AI Capabilities Research Articles

Descriptive Case Analysis on the Application of Prompt Engineering in Business Management

In recent years, the application of AI has been witnessed in various domains and disciplines, mainly AI in terms of large-scale pre-trained language models (LMs) and Visual Language models which are the prompt engineering types that have gained increasing attention in the field of Business and Management field. The prompt engineering approach is considered vital leverage in the areas of BM tasks. In this AI-generated review, we will try to discuss various aspects of prompt engineering encompassing the domain of management and also highlight various applications in this area. This paper also tries to examine the challenges of applying prompt engineering in the context of business management. This is a descriptive case study research exploring various facets of prompt engineering and application on the spectrum of business management. Through this exploration, this paper intends to contribute to the evolving dialogue on the integration of human creativity with generative AI capabilities, offering insights into the future of effective and ethical AI interaction. Prompt engineering, Artificial intelligence, large language models, Visual language models, Business Management, Human-AI interaction, Chat GPT, Generative AI

The Framework of GeoSOT-3D Grid Modeling for Spatial Artificial Intelligence

Abstract. With the development of society, spatial artificial intelligence (spatial AI) research is gradually able to play a greater role. However, spatial AI has problems such as data alignment, poor interpretability, and cross domain learning. Therefore, this paper proposes an innovative GeoSOT-3D grid modeling framework for spatial AI research, which enhances the application capabilities of spatial AI. Grid modeling will be able to run through the upstream and downstream of spatial AI research, providing encoding calculations and spatial neighborhood embedding matrices for spatial data. This paper also uses task examples to demonstrate how to effectively organize and index spatial data using GeoSOT-3D grids and conduct spatial AI research. The use of GeoSOT-3D grids for spatial AI analysis has enormous potential and broad application prospects, which will help promote the further development and application of spatial AI.

Fostering Tech Innovation

This study aimed to interpret the value of integration of TRIZ (Teoriya Resheniya Izobretatelskih Zadach; Theory of Inventive Problem Solving) and ChatGPT(Chat Generative Pre-trained Transformer) to enhance technological and entrepreneurial problem-solving and decision-making. TRIZ offers a structured approach to innovation, while ChatGPT excels in generating diverse and innovative responses through advanced natural language processing. From the combination of them, we tried to discover synergies that promote innovation in highly competitive business areas. Through the analysis of case studies, including "Imperfect Waterproof Zipper" and "Drilling a Hole in a Thin-Walled Tube", we discovered that this integration not only aligns with actual problem-solving outcomes but also enhances the quality of solutions, particularly benefiting developers with limited TRIZ knowledge. We identified that leveraging ChatGPT enables developers and entrepreneurs to approach challenges with enhanced creativity, yielding practical and innovative solutions through these case studies. Our approach, focusing on real-world applications, demonstrates the study's contribution by providing a novel strategy for combining structured problem-solving with AI capabilities. The primary motivation behind this research was to ascertain whether AI can amplify the problem-solving framework of TRIZ, thereby extending its utility beyond traditional domains. The findings underline the importance of AI in creative problem-solving, suggesting that even those unfamiliar with TRIZ can apply its principles effectively with the aid of ChatGPT. This research adds to the existing knowledge by showcasing how AI can be a powerful ally in the creative process, offering new avenues for problem-solving and strategic decision-making. In conclusion, our study demonstrates that the collaboration between TRIZ and ChatGPT not only elevates creativity but also equips developers and entrepreneurs with competitive strategies, emphasizing the role of AI in driving forward human innovation and creativity.

Adapting to the AI Era: ChatGPT in the Classroom

The emergence of ChatGPT, a conversational AI developed by OpenAI, has raised profound questions about its impact on the traditional university experience. This article addresses the pervasive discourse surrounding ChatGPT by investigating the actual capabilities of AI writing at the undergraduate level. To study the strengths and limitations of AI in the classroom, we conducted a survey experiment involving faculty assessment of essays generated by ChatGPT. Contrary to concerns of diminishing academic rigor and standards, our findings suggest that ChatGPT’s effectiveness is influenced by user knowledge, indicating potential for symbiotic integration into pedagogical approaches. Rather than spell the end of the college essay, we suggest that ChatGPT offers opportunities to open discussions about information literacy and verification of information. For universities, our study suggests revising university AI-usage policies and incorporating AI into teaching and learning objectives.

