受认知科学启发，提出可自主切换学习模式的新型AI架构。
Why AI systems don't learn and what to do about it: Lessons on autonomous learning from cognitive science
- 结合观察学习与主动行为学习，动态切换模式
- 通过元控制信号调节学习策略，提升适应性
- 适合研究自主智能系统或具身学习的学者
我们批判性地审视了当前AI模型在实现自主学习方面的局限性，并提出一种受人类和动物认知启发的学习架构。该框架融合了基于观察的学习（系统A）与基于主动行为的学习（系统B），并可根据内部生成的元控制信号（系统M）灵活切换学习模式。我们探讨了如何借鉴生物体在演化和发育时间尺度上适应真实动态环境的方式，构建这一学习体系。
原文摘要 · Abstract (English)
We critically examine the limitations of current AI models in achieving autonomous learning and propose a learning architecture inspired by human and animal cognition. The proposed framework integrates learning from observation (System A) and learning from active behavior (System B) while flexibly switching between these learning modes as a function of internally generated meta-control signals (System M). We discuss how this could be built by taking inspiration on how organisms adapt to real-world, dynamic environments across evolutionary and developmental timescales.
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