Big data have the characteristics of enormous volume, high velocity, diversity, value-sparsity, and uncertainty, which lead the knowledge learning from them full of challenges. With the emergence of crowdsourcing, versatile information can be obtained on-demand so that the wisdom of crowds is easily involved to facilitate the knowledge learning process. During the past thirteen years, researchers in the AI community made great efforts to remove the obstacles in the field of learning from crowds. This concentrated survey paper comprehensively reviews the technical progress in crowdsourcing learning from a systematic perspective that includes three dimensions of data, models, and learning processes. In addition to reviewing existing important work, the paper places a particular emphasis on providing some promising blueprints on each dimension as well as discussing the lessons learned from our past research work, which will light up the way for new researchers and encourage them to pursue new contributions.

翻译：大型数据具有巨大数量、高速、多样性、价值差异和不确定性的特点,这些特点引导着知识从中学习,充满了挑战。随着众包的出现,人们可以按需要获得多种信息,从而便于人群的智慧参与知识学习过程。在过去十三年中,大赦国际的研究人员为消除人群学习领域的障碍作出了巨大努力。这份集中调查文件从系统的角度,从包括数据、模型和学习过程三个层面的系统角度,全面审查了众包学习的技术进步。除了审查现有的重要工作外,该文件特别强调就每个层面提供一些有希望的蓝图,并讨论我们过去研究工作的经验教训,这将为新的研究人员指明道路,鼓励他们作出新的贡献。