This paper summarizes some of the technical background, research ideas, and possible development strategies for achieving machine common sense. Machine common sense has long been a critical-but-missing component of Artificial Intelligence (AI). Recent advances in machine learning have resulted in new AI capabilities, but in all of these applications, machine reasoning is narrow and highly specialized. Developers must carefully train or program systems for every situation. General commonsense reasoning remains elusive. The absence of common sense prevents intelligent systems from understanding their world, behaving reasonably in unforeseen situations, communicating naturally with people, and learning from new experiences. Its absence is perhaps the most significant barrier between the narrowly focused AI applications we have today and the more general, human-like AI systems we would like to build in the future. Machine common sense remains a broad, potentially unbounded problem in AI. There are a wide range of strategies that could be employed to make progress on this difficult challenge. This paper discusses two diverse strategies for focusing development on two different machine commonsense services: (1) a service that learns from experience, like a child, to construct computational models that mimic the core domains of child cognition for objects (intuitive physics), agents (intentional actors), and places (spatial navigation); and (2) service that learns from reading the Web, like a research librarian, to construct a commonsense knowledge repository capable of answering natural language and image-based questions about commonsense phenomena.
A generalized concept-cognitive learning: A machine learning viewpoint
University of Chinese Academy of Sciences（中国科学院大学）；Chinese Academy of Sciences（中国科学院）；University of Nebraska at Omaha（内布拉斯加大学奥马哈分校）；Kunming University of Science and Technology（昆明理工大学）
A Pluggable Common Sense-Enhanced Framework for Knowledge Graph Completion
School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）；College of Information and Communication Engineering, Hainan University（海南大学信息与通信工程学院）
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；Saarland University（萨尔大学）；J. Mack Robinson College of Business, Georgia State University（佐治亚州立大学J·麦克·罗宾逊商学院）
Optimization paper production through digitalization by developing an assistance system for machine operators including quality forecast: a concept
Fraunhofer Institute for Process Engineering and Packaging（弗劳恩霍夫过程工程与包装研究所）；Consulting Talents AG（Consulting Talents股份公司）；University Siegen（锡根大学）；LEIPA Group GmbH（LEIPA集团有限公司）
Discover physical concepts and equations with machine learning
Department of Physics, Shanghai University（上海大学物理系）；Department of Physics, Ludwig-Maximilians-Universität（路德维希-马克西米利安大学物理系）；Center for Gravitation and Cosmology, Yangzhou University（扬州大学引力与宇宙学中心）
On Interpretability and Similarity in Concept-Based Machine Learning
Bern University of Applied Sciences（伯尔尼应用科学大学）；National Research University Higher School of Economics（国立研究型大学高等经济学院）；St. Petersburg Department of Steklov Mathematical Institute of Russian Academy of Sciences（俄罗斯科学院斯捷克洛夫数学研究所圣彼得堡分所）
Machine Unlearning in Hyperbolic vs. Euclidean Multimodal Contrastive Learning: Adapting Alignment Calibration to MERU
Aalborg University（奥尔堡大学）；University of Barcelona（巴塞罗那大学）；Computer Vision Center（计算机视觉中心）；Milestone Systems（里程碑系统公司）；Pioneer Center for Artificial Intelligence（先锋人工智能中心）
