This article reviews meta-learning also known as learning-to-learn which
seeks rapid and accurate model adaptation to unseen tasks with applications in
highly automated AI, few-shot learning, natural language processing and
robotics. Unlike deep learning, meta-learning can be applied to few-shot
high-dimensional datasets and considers further improving model generalization
to unseen tasks. Deep learning is focused upon in-sample prediction and
meta-learning concerns model adaptation for out-of-sample prediction.
Meta-learning can continually perform self-improvement to achieve highly
autonomous AI. Meta-learning may serve as an additional generalization block
complementary for original deep learning model. Meta-learning seeks adaptation
of machine learning models to unseen tasks which are vastly different from
trained tasks. Meta-learning with coevolution between agent and environment
provides solutions for complex tasks unsolvable by training from scratch.
Meta-learning methodology covers a wide range of great minds and thoughts. We
briefly introduce meta-learning methodologies in the following categories:
black-box meta-learning, metric-based meta-learning, layered meta-learning and
Bayesian meta-learning framework. Recent applications concentrate upon the
integration of meta-learning with other machine learning framework to provide
feasible integrated problem solutions. We briefly present recent meta-learning
advances and discuss potential future research directions.

该论文回顾了元学习的概念和应用，其与深度学习的不同之处在于其能够适应于少量高维数据集，可以用于模型自适应，可不断自我完善实现高度自主的人工智能。元学习不断发展创新，不同的发展方法已有不同的应用和研究方向，进一步拓展了机器学习的应用领域。

元学习最近进展的综述和调查

A Comprehensive Overview and Survey of Recent Advances in Meta-Learning

What if we delegated so much to autonomous AI and intelligent machines that
They passed a law that forbids humans to carry out a number of professions? We
conceive the plot of a new episode of Black Mirror to reflect on what might
await us and how we can deal with such a future.

黑镜中的一个情节，探讨的是如果我们将许多职业委派给自主 AI 和智能机器，是否会出台法律禁止人类从事某些职业，这样的未来将会带来什么影响以及我们如何应对。