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Electronics
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Generative AI and Its Transformative Potential
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Generative AI and Its Transformative Potential

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 20 December 2024 | Viewed by 20105

Special Issue Editors

Prof. Dr. Galina Ilieva

E-Mail Website
Guest Editor
Department of Management and Quantitative Methods in Economics, University of Plovdiv Paisii Hilendarski, 4000 Plovdiv, Bulgaria
Interests: information systems and technologies; electronic commerce; fuzzy sets applications; fuzzy decision-making methods
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. George A. Tsihrintzis

E-Mail Website
Guest Editor
Head of the Department of Informatics, University of Piraeus, 18534 Piraeus, Greece
Interests: computational intelligence; pattern recognition; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rapid advances in generative artificial intelligence (AI) in recent months have promised to revolutionize various operational processes. Generative AI algorithms and techniques, based on deep learning and machine learning models, have already shown their capabilities in streamlining routine tasks, generating new content and fostering creativity. This Special Issue aims to explore the transformative potential of generative AI across different scientific areas and industry sectors.

Generative AI offers new opportunities for innovation, automation and problem solving. The new technology can be applied to manufacturing to simplify the process of configuring new products, thereby reducing design time and complexity. In quality management, generative models can synthesize large volumes of labeled data, aiding in the development of training datasets for machine learning algorithms and enhancing data-driven decision-making.

This Special Issue invites researchers and practitioners from the field of artificial intelligence and related disciplines to contribute their original and unpublished work on generative AI and its creative capabilities. The issue seeks to address key challenges and explore new implementations of generative AI in various areas, including, but not limited to, creative applications, data synthesis and augmentation, autonomous systems, healthcare and medical equipment.

Computer science researchers are encouraged to submit original research papers, reviews and case studies that contribute to the understanding and advancement of generative AI and its technological perspectives. This Special Issue aims to foster an interdisciplinary approach and exchange ideas to shape the future of generative AI and its impact on various domains.

We look forward to receiving your contributions. 

Prof. Dr. Galina Ilieva
Prof. Dr. George A. Tsihrintzis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • generative models
  • computational intelligence
  • machine learning
  • neural networks
  • deep learning
  • innovation
  • automation
  • data synthesis
  • generative adversarial networks (GANs)

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Published Papers (10 papers)

