This article is an overview of the "SP theory of intelligence". The theory aims to simplify and integrate concepts across artificial intelligence, mainstream computing and human perception and cognition, with information compression as a unifying theme. It is conceived as a brain-like system that receives 'New' information and stores some or all of it in compressed form as 'Old' information. It is realised in the form of a computer model -- a first version of the SP machine. The concept of "multiple alignment" is a powerful central idea. Using heuristic techniques, the system builds multiple alignments that are 'good' in terms of information compression. For each multiple alignment, probabilities may be calculated. These provide the basis for calculating the probabilities of inferences. The system learns new structures from partial matches between patterns. Using heuristic techniques, the system searches for sets of structures that are 'good' in terms of information compression. These are normally ones that people judge to be 'natural', in accordance with the 'DONSVIC' principle -- the discovery of natural structures via information compression. The SP theory may be applied in several areas including 'computing', aspects of mathematics and logic, representation of knowledge, natural language processing, pattern recognition, several kinds of reasoning, information storage and retrieval, planning and problem solving, information compression, neuroscience, and human perception and cognition. Examples include the parsing and production of language including discontinuous dependencies in syntax, pattern recognition at multiple levels of abstraction and its integration with part-whole relations, nonmonotonic reasoning and reasoning with default values, reasoning in Bayesian networks including 'explaining away', causal diagnosis, and the solving of a geometric analogy problem.
Deep Neural Network Based Relation Extraction: An Overview
Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province（四川省可信云计算与大数据重点实验室）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）
知识是一种形式化的认识世界的方式，为下一代人工智能提供了人类水平的认知和智能。知识的表示之一是实体之间的语义关系。关系抽取（RelationExtraction，RE）是信息抽取的一个子任务，是自动获取这些重要知识的有效途径，在自然语言处理（Natural Language Processing，NLP）中起着至关重要的作用。其目的是从自然语言文本中识别实体之间的语义关系。到目前为止，有几个研究RE在以前的作品，其中记录了这些技术的基础上，深度神经网络（DNN）成为一个流行的技术在这项研究中。特别是基于DNN的监督和远程监督方法是RE最流行和最可靠的解决方案。本文1）介绍了一些基本概念，2）从两个角度对分布式神经网络在RE中的应用进行了全面的概述：监督RE，它试图改进标准RE系统;远程监督RE，它采用分布式神经网络设计句子编码器和去噪方法。我们进一步3）涵盖了一些新的方法和最近的趋势，以及讨论可能的未来研究方向，这项任务。
Bringing AI To Edge: From Deep Learning's Perspective
HP-NTU Digital Manufacturing Corporate Lab, Nanyang Technological University（南洋理工大学惠普-南大数字制造企业实验室）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；Innovations and Experiences–Business Personal Systems, HP Inc.（惠普公司创新与体验-个人系统业务部）
An Unified Intelligence-Communication Model for Multi-Agent System Part-I: Overview
Artificial Intelligence Research Center, National Institute of Defense Technology Innovation（国防科技创新研究院人工智能研究中心）；Academy of Military Sciences（军事科学院）；National University of Defense Technology（国防科技大学）；Southampton University（南安普顿大学）
Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview
Ozyegin University（厄兹耶因大学）；Ege University（爱琴海大学）
软件定义网络（Software defined networking，SDN）是一种结合了集中管理和网络可编程性的网络架构。SDN将控制平面与数据平面分离，并将网络管理转移到一个中心点，称为控制器，可以编程并用作网络的大脑。最近，研究界已经表现出越来越多的趋势，受益于人工智能（AI）领域的最新进展，以提供学习能力和更好的决策在SDN。在这项研究中，我们详细概述了最近将AI纳入SDN的努力。我们的研究表明，研究工作集中在人工智能的三个主要子领域，即：机器学习，元推理和模糊推理系统。因此，在这项工作中，我们调查了它们的不同应用领域和潜在用途，以及通过在SDN范式中包含基于AI的技术所实现的改进。
Can Complexity and Uncomputability Explain Intelligence? SuperARC: A Test for Artificial Super Intelligence Based on Recursive Compression
Oxford Immune Algorithmics（牛津免疫算法公司）；Oxford University Innovation（牛津大学创新中心）；London Institute for Healthcare Engineering（伦敦医疗工程研究所）；Algorithmic Dynamics Lab（算法动力实验室）；Center of Molecular Medicine, Karolinska Institute（卡罗林斯卡研究所分子医学中心）；King’s College London（伦敦大学国王学院）；Centre for Logic, Epistemology and the History of Science, University of Campinas (UNICAMP)（逻辑、知识论和科学史研究中心，坎皮纳斯大学）；DEXL, National Laboratory for Scientific Computing (LNCC)（DEXL，国家科学计算实验室）；King’s Institute for Artificial Intelligence, King’s College London（伦敦大学国王学院人工智能研究所）
The Bioelectrical Information Theory: Investigating the theoretical compression limit of bioelectrical signals under artificial intelligence
College of Computer Science and Artificial Intelligence（计算机科学与人工智能学院）；Shanghai Key Laboratory of Intelligent Information Processing（上海智能信息处理重点实验室）；Fudan University（复旦大学）
An Overview of the Prospects and Challenges of Using Artificial Intelligence for Energy Management Systems in Microgrids
Department of Electrical Engineering, University of Sharjah（电气工程系，谢贾哈大学）；Department of Electric, Electronic and Computer Engineering, University of Pretoria（电气电子与计算机工程系，普里蒂亚大学）
A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence
University of Macau（澳门大学）；Future Network of Intelligence Institute（未来网络智能研究院）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Southeast University（东南大学）；Purple Mountain Laboratories（紫金山实验室）；University of New South Wales（新南威尔士大学）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；Friedrich-Alexander-University Erlangen-Nurnberg（弗里德里希-亚历山大-埃尔朗根-纽伦堡大学）；University of California, Irvine（加州大学欧文分校）
UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility
The State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）；Macau University of Science and Technology（澳门科技大学）；School of Automation, Beijing Institute of Technology（北京理工大学自动化学院）；School of Software, Northwestern Polytechnical University（西北工业大学软件学院）；College of Mechanical and Vehicle Engineering, Hunan University（湖南大学机械与运载工程学院）；John von Neumann Faculty of Informatics, Obuda University（欧布达大学约翰·冯·诺依曼信息学院）
Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Saarland University（萨尔大学）
AI-Native Open RAN for Non-Terrestrial Networks: An Overview
CEMSE Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia (KSA)（卡奥斯特大学科学与技术学院，卡萨尔王国沙特阿拉伯 Thuwal 分校）；Interdisciplinary Research Center for Communication Systems and Sensing (IRC-CSS), Department of Electrical Engineering, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, KSA（通信系统与感知交叉研究中心，国王法赫德石油矿物大学电子工程系）；Centre for Future Transport and Cities, Coventry University, U.K.（未来交通与城市研究中心，考文垂大学）
