  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.