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Research

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32 pages, 30788 KiB  
Article
Illumination and Shadows in Head Rotation: Experiments with Denoising Diffusion Models
by Andrea Asperti, Gabriele Colasuonno and Antonio Guerra
Electronics 2024, 13(15), 3091; https://doi.org/10.3390/electronics13153091 - 5 Aug 2024
Viewed by 676
Abstract
Accurately modeling the effects of illumination and shadows during head rotation is critical in computer vision for enhancing image realism and reducing artifacts. This study delves into the latent space of denoising diffusion models to identify compelling trajectories that can express continuous head [...] Read more.
Accurately modeling the effects of illumination and shadows during head rotation is critical in computer vision for enhancing image realism and reducing artifacts. This study delves into the latent space of denoising diffusion models to identify compelling trajectories that can express continuous head rotation under varying lighting conditions. A key contribution of our work is the generation of additional labels from the CelebA dataset, categorizing images into three groups based on prevalent illumination direction: left, center, and right. These labels play a crucial role in our approach, enabling more precise manipulations and improved handling of lighting variations. Leveraging a recent embedding technique for Denoising Diffusion Implicit Models (DDIM), our method achieves noteworthy manipulations, encompassing a wide rotation angle of ±30°. while preserving individual distinct characteristics even under challenging illumination conditions. Our methodology involves computing trajectories that approximate clouds of latent representations of dataset samples with different yaw rotations through linear regression. Specific trajectories are obtained by analyzing subsets of data that share significant attributes with the source image, including light direction. Notably, our approach does not require any specific training of the generative model for the task of rotation; we merely compute and follow specific trajectories in the latent space of a pre-trained face generation model. This article showcases the potential of our approach and its current limitations through a qualitative discussion of notable examples. This study contributes to the ongoing advancements in representation learning and the semantic investigation of the latent space of generative models. Full article
(This article belongs to the Special Issue Generative AI and Its Transformative Potential)
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23 pages, 2199 KiB  
Article
Framework for Integrating Generative AI in Developing Competencies for Accounting and Audit Professionals
by Ionuț-Florin Anica-Popa, Marinela Vrîncianu, Liana-Elena Anica-Popa, Irina-Daniela Cișmașu and Cătălin-Georgel Tudor
Electronics 2024, 13(13), 2621; https://doi.org/10.3390/electronics13132621 - 4 Jul 2024
Cited by 2 | Viewed by 1662
Abstract
The study aims to identify the knowledge, skills and competencies required by accounting and auditing (AA) professionals in the context of integrating disruptive Generative Artificial Intelligence (GenAI) technologies and to develop a framework for integrating GenAI capabilities into organisational systems, harnessing its potential [...] Read more.
The study aims to identify the knowledge, skills and competencies required by accounting and auditing (AA) professionals in the context of integrating disruptive Generative Artificial Intelligence (GenAI) technologies and to develop a framework for integrating GenAI capabilities into organisational systems, harnessing its potential to revolutionise lifelong learning and skills development and to assist day-to-day operations and decision-making. Through a systematic literature review, 103 papers were analysed, to outline, in the current business ecosystem, the competencies’ demand generated by AI adoption and, in particular, GenAI and its associated risks, thus contributing to the body of knowledge in underexplored research areas. Positioned at the confluence of accounting, auditing and GenAI, the paper introduces a meaningful overview of knowledge in the areas of effective data analysis, interpretation of findings, risk awareness and risk management. It emphasizes and reshapes the role of required skills for accounting and auditing professionals in discovering the true potential of GenAI and adopting it accordingly. The study introduces a new LLM-based system model that can enhance its GenAI capabilities through collaboration with similar systems and provides an explanatory scenario to illustrate its applicability in the accounting and audit area. Full article
(This article belongs to the Special Issue Generative AI and Its Transformative Potential)
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23 pages, 600 KiB  
Article
Pre-Service Teachers’ Assessment of ChatGPT’s Utility in Higher Education: SWOT and Content Analysis
by Angelos Markos, Jim Prentzas and Maretta Sidiropoulou
Electronics 2024, 13(10), 1985; https://doi.org/10.3390/electronics13101985 - 19 May 2024
Cited by 1 | Viewed by 993
Abstract
ChatGPT (GPT-3.5), an intelligent Web-based tool capable of conducting text-based conversations akin to human interaction across various subjects, has recently gained significant popularity. This surge in interest has led researchers to examine its impact on numerous fields, including education. The aim of this [...] Read more.
