News & Activities

The UK’s National AI Strategy If you are interested in learning more about the UK’s National AI Strategy, additional details can be found on the original platform at the following link:https://aistandardshub.org/the-national-ai-strategyhttps://www.linkedin.com/company/ai-standards-hub/The following information presents the UK government’s position on AI as set out in the UK’s National AI Strategy, and highlights how the AI Standards Hub will contribute to the delivery of the UK’s vision and outcomes for AI. The National AI Strategy outlines the United Kingdom’s long-term vision for becoming a global leader in artificial intelligence by promoting innovation, investment, and responsible AI governance. The strategy focuses on three major pillars: strengthening the AI ecosystem through research and talent development, supporting the transition to an AI-enabled economy across industries, and establishing effective national and international AI governance frameworks.A key component of the strategy is the development of trustworthy and ethical AI through international technical standards. The UK established the AI Standards Hub to support collaboration among government, academia, industry, and international partners in shaping global AI standards. The initiative aims to improve AI reliability, transparency, security, and interoperability while encouraging innovation and public trust in AI technologies.The strategy also emphasizes a “pro-innovation” approach to AI governance by balancing regulation with technological advancement. The government supports the creation of AI governance tools, standards, and policy frameworks that encourage responsible AI adoption while protecting public values, human rights, and economic competitiveness. In addition, the strategy highlights the importance of international cooperation, AI ethics, and long-term AI safety in supporting sustainable digital transformation.LSE to launch Global Forum on AI and the Social Sciences with $2m award from the MacArthur Foundationhttps://www.lse.ac.uk/dsi/news/2026/global-forum-announcedThe London School of Economics and Political Science (LSE) has announced the launch of a new Global Forum on AI and the Social Sciences, supported by a $2 million grant from the John D. and Catherine T. MacArthur Foundation. The initiative aims to bring together policymakers, researchers, industry leaders, and civil society representatives to explore how artificial intelligence can better serve society and human needs through evidence-based research and international collaboration.The Forum will be led by professors from LSE’s Data Science Institute (DSI) and will focus on the social and economic impacts of AI. Each year, the Forum will address a different major AI-related issue. The inaugural event, scheduled for September 2026 at LSE, will examine how AI is transforming the future of work and how governments, businesses, and workers can prepare for these changes.In addition, LSE plans to publish an annual State of AI and Society Report to summarize current research findings, identify knowledge gaps, and support informed policymaking. The initiative will also introduce a Commitment Charter, encouraging cooperation among governments, researchers, industry, and funding organizations to promote responsible and human-centered AI development. Overall, the Forum aims to position social science research at the center of global AI discussions and governance.Ethics of AIOnline coursehttps://cmu.to/92C4MThe LSE Ethics of AI Online Course is a short online program developed by London School of Economics and Political Science that focuses on the ethical, social, and political implications of artificial intelligence. The course is designed to help learners understand how AI affects governments, businesses, and society while developing practical skills for addressing ethical challenges related to AI technologies.The program explores major topics such as algorithmic bias, fairness, transparency, privacy, democracy, workplace inequality, and AI governance. Participants learn how AI systems can influence decision-making, hiring processes, public administration, and social structures. The course also discusses the responsibilities of governments and multinational companies in designing and deploying responsible AI systems.The course is structured into three main modules covering AI and the state, AI and business, and AI and society. Through case studies, discussions, and practical activities, learners develop critical thinking skills to evaluate ethical dilemmas and understand global approaches to AI regulation and governance. Overall, the course emphasizes human-centered and responsible AI development while preparing professionals to navigate the growing impact of AI in real-world contexts.

The 9th International Conference on Machine Learning and Machine Intelligence (MLMI 2026)Conference Theme: "AI-Driven Innovations for a Sustainable Future"https://mlmi.netSubmission Deadline: February 5, 2026Notification Deadline: March 5, 2026Registration Deadline: March 20, 2026 We are thrilled to announce that the 9th International Conference on Machine Learning and Machine Intelligence (MLMI 2026) will be held in Tokyo, Japan, from July 17 to 20, 2026. Organized by Rikkyo University, Japan, MLMI 2026 will bring together leading researchers, academics, and industry professionals to explore the latest advancements in machine learning and machine intelligence. The conference will feature insightful presentations, engaging discussions, and vibrant networking opportunities, all set in the culturally rich city of Tokyo. MLMI 2026 aims to advance the field of machine learning and machine intelligence by fostering interdisciplinary collaboration and showcasing cutting-edge research. The scope of MLMI 2026 includes, but is not limited to, deep learning, reinforcement learning, natural language processing, computer vision, robotics, AI ethics, and the integration of machine intelligence in domains such as healthcare, finance, autonomous systems, and environmental sustainability. By providing a platform for knowledge exchange and collaboration, MLMI 2026 seeks to drive innovation and inspire the development of intelligent systems that benefit society.MLMI 2026 offers a unique opportunity to engage with the latest research, connect with global experts, and contribute to the future of machine intelligence. We invite you to join us for four days of learning and networking. You are guaranteed to leave the event with a suitcase full of knowledge and inspiration.Submitted papers will be peer reviewed by conference committees, and accepted papers after proper registration and presentation will be published into Lecture Notes in Electrical Engineering (Electronic ISSN: 1876-1119 & Print ISSN: 1876-1100) as a proceedings book volume. The book series will be indexed by EI Compendex, SCOPUS, INSPEC, SCImago and other database.Topics of interest for submission include, but are not limited to:Track 1: Deep Learning and Neural Architectures Track 2: Reinforcement Learning and Sequential Decision Making▪ Architectural Advances ▪ Advanced Reinforcement Learning Algorithms▪ Efficient Training Techniques ▪ Multi-Agent Reinforcement Learning▪ Graph Neural Networks ▪ Time Series and Sequential Data▪ Generative Models ▪ Predictive Modeling▪ Neural Architecture Search ▪ Anomaly Detection▪ Transfer Learning ▪ Sequential Decision Making ▪ Real-Time AI Systems Track 3: Applied Machine Intelligence Track 4: Natural Language Processing and Multimodal Learning▪ Machine Learning in Healthcare ▪ Large Language Models▪ AI in Robotics ▪ Multilingual and Low-Resource NLP▪ AI for Cybersecurity ▪ Multimodal Machine Learning▪ Biometric and Behavioral Analytics ▪ Cross-Modal Retrieval and Generation▪ Industry-Specific Applications Track 5: Emerging Paradigms and Future Directions Track 6: Explainable, Ethical, and Human-Centered AI▪ Quantum Machine Learning ▪ Explainable AI (XAI)▪ Federated and Distributed Learning ▪ Ethical AI and Fairness▪ AutoML and Meta-Learning ▪ Privacy-Preserving Machine Learning▪ Sustainable and Green AI ▪ Human-Centered AI▪ Hybrid Models ▪ Augmented Intelligence

