Bentham Science Publishers has announced the launch of Current Artificial Intelligence, a new international, peer-reviewed journal focused on research shaping the next generation of artificial intelligence. The publication is now accepting manuscripts from researchers, academics, technology professionals, and innovators worldwide, positioning itself as a new venue for studies examining how AI systems are designed, tested, deployed, and governed across an increasingly digital society.
The journal arrives at a moment when artificial intelligence is moving rapidly from experimental laboratories into hospitals, factories, financial systems, classrooms, scientific institutions, and public services. Advances in machine learning, deep neural networks, generative models, robotics, and automated decision-making are producing powerful new capabilities, but they are also raising urgent questions about reliability, transparency, safety, bias, privacy, and accountability. Current Artificial Intelligence aims to bring these technical and societal discussions together within a multidisciplinary research platform.
Its scope covers both the fundamental science behind AI and the practical systems built from it. Research in machine learning and deep learning may address the development of algorithms that identify patterns in large datasets, optimize decisions, or learn representations without explicit programming. Computational intelligence, including evolutionary computation, fuzzy systems, and swarm-based methods, also falls within the journal’s remit. Such approaches can be especially valuable when problems are complex, uncertain, or difficult to model using conventional mathematical techniques.
Natural language processing and large language models represent another major area of interest. These systems use statistical learning and neural architectures to analyze, generate, translate, and summarize human language. Research may focus on model training, retrieval-augmented generation, multimodal learning, factual accuracy, computational efficiency, or methods for reducing hallucinations. Generative AI studies can also examine how text, images, audio, video, and code are produced, evaluated, and integrated into professional and scientific workflows.
The journal also welcomes work in computer vision, pattern recognition, and intelligent data analytics. Computer vision systems extract information from images and video, supporting applications such as medical diagnosis, industrial inspection, environmental monitoring, and autonomous navigation. Pattern-recognition research can improve the classification of complex signals, while data-analytics methods help convert high-dimensional or rapidly changing datasets into actionable knowledge. Automated reasoning, knowledge representation, and expert systems extend these capabilities by allowing machines to organize information, draw inferences, and support decisions through structured rules or learned models.
Robotics and autonomous systems form an additional part of the journal’s broad agenda. Research in these fields may combine perception, planning, control, and reinforcement learning to enable machines to operate in uncertain environments. Autonomous vehicles, service robots, industrial machines, and intelligent agents must continuously interpret sensor data, predict outcomes, and select actions while meeting safety constraints. Studies of human–AI interaction are equally important, particularly when people and intelligent systems collaborate in workplaces, healthcare settings, research laboratories, or everyday environments.
A central theme of the new publication is the development of trustworthy and explainable AI. High-performing systems are not necessarily dependable if their decisions cannot be understood, reproduced, or challenged. Explainable AI seeks to clarify how models arrive at their outputs, while trustworthy AI considers factors such as robustness, fairness, privacy, security, accountability, and resistance to manipulation. Research may investigate interpretable model architectures, post-hoc explanation techniques, bias detection, uncertainty estimation, adversarial robustness, or governance frameworks for deploying AI responsibly.
Applications across healthcare, life sciences, engineering, finance, law, education, and the social sciences are also included. In healthcare, AI can assist with medical-image analysis, clinical prediction, drug discovery, and personalized treatment, although these systems require rigorous validation before they can influence patient care. In engineering and finance, intelligent algorithms can support predictive maintenance, resource optimization, risk assessment, and anomaly detection. Across all these domains, the journal emphasizes the importance of evaluating methods against meaningful benchmarks rather than presenting performance claims in isolation.
To strengthen reproducibility, authors are encouraged to validate proposed methods using publicly available datasets whenever appropriate. Open datasets allow independent researchers to repeat experiments, compare algorithms under consistent conditions, and identify whether reported improvements generalize beyond a single sample or institution. Technical reporting of data-processing procedures, model architectures, training settings, evaluation metrics, and computational requirements can further help the research community assess whether an AI method is robust, efficient, and transferable to real-world use.
Current Artificial Intelligence will publish original research articles, comprehensive reviews, mini-reviews, letters, case reports, and guest-edited thematic issues. The journal also states that generative AI tools cannot be credited as authors under current publication-ethics guidance. Any use of such tools in preparing a manuscript, or during peer review, must be disclosed. Through its focus on technical progress, transparent evaluation, and responsible use, the new journal seeks to provide a forum for research addressing both the extraordinary potential of artificial intelligence and the challenges that will determine whether its benefits can be trusted by society.
Subject of Research: Artificial intelligence, machine learning, deep learning, generative AI, natural language processing, computer vision, robotics, explainable and trustworthy AI, and interdisciplinary AI applications.
Keywords: Artificial intelligence; machine learning; deep learning; computational intelligence; natural language processing; large language models; generative AI; computer vision; pattern recognition; intelligent data analytics; automated reasoning; knowledge representation; robotics; autonomous systems; human–AI interaction; explainable AI; trustworthy AI; ethical AI; AI applications; reproducibility.
