数字双胞胎研究逆向工程视觉智能的起源
Justin N Wood1,2,3, Lalit Pandey1, Samantha M W Wood1,2,3
1Informatics Department, Indiana University Bloomington, Bloomington, Indiana, USA; email: woodjn@indiana.edu, lpandey@iu.edu, sw113@iu.edu.
Annual review of vision science
|September 18, 2024
概括
数字双胞胎研究表明,域一般学习算法可以解释大脑中的先天知识和学习能力. 这表明"时空适配"的普遍原则是跨物种和机器的智能基础.
科学领域:
- 认知科学 认知科学
- 神经科学是一个神经科学.
- 人工智能的人工智能
背景情况:
- 关于智力起源的辩论集中在先天的知识与从经验中学习.
- 民族主义假设先天性,领域特定的系统,而经验主义则倾向于领域一般的学习系统.
研究的目的:
- 研究新生儿大脑中的核心学习算法.
- 用数字双胞胎研究来解决本土主义与经验主义争论.
主要方法:
- 数字双胞胎研究比较新生动物和人工代理在相同的环境和任务.
- 在模拟新生儿大脑中逆向工程学习算法.
主要成果:
- 域一般算法从出生后的视觉体验中获得了类似动物的感知,支持经验主义.
- 域一般算法从产前经验 (视网膜波) 中产生了先天的,域特定的知识,支持本地主义.
结论:
- 一个普遍的原则,称为"时空适配",统一了原生主义和经验主义的发现.
- 时空适配为理解人类,动物和机器智能的起源提供了一个框架.
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