- 学术相关
- Building Socially Intelligent AI Systems: Evidence from the Trust Game Using Artificial Agents with Deep Learning
- Reciprocal Human-Machine Learning: A Theory and an Instantiation for the Case of Message Classification
- A contrast-composition-distraction framework to understand product photo background’s impact on consumer interest in E-commerce
- A model of behavioral manipulation
- 技术技巧
学术相关
Building Socially Intelligent AI Systems: Evidence from the Trust Game Using Artificial Agents with Deep Learning1
Reciprocal Human-Machine Learning: A Theory and an Instantiation for the Case of Message Classification2
A contrast-composition-distraction framework to understand product photo background’s impact on consumer interest in E-commerce3
A model of behavioral manipulation4
技术技巧
Chorme即将推出自动增加textarea高度的CSS属性
基于截图自动生成前端
基于ChatGPT 4和DALL-E 3。
tldrow基于绘图前端生成
基于OpenAI。
基于自然语言生成Emoji
AI前端评审与设计建议
GPT-4V 自动生成图表
ChatGPT-4 Vision可以基于图片生成图表。
基于ChatGPT应用导航网站
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Wu, J. X., Wu, Y. (Diana), Chen, K.-Y., & Hua, L. (2023). Building Socially Intelligent AI Systems: Evidence from the Trust Game Using Artificial Agents with Deep Learning. Management Science. https://doi.org/10.1287/mnsc.2023.4782 ↩
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Te’eni, D., Yahav, I., Zagalsky, A., Schwartz, D., Silverman, G., Cohen, D., Mann, Y., & Lewinsky, D. (2023). Reciprocal Human-Machine Learning: A Theory and an Instantiation for the Case of Message Classification. Management Science. https://doi.org/10.1287/mnsc.2022.03518 ↩
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Wang, M., Li, X., Liu, Y., Chau, P., & Chen, Y. (2023). A contrast-composition-distraction framework to understand product photo background’s impact on consumer interest in E-commerce. Decision Support Systems, 114124. https://doi.org/10.1016/j.dss.2023.114124 ↩
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Acemoglu, D., Makhdoumi, A., Malekian, A., & Ozdaglar, A. (2022). A model of behavioral manipulation. Work in Progress. ↩