Was eine KI niemals können wird, wird auch der Mensch niemals können

Abstract In this article, I will argue in favor of a broad concept of AI that is not restricted to a particular kind of technology. Arguments based on such a concept will stand the test of time and will not become obsolete as technology advances. Arguments concerning the limits and possibilities of technology based solely on steam engine technology, for example, would no longer be relevant today. The same holds for arguments concerning symbolic-logical AI – and will probably be similar in the future with arguments concerning current artificial neural networks. In a second step I develop a technology-independent argument to show that no system – no matter how advanced it may be – can ever fully understand itself. Furthermore, I will show that with a broad concept of AI – which also includes biological systems, for example – AI with human-level capabilities is, at least, possible. However, if artificial intelligence with human-level capabilities is possible, then the fundamental or absolute limitations of AI capabilities would also be fundamental limitations of human capabilities. In other words: what AI can never do, humans can’t do either.

Building a Data Lake on AWS: From Data Migration to AI-Driven Insights

As organizations generate and process increasing amounts of data, building data lakes on cloud platforms like AWS has become crucial to managing large datasets efficiently. This paper outlines the key steps in constructing a scalable data lake on AWS, starting from data migration to leveraging AI for insights. It explores how AWS services like S3, Glue, and SageMaker work together to facilitate data storage, transformation, and machine learning. In addition, it highlights the importance of orchestrating data pipelines with automation tools like AWS Lambda and Apache Airflow to ensure smooth, scalable, and efficient workflows. This paper explores the end-to-end process of migrating data to AWS, constructing scalable data lakes, and leveraging AI capabilities to drive actionable insights. Through practical examples, diagrams, and pseudocode, this paper provides a comprehensive guide to implementing data lakes with AWS services such as S3, Glue, and SageMaker, highlighting key considerations around data migration, storage, processing, and analytics. The role of automation tools like AWS Lambda and Airflow in orchestrating these pipelines is also discussed. Keywords AWS, Data Lake, AI-driven Insights, Data Migration, Amazon S3, AWS Glue, Amazon SageMaker, Cloud Analytics, Data Pipeline, ETL, Machine Learning

GPT-4 and Beyond : Advancements in AI Language Models

This comprehensive article explores the groundbreaking advancements and implications of GPT-4, the latest iteration in the Generative Pre-trained Transformer series. It delves into GPT-4's enhanced comprehension and contextual awareness, multilingual proficiency, expanded knowledge base, multimodal capabilities, and improved safety and ethical considerations. The article provides quantifiable improvements and practical implications across various domains, including natural language processing, machine translation, research assistance, education, and content creation. It also addresses the challenges and limitations of the model, such as biases and computational requirements, while discussing future directions in AI development. The piece concludes with projections for future AI capabilities, emphasizing more human-like reasoning, handling of complex tasks, and seamless integration with other systems, while also highlighting the ethical considerations and challenges that accompany these advancements.

Exploiting AI Capabilities: An in-Depth Analysis of Artificial Intelligence Integration in Cybersecurity for Threat Detection and Response

Exploiting AI Capabilities: An in-Depth Analysis of Artificial Intelligence Integration in Cybersecurity for Threat Detection and Response

AI-Enhanced Edge Device for Real-Time Snoring Detection

This paper presents the development of a cutting-edge, non-invasive edge device designed to monitor snoring and provide timely, moderate haptic feedback to users. Utilizing the Qualcomm Snapdragon 8cx Gen 3 processor, the device offers robust computing power and AI capabilities for real-time processing, making it a versatile tool for health monitoring applications. The system integrates a high-fidelity MEMS microphone array capable of capturing nuanced audio signals and a TDK piezoelectric haptic actuator, which delivers precise alerts through customized vibrations. The research explores the potential of this advanced hardware in detecting and managing obstructive sleep apnea (OSA), a condition often underdiagnosed due to a lack of patient awareness. By leveraging state-of-the-art digital signal processing and deep learning techniques, the device aims to enhance user awareness and intervention in sleep-related disorders, offering a promising new avenue for improving patient outcomes and quality of life.

