Artificial Intelligence Revolution Research Articles

‘Quantum of information’ functionality as a measure of subjectivity beyond the capabilities of deep learning

The potential of conscious artificial intelligence (AI), with its functional systems that surpass automation and rely on elements of understanding, is a beacon of hope in the AI revolution. The shift from automation to conscious AI, once replaced with machine understanding, offers a future where AI can comprehend without needing to experience, thereby revolutionizing the field of AI. In this context, the proposed Dynamic Organicity Theory of consciousness (DOT) stands out as a promising and novel approach for building artificial consciousness that is more like the brain with physiological nonlocality and diachronicity of self-referential causal closure. However, deep learning algorithms utilize "black box" techniques such as “dirty hooks” to make the algorithms operational by discovering arbitrary functions from a trained set of dirty data rather than prioritizing models of consciousness that accurately represent intentionality as intentions-in-action. The limitations of the “black box” approach in deep learning algorithms present a significant challenge as quantum information biology, or intrinsic information, is associated with subjective physicalism and cannot be predicted with Turing computation. This paper suggests that deep learning algorithms effectively decode labeled datasets but not dirty data due to unlearnable noise, and encoding intrinsic information is beyond the capabilities of deep learning. New models based on DOT are necessary to decode intrinsic information by understanding meaning and reducing uncertainty. The process of “encoding” entails functional interactions as evolving informational holons, forming informational channels in functionality space of time consciousness. The “quantum of information” functionality is the motivity of (negentropic) action as change in functionality through thermodynamic constraints that reduce informational redundancy (also referred to as intentionality) in informational pathways. It denotes a measure of epistemic subjectivity towards machine understanding beyond the capabilities of deep learning.

AI - Current Landscape and Future Predictions

This paper is the introduction to the Artificial Intelligence (AI) landscape in the present day and its future properties. AI is now being used in numerous fields, such as technology, finance and banking, and healthcare to name a few. The current use of AI is in automation, to perform tasks at the same level or even better than humans can in terms of efficiency, accuracy and speed. This is why AI is being used in more businesses, and why understanding AI has become more important than ever before. The majority of people don't truly understand what AI even is, and assume that it’s a dangerous field filled with killer robots, however that couldn’t be farther than the truth. The following chapters in this paper will explain what AI actually is as well as comparing it to the public’s perception. Once a proper understanding of AI is established, the paper aims to show how AI is being applied into industries, and how they can skyrocket the success of all of them in vast ways that were never possible before the AI revolution.

The impact of artificial intelligence in the early retirement decision

AbstractThis paper examines the impact of Artificial Intelligence (AI) on early retirement (ER) decisions in Europe. For the analysis, we utilize microdata from the Survey of Health, Ageing and Retirement in Europe, along with occupation-level data on AI advances and AI exposure. Initially, we investigate the influence of AI advances and AI exposure separately, finding in both instances a significant reduction in ER likelihood, though this only applies to workers with higher education. Subsequently, we explore the interaction between AI advances and AI exposure concerning ER probability. This interaction proves critical in determining AI’s impact on ER transitions. Specifically, we observe a significant reduction in ER probabilities for workers whose occupations exhibit high levels of AI advances and high expectations for further implementation of this technology in the future. Finally, we jointly analyse the interaction between AI advances, AI exposure, and education level. This analysis highlights that workers’ ER probabilities may either increase or decrease in response to the AI revolution, depending on their education level and the characteristics of their occupations in terms of AI advances and AI exposure.

The AI Revolution: How Artificial Intelligence is Reshaping Marketing Strategies

This paper explores the transformative impact of Artificial Intelligence (AI) on modern marketing practices. It examines how AI applications are revolutionizing various aspects of marketing, including customer experience, campaign personalization, content creation, and market research. The paper analyzes real- world examples and data to showcase the effectiveness of AI implementation. It also discusses potential challenges and ethical considerations surrounding AI in marketing. Finally, the paper explores the future potential of AI and its role in shaping the marketing landscape of tomorrow.

