This article is about how the "SP theory of intelligence" and its realisation in the "SP machine" may, with advantage, be applied to the management and analysis of big data. The SP system -- introduced in the article and fully described elsewhere -- may help to overcome the problem of variety in big data: it has potential as "a universal framework for the representation and processing of diverse kinds of knowledge" (UFK), helping to reduce the diversity of formalisms and formats for knowledge and the different ways in which they are processed. It has strengths in the unsupervised learning or discovery of structure in data, in pattern recognition, in the parsing and production of natural language, in several kinds of reasoning, and more. It lends itself to the analysis of streaming data, helping to overcome the problem of velocity in big data. Central in the workings of the system is lossless compression of information: making big data smaller and reducing problems of storage and management. There is potential for substantial economies in the transmission of data, for big cuts in the use of energy in computing, for faster processing, and for smaller and lighter computers. The system provides a handle on the problem of veracity in big data, with potential to assist in the management of errors and uncertainties in data. It lends itself to the visualisation of knowledge structures and inferential processes. A high-parallel, open-source version of the SP machine would provide a means for researchers everywhere to explore what can be done with the system and to create new versions of it.
Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big data
University of Pavia（帕维亚大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Oden Institute for Computational Engineering and Sciences（奥登计算工程与科学研究所）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Harvard University（哈佛大学）；Massachusetts Institute of Technology（麻省理工学院）；Dartmouth College（达特茅斯学院）；Geisel School of Medicine at Dartmouth（达特茅斯盖泽尔医学院）；Livestrong Cancer Institutes（利文斯顿癌症研究所）；Purdue University（普渡大学）；Weldon School of Biomedical Engineering（韦尔登生物医学工程学院）；University of Colorado Anschutz（科罗拉多大学安舒茨分校）
Inertial Sensing Meets Artificial Intelligence: Opportunity or Challenge?
University of Calgary（卡尔加里大学）；Wuhan University（武汉大学）；China University of Geosciences (Beijing)（中国地质大学（北京））；Beijing University of Posts and Telecommunications（北京邮电大学）
Federated Learning in Big Model Era: Domain-Specific Multimodal Large Models
ENN Group Co.,Ltd.（ENN集团有限公司）；China University of Petroleum（中国石油大学）；Unicom (Shanghai) Industrial Internet Co.,Ltd.（联通（上海）工业互联网有限公司）；Qingdao Windaka Technology Co.,Ltd.（青岛Windaka科技有限公司）
College of Cyber Security, Jinan University（暨南大学网络空间安全学院）；College of Mathematics and Computer Science, Shantou University（汕头大学数学与计算机科学学院）；School of Information Engineering, Guangdong Eco-Engineering Polytechnic（广东生态工程职业学院信息工程学院）；Department of Computer Science, University of Illinois Chicago（芝加哥伊利诺伊大学计算机科学系）
Towards a New Science of a Clinical Data Intelligence
Siemens AG（西门子股份公司）；University Hospital Erlangen（埃尔朗根大学医院）；Friedrich-Alexander-University Erlangen-Nuremberg（弗里德里希-亚历山大大学埃尔朗根-纽伦堡分校）；Charité - Universitätsmedizin Berlin（柏林夏里特医学院）；Averbis GmbH（Averbis有限公司）；DFKI GmbH（德国人工智能研究中心有限公司）；Fraunhofer Institute for Integrated Circuits（弗劳恩霍夫集成电路研究所）；Institut für Frauengesundheit GmbH（女性健康研究所有限公司）；Ludwig Maximilian University of Munich（慕尼黑路德维希-马克西米利安大学）
Artificial Intelligence as an Anti-Corruption Tool (AI-ACT) -- Potentials and Pitfalls for Top-down and Bottom-up Approaches
Max-Planck-Institute for Human Development（马克斯·普朗克人类发展研究所）；Heinrich Heine University Düsseldorf（杜塞尔多夫海因里希·海涅大学）；Heine Center for Artificial Intelligence and Data Science（海涅人工智能与数据科学中心）
A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data and LLMs Perspective
Southwestern University of Finance and Economics（西南财经大学）；Chengdu University（成都大学）；Universität Hamburg（汉堡大学）；The University of Hong Kong（香港大学）；Hunan University of Technology and Business（湖南工业大学）；Xiangjiang Laboratory（湘江实验室）；Emory University（埃默里大学）
Big Bang, Low Bar -- Risk Assessment in the Public Arena
Trinity College, Cambridge（剑桥大学三一学院）；Leverhulme Centre for the Future of Intelligence, Cambridge（剑桥大学莱弗休姆未来智能研究中心）；Centre for Science and Thought, University of Bonn（波恩大学科学与思想中心）
Artificial Intelligence for Atmospheric Sciences: A Research Roadmap
Department of Computer Science and Institute for Atmospheric Science and Earth System Research (INAR), University of Helsinki, Finland（计算机科学系和大气科学与地球系统研究所（INAR）、赫尔辛基大学，芬兰）；Department of Computer Science, University of Helsinki, Finland（计算机科学系、赫尔辛基大学，芬兰）；Department of Computer Science and Helsinki Institute of Sustainability Science (HELSUS), University of Helsinki, Finland（计算机科学系和赫尔辛基可持续科学研究所（HELSUS）、赫尔辛基大学，芬兰）；Institute for Atmospheric Science and Earth System Research (INAR), University of Helsinki, Finland（大气科学与地球系统研究所（INAR）、赫尔辛基大学，芬兰）
Big Data and the Computational Social Science of Entrepreneurship and Innovation
Booth School of Business, University of Chicago（芝加哥大学博斯商学院）；Department of Sociology, University of Chicago（芝加哥大学社会学系）；Knowledge Lab, University of Chicago（芝加哥大学知识实验室）；Santa Fe Institute, \cny USA（圣达菲研究所）
