Artificial intelligence models trained through loss minimization have demonstrated significant success, grounded in principles from fields like information theory and statistical physics. This work explores these established connections through the lens of statistical mechanics, starting from first-principles sample concentration behaviors that underpin AI and machine learning. Our development of statistical mechanics for modeling highlights the key role of exponential families, and quantities of statistics, physics, and information theory.

翻译：通过损失最小化训练的人工智能模型已取得显著成功，其理论基础源于信息论与统计物理学等领域的原理。本研究从支撑人工智能与机器学习的第一性原理样本集中行为出发，通过统计力学视角探讨这些既有的关联。我们为建模而发展的统计力学框架凸显了指数族分布的核心作用，以及统计学、物理学与信息论中的关键量。