Research Area:  Machine Learning

Abstract:

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.

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Author(s) Name:  Huimin Peng

Publisher name:  arXiv:2101.04283

DOI:  10.48550/arXiv.2101.04283

Volume Information:  

Paper Link:   https://arxiv.org/abs/2101.04283