意识的形态空间:思想和机器的三种复杂性
Xerxes D Arsiwalla1, Ricard Solé2,3,4,5, Clément Moulin-Frier6
1Departament of Information and Communication Technologies, Universitat Pompeu Fabra (UPF), 08018 Barcelona, Spain.
NeuroSci
|November 1, 2024
概括
一个新的形态空间框架,使用信息理论措施,比较生物和人工智能 (AI) 系统. 这种基于复杂性的方法有助于在各种各样的代理人中定义意识和智力.
科学领域:
- 认知科学 认知科学
- 人工智能的人工智能
- 理论生物学 理论生物学
背景情况:
- 比较生物制剂和人工智能 (AI) 系统需要一个统一的框架.
- 现有的意识和智力指标往往是特定领域的.
研究的目的:
- 引入一个信息理论形态空间来比较生物和人工智能系统.
- 探索复杂性,意识和智力之间的关系.
- 提出一个框架来识别不同类型的意识.
主要方法:
- 开发一个形态空间,轴代表自主,计算和社会复杂性.
- 将各种生物代理 (从细菌到人类) 和人工智能系统 (从深度神经网络到沃森) 映射到这个形态空间上.
- 分析意识的认知和临床指标,包括意识和清醒.
主要成果:
- 形态空间有效地根据复杂性对生物和人工智能系统进行分类.
- 意识和清醒与计算和自主复杂性有关.
- 社会复杂性成为理解意识的关键因素,特别是在进化背景下.
结论:
- 拟议的形态空间为比较思想和机器提供了一个有价值的概念工具.
- 它表明了合成,集体和模拟意识形式的潜力.
- 这一框架有助于识别智能系统的定义特征和设计原则.
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