借鉴生物智能机制，设计更适应环境的AI系统。
Bio-inspired AI: Integrating Biological Complexity into Artificial Intelligence
- 从生物计算中提炼上下文感知、分层处理等核心原理。
- 强调自适应交互与顶层因果关系对智能系统的重要性。
- 适合关注下一代通用人工智能的科研人员参考。
追求创造人工智能的过程，反映了人类长期对自身智能的好奇。从塔洛斯的传说、亚里士多德逻辑到赫伦的发明，我们始终试图复制心智的奇迹。尽管近年来人工智能进展显著，但单一方法常难以捕捉智能的本质。本文探讨生物计算中的基础原则——如情境依赖的分层信息处理、试错启发式和多尺度组织结构——如何指导真正智能系统的构建。通过分析生物智能的复杂机制，例如自上而下的因果作用与环境的动态适应性交互，我们揭示现有人工系统可能存在的局限。目标是建立一个受生物系统启发的框架，以设计出更具适应性和鲁棒性的智能系统。
原文摘要 · Abstract (English)
The pursuit of creating artificial intelligence (AI) mirrors our longstanding fascination with understanding our own intelligence. From the myths of Talos to Aristotelian logic and Heron's inventions, we have sought to replicate the marvels of the mind. While recent advances in AI hold promise, singular approaches often fall short in capturing the essence of intelligence. This paper explores how fundamental principles from biological computation--particularly context-dependent, hierarchical information processing, trial-and-error heuristics, and multi-scale organization--can guide the design of truly intelligent systems. By examining the nuanced mechanisms of biological intelligence, such as top-down causality and adaptive interaction with the environment, we aim to illuminate potential limitations in artificial constructs. Our goal is to provide a framework inspired by biological systems for designing more adaptable and robust artificial intelligent systems.
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