The 10th International Conference on Deep Learning TechnologiesJuly 17-19, 2026, Kunming, Chinahttps://www.icdlt.orgSubmission Deadline: March 1, 2026Notification of Acceptance: April 1, 2026Camera Ready Deadline: April 15, 2026Registration Deadline: April 15, 2026Conference Dates: July 17-19, 2026You are invited to attend 2026 10th International Conference on Deep Learning Technologies will be held during July 17-19, 2026 in Kunming, China. It's sponsored by Kunming University of Science and Technology, China, organized by the Faculty of Information Engineering and Automation of Kunming University of Science and Technology. It's to provide a valuable opportunity for researchers, scholars and scientists to exchange their ideas face to face in Deep Learning Technologies.We invite researchers from deep learning technologies to participate and submit their work to the program. Likewise, any work on deep learning that has a relation to any of these fields or potential for the usage in any of them is welcome. Please refer to the different submission categories under "Call for Papers" above for further details.Track 1: Deep Learning Model and AlgorithmRecurrent Neural Network (RNN) Sparse Coding Neuro-Fuzzy Algorithms Evolutionary Methods Convolutional Neural Networks (CNN) Deep Hierarchical Networks (DHN) Dimensionality Reduction Unsupervised Feature Learning Deep Boltzmann Machines Generative Adversarial Networks (GAN) Autoencoders Deep Belief Networks Meta-Learning and Deep Networks Deep Metric Learning Methods MAP Inference in Deep Networks Deep Reinforcement Learning Learning Deep Generative Models Deep Kernel Learning Graph Representation Learning Gaussian Processes for Machine Learning Clustering, Classification and Regression Classification ExplainabilityTrack 2: Machine Learning Theory and TechnologyNovel machine and deep learningActive learningIncremental learning and online learningAgent-based learningManifold learningMulti-task learningBayesian networks and applicationsCase-based reasoning methodsStatistical models and learningComputational learningEvolutionary algorithms and learningFuzzy logic-based learningGenetic optimizationClustering, classification and regressionNeural network models and learningParallel and distributed learningReinforcement learningSupervised, semi-supervised and unsupervised learningTensor Learning Deep and Machine Learning for Big Data Analytics:Deep/Machine learning based theoretical and computational modelsMachine learning (e.g., deep, reinforcement, statistical relational, transfer)Model-based reasoningTrack 3: Deep and Machine Learning ApplicationsDeep Learning for Computing and Network PlatformsRecommender systemsDeep Learning for Social media and networksDeep Learning in Computer VisionDeep learning in speech recognitionDeep Learning in Nature Language Processing,Deep Learning in Machine TranslationDeep learning in bioinformaticsDeep Learning in Medical Image AnalysisDeep Learning in Climate ScienceDeep Learning in Board Game ProgramsDeep and Machine Learning for Data Mining and KnowledgeTrack 4: Responsible AI, Security, and GovernanceAI safetyPrivacy preservationAlgorithmic FairnessFairness and ethicsClassification Explainability and Explainable AI (XAI)AI EthicsAI governanceGreen Deep LearningSynthetic Data GenerationSocial and Economic Impact of AISustainable AIAI Risk AssessmentThe accepted paper will be included into ICDLT 2026 Conference ProceedingsPUBLICATION HISTORYICDLT 2025 Proceedings - 979-8-4007-1852-6 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2024 Proceedings - 979-8-4007-1686-7 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2023 Proceedings - 979-8-4007-0752-0 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2022 Proceedings - 978-1-4503-9693-6 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2021 Proceedings - 978-1-4503-9016-3 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2020 Proceedings - 978-1-4503-7548-1 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2019 Proceedings - 978-1-4503-7160-5 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2018 Proceedings - 978-1-4503-6473-7 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2017 Proceedings - 978-1-4503-5232-1 | ACM Digital Library | Ei & SCOPUS Index