标题： 小数据解释器 -- 小数据方法在日常生活中的影响 显示英文标题

标题： Small Data Explainer -- The impact of small data methods in everyday life

摘要： 突破性人工智能（AI）技术的出现促使人们重新关注在小数据环境下，即信息有限的环境下，如何从这些发展受益。 这包括社会问题，例如如何最好地将代表性不足的群体纳入数据驱动的政策和决策制定中，或者可穿戴设备等辅助技术的健康益处。 我们提供了一个概念性概述，特别是将小数据与大数据进行对比，并从典型的案例研究和应用领域中识别出共同主题。 潜在的解决方案在对当前数据分析和建模技术的更详细的技术概述中进行了描述，强调了不同学科的贡献，例如来自统计学的知识驱动建模和来自计算机科学的数据驱动建模。 通过将应用环境、概念性贡献和具体技术联系起来，我们突出了目前已可行的内容，并提出了一个全面利用小数据的议程可能的面貌。 显示英文摘要

摘要： The emergence of breakthrough artificial intelligence (AI) techniques has led to a renewed focus on how small data settings, i.e., settings with limited information, can benefit from such developments. This includes societal issues such as how best to include under-represented groups in data-driven policy and decision making, or the health benefits of assistive technologies such as wearables. We provide a conceptual overview, in particular contrasting small data with big data, and identify common themes from exemplary case studies and application areas. Potential solutions are described in a more detailed technical overview of current data analysis and modelling techniques, highlighting contributions from different disciplines, such as knowledge-driven modelling from statistics and data-driven modelling from computer science. By linking application settings, conceptual contributions and specific techniques, we highlight what is already feasible and suggest what an agenda for fully leveraging small data might look like.