自主适应性智能和人工通用智能的神经网络模型:我们的大脑如何学习大型语言模型及其含义
1Departments of Mathematics and Statistics, Psychological and Brain Sciences, and Biomedical Engineering, Boston University, Boston, MA, United States.
Frontiers in systems neuroscience
|August 14, 2025
概括
本研究介绍了一种生物神经网络模型ChatSOME,该模型通过自我组织过程解释了人类对大型语言模型 (LLM) 的学习及其含义. 它详细介绍了人类如何有意识地感知,识别和将视觉场景与情感和语言联系起来,这些能力缺乏像ChatGPT这样的当前AI.
科学领域:
- 认知神经科学 认知神经科学
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 人类的学习涉及复杂的自我组织的大脑过程,包括记忆,感知和情绪.
- 像ChatGPT这样的现有AI模型缺乏在人类中看到的细微的理解和关联式学习能力.
- 之前的工作集中在小语言模型上;本研究涉及大型语言模型 (LLM).
研究的目的:
- 介绍一个生物神经网络模型,ChatSOME (自组织意义),它解释了人类对LLMs的理解.
- 阐明人类如何学习有意识地感知,识别和将视觉场景与情感和语言联系起来.
- 突出人类认知情感学习与当前人工智能能力之间的差异.
主要方法:
- 开发一种生物神经网络模型 (ChatSOME),结合各种自我组织的大脑过程.
- 利用自适应共振理论电路用于模型学习和记忆稳定.
- 模拟视觉场景,情绪和语言之间的双向关联学习.
主要成果:
- 聊天SOME模型展示了人类如何通过共享经验和自我组织从LLM学习意义.
- 它解释了无限视觉场景的有意识识别和与之相关的情绪和语言的稳定记忆形成.
- 场景,情绪和发言之间的双向关联链接被学习并稳定地记住.
结论:
- 人类对LLM的理解涉及复杂的认知情感互动和自我组织的记忆,在当前的AI中没有复制.
- 聊天SOME模型为理解语言和意义的生物学合理学习提供了一个框架.
- 这项研究促进了对神经符号计算和人工智能意识感知的理解.
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