Artificial Intelligence (AI) is making a profound impact in almost every
domain. A vital enabler of its great success is the availability of abundant
and high-quality data for building machine learning models. Recently, the role
of data in AI has been significantly magnified, giving rise to the emerging
concept of data-centric AI. The attention of researchers and practitioners has
gradually shifted from advancing model design to enhancing the quality and
quantity of the data. In this survey, we discuss the necessity of data-centric
AI, followed by a holistic view of three general data-centric goals (training
data development, inference data development, and data maintenance) and the
representative methods. We also organize the existing literature from
automation and collaboration perspectives, discuss the challenges, and tabulate
the benchmarks for various tasks. We believe this is the first comprehensive
survey that provides a global view of a spectrum of tasks across various stages
of the data lifecycle. We hope it can help the readers efficiently grasp a
broad picture of this field, and equip them with the techniques and further
research ideas to systematically engineer data for building AI systems. A
companion list of data-centric AI resources will be regularly updated on
this https URL

本文综述了数据驱动人工智能的必要性，以及针对训练数据开发、推理数据开发和数据维护三个方面的代表性方法，同时讨论了该领域的挑战和各种任务的基准，并提供了一份数据驱动人工智能资源列表。