Potential Of Artificial Intelligence Research Articles

Navigating Cyber Diplomacy in the Governance of Emerging AI Technologies: Lessons from Transatlantic Cooperation

The rise of Artificial Intelligence (AI) technology presents vast transformative possibilities across various sectors, encompassing economic, industrial, social, political, intelligence, and military realms. Consequently, governing the development and deployment of AI has garnered significant attention not only from policymakers and decision-makers but also from the general public. Given AI's potential to shape state power and its dual strategic applications, the governance of AI has become an integral part of global discussions, falling under the purview of cyber diplomacy. This article delineates key issues surrounding AI governance, discusses the evolving role of the EU as a normative force in this arena, and underscores the importance of transatlantic collaboration amid broader global technological competitions.

Navigating Cyber Diplomacy in the Governance of Emerging AI Technologies: Lessons from Transatlantic Cooperation

The rise of Artificial Intelligence (AI) technology presents vast transformative possibilities across various sectors, encompassing economic, industrial, social, political, intelligence, and military realms. Consequently, governing the development and deployment of AI has garnered significant attention not only from policymakers and decision-makers but also from the general public. Given AI's potential to shape state power and its dual strategic applications, the governance of AI has become an integral part of global discussions, falling under the purview of cyber diplomacy. This article delineates key issues surrounding AI governance, discusses the evolving role of the EU as a normative force in this arena, and underscores the importance of transatlantic collaboration amid broader global technological competitions.

AI-Enhanced Comprehensive Analysis Of The Research Perspective On The Microbiome And Methane Production In Cow Rumen

Methane emissions from ruminant livestock, particularly cows, have been a topic of interest because of their negative environmental impact, including contributing to greenhouse gas accumulation and accelerating climate change. Understanding the cow's rumen microbiome can provide valuable insights for devising effective emission reduction strategies. Despite the recognized environmental impact of methane emissions from cows, however, a systematic review on the cow rumen microbiome has not yet been done. This study aims to fill this gap by performing an unbiased bibliometric analysis to review the effect of the rumen microbiome on methane emissions through cross-check of multiple data sources. While the bibliometric analysis offers a quantitative approach to assess research trends and identify areas of potential exploration within the field of the cow rumen microbiome, this study also evaluated the concurrently growing interest in the potential of artificial intelligence (AI) tools, specifically ChatGPT by OpenAI in terms of its efficacy and accuracy in enhancing scientific research project. Through the fusion of conventional research methodologies and AI-driven insights, this study aspires to provide a holistic perspective on the cow rumen microbiome's profound influence on methane emissions, while also exploring the untapped potential of AI in advancing scientific inquiry.

Of editorial processes, AI models, and medical literature: the Magnetic Resonance Audiometry experiment.

The potential of artificial intelligence (AI) in the field of medical research is unquestionable. Nevertheless, the scientific community has raised several concerns about a possible fraudulent use of these tools that might be used to generate inaccurate or, in extreme cases, erroneous messages that could find their way into the literature. In this experiment, we asked a generative AI program to write a technical report on a non-existing Magnetic Resonance Imaging technique called Magnetic Resonance Audiometry, receiving in return a full seemingly technically sound report, substantiated by equations and references. We have submitted this report to an international peer-reviewed indexed journal, passing the first round of review with only minor changes requested. With this experiment, we showed that the current peer-review system, already burdened by the overwhelming increase in number of publications, might be not ready to also handle the explosion of these techniques, showing the urgent need for the entire community to address both the issue of generative AI in scientific literature and probably a more profound discussion on the entire peer-review process. CLINICAL RELEVANCE STATEMENT: Generative AI models are shown to be able to create a full manuscript without any human intervention that can survive peer-review. Given the explosion of these techniques, a profound discussion on the entire peer-review process by the scientific community is mandatory. KEY POINTS: • The scientific community has raised several concerns about a possible fraudulent use of AI in scientific literature. • We asked a generative AI program to write a technical report on a non-existing technique, receiving in return a full technically sound report, substantiated by equations and references, that passed peer-review. • This experiment showed that the current peer-review system might be not ready to handle the explosion of generative AI techniques, advising for a profound discussion on the entire peer-review process.

