Advancing Automatic Recognition through Digital Transformation:Balancing Automation with a Human-Centric Approach

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The Emerging Role of Vision-Language Models in the Automation of Railway Asset Management: A Review and Future Perspective

This review paper argues that Vision-Language Models (VLMs), a paradigm whose rapid maturation is evidenced by recent comprehensive surveys offer a transformative solution for rail asset management, and provides a focused overview of the limitations of current CV systems.

From Human to Hybrid: Artificial Intelligence and the Transformation of Language Services

The growing presence of artificial intelligence (AI) in everyday life has generated debate regarding its impact on language services, where tools such as Large Language Models (LLMs) are increasingly being integrated. Framed as an exploratory and preliminary study, this article examines how a self-selected sample of 60 language-service professionals working in European contexts perceive the adoption of AI, particularly LLMs, in relation to professional practices, quality, ethical concerns, and emerging competence requirements. An embedded mixed-methods design was adopted, combining descriptive quantitative analysis with the thematic analysis of open-ended responses. The findings suggest a cautious and selective adoption of LLMs. While respondents recognise potential advantages related to speed, productivity, and support for specific tasks, they also identify persistent limitations concerning quality, terminology, contextual adequacy, cultural sensitivity, and the need for human revision. Respondents also report concerns about professional devaluation, changing work conditions, and the need for reskilling, particularly in relation to general translation and AI-assisted workflows. At the same time, some participants identify opportunities for innovation, enhanced human oversight, and the revaluation of specialised expertise. Overall, the study suggests that, from the perspective of the surveyed professionals, AI is contributing to the reconfiguration of language-service practices, while reinforcing the continued importance of human judgement, linguistic expertise, ethical responsibility, and critical engagement with AI-generated outputs.

Advancements in AI Research and Social Responsibility : From Technology to Control and Governance

Drawing upon recent academic publications and reports, this paper systematically examines current developments in artificial intelligence (AI) through a five-stage framework encompassing technology, applications, expectations and reality, risks and safety, and control and governance. Based on this analysis, the paper identifies key challenges and proposes future directions for AI research and development. Following the emergence of ChatGPT, large language models (LLMs) have rapidly proliferated. Their core component, the transformer, has expanded beyond natural language processing into diverse domains such as computer vision (Vision Transformer, ViT) and multivariate time-series analysis (Time Series Transformer, TST). Furthermore, LLMs are evolving into agent-based systems and are being applied to the automation of scientific research, as exemplified by Google’s Co-Scientist, AlphaEvolve, and AlphaGenome. These developments have significantly heightened expectations regarding the transformative potential of AI across society. However, a gap remains between these expectations and the current state of technology, as structural issues such as data bias and hallucination continue to pose substantial risks. In particular, hallucination is interpreted as a phenomenon arising from the model’s tendency to maximize expected evaluation outcomes, and it is identified as a critical challenge for ensuring AI safety. Accordingly, the safe deployment of AI requires effective monitoring of model behavior and improved interpretability of chain-of-thought (CoT) reasoning processes, red-teaming activities at both macro- and micro-levels, and the establishment of international governance frameworks, including those in the healthcare domain such as guidelines from the World Health Organization (WHO). In conclusion, while AI is driving profound changes not only in science and technology but also across society as a whole, addressing technical challenges— such as mitigating hallucination, preventing catastrophic forgetting in continual learning, and improving data efficiency—must be accompanied by the development of control and governance systems aligned with human values. In particular, international governance initiatives are needed to reduce disparities between countries and address polarization at the global level.

The Evolution of Quality Engineering: From Scripted Automation to AI Autonomy A Comprehensive Literature Review

Software testing is moving away from rigid, hand-written scripts toward AI systems that can adapt on their own. This review traces how quality engineering has changed, from rule-based automation to self-adjusting test frameworks, and looks at the technology behind Autonomous Quality Agents: Large Language Models (LLMs) that generate code from requirements, Computer Vision that handles visual regression, and Reinforcement Learning that drives exploratory testing. It also examines two ongoing problems: the difficulty of understanding how AI models make decisions, and the extra work needed to keep older, script-based automation running. The review closes with a proposed framework for where autonomous software assurance is headed next. This proposed framework, termed Autonomous Quality Assurance (AQA), is organised around three layers, perception (visual and DOM-based sensing), cognition (LLM-driven reasoning and test generation), and governance (interpretability and verification), intended to give practitioners and researchers a shared structure for locating where a given tool or technique sits today and what would need to mature before autonomous testing can be trusted at industrial scale.

Artificial Intelligence and Digital Libraries: A Transformative Partnership

Artificial Intelligence (AI) has emerged as one of the most significant technological innovations influencing the transformation of digital libraries. Modern digital libraries are no longer mere repositories of electronic resources; they are evolving into intelligent knowledge ecosystems capable of understanding user needs, providing personalized services, and supporting research through advanced data analytics. AI technologies—including Machine Learning (ML), Natural Language Processing (NLP), Optical Character Recognition (OCR), computer vision, recommendation systems, and conversational AI—are revolutionizing information organization, retrieval, preservation, and dissemination. These technologies enable libraries to enhance accessibility, automate repetitive tasks, improve metadata quality, and provide 24×7 user support. The integration of AI also introduces challenges related to data privacy, algorithmic bias, digital inequality, intellectual property, and the need for continuous professional development among librarians. This article examines the transformative relationship between AI and digital libraries, discusses current applications and emerging trends, evaluates ethical concerns, and highlights the evolving role of librarians in creating intelligent, inclusive, and user-centered digital information environments.