Non-equilibrium physics: from spin glasses to machine and neural learning
It addresses the problem of understanding intelligent behaviors in disordered systems for AI researchers, though it appears incremental as it builds on existing empirical successes without claiming major breakthroughs.
The thesis characterized emergent intelligence in disordered systems using statistical physics, uncovering relationships between learning mechanisms and physical dynamics to guide intelligent system design.
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.