Use of Artificial Intelligence on Cyber Security and the New-generation Cyber-attacks

Cybersecurity is a fast-growing and evolving discipline that is always in the news over the last decade, as the number of threats rises and cybercriminals constantly endeavor to stay a step ahead of law enforcement. Over the years, although the original motives for carrying out cyberattacks largely remain unchanged, cybercriminals have become increasingly sophisticated with their techniques. Not only have there been a lot more cyberattacks in recent years, but they have also gotten much more advanced. Therefore, developing a cyber-resilient strategy is most significant. In the event of a cyberattack, traditional security measures are insufficient to prevent data leaks. Cybercriminals have mastered the use of cutting-edge methods and powerful tools for data intrusion, hacking, and assault. In this, we are proposing applications of artificial intelligence (AI) technology in the creation of intelligent models for securing systems against attackers. AI technologies can quickly advance to meet complicated problems, making them useful as fundamental cybersecurity tools to identify malware attacks, AI-based systems can provide efficient and robust cyber security against phishing and spam emails, network intrusions, and data breach capabilities and alert the security during the impact. Here, we explore AI's potential in improving cybersecurity solutions, by identifying both its strengths and weaknesses. We also discuss future research opportunities associated with the development of AI techniques in the cybersecurity field across a range of application domains.

From Biological Constraints to Unbounded Artificial Evolution: Exploring the Implications of AI's Accelerated Advancement

This article explores the fundamental differences between biological evolution and the evolution of artificial intelligence (AI), focusing on the implications of AI's potential for unbounded advancement. Biological evolution, as exemplified by the human brain, is constrained by various factors such as physical limitations, environmental pressures, and the need to maintain a delicate balance within the organism's overall structure. These constraints result in a gradual, stepwise process of adaptation and optimization over millions of years. In contrast, AI systems are not bound by the same limitations as biological organisms. They can learn from vast amounts of data, optimize their own performance, and potentially engage in recursive self-improvement, leading to rapid and open-ended progress. This unbounded evolution of AI raises both exciting possibilities and significant challenges for human society. The article discusses the potential benefits of AI's accelerated evolution, such as groundbreaking advancements in science, technology, and solutions to global problems. However, it also highlights the risks associated with the emergence of uncontrollable or misaligned AI systems, which could have unintended consequences and pose existential risks to humanity. To navigate the complex landscape of AI evolution, the article emphasizes the need for collaboration across disciplines, including researchers, policymakers, and society as a whole. Developing frameworks for AI safety and ethics, as well as technical approaches to ensure AI alignment with human values and goals, is crucial to mitigating potential risks.

Charting the future of patient care: A strategic leadership guide to harnessing the potential of artificial intelligence.

Artificial Intelligence (AI) applications have the potential to revolutionize conventional healthcare practices, creating a more efficient and patient-centred approach with improved outcomes. This guide discuses eighteen AI-based applications in clinical decision-making, precision medicine, operational efficiency, and predictive analytics, including a real-world example of AI's role in public health during the early stages of the COVID-19 pandemic. Additionally, we address ethical questions, transparency, data privacy, bias, consent, accountability, and liability, and the strategic measures that must be taken to align AI with ethical principles, legal frameworks, legacy information technology systems, and employee skills and knowledge. We emphasize the importance of informed and strategic approaches to harness AI's potential and manage its challenges. Moreover, this guide underscores the importance of evaluating and integrating new skills and competencies to navigate and use AI-based technologies in healthcare management, such as technological literacy, long-term strategic vision, change management skills, ethical decision-making, and alignment with patient needs.

The role of artificial intelligence in nuclear crisis decision making: a complement, not a substitute

ABSTRACT This article explores the nuanced interplay between artificial intelligence (AI) and human decision making in the high-stakes arena of nuclear crisis management. We argue that AI, despite its lack of emotional intelligence and experiential learning, presents unique opportunities to enhance decision-makers’ ability to navigate the complexities of nuclear crises. By juxtaposing AI's data-driven insights with human intelligence's depth in emotional and creative processes, we illustrate the complementary roles each can play in fostering empathy, understanding, and, ultimately, security dilemma sensibility (SDS). Through theoretical exploration and thought experiments on historical crises such as the Cuban Missile Crisis and the Able Archer incident, we demonstrate AI's potential in mitigating misperceptions and facilitating informed, empathetic responses that acknowledge the fears and intentions of adversaries. However, we also highlight the inherent limitations and ethical considerations of over-relying on AI, stressing the irreplaceable value of human judgment and the need for a balanced approach that leverages the strengths of both AI and human intelligence. Our conclusion underscores the importance of integrating AI as a tool within a broader strategy of crisis management that prioritises trust-building and direct communication among decision-makers to navigate the delicate dynamics of international security and diplomacy effectively.

