responsibility. We detail the theoretical foundations, system design, and operational principles that enable AI to learn continuously and contextually while minimizing carbon footprints and human annotation costs. Our approach addresses pressing challenges in active learning, continual adaptation, and energy-efficient model deployment, offering a pathway toward responsible, human-centered artificial intelligence.
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