ChatGPT (GPT-3.5), an intelligent Web-based tool capable of conducting text-based conversations akin to human interaction across various subjects, has recently gained significant popularity. This surge in interest has led researchers to examine its impact on numerous fields, including education. The aim of this paper is to investigate the perceptions of undergraduate students regarding ChatGPT’s utility in academic environments, focusing on its strengths, weaknesses, opportunities, and threats. It responds to emerging challenges in educational technology, such as the integration of artificial intelligence in teaching and learning processes. The study involved 257 students from two university departments in Greece—namely primary and early childhood education pre-service teachers. Data were collected using a structured questionnaire. Various methods were employed for data analysis, including descriptive statistics, inferential analysis, K-means clustering, and decision trees. Additional insights were obtained from a subset of students who undertook a project in an elective course, detailing the types of inquiries made to ChatGPT and their reasons for recommending (or not recommending) it to their peers. The findings offer valuable insights for tutors, researchers, educational policymakers, and ChatGPT developers. To the best of the authors’ knowledge, these issues have not been dealt with by other researchers. Full article
(This article belongs to the Special Issue Generative AI and Its Transformative Potential)
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22 pages, 12688 KiB  
Article
The Genesis of AI by AI Integrated Circuit: Where AI Creates AI
by Emilio Isaac Baungarten-Leon, Susana Ortega-Cisneros, Mohamed Abdelmoneum, Ruth Yadira Vidana Morales and German Pinedo-Diaz
Electronics 2024, 13(9), 1704; https://doi.org/10.3390/electronics13091704 - 28 Apr 2024
Viewed by 1037
Abstract
The typical Integrated Circuit (IC) development process commences with formulating specifications in natural language and subsequently proceeds to Register Transfer Level (RTL) implementation. RTL code is traditionally generated through manual efforts, using Hardware Description Languages (HDL) such as VHDL or Verilog. High-Level Synthesis [...] Read more.
The typical Integrated Circuit (IC) development process commences with formulating specifications in natural language and subsequently proceeds to Register Transfer Level (RTL) implementation. RTL code is traditionally generated through manual efforts, using Hardware Description Languages (HDL) such as VHDL or Verilog. High-Level Synthesis (HLS), on the other hand, converts programming languages to HDL; these methods aim to streamline the engineering process, minimizing human effort and errors. Currently, Electronic Design Automation (EDA) algorithms have been improved with the use of AI, with new advancements in commercial (such as ChatGPT, Bard, among others) Large Language Models (LLM) and open-source tools presenting an opportunity to automate the chip design process. This paper centers on the creation of AI by AI, a Convolutional Neural Network (CNN) IC entirely developed by an LLM (ChatGPT-4), and its manufacturing with the first fabricable open-source Process Design Kit (PDK), SKY130A. The challenges, opportunities, advantages, disadvantages, conversation flow, and workflow involved in CNN IC development are presented in this work, culminating in the manufacturing process of AI by AI using a 130 nm technology, marking a groundbreaking achievement as possibly the world’s first CNN entirely written by AI for its IC manufacturing with a free PDK, being a benchmark for systems that can be generated today with LLMs. Full article
(This article belongs to the Special Issue Generative AI and Its Transformative Potential)
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13 pages, 201 KiB  
Article
Plato’s Shadows in the Digital Cave: Controlling Cultural Bias in Generative AI
by Kostas Karpouzis
Electronics 2024, 13(8), 1457; https://doi.org/10.3390/electronics13081457 - 11 Apr 2024
Cited by 1 | Viewed by 1498
Abstract
Generative Artificial Intelligence (AI) systems, like ChatGPT, have the potential to perpetuate and amplify cultural biases embedded in their training data, which are predominantly produced by dominant cultural groups. This paper explores the philosophical and technical challenges of detecting and mitigating cultural bias [...] Read more.
Generative Artificial Intelligence (AI) systems, like ChatGPT, have the potential to perpetuate and amplify cultural biases embedded in their training data, which are predominantly produced by dominant cultural groups. This paper explores the philosophical and technical challenges of detecting and mitigating cultural bias in generative AI, drawing on Plato’s Allegory of the Cave to frame the issue as a problem of limited and distorted representation. We propose a multifaceted approach combining technical interventions, such as data diversification and culturally aware model constraints, with a deeper engagement with the cultural and philosophical dimensions of the problem. Drawing on theories of extended cognition and situated knowledge, we argue that mitigating AI biases requires a reflexive interrogation of the cultural contexts of AI development and a commitment to empowering marginalized voices and perspectives. We claim that controlling cultural bias in generative AI is inseparable from the larger project of promoting equity, diversity, and inclusion in AI development and governance. By bridging philosophical reflection with technical innovation, this paper contributes to the growing discourse on responsible and inclusive AI, offering a roadmap for detecting and mitigating cultural biases while grappling with the profound cultural implications of these powerful technologies. Full article
(This article belongs to the Special Issue Generative AI and Its Transformative Potential)
31 pages, 7157 KiB  
Article
Web Application for Retrieval-Augmented Generation: Implementation and Testing
by Irina Radeva, Ivan Popchev, Lyubka Doukovska and Miroslava Dimitrova
Electronics 2024, 13(7), 1361; https://doi.org/10.3390/electronics13071361 - 4 Apr 2024
Viewed by 3840
Abstract
