  This paper briefly reviews the history of meta-learning and describes its
contribution to general AI. Meta-learning improves model generalization
capacity and devises general algorithms applicable to both in-distribution and
out-of-distribution tasks potentially. General AI replaces task-specific models
with general algorithmic systems introducing higher level of automation in
solving diverse tasks using AI. We summarize main contributions of
meta-learning to the developments in general AI, including memory module,
meta-learner, coevolution, curiosity, forgetting and AI-generating algorithm.
We present connections between meta-learning and general AI and discuss how
meta-learning can be used to formulate general AI algorithms.
