让绿色增长成为现实:调和清醒与利益相关者的满意度
Caroline Gans-Combe1, Jae-Yun Jun2, Waleed Mouhali2
1INSEEC Business School, OMNES Education, Paris, France.
PloS one
|August 23, 2023
概括
通过优化机器学习 (ML) 模型训练数据,可以实现数字,经济和生态清醒. 这种方法可以保持模型性能,同时减少资源消耗和环境影响,支持绿色增长.
科学领域:
- 能源转型 能源转型
- 可持续的计算 可持续的计算
- 绿色增长 绿色增长 绿色增长
背景情况:
- 清醒对于能源转型场景至关重要,但往往被消极地看待.
- 将清醒与用户满意度联系起来,可以支持绿色增长.
- 数字,经济和生态的清醒可以同时实现.
研究的目的:
- 展示一种实现数字,经济和生态清醒的方法.
- 通过资源优化来确保最终用户的满意度得到维持.
- 探索人工智能 (AI) 在资源最小化方面的潜力.
主要方法:
- 开发了一个基于边际效用的满意度函数.
- 使用基于活动的成本计算 (ABC方法) 来分配资源.
- 专注于人工智能概念验证来控制训练数据集大小.
主要成果:
- 通过减少训练数据大小而不会影响AI模型性能 (准确性) 来实现数值清醒.
- 在保持效率的同时,在人工智能过程中显著减少了资源.
- 证实了"清醒三角"概念,将数字,经济和生态主权联系在一起.
结论:
- 人工智能生产流程为清醒和资源最小化提供了巨大的潜力.
- 与人工智能过程相同的活动可能具有类似的清醒特征.
- 这种方法为可持续的绿色增长铺平了道路.
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