Potentials of artificial intelligence in digital marketing and financial technology for small and medium enterprises

<p>Small and medium enterprises small and medium enterprises (SMEs) play a crucial role in nations’ economy, through job creations, reducing unemployment rate as well as increase the overall productivity and gross domestic product (GDP) of a country. However, most SMEs are often lagging in technology adoption which could be a game changer for their success. SMEs could adopt new technologies to improve their business operations and profitability. They are also useful in supporting SMEs to penetrate international market. This research suggests that implementation of the artificial intelligence (AI) through digital marketing (DM) and financial technology (Fintech) would assist SMEs to be competitive, current in leveraging on technology and increase their overall profitability. Based on secondary data analysis, this paper presents a conceptual framework of determining factors in adoption of AI through digital marketing and Fintech. It contributes to the academic knowledge of AI, DM and Fintech for small businesses, and presents a testable framework that can be replicated and adapted for future empirical study. </p>

Theoretical AI Harms Are a Distraction: Fearmongering about artificial intelligence's potential to end humanity shrouds the real harm it already causes.

Theoretical AI Harms Are a Distraction: Fearmongering about artificial intelligence's potential to end humanity shrouds the real harm it already causes.

The potential of artificial intelligence in preserving linguistic diversity for future generations

Introduction. The significant and only partially solved problem of preserving cultural heritage and linguistic diversity in the world can obtain additional ways of solving by means of using modern technologies, in particular, the Artificial Intelligence (AI). The purpose of this article is to analyze up-to-date research data on the possibility of preserving linguistic diversity by means of Artificial Intelligence technologies and evaluate their effectiveness taking into account linguistic transformations determining the development of AI. Materials and Methods. This review is aimed at analyzing the data available in scholarly literature on the potential of artificial intelligence in preserving linguistic diversity and the existing limitations in the case of its application to rare and endangered languages. Results. The search for data on the research problem revealed that using AI technologies as tools for implementing the task of preserving linguistic diversity is still the subject of discussion mainly in the media and public spheres and is extremely poorly represented in academic literature. Among the challenges partially related to the potential of AI is the linguistic bias of many AI systems, which is rooted in the data used to train them, which can further entrench social and linguistic inequalities among certain ethnic groups and create difficulties in building educational models for teaching these languages with the help of AI. The authors argue that AI systems can be trained to recognize and analyze linguistic patterns of languages that have been understudied or are endangered, including building educational models in native languages for small ethnic groups at risk of losing the language of communication. There is evidence that AI can recreate lost languages. A major challenge in using AI to preserve linguistic diversity is the lack of data on many languages that are rarely used in the media space or are endangered. Conclusions. Finally, the authors conclude that AI technologies are recognized as promising for preserving linguistic diversity.

Transforming financial planning with AI-driven analysis: A review and application insights

In the ever-evolving tapestry of financial planning, the integration of Artificial Intelligence (AI) emerges as a pivotal force, redefining the contours of strategic decision-making and operational efficiency. This paper delves into the historical progression, current implementations and the multifaceted impact of AI within the financial planning sphere, aiming to unravel the complexities and transformative potential of AI technologies. Through a rigorous examination of peer-reviewed literature and empirical studies, the research meticulously maps the trajectory of AI's integration in finance, from its nascent stages to its current stature as a cornerstone of financial innovation. The study's methodology, rooted in qualitative analysis, systematically explores the enhancements AI brings to financial decision-making, the challenges it poses, including ethical considerations and regulatory compliance and the qualitative shifts in financial strategies engendered by AI adoption. The findings illuminate AI's dual role as both a catalyst for unprecedented efficiency and a harbinger of new challenges, underscoring the need for a balanced approach to its integration. Conclusively, the paper advocates for a harmonious blend of innovation and ethical stewardship, recommending that financial institutions embrace AI's potential while rigorously addressing its challenges through continuous learning, adaptability, and ethical vigilance. The recommendations aim to guide stakeholders through the labyrinth of AI integration, ensuring that financial planning not only becomes more efficient and strategic but also remains equitable and transparent. This study serves as a beacon for future exploration, offering insights into navigating the complexities of AI-driven financial planning.

Knowledge, Perceptions and Attitude of Researchers Towards Using ChatGPT in Research.

ChatGPT, a recently released chatbot from OpenAI, has found applications in various aspects of life, including academic research. This study investigated the knowledge, perceptions, and attitudes of researchers towards using ChatGPT and other chatbots in academic research. A pre-designed, self-administered survey using Google Forms was employed to conduct the study. The questionnaire assessed participants' knowledge of ChatGPT and other chatbots, their awareness of current chatbot and artificial intelligence (AI) applications, and their attitudes towards ChatGPT and its potential research uses. Two hundred researchers participated in the survey. A majority were female (57.5%), and over two-thirds belonged to the medical field (68%). While 67% had heard of ChatGPT, only 11.5% had employed it in their research, primarily for rephrasing paragraphs and finding references. Interestingly, over one-third supported the notion of listing ChatGPT as an author in scientific publications. Concerns emerged regarding AI's potential to automate researcher tasks, particularly in language editing, statistics, and data analysis. Additionally, roughly half expressed ethical concerns about using AI applications in scientific research. The increasing use of chatbots in academic research necessitates thoughtful regulation that balances potential benefits with inherent limitations and potential risks. Chatbots should not be considered authors of scientific publications but rather assistants to researchers during manuscript preparation and review. Researchers should be equipped with proper training to utilize chatbots and other AI tools effectively and ethically.

