  Information field theory (IFT), the information theory for fields, is a
mathematical framework for signal reconstruction and non-parametric inverse
problems. Artificial intelligence (AI) and machine learning (ML) aim at
generating intelligent systems including such for perception, cognition, and
learning. This overlaps with IFT, which is designed to address perception,
reasoning, and inference tasks. Here, the relation between concepts and tools
in IFT and those in AI and ML research are discussed. In the context of IFT,
fields denote physical quantities that change continuously as a function of
space (and time) and information theory refers to Bayesian probabilistic logic
equipped with the associated entropic information measures. Reconstructing a
signal with IFT is a computational problem similar to training a generative
neural network (GNN) in ML. In this paper, the process of inference in IFT is
reformulated in terms of GNN training. In contrast to classical neural
networks, IFT based GNNs can operate without pre-training thanks to
incorporating expert knowledge into their architecture. Furthermore, the
cross-fertilization of variational inference methods used in IFT and ML are
discussed. These discussions suggests that IFT is well suited to address many
problems in AI and ML research and application.
