Neuroscience-inspired continuous learning: a sustainable approach to AI energy challenge

作者：Dongsheng Xiao · 年份：2023 · DOI：10.31219/osf.io/twn9q · 被引用次数：3 · 研究领域：Advanced Memory and Neural Computing

The rapid advancement of Artificial Intelligence (AI) has revolutionized various sectors, from industry to healthcare, driven by significant strides in computational power, data availability, and algorithmic innovations. However, this progress has escalated AI's computational demands, leading to increased energy consumption and substantial carbon emissions, thereby posing significant environmental challenges. In contrast, the human brain offers a model of exceptional efficiency and adaptability, capable of continuous learning with minimal resource consumption. This perspective article explores the potential of harnessing neuroscience principles to develop energy-efficient AI systems, drawing inspiration from the brain's mechanisms for continuous multi-task learning, such as modular organization, dynamic neural network reconfiguration, and synaptic plasticity. These principles suggest a path towards AI models that reduce energy demands by minimizing repetitive training cycles, akin to the brain's efficient learning processes. The article delves into the integration of these neuroscience-inspired models into AI, addressing the challenges of replicating complex neural processes and adapting AI architectures for incremental learning. It proposes a sustainable framework for AI development, emphasizing the need to align technological advancements with environmental conservation. The discussion extends to the societal and ethical implications of AI's energy and carbon footprint, hig...