在科学背景下对文本到图像生成AI模型进行比较分析:关于核电的案例研究
Veda Joynt1, Jacob Cooper1, Naman Bhargava2
1Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI, 48109, USA.
Scientific reports
|December 5, 2024
概括
生成型人工智能显示出对清洁能源通信的承诺,但与技术准确性和偏见作斗争. 需要专门的工具来提高公众的能源素养和对核能的参与.
科学领域:
- 人工智能的人工智能
- 能源系统 能源系统
- 公众参与 公众参与
背景情况:
- 生成型人工智能 (AI) 提供了公众参与清洁能源讨论的潜力.
- 提高能源素养可以促进公众参与能源系统决策.
- 向公众传达关于核能等低碳能源的技术信息是一项挑战.
研究的目的:
- 评估生成人工智能的潜力,以促进公众对清洁能源的参与.
- 探索AI在传达技术核能信息方面的有效性.
- 确定当前人工智能模型在表示能源系统和社会背景方面的局限性.
主要方法:
- 评估了20个由人工智能驱动的文本到图像生成器.
- 对一般和科学核相关提示的模型性能进行比较.
- 评估图像的相关性,技术准确性,偏差表示和景观描绘.
主要成果:
- DALL-E,DreamStudio和Craiyon显示出对一般核主题的承诺.
- 人工智能模型无法准确地描述技术能源系统的细节.
- 模型重现了性别偏见,并不准确地代表了土著景观.
结论:
- 目前的生成人工智能工具不足以让公众对核能有细微的参与.
- 需要专门的AI工具来提高能源素养和准确的表示.
- 开发改进的人工智能对于有效的公众讨论低碳能源至关重要.
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