对视觉类比有两种计算方法:具体任务模型与域一般映射
Nicholas Ichien1, Qing Liu2, Shuhao Fu3
1Department of Psychology, University of Pennsylvania.
Cognitive science
|September 18, 2023
概括
人类在视觉类比中的推理依赖于心理表征的域一般比较,而不是特定任务的深度学习. 一个基于部分的比较模型与人类的表现相匹配,这表明了人工通用智能的途径.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 人类推理的性质,特别是模拟推理,是有争议的:任务特定的知识与对象的域一般操纵.
- 人工智能 (AI) 的进步迅速重新评估了人类的智能和潜在的机器仿真.
研究的目的:
- 调查人类视觉类比推理是否依赖于特定任务的知识或域一般表示比较.
- 将人类的表现与深度学习模型和在视觉类比任务上的新型域-通用模型进行比较.
主要方法:
- 在视觉类比问题中对视角,部分关系和属性的系统操纵,使用现实的3D对象图像 (汽车).
- 与语网络和关系网络 (任务特定的深度学习模型) 进行人类表现的比较.
- 基于部分的比较模型 (PCM) 的开发和评估,采用学到表示的域一般映射.
主要成果:
- 域通用PCM模型在四项和开放式视觉类比中都表现出类似人类的性能.
- 特定任务的深度学习模型 (罗网络,关系网络) 并没有复制人类类比推理的关键方面.
- 类似人类的模拟推理似乎依赖于学习表征,为多个任务编码结构信息.
结论:
- 目前使用大数据的深度学习方法可能无法实现对特定问题的类似人类模拟推理.
- 人类的类比推理可能涉及到域一般的映射和学习的结构信息表示的比较.
- 未来的AI开发可能会从专注于关系相似性计算的域一般方法中受益.
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