Academic challenges comprise effective means for (i) advancing the state of the art, (ii) putting in the spotlight of a scientific community specific topics and problems, as well as (iii) closing the gap for under represented communities in terms of accessing and participating in the shaping of research fields. Competitions can be traced back for centuries and their achievements have had great influence in our modern world. Recently, they (re)gained popularity, with the overwhelming amounts of data that is being generated in different domains, as well as the need of pushing the barriers of existing methods, and available tools to handle such data. This chapter provides a survey of academic challenges in the context of machine learning and related fields. We review the most influential competitions in the last few years and analyze challenges per area of knowledge. The aims of scientific challenges, their goals, major achievements and expectations for the next few years are reviewed.

翻译：学术挑战构成了推进技术发展水平、(ii) 将特定主题和问题置于科学界关注焦点，以及(iii) 缩小代表性不足群体在获取并参与塑造研究领域方面的差距的有效手段。竞赛的历史可追溯至数百年前，其成就对当今世界产生了深远影响。近年来，随着各领域海量数据的生成，以及突破现有方法边界与处理此类数据可用工具需求的增强，竞赛（重新）获得了广泛关注。本章对机器学习及相关领域的学术挑战进行了综述。我们回顾了近些年来最具影响力的竞赛，并按知识领域分析了各类挑战。同时，还探讨了科学挑战的目标、主要成就以及未来数年的发展预期。