Rapid Advancements in Large Language Models for Quantitative Remote Sensing: The Case of Water Depth Inversion

This study presents a comparative analysis of two advanced AI models, ChatGPT and ERNIE, in the context of water depth inversion. Utilizing satellite spectral data and in-situ bathymetric measurements collected from Rushikonda Beach, India, we processed and analyzed the data to generate high-resolution bathymetric maps. Both models demonstrated significant accuracy, with ChatGPT slightly outperforming ERNIE in terms of mean absolute error. The study highlights the advantages of AI models, such as efficient data processing and the ability to integrate multi-modal inputs, while also discussing challenges related to data quality, interpretability, and computational demands. The findings suggest that while both models are highly effective for water depth inversion, ongoing improvements in data handling and model transparency are essential for their broader application in environmental monitoring. This research contributes to the understanding of AI capabilities in geospatial analysis and sets the stage for future enhancements in the field.

Holistic AI-Enhanced Marketing Framework Theory: Bridging Human Creativity and AI for Ethical Marketing

The Holistic AI-Enhanced Marketing Framework Theory (HAEMF) presents a holistic paradigm that combines AI capabilities with human creativity, ethics, inclusivity, transparency, and security to improve user acceptance of AI-enhanced marketing techniques. To address shortcomings in existing technological acceptance models, HAEMF incorporates constructs such as perceived creativity and emotional resonance, ethical transparency and trust, inclusivity and bias mitigation, cybersecurity and data privacy, and digital inclusivity and accessibility. These structures operate together in a balanced framework to ensure that AI-powered marketing is efficient, data-driven, and consistent with human values and ethical norms. The model's qualitative study reveals that transparent and inclusive AI methods, human emotional intelligence, and ethical monitoring dramatically improve user engagement and trust. HAEMF's adaptable, cross-sector approach serves as a road map for practitioners looking to implement AI in marketing responsibly, balancing technological innovation with ethical considerations and user-centric experiences, ultimately leading to greater acceptance of AI-enhanced marketing practices.

The integration of Artificial Intelligence in demand forecasting and inventory management in the United States

The integration of Artificial Intelligence in demand forecasting and inventory management in the United States has been examined in this paper. Demand forecasting and inventory management are two of critical areas in supply chain management, which is a veritable tool for promoting industrialization, manufacturing capabilities, and customers satisfaction. The use of AI in form of robotics, machine learning, deep learning, and predictive analytics, among others in all aspects of supply chain operations is gaining ground by the day. The integration of AI into the supply chain process can sustain multi-billion dollars trades in the United States, by reducing the cost of production and distribution, reducing human errors causing inaccurate demand forecasts, return shipment and cancellations of orders, etc. The challenges relating to the use of AI in demand forecasting and inventory management such as high cost of installation and maintenance, data privacy violations, requirement of skilled personnel, which are limited in global supply, and employees’ resistance to change were also identified. The outlook of relationship between Artificial Intelligence and supply chain management looks hopeful, brighter, and encouraging. This will be made possible by continuous development of AI capabilities and reducing the challenges of its widespread integration.

