# A Brief Survey of Associations Between Meta-Learning and General AI

arXiv: https://arxiv.org/abs/2101.04283 · HTML: https://mlsanity.com/paper/2101.04283/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 12 Jan 2021.

## Builds on (most cited first, 7 of 7)

- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Exploring Generalization in Deep Learning](https://mlsanity.com/paper/1706.08947/) (2017)
- [Highway Networks](https://mlsanity.com/paper/1505.00387/) (2015)
- [AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence](https://mlsanity.com/paper/1905.10985/) (2019)
- [On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models](https://mlsanity.com/paper/1511.09249/) (2015)
- [The unreasonable effectiveness of the forget gate](https://mlsanity.com/paper/1804.04849/) (2018)
- [A Comprehensive Overview and Survey of Recent Advances in Meta-Learning](https://mlsanity.com/paper/2004.11149/) (2020)

## Built on by (most cited first, 2 of 2)

- [A Brief Summary of Interactions Between Meta-Learning and Self-Supervised Learning](https://mlsanity.com/paper/2103.00845/) (2021)
- [User-Oriented Smart General AI System under Causal Inference](https://mlsanity.com/paper/2103.14561/) (2021)

## Abstract (arXiv)

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.