A Brief Survey of Associations Between Meta-Learning and General AI
It addresses the problem of developing more automated and generalizable AI systems for researchers and practitioners, but it is incremental as it surveys existing connections.
This paper reviews how meta-learning contributes to general AI by improving model generalization and enabling algorithms applicable to both in-distribution and out-of-distribution tasks, summarizing key contributions such as memory modules and meta-learners.
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.