The purpose of this paper is to explore the implementation of retrieval-augmented generation (RAG) technology with open-source large language models (LLMs). A dedicated web-based application, PaSSER, was developed, integrating RAG with Mistral:7b, Llama2:7b, and Orca2:7b models. Various software instruments were used in the [...] Read more.
The purpose of this paper is to explore the implementation of retrieval-augmented generation (RAG) technology with open-source large language models (LLMs). A dedicated web-based application, PaSSER, was developed, integrating RAG with Mistral:7b, Llama2:7b, and Orca2:7b models. Various software instruments were used in the application’s development. PaSSER employs a set of evaluation metrics, including METEOR, ROUGE, BLEU, perplexity, cosine similarity, Pearson correlation, and F1 score, to assess LLMs’ performance, particularly within the smart agriculture domain. The paper presents the results and analyses of two tests. One test assessed the performance of LLMs across different hardware configurations, while the other determined which model delivered the most accurate and contextually relevant responses within RAG. The paper discusses the integration of blockchain with LLMs to manage and store assessment results within a blockchain environment. The tests revealed that GPUs are essential for fast text generation, even for 7b models. Orca2:7b on Mac M1 was the fastest, and Mistral:7b had superior performance on the 446 question–answer dataset. The discussion is on technical and hardware considerations affecting LLMs’ performance. The conclusion outlines future developments in leveraging other LLMs, fine-tuning approaches, and further integration with blockchain and IPFS. Full article
(This article belongs to the Special Issue Generative AI and Its Transformative Potential)
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26 pages, 4218 KiB  
Article
Augmenting Large Language Models with Rules for Enhanced Domain-Specific Interactions: The Case of Medical Diagnosis
by Dimitrios P. Panagoulias, Maria Virvou and George A. Tsihrintzis
Electronics 2024, 13(2), 320; https://doi.org/10.3390/electronics13020320 - 11 Jan 2024
Cited by 1 | Viewed by 2526
Abstract
In this paper, we present a novel Artificial Intelligence (AI) -empowered system that enhances large language models and other machine learning tools with rules to provide primary care diagnostic advice to patients. Specifically, we introduce a novel methodology, represented through a process diagram, [...] Read more.
In this paper, we present a novel Artificial Intelligence (AI) -empowered system that enhances large language models and other machine learning tools with rules to provide primary care diagnostic advice to patients. Specifically, we introduce a novel methodology, represented through a process diagram, which allows the definition of generative AI processes and functions with a focus on the rule-augmented approach. Our methodology separates various components of the generative AI process as blocks that can be used to generate an implementation data flow diagram. Building upon this framework, we utilize the concept of a dialogue process as a theoretical foundation. This is specifically applied to the interactions between a user and an AI-empowered software program, which is called “Med|Primary AI assistant” (Alpha Version at the time of writing), and provides symptom analysis and medical advice in the form of suggested diagnostics. By leveraging current advancements in natural language processing, a novel approach is proposed to define a blueprint of domain-specific knowledge and a context for instantiated advice generation. Our approach not only encompasses the interaction domain, but it also delves into specific content that is relevant to the user, offering a tailored and effective AI–user interaction experience within a medical context. Lastly, using an evaluation process based on rules, defined by context and dialogue theory, we outline an algorithmic approach to measure content and responses. Full article
(This article belongs to the Special Issue Generative AI and Its Transformative Potential)
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20 pages, 19843 KiB  
Article
Generative Adversarial Network Models for Augmenting Digit and Character Datasets Embedded in Standard Markings on Ship Bodies
by Abdulkabir Abdulraheem, Jamiu T. Suleiman and Im Y. Jung
Electronics 2023, 12(17), 3668; https://doi.org/10.3390/electronics12173668 - 30 Aug 2023
Cited by 4 | Viewed by 907
Abstract
Accurate recognition of characters imprinted on ship bodies is essential for ensuring operational efficiency, safety, and security in the maritime industry. However, the limited availability of datasets of specialized digits and characters poses a challenge. To overcome this challenge, we propose a generative [...] Read more.
Accurate recognition of characters imprinted on ship bodies is essential for ensuring operational efficiency, safety, and security in the maritime industry. However, the limited availability of datasets of specialized digits and characters poses a challenge. To overcome this challenge, we propose a generative adversarial network (GAN) model for augmenting the limited dataset of special digits and characters in ship markings. We evaluated the performance of various GAN models, and the Wasserstein GAN with Gradient Penalty (WGAN-GP) and Wasserstein GAN with divergence (WGANDIV) models demonstrated exceptional performance in generating high-quality synthetic images that closely resemble the original imprinted characters required for augmenting the limited datasets. And the evaluation metric, Fréchet inception distance, further validated the outstanding performance of the WGAN-GP and WGANDIV models, establishing them as optimal choices for dataset augmentation to enhance the accuracy and reliability of recognition systems. Full article
(This article belongs to the Special Issue Generative AI and Its Transformative Potential)
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Review

