Current Issue : October-December Volume : 2025 Issue Number : 4 Articles : 5 Articles
The cornerstone of the global economy, oil and gas reservoir development, faces numerous challenges such as resource depletion, operational inefficiencies, safety concerns, and environmental impacts. In recent years, the integration of artificial intelligence (AI), particularly artificial general intelligence (AGI), has gained significant attention for its potential to address these challenges. This review explores the current state of AGI applications in the oil and gas sector, focusing on key areas such as data analysis, optimized decision and knowledge management, etc. AGIs, leveraging vast datasets and advanced retrieval-augmented generation (RAG) capabilities, have demonstrated remarkable success in automating data-driven decision-making processes, enhancing predictive analytics, and optimizing operational workflows. In exploration, AGIs assist in interpreting seismic data and geophysical surveys, providing insights into subsurface reservoirs with higher accuracy. During production, AGIs enable real-time analysis of operational data, predicting equipment failures, optimizing drilling parameters, and increasing production efficiency. Despite the promising applications, several challenges remain, including data quality, model interpretability, and the need for high-performance computing resources. This paper also discusses the future prospects of AGI in oil and gas reservoir development, highlighting the potential for multi-modal AI systems, which combine textual, numerical, and visual data to further enhance decision-making processes. In conclusion, AGIs have the potential to revolutionize oil and gas reservoir development by driving automation, enhancing operational efficiency, and improving safety. However, overcoming existing technical and organizational challenges will be essential for realizing the full potential of AI in this sector....
The integration of large language models (LLMs) like ChatGPT is transforming education the health sciences. This study evaluated the applicability of ChatGPT-4 and ChatGPT-4o in endodontics, focusing on their reliability and repeatability in responding to practitioner-level questions. Thirty closed-clinical questions, based on international guidelines, were each submitted thirty times to both models, generating a total of 1800 responses. These responses were evaluated by endodontic experts using a 3-point Likert scale. ChatGPT-4 achieved a reliability score of 52.67%, while ChatGPT-4o slightly outperformed it with 55.22%. Notably, ChatGPT-4o demonstrated greater response consistency, showing superior repeatability metrics such as Gwet’s AC1 and percentage agreement. While both models show promise in supporting learning, ChatGPT-4o may provide more consistent and pedagogically coherent feedback, particularly in contexts where response dependability is essential. From an educational standpoint, the findings support ChatGPT’s potential as a complementary tool for guided study or formative assessment in dentistry. However, due to moderate reliability, unsupervised use in specialized or clinically relevant contexts is not recommended. These insights are valuable for educators and instructional designers seeking to integrate AI into digital pedagogy. Further research should examine the performance of LLMs across diverse disciplines and formats to better define their role in AI-enhanced education....
Museum use of artificial intelligence (AI) is becoming increasingly common, but its contribution to museum attendance is yet to be confirmed. This paper investigates whether the adoption of AI impacts museum visitation using data from 19 museums. Statistical analyses, including ANOVA and Spearman correlation, were conducted to determine if the use of AI has significant effects on visitors. The findings indicate no statistically significant difference between museums that use AI and those that do not (ANOVA: p = 0.263, F = 1.34), but the Spearman correlation (r = 0.448, p = 0.055) indicates a moderate positive correlation that is not statistically significant. The findings suggest that AI enhances visitor experience rather than increasing attendance. Additionally, this study proposes a conceptual framework for AI prototyping in museums. The study contributes to the ongoing debate on AI in cultural institutions by emphasizing that future research should incorporate longitudinal studies and qualitative visitor feedback in order to capture the overall impact of AI on engagement and sustainability in museums....
The integration of artificial intelligence (AI) in special education has the potential to transform learning experiences and improve outcomes for students with disabilities. This systematic literature review examines the application of AI technologies in special education, focusing on personalized learning, cognitive and behavioral interventions, communication, emotional support, and physical independence. Through an analysis of 15 studies conducted between 2019 and 2024, the review synthesizes evidence on the effectiveness of AI tools, including intelligent tutoring systems, adaptive learning platforms, assistive communication devices, and robotic aids. The findings suggest that AI-driven technologies significantly enhance students’ academic performance, communication skills, emotional regulation, and physical mobility by providing tailored interventions that address individual needs. This review also highlights several challenges, including limited access to AI technologies in low-resource settings, the need for more comprehensive teacher training, and ethical concerns related to data privacy and algorithmic bias. Additionally, the geographic focus of the current research is primarily on developed countries, overlooking the specific challenges of implementing AI in resource-constrained environments. This review emphasizes the need for more diverse and ethical research to fully realize the potential of AI in supporting students with disabilities and promoting inclusive education....
Brain-inspired models in artificial intelligence (AI) originated from foundational insights in neuroscience. In recent years, this relationship has been moving toward a mutually reinforcing feedback loop. Currently, AI is significantly contributing to advancing our understanding of neuroscience. In particular, when combined with wireless optogenetics, AI enables experiments without physical constraints. Furthermore, AI-driven real-time analysis facilitates closed-loop control, allowing experimental setups across a diverse range of scenarios. And a deeper understanding of these neural networks may, in turn, contribute to future advances in AI. This work demonstrates the synergy between AI and miniaturized neural technology, particularly through wireless optogenetic systems designed for closed-loop neural control. We highlight how AI is now revolutionizing neuroscience experiments from decoding complex neural signals and quantifying behavior, to enabling closed-loop interventions and high-throughput phenotyping in freely moving subjects. Notably, AI-integrated wireless implants can monitor and modulate biological processes with unprecedented precision. We then recount how neuroscience insights derived from AIintegrated neuroscience experiments can potentially inspire the next generation of machine intelligence. Insights gained from these technologies loop back to inspire more efficient and robust AI systems. We discuss future directions in this positive feedback loop between AI and neuroscience, arguing that the coevolution of the two fields, grounded in technologies like wireless optogenetics and guided by reciprocal insight, will accelerate progress in both, while raising new challenges and opportunities for interdisciplinary collaboration....
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