AI IR: Charting International Relations in the Age of Artificial Intelligence

Abstract Over the past decade, rapid progress in artificial intelligence (AI) has transformed a range of areas, from medicine to strategic games and communication technologies, from art and culture to everyday office work. It would be naïve to assume that this evolution does not permeate and alter international affairs. Building on, and solidifying, a thriving yet still fragmented emerging literature on “AI IR,” this forum gathers several critical diagnoses of the way AI technologies impact on various areas of international relations. Introducing new concepts and charting emerging empirical realities, contributors explore how AI advances, such as autonomous lethal systems, synthetic imagery and text, or intelligent systems, are already creating new landscapes of violent and nonviolent international interactions. Yet, behind their distinct takes, contributions together stress the need to correctly locate and evaluate specific sites of AI impact, thus offering a nuanced appraisal scrutinizing grand declarations of an “AI revolution” in global politics.

The generative artificial intelligence revolution: How hospitalists can lead the transformation of medical education.

The generative artificial intelligence revolution: How hospitalists can lead the transformation of medical education.

Redefining Intelligence: The Deep Learning Revolution in AI

In the ever-evolving realm of artificial intelligence (AI), deep learning stands as a beacon of innovation, transforming the essence and trajectory of AI systems. This paper ventures into the heart of deep learning’s transformative power, charting its journey from the early neural network concepts to the advanced architectures that propel today’s technological breakthroughs. We delve into the harmonious interplay between deep learning and the surge in computational prowess, amplified by the vast seas of big data, which have jointly propelled AI to unprecedented levels of functionality and societal integration. Through a tapestry of case studies, we illuminate the tangible applications of deep learning across diverse sectors such as healthcare, finance, and autonomous navigation, showcasing how these intelligent algorithms have not only sharpened efficiency and accuracy but also brought forth pressing ethical dilemmas. The conversation on AI’s ethical landscape, with a spotlight on transparency, accountability, and privacy, emerges as an indispensable facet of conscientious AI evolution. Peering into the horizon, we ponder the onward march of deep learning, recognizing the promise it holds for awe-inspiring innovation as well as the hurdles that await. The paper underscores the imperative for ongoing inquiry into algorithmic refinement, data morality, and the ecological footprint of AI technologies. In our closing reflections, this paper celebrates the profound sway of deep learning over AI, with a nod to the indomitable human spirit that fuels technological advancement. It is this fusion of human ingenuity, moral vision, and technical mastery that will chart the course for AI’s future, steering it towards our shared ideals and dreams for a brighter tomorrow

AI in higher education: Booster or stumbling block for developing digital competence?

Since the Artificial intelligence (AI) revolution catalyzed by ChatGPT, the discourse of students’ digital competence has become prevalent in German higher education institutions (HEIs). While educators recognize the potential for using AI in higher education, concerns persist about students needing more necessary skills. This paper presents findings from a comprehensive lecturer survey that provides insights into educators’ perspectives on the opportunities and challenges associated with AI integration in HEIs. Furthermore, it addresses the conditions required for successful AI implementation in German HEIs to promote, rather than hinder, students’ digital competence and future skills.

Unveiling the powerhouses of AI: A comprehensive study of GPU, FPGA, and ASIC accelerators

In the ever-evolving realm of technology, Artificial Intelligence (AI) has ushered in a transformative era, reshaping our interactions with digital systems, and expanding the horizons of machine capabilities. At the core of this AI revolution are specialized hardware entities known as AI accelerators. These accelerators, including Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Application-Specific Integrated Circuits (ASICs), play a pivotal role in advancing AI applications across diverse domains. This paper delves into these accelerators, offering an in-depth exploration of their unique attributes and application domains. GPUs, initially designed for graphics, have evolved into versatile tools, thanks to their parallel computing prowess and efficient memory utilization. FPGAs, with reconfigurability and low latency, prove valuable in aerospace and neural network implementations, though they come with cost and expertise challenges. ASICs, engineered for specific functions, excel in performance and power efficiency for mass production but require significant time and resources for development. Furthermore, this paper presents practical application analyses, showcasing how these accelerators are effectively deployed in real-world scenarios. With this comprehensive exploration, readers gain a deeper understanding of AI accelerators and their transformative impact on the AI landscape.