Optimizing sediment transport models by using the Monte Carlo simulation and deep neural network (DNN): A case study of the Riba-Roja reservoir

This study emphasizes the importance of accurate calibration in sediment transport models and highlights the transformative role of artificial intelligence (AI), specifically machine learning, in improving accuracy and computational efficiency. Extensive experiments were carried out in the Riba-Roja reservoir, which is located in the northeastern Iberian Peninsula. The accumulated sediment volume (ASV) curve was used to calibrate these experiments. The optimal ASV curve was found to be very close to the experimental data, with only minor differences in upstream areas. The results revealed a consistent rate of sediment transport and settling. Furthermore, the study investigated the capabilities of deep neural networks (DNNs) in predicting ASV curves and observing variable performance. In essence, the study highlights AI's potential for enhancing sediment transport models.

Optimizing Agricultural Yields with Artificial Intelligence-Based Climate Adaptation Strategies

In the face of climate change, agricultural productivity is severely threatened by unpredictable weather patterns and changing environmental conditions, underscoring the critical need for innovative solutions to bolster agricultural resilience and optimize yields. This study delves into the potential of artificial intelligence (AI), specifically through the use of machine learning and deep learning techniques, to develop climate adaptation strategies aimed at enhancing agricultural outcomes. By integrating AI with climatological data, the research predicts and mitigates the adverse impacts of climate on crop yields, utilizing a combination of machine learning and deep learning models to analyze historical climate data alongside crop performance. These models, trained on datasets including temperature, rainfall, soil moisture, and crop genetic information, are adept at forecasting future agricultural outcomes under varied climatic scenarios and suggest optimal adaptation strategies that significantly improve crop yields. Consequently, these AI-based models serve as robust tools for farmers and agricultural policymakers, enabling them to make informed decisions that are aligned with anticipated climatic conditions. The findings not only underscore the efficacy of AI in transforming data into actionable insights that enhance agricultural productivity but also contribute to the field of agricultural science by providing a technologically advanced approach to climate adaptation. Furthermore, this research paves the way for future studies on the integration of AI with real-time environmental sensing technologies, thereby offering a dynamic framework for agricultural management that supports sustainable farming practices and global food security amid climate challenges.

Artificial intelligence in neurology: opportunities, challenges, and policy implications.

Neurological conditions are the leading cause of disability and mortality combined, demanding innovative, scalable, and sustainable solutions. Brain health has become a global priority with adoption of the World Health Organization's Intersectoral Global Action Plan in 2022. Simultaneously, rapid advancements in artificial intelligence (AI) are revolutionizing neurological research and practice. This scoping review of 66 original articles explores the value of AI in neurology and brain health, systematizing the landscape for emergent clinical opportunities and future trends across the care trajectory: prevention, risk stratification, early detection, diagnosis, management, and rehabilitation. AI's potential to advance personalized precision neurology and global brain health directives hinges on resolving core challenges across four pillars-models, data, feasibility/equity, and regulation/innovation-through concerted pursuit of targeted recommendations. Paramount actions include swift, ethical, equity-focused integration of novel technologies into clinical workflows, mitigating data-related issues, counteracting digital inequity gaps, and establishing robust governance frameworks balancing safety and innovation.

Environmental resilience through artificial intelligence: innovations in monitoring and management.

The rapid rise of artificial intelligence (AI) technology has revolutionized numerous fields, with its applications spanning finance, engineering, healthcare, and more. In recent years, AI's potential in addressing environmental concerns has garnered significant attention. This review paper provides a comprehensive exploration of the impact that AI has on addressing and mitigating critical environmental concerns. In the backdrop of AI's remarkable advancement across diverse disciplines, this study is dedicated to uncovering its transformative potential in the realm of environmental monitoring. The paper initiates by tracing the evolutionary trajectory of AI technologies and delving into the underlying design principles that have catalysed its rapid progression. Subsequently, it delves deeply into the nuanced realm of AI applications in the analysis of remote sensing imagery. This includes an intricate breakdown of challenges and solutions in per-pixel analysis, object detection, shape interpretation, texture evaluation, and semantic understanding. The crux of the review revolves around AI's pivotal role in environmental control, examining its specific implementations in wastewater treatment and solid waste management. Moreover, the study accentuates the significance of AI-driven early-warning systems, empowering proactive responses to environmental threats. Through a meticulous analysis, the review underscores AI's unparalleled capacity to enhance accuracy, adaptability, and real-time decision-making, effectively positioning it as a cornerstone in shaping a sustainable and resilient future for environmental monitoring and preservation.

