生物与机器的适应原理I：机器学习、信息论与热力学
The principles of adaptation in organisms and machines I: machine learning, information theory, and thermodynamics
摘要：
生物体如何通过获得关于世界的知识来认识它们的环境，以及它们根据这些知识采取什么行动？本文从机器学习、信息论和热力学的角度考察了关于生物体适应环境的假设。我们首先构建一个世界的分层模型作为大脑的内部模型，并回顾标准的机器学习方法，通过在最大似然原理下近似学习模型来推断原因。这反过来又提供了一个有机体的自由能原理的概述，这是一个从最小惊喜原则解释感知和行动的假设。将这种统计学习视为世界与大脑之间的交流，学习被解释为最大化关于世界的信息的过程。我们研究如何感知的经典理论，如infomax原则与学习层次模型。然后，我们提出了一种基于热力学的识别和学习的方法，表明因果学习的结果在热力学第二定律的适应，而推理动力学，融合观察与先验知识形成一个热力学过程。这些提供了关于生物体适应环境的统一观点。
英文摘要：
How do organisms recognize their environment by acquiring knowledge about the world, and what actions do they take based on this knowledge? This article examines hypotheses about organisms' adaptation to the environment from machine learning, information-theoretic, and thermodynamic perspectives. We start with constructing a hierarchical model of the world as an internal model in the brain, and review standard machine learning methods to infer causes by approximately learning the model under the maximum likelihood principle. This in turn provides an overview of the free energy principle for an organism, a hypothesis to explain perception and action from the principle of least surprise. Treating this statistical learning as communication between the world and brain, learning is interpreted as a process to maximize information about the world. We investigate how the classical theories of perception such as the infomax principle relates to learning the hierarchical model. We then present an approach to the recognition and learning based on thermodynamics, showing that adaptation by causal learning results in the second law of thermodynamics whereas inference dynamics that fuses observation with prior knowledge forms a thermodynamic process. These provide a unified view on the adaptation of organisms to the environment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
