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.

翻译：人工智能的快速发展带来了前所未有的计算需求，引发了重大的环境与伦理关切。本文批判了当前对大规模静态数据集和单一训练范式的依赖，倡导转向受人类启发的可持续人工智能解决方案。我们提出了一种新颖的框架——人类人工智能，该框架强调增量学习、碳感知优化和人机协同，以提升适应性、效率与可问责性。通过类比生物认知机制并利用动态架构，人类人工智能旨在平衡性能与生态责任。我们详细阐述了其理论基础、系统设计和运行原则，使人工智能能够在持续情境化学习的同时，最小化碳足迹和人工标注成本。我们的方法应对了主动学习、持续适应和节能模型部署中的紧迫挑战，为构建负责任、以人为本的人工智能提供了一条可行路径。