Turing tests in chess: An experiment revealing the role of human subjectivity

With the growing capabilities of AI, technology is increasingly able to match or even surpass human performance. In the current study, focused on the game of chess, we investigated whether chess players could distinguish whether they were playing against a human or a computer, and how they achieved this. A total of 24 chess players each played eight 5 + 0 Blitz games from different starting positions. They played against (1) a human, (2) Maia, a neural network-based chess engine trained to play in a human-like manner, (3) Stockfish 16, the best chess engine available, downgraded to play at a lower level, and (4) Stockfish 16 at its maximal level. The opponent’s move time was fixed at 10 s. During the game, participants verbalized their thoughts, and after each game, they indicated by means of a questionnaire whether they thought they had played against a human or a machine and if there were particular moves that revealed the nature of the opponent. The results showed that Stockfish at the highest level was usually correctly identified as an engine, while Maia was often incorrectly identified as a human. The moves of the downgraded Stockfish were relatively often labeled as ‘strange’ by the participants. In conclusion, the Turing test, as applied here in a domain where computers can perform superhumanly, is essentially a test of whether the chess computer can devise suboptimal moves that correspond to human moves, and not necessarily a test of computer intelligence.

Tapping generative AI capabilities: a study to examine continued intention to use ChatGPT in the travel planning

ABSTRACT Generative AI has lately been established as a very effective tool impacting consumer decision-making. Thus, this study aims to evaluate the variables affecting the flow of controversial AI in travel and tourism. Despite the increasing interest in ChatGPT, little research has examined what factors influence the uptake of this novel AI tool and its implications in the travel planning industry. This work builds its conceptual model on UTAUT2 and PCI Theory and tests the antecedents of Flow which impacts continuance intention to use ChatGPT in travel planning. An online survey was administered among 408 individuals in India. Of the factors of UTAUT2 and PCI, social influence, compatibility, and image do not impact the flow state significantly. Additionally, the study establishes the relationship between Flow and the continued intention to use ChatGPT for travel planning with “external influence” as a moderator.

AI in the spotlight: The impact of artificial intelligence disclosure on user engagement in short-form videos

This paper aims to investigate how AI disclosure affects user engagement intention. We try to comprehensively explore the fundamental mechanisms behind AI disclosure and user engagement, along with the boundary conditions that impact the relationship. Based on the heuristic-systematic model, this study built a moderated mediation model and conducted an online experiment on “Credamo” platform. The 479 valid experimental data of the study were analyzed by partial least squares structural equation modeling (PLS-SEM). The results indicate that AI disclosure has a direct, positive impact on user engagement intention, while it could also diminish user engagement intention by lowering users' perceived content quality. However, the negative impact on perceived content quality can be mitigated by improving users’ perceived AI capabilities. This study expands the research focus of AI disclosure and the practical application of heuristic-systematic model, which provides theoretical insights to the artificial intelligence related literature. In addition, we put forward to informed practical recommendations for video content creators and publishers in creating content and promoting user interaction.

Using AI to Mitigate the Employee Misclassification Problem

Misclassification of employees as independent contractors is widespread. This article aims to make two contributions. My first goal is to sharpen the explanation of why misclassifications persist; I argue that three well‐known problems – the indeterminacy of employee status tests, the barriers to self‐enforcement, and the inequality of bargaining power – together combine to give employers de facto power to set the default legal status. Putting the burden on the worker to initiate legal proceedings and challenge their classification as an independent contractor is the ultimate reason for persistent misclassifications. The second and main contribution is to propose a solution that relies on new AI capabilities. Thanks to technological advancements it is now possible to require employers to seek pre‐authorisation before engaging with someone as an independent contractor. The authorisation would be granted (or refused) by a state‐run automated system, based on an AI prediction about the law. Both parties would still be able to bring the case before a court of law; but the power to set the default legal status would be taken away from employers. The article considers the difficulties with relying on AI predictions, and argues that those difficulties can be addressed, proposing a model that can be justified.

GENERATIVE AI: A TOOL FOR ADDRESSING DATA SCARCITY IN SCIENTIFIC RESEARCH

Generative AI, a pivotal advancement in data science, addresses data scarcity by producing high-quality synthetic data that mirrors real-world data. This article explores Generative AI's capabilities, including data augmentation, privacy-preserving anonymization, simulation of rare events, and cost-efficient data collection. Techniques such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are discussed, highlighting their role in creating realistic and diverse synthetic data. The practical applications span healthcare, finance, and climate science, demonstrating Generative AI's transformative potential in enhancing research across various scientific disciplines.

