Tracking the carbon footprint of global generative artificial intelligence
Zhaohao Ding, Jianxiao Wang, Yiyang Song, Xiaokang Zheng, Guannan He, Xiupeng Chen, Tiance Zhang, Wei‐Jen Lee 等 9 位
North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Peking University University of Hong Kong
阅读操作
确认中在文库中上传 PDF 后可生成中文音频讲解。
摘要与影响
In recent years, generative artificial intelligence (GAI) has gained unprecedented attention. Unlike conventional AI, GAI can generate innovative and meaningful content across texts, images, and videos. The success of OpenAI’s ChatGPT has driven global tech companies to develop high-performance models and integrate GAI into products.1 This AI arms race continues, as shown by OpenAI’s text-to-video model, Sora, and Anthropic’s new large language model, Claude 3. The release of DeepSeek V3/R1 has sparked a global AI cost revolution.
逐年被引趋势
关键指标
同类平均 = 1
同领域 · 同年份 · 同类型
Google Scholar 与 OpenAlex 的被引统计范围不同，数值存在差异属正常。
AI 辅助阅读
依据：摘要
可就本文提问；依据不足时会说明。
学术脉络
学科主题
物理Space Science and Extraterrestrial Life
参考文献 7
此处列出前 3 条
引用本文 32
按被引量排序，此处列出前 3 条