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相关实验视频

Updated: Sep 10, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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复杂系统科学对人工智能治理的教训

Noam Kolt1,2, Michal Shur-Ofry1, Reuven Cohen3

  • 1Faculty of Law, Hebrew University, Jerusalem, Israel.

Patterns (New York, N.Y.)
|August 22, 2025
PubMed
概括
此摘要是机器生成的。

复杂的自适应系统原则为管理人工智能 (AI) 提供了至关重要的见解. 应用这些教训有助于管理人工智能

关键词:
连续发生的风险复杂的适应性系统出现情况反循环监管和治理进行缩放

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科学领域:

  • 复杂的适应系统科学的跨学科应用.
  • 整合来自物理,生物学和社会科学的见解来实现人工智能治理.
  • 利用复杂系统理论来理解人工智能行为和风险.

背景情况:

  • 当代人工智能 (AI) 系统表现出复杂的适应性系统的特征.
  • 人工智能环境显示非线性增长,新兴现象和级联失败.
  • 人工智能治理的挑战源于反循环和关键基础设施的相互依赖.

研究的目的:

  • 探索复杂的适应性系统原则对人工智能治理的适用性.
  • 通过复杂的系统科学来确定人工智能治理的关键挑战.
  • 为复杂性兼容的人工智能治理提出框架.

主要方法:

  • 在复杂的自适应系统和人工智能行为之间进行并行.
  • 通过复杂系统的视角分析人工智能治理挑战.
  • 审查公共卫生和气候变化的案例研究,以获得治理见解.

主要成果:

  • 人工智能系统及其环境与复杂的适应性系统具有共同的特性.
  • 目前对人工智能治理的努力的特点是深层不确定性.
  • 像合成数据反循环这样的特定人工智能功能带来了治理挑战.

结论:

  • 人工智能治理需要与复杂的适应性系统兼容的原则.
  • 建议的目标包括早期/可扩展干预,适应性机构和校准风险值.
  • 有效的人工智能治理需要适应性策略来管理不确定性和新出现的风险.