The digital divide in action: how experiences of digital technology shape future relationships with artificial intelligence

AbstractThe digital divide remains an ongoing societal concern, with digital exclusion shown to have a significantly detrimental impact on people’s quality of life. Artificial intelligence (AI), the latest wave of digitalisation, is being integrated into the fabric of society at an accelerated rate, the speed of which has prompted ethical concerns. Without addressing the digital divide, the AI revolution risks exacerbating the existing consequences of digital exclusion and limiting the potential for all people to reap the benefits provided by AI. To understand the factors that might contribute to experiences of AI, and how these might be related to digital exclusion, we surveyed a diverse online community sample (N = 303). We created a novel measure of digital confidence capturing individual levels of awareness, familiarity, and sense of competence with digital technology. Results indicated that measures of digital confidence were predicted by structural, behavioural, and psychological differences, such that women, older people, those on lower salaries, people with less digital access, and those with lower digital well-being, reported significantly less digital confidence. Furthermore, digital confidence significantly moderated the relationship between people’s experiences with everyday AI technologies and their general attitudes towards AI. This understanding of the spill-over effects of digital exclusion onto experiences of AI is fundamental to the articulation and delivery of inclusive AI.

Formation and analysis of networks of events in the field of parliamentary control based on the application of artificial intelligence systems

The article introduces a methodology for forming and analyzing the network of events in news reports related to parliamentary control. This methodology relies on the application of generative artificial intelligence, and the article provides examples of its practical implementation. The revolution in artificial intelligence enables the solution of tasks not only related to identification but also to the formation of causal networks of events, where the causes and consequences are clearly presented. The utilization of large linguistic models has yielded convenient methods for extracting events from texts, filtering them, and clustering. Artificial intelligence is employed for identifying cause-and-effect relationships, significantly simplifying the processing of natural language. Visualization and cluster analysis of the formed networks can be performed using traditional tools for network analysis.

Navigating the AI Revolution: Implications for Business Education and Pedagogy

Generative artificial intelligence (AI) is rapidly emerging as a transformative force across various sectors. As education shifts towards an AI-focused future, adapting teaching methodologies and evaluation strategies to this technological evolution becomes more and more important. This paper delves into the profound implications of generative AI on business education, critically analyzing its influence on broad program learning outcomes as well as on specific assessment tasks, ranging from quizzes to work-integrated learning projects. By examining and assessing responses from ChatGPT, we evaluate their structural coherence and the potential to enhance or replace key skills evaluated in students. Our findings primarily indicate that for formulaic quizzes and short essay questions, GPT-4 often delivers accurate solutions. In the context of research reports and reflection journals, GPT-4 serves more as a guide and scaffolding tool. As AI continues to advance, it becomes increasingly crucial for business educators to re-examine their learning objective frameworks. This includes utilizing the technology’s potential while navigating its complexities, ensuring that education remains effective, relevant, and in step with the pace of innovation.

Overlay databank unlocks data-driven analyses of biomolecules for all

Tools based on artificial intelligence (AI) are currently revolutionising many fields, yet their applications are often limited by the lack of suitable training data in programmatically accessible format. Here we propose an effective solution to make data scattered in various locations and formats accessible for data-driven and machine learning applications using the overlay databank format. To demonstrate the practical relevance of such approach, we present the NMRlipids Databank—a community-driven, open-for-all database featuring programmatic access to quality-evaluated atom-resolution molecular dynamics simulations of cellular membranes. Cellular membrane lipid composition is