Examining Occupation Fields of Programs According to Artificial Intelligence: Anadolu University Open Education System Case

Anadolu University's Open Education System (OES) accommodates over one million students and has incorporated an AI-based Virtual Assistant for non-academic support since 2022. While OES offers abundant information about its programs on its website, there is a notable absence of support services providing job recommendations related to students' chosen programs. This gap in student support extends to the post-graduation phase, with the Virtual Assistant lacking a concept for guiding students in finding employment opportunities. Recognizing the need for comprehensive assistance, this study sought to leverage AI capabilities to offer job recommendations by extracting information from the objectives of 63 OES programs. The initial inquiry involved requesting AI-generated job recommendations based on the stated objectives of these programs. Subsequently, the Virtual Assistant was tasked with providing insights into the occupation fields associated with OES programs. Analysis of the AI's responses, along with the classification of occupations according to the International Standards of Classifications of Occupations (ISCO) and the International Standard Classification of Education (ISCED), forms the core of this study. Contrary to trends observed in most European countries, the predominant number of graduates in Türkiye emerges from business and management fields. However, the correlation between graduation rates and subsequent job placements appears suboptimal within the labor force and employment landscape. The study advocates for the integration of AI in offering job recommendations, incorporating graduation and employment rates. This approach enables students to seek guidance on suitable programs aligned with their skills, fostering a more informed decision-making process. The study underscores the potential for higher education institutes to share employment and labor force data.

Review on synergizing the Metaverse and AI-driven synthetic data: enhancing virtual realms and activity recognition in computer vision

The Metaverse’s emergence is redefining digital interaction, enabling seamless engagement in immersive virtual realms. This trend’s integration with AI and virtual reality (VR) is gaining momentum, albeit with challenges in acquiring extensive human action datasets. Real-world activities involve complex intricate behaviors, making accurate capture and annotation difficult. VR compounds this difficulty by requiring meticulous simulation of natural movements and interactions. As the Metaverse bridges the physical and digital realms, the demand for diverse human action data escalates, requiring innovative solutions to enrich AI and VR capabilities. This need is underscored by state-of-the-art models that excel but are hampered by limited real-world data. The overshadowing of synthetic data benefits further complicates the issue. This paper systematically examines both real-world and synthetic datasets for activity detection and recognition in computer vision. Introducing Metaverse-enabled advancements, we unveil SynDa’s novel streamlined pipeline using photorealistic rendering and AI pose estimation. By fusing real-life video datasets, large-scale synthetic datasets are generated to augment training and mitigate real data scarcity and costs. Our preliminary experiments reveal promising results in terms of mean average precision (mAP), where combining real data and synthetic video data generated using this pipeline to train models presents an improvement in mAP (32.35%), compared to the mAP of the same model when trained on real data (29.95%). This demonstrates the transformative synergy between Metaverse and AI-driven synthetic data augmentation.

A review of artificial intelligence methods enabled music-evoked EEG emotion recognition and their applications.

Music is an archaic form of emotional expression and arousal that can induce strong emotional experiences in listeners, which has important research and practical value in related fields such as emotion regulation. Among the various emotion recognition methods, the music-evoked emotion recognition method utilizing EEG signals provides real-time and direct brain response data, playing a crucial role in elucidating the neural mechanisms underlying music-induced emotions. Artificial intelligence technology has greatly facilitated the research on the recognition of music-evoked EEG emotions. AI algorithms have ushered in a new era for the extraction of characteristic frequency signals and the identification of novel feature signals. The robust computational capabilities of AI have provided fresh perspectives for the development of innovative quantitative models of emotions, tailored to various emotion recognition paradigms. The discourse surrounding AI algorithms in the context of emotional classification models is gaining momentum, with their applications in music therapy, neuroscience, and social activities increasingly coming under the spotlight. Through an in-depth analysis of the complete process of emotion recognition induced by music through electroencephalography (EEG) signals, we have systematically elucidated the influence of AI on pertinent research issues. This analysis offers a trove of innovative approaches that could pave the way for future research endeavors.