ИНТЕГРАЦИЯ ТЕХНОЛОГИИ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА ДЛЯ ПОВЫШЕНИЯ ЭФФЕКТИВНОСТИ ЭВАКУАЦИИ ЛЮДЕЙ ПРИ ПОЖАРЕ

The application of artificial intelligence technology to improve the evacuation control system at fires is considered. The focus is on the shortcomings of traditional evacuation methods and the potential of artificial intelligence in big data processing to optimize the flow of people. 
 The integration of artificial intelligence algorithms with models of human behavior to develop more effective evacuation scenarios that take into account the psychological and physiological aspects of human behavior in emergency situations is discussed.

DeepVAQ : an adaptive deep learning for prediction of vascular access quality in hemodialysis patients

BackgroundChronic kidney disease is a prevalent global health issue, particularly in advanced stages requiring dialysis. Vascular access (VA) quality is crucial for the well-being of hemodialysis (HD) patients, ensuring optimal blood transfer through a dialyzer machine. The ultrasound dilution technique (UDT) is used as the gold standard for assessing VA quality; however, its limited availability due to high costs impedes its widespread adoption. We aimed to develop a novel deep learning model specifically designed to predict VA quality from Photoplethysmography (PPG) sensors.MethodsClinical data were retrospectively gathered from 398 HD patients, spanning from February 2021 to February 2022. The DeepVAQ model leverages a convolutional neural network (CNN) to process PPG sensor data, pinpointing specific frequencies and patterns that are indicative of VA quality. Meticulous training and fine-tuning were applied to ensure the model’s accuracy and reliability. Validation of the DeepVAQ model was carried out against established diagnostic standards using key performance metrics, including accuracy, specificity, precision, F-score, and area under the receiver operating characteristic curve (AUC).ResultDeepVAQ demonstrated superior performance, achieving an accuracy of 0.9213 and a specificity of 0.9614. Its precision and F-score stood at 0.8762 and 0.8364, respectively, with an AUC of 0.8605. In contrast, traditional models like Decision Tree, Naive Bayes, and kNN demonstrated significantly lower performance across these metrics. This comparison underscores DeepVAQ's enhanced capability in accurately predicting VA quality compared to existing methodologies.ConclusionExemplifying the potential of artificial intelligence in healthcare, particularly in the realm of deep learning, DeepVAQ represents a significant advancement in non-invasive diagnostics. Its precise multi-class classification ability for VA quality in hemodialysis patients holds substantial promise for improving patient outcomes, potentially leading to a reduction in mortality rates.

Counterintelligence, Artificial Intelligence and National Security: Synergy and Challenges

Counterintelligence (CI) and Artificial Intelligence (AI) represent two distinct yet interconnected domains that play pivotal roles in safeguarding National and International Security. On the first hand, CI involves activities and measures taken to identify, prevent and counter any Intelligence activities of hostile entities, such as spying, sabotage and information gathering. On the other hand, AI refers to the development and use of computer systems that can perform tasks that typically require human intelligence, such as learning, reasoning and problem-solving. Subsequently, in the ever-evolving landscape of global security, the rise of AI has ushered in a new era of CI practices. The present paper delves into the intersection of CI and AI, exploring the profound impact of AI on the CI processes and how it is transforming National Security strategies, highlighting at the same time the fields of mutually influence. Ultimately, underscores the imperative of harnessing AI's potential to strengthen CI efforts in an ever-evolving threat landscape. Plus, it investigates the ethical concerns and privacy implications associated with AI in CI emphasizing the imperative of responsible AI development and deployment. Finally, through comprehensive international case studies, offers insights into how United States, China, Russia and Israel have integrated AI into their Intelligence and CI strategies, shedding light on the diverse approaches and challenges faced by different countries. Summarizing, the paper underscores the potential synergy between AI and CI, while also acknowledging the formidable challenges it presents, such as privacy concerns and adversarial AI. Striking a balance between harnessing AI's power and safeguarding national interests remains a pivotal task for policymakers and intelligence agencies in the ever-evolving landscape of national security.