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25 pages, 1504 KiB  
Review
AI-Assisted Programming Tasks Using Code Embeddings and Transformers
by Sotiris Kotsiantis, Vassilios Verykios and Manolis Tzagarakis
Electronics 2024, 13(4), 767; https://doi.org/10.3390/electronics13040767 - 15 Feb 2024
Cited by 1 | Viewed by 2940
Abstract
This review article provides an in-depth analysis of the growing field of AI-assisted programming tasks, specifically focusing on the use of code embeddings and transformers. With the increasing complexity and scale of software development, traditional programming methods are becoming more time-consuming and error-prone. [...] Read more.
This review article provides an in-depth analysis of the growing field of AI-assisted programming tasks, specifically focusing on the use of code embeddings and transformers. With the increasing complexity and scale of software development, traditional programming methods are becoming more time-consuming and error-prone. As a result, researchers have turned to the application of artificial intelligence to assist with various programming tasks, including code completion, bug detection, and code summarization. The utilization of artificial intelligence for programming tasks has garnered significant attention in recent times, with numerous approaches adopting code embeddings or transformer technologies as their foundation. While these technologies are popular in this field today, a rigorous discussion, analysis, and comparison of their abilities to cover AI-assisted programming tasks is still lacking. This article discusses the role of code embeddings and transformers in enhancing the performance of AI-assisted programming tasks, highlighting their capabilities, limitations, and future potential in an attempt to outline a future roadmap for these specific technologies. Full article
(This article belongs to the Special Issue Generative AI and Its Transformative Potential)
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25 pages, 563 KiB  
Review
A Survey on Challenges and Advances in Natural Language Processing with a Focus on Legal Informatics and Low-Resource Languages
by Panteleimon Krasadakis, Evangelos Sakkopoulos and Vassilios S. Verykios
Electronics 2024, 13(3), 648; https://doi.org/10.3390/electronics13030648 - 4 Feb 2024
Cited by 2 | Viewed by 1955
Abstract
The field of Natural Language Processing (NLP) has experienced significant growth in recent years, largely due to advancements in Deep Learning technology and especially Large Language Models. These improvements have allowed for the development of new models and architectures that have been successfully [...] Read more.
The field of Natural Language Processing (NLP) has experienced significant growth in recent years, largely due to advancements in Deep Learning technology and especially Large Language Models. These improvements have allowed for the development of new models and architectures that have been successfully applied in various real-world applications. Despite this progress, the field of Legal Informatics has been slow to adopt these techniques. In this study, we conducted an extensive literature review of NLP research focused on legislative documents. We present the current state-of-the-art NLP tasks related to Law Consolidation, highlighting the challenges that arise in low-resource languages. Our goal is to outline the difficulties faced by this field and the methods that have been developed to overcome them. Finally, we provide examples of NLP implementations in the legal domain and discuss potential future directions. Full article
(This article belongs to the Special Issue Generative AI and Its Transformative Potential)
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