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.

混沌多体系统的新兴智能行为可以通过统计物理学来表征，探索学习机制与物理动力学之间的关系，以设计适用于人工智能应用的智能系统。