社会智能机器从人类中学习,并帮助人类学习
Hyowon Gweon1, Judith Fan1,2, Been Kim3
1Department of Psychology, Stanford University, Stanford, CA 94305, USA.
概括
我们建议开发能够进行推断式社会学习 (ISL) 的社会智能机器. 这些人工智能系统将通过考虑人类的价值观,意图和信仰来学习和教导人类,以加强人类-人工智能合作.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 人类智能的特点是理解和影响他人的思想.
- 推断式社会学习 (ISL) 涉及使用常识心理学来学习和教学.
- 人工智能的进步引发了关于支持ISL的人机交互的问题.
研究的目的:
- 想象社会智能机器可以通过ISL学习,教学和沟通.
- 超越仅仅预测行为或模仿社会线索的AI.
- 开发能够主动考虑人类价值观,意图和信仰的AI.
主要方法:
- 社会智能机器的概念框架.
- 专注于积极考虑人类精神状态.
- 强调人类和机器之间的相互理解.
主要成果:
- 社会智能机器可以增强人工智能对人类 (作为学习者) 的学习能力.
- 这些机器可以帮助人类获得知识 (作为教师).
- 需要科学研究人类对机器大脑的推理.
结论:
- 开发社会智能机器需要向理解人类价值观和意图的转变.
- 未来的人工智能应该促进有效的人工智能社会学习.
- 人工智能/ML和认知科学之间的合作对于推进自然和人工智能至关重要.
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