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人工创造力:从预测性AI到生成系统3
Juan Carlos Chávez-Autor1,2,3,4
1College of Psychology, Keiser University, Fort Lauderdale, FL, United States.
Frontiers in artificial intelligence
|October 31, 2025
概括
由于缺乏评估和控制机制,大型语言模型与创造力作斗争. 生成系统3 (GS-3) 引入了内部批评和自适应增益控制,以增强人工智能系统中的人工创造力和新性.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
- 计算创造力的创造力
背景情况:
- 大型语言模型 (LLM) 在文本生成方面表现出色,但缺乏持续的新性,任务相关性和多样性.
- 人类的创造力涉及三种过程循环:自发的构想 (系统1),目标导向的评估 (系统2) 和元认知控制 (系统3).
- 目前的LLM只实现这一循环的片段,阻碍了他们的创造潜力.
研究的目的:
- 介绍生成系统3 (GS-3),一个建筑不可知的设计模式,以培养人工创造力.
- 解决当前LLM在扩展背景下生成新,相关和多样化的内容方面的局限性.
- 为开发真正创造性的生成系统提供正式的框架和可测试的路线图.
主要方法:
- 概念分析将新性,有用性和多样性与操作定义正式化.
- 开发具有稳定性约束的多重增益更新策略 (指数式,线性,后勤式).
- 可伪造的行为指数的推导:关联距离密度,分析验证比率和趋同延迟.
- 关于GS-3的概念验证蓝图和评估协议的建议.
主要成果:
- GS-3集成了一个高生成器,一个学术批评器和一个自适应增益控制器.
- 识别了人工创造力的关键缺失组件:内部评估,对采样的内源控制和自适应先验.
- 定义了用于评估创造性AI系统的正式指标和通过-失败标准.
- 概述了伦理方面的考虑,包括减轻偏见和预防奖励游戏.
结论:
- GS-3提供了一个可测试的路线图,用于推进创造性人工智能,超越仅仅是预测,朝着真正的创造力发展.
- 拟议的框架允许开发能够持续创新,相关性和多样性的AI系统.
- GS-3为更复杂的共同创作系统和对人工创造力的更深入理解提供了基础.
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