Communications AI & Computing is an open-access journal from the Nature Portfolio that publishes high-quality research, reviews and commentary pieces relevant to computer scientists. The papers we publish represent notable advances covering developments, implementations and applications of computer science and AI. We also provide a community forum for issues of importance to scientists working on AI and computer science research, regardless of sub-discipline. The scope of the journal covers all areas of AI and computer science, including (but not limited to): Artificial Intelligence & Machine Learning Supervised, unsupervised, and reinforcement learning Deep learning architectures and optimization Generative models and representation learning Explainable and interpretable AI Causal inference and reasoning Single- and Multi-agent systems Human-in-the-loop and interactive learning Continual, transfer, and meta-learning Federated and privacy-preserving learning Biologically-inspired learning algorithms and AI architectures Natural Language Processing Language modeling and understanding Machine translation and multilingual NLP Dialogue systems and conversational AI Information retrieval and question answering Speech recognition and synthesis Multimodal language processing Computer Vision Image and video understanding Object detection and recognition 3D vision and scene understanding Vision-language integration Computational Science High-performance computing (HPC) and parallel algorithms Numerical methods and simulation Computational modeling in physics, chemistry, biology, and engineering AI for scientific discovery and automation Data-driven modeling and surrogate modeling Digital twins and simulation-based inference Algorithms and data structures Theoretical computer science Scientific Computing Biomedical informatics Computational neuroscience AI applications in psychology and cognitive science Social computing Geospatial computing and geography Computational chemistry and materials science Computational biology Digital humanities Quantum and Unconventional Computing Quantum algorithms and complexity Quantum machine learning Quantum simulation and optimization Quantum software and programming frameworks Hybrid quantum-classical systems Neuromorphic computing Molecular and biochemical computing Ethics, Policy & Societal Impact Fairness, accountability, and transparency in AI Bias detection and mitigation AI governance and regulation Environmental sustainability of computing Social, economic, and cultural implications of AI The assessment and peer-review processes are managed by in-house professional editors supported by editorial board members, who provide technical expertise across the breadth of AI and computer science. We are committed to rapid dissemination of important research results. Articles are published on a continuous basis to ensure swift communication of findings.   Criteria for publication To be published in Communications AI & Computing a paper needs to meet several general criteria: The conclusions are novel (novelty is not compromised by either abstracts or internet preprints) The paper provides strong evidence for the main conclusions The work follows high standard reporting guidelines, including data curation and cleaning, statistical analysis, and suitable comparisons with baselines and benchmarks if available The underlying data and codes are made available to the community following FAIR data deposition standards The manuscript is important to scientists in a specific sub-field of computational science or AI research In general, to be acceptable, a paper should meet these criteria and represent an advance in understanding that is expected to influence thinking in the field.