Disordered many-body systems exhibit a wide range of emergent phenomena across different scales. These complex behaviors can be utilized for various information processing tasks such as error correction, learning, and optimization. Despite the empirical success of utilizing these systems for intelligent tasks, the underlying principles that govern their emergent intelligent behaviors remain largely unknown. In this thesis, we aim to characterize such emergent intelligence in disordered systems through statistical physics. We chart a roadmap for our efforts in this thesis based on two axes: learning mechanisms (long-term memory vs. working memory) and learning dynamics (artificial vs. natural). Throughout our journey, we uncover relationships between learning mechanisms and physical dynamics that could serve as guiding principles for designing intelligent systems. We hope that our investigation into the emergent intelligence of seemingly disparate learning systems can expand our current understanding of intelligence beyond neural systems and uncover a wider range of computational substrates suitable for AI applications.

翻译：无序多体系统在不同尺度上展现出广泛的涌现现象。这些复杂行为可用于多种信息处理任务，如错误纠正、学习和优化。尽管利用这些系统完成智能任务已取得实证成功，但支配其涌现智能行为的基本原理仍大多未知。在本论文中，我们旨在通过统计物理学刻画无序系统中的这种涌现智能。我们基于两条轴线勾勒出本研究的路线图：学习机制（长期记忆 vs. 工作记忆）和学习动力学（人工 vs. 自然）。在整个探索过程中，我们揭示了学习机制与物理动力学之间的联系，这些联系可作为设计智能系统的指导原则。我们期望通过对看似迥异的学习系统中涌现智能的研究，不仅能拓展当前超越神经系统的智能理解，更能发现适用于人工智能应用的更广泛计算基质。