Towards Sustainable AI: Exploring Cyclical and Adaptive Approaches
Introduction The advent of large generative models, starting with GPT-3 in 2020, has revolutionized artificial intelligence. However, the significant computational resources required by these models have spurred a growing interest in sustainable AI. This analysis explores emerging approaches that move beyond the traditional "extractive" AI model towards cyclical, adaptive, and resource-conscious systems. We'll examine how concepts like continuous learning, resource optimization, and ecological principles can be integrated into AI infrastructure to minimize environmental impact and promote long-term viability. While the vision of fully "Regenerative AI Systems (RAIS)" remains largely theoretical, this analysis will focus on practical steps and research directions currently being pursued. The Challenge of Extractive AI Traditional AI development often follows a linear "extractive" model: Traditional AI Paradigm: [Data Extraction] → [Computation...