从动物适应行为中汲取灵感，构建能实时调整的智能系统
Adaptive Intelligence: leveraging insights from adaptive behavior in animals to build flexible AI systems
- 借鉴动物学习与环境反馈的机制，设计可在线学习的智能体
- 提出生物启发式算法框架，支持快速环境适应与泛化能力
- 适合研究自适应系统、神经科学与人工智能交叉方向的学者
生物智能具有内在的适应性——动物会根据环境反馈持续调整行为。然而，构建自适应的人工智能仍是重大挑战。下一代目标是超越传统AI，发展‘自适应智能’，即利用生物智能的洞见，构建能够在线学习、泛化并快速适应环境变化的智能体。近期神经科学进展提供了新启示，研究日益聚焦于动物如何自然地学习和更新其世界模型。本文将回顾自适应生物智能的行为与神经基础，对比人工智能的最新进展，并探索脑启发式方法在构建更自适应算法中的潜力。
原文摘要 · Abstract (English)
Biological intelligence is inherently adaptive -- animals continually adjust their actions based on environmental feedback. However, creating adaptive artificial intelligence (AI) remains a major challenge. The next frontier is to go beyond traditional AI to develop "adaptive intelligence," defined here as harnessing insights from biological intelligence to build agents that can learn online, generalize, and rapidly adapt to changes in their environment. Recent advances in neuroscience offer inspiration through studies that increasingly focus on how animals naturally learn and adapt their world models. In this Perspective, I will review the behavioral and neural foundations of adaptive biological intelligence, the parallel progress in AI, and explore brain-inspired approaches for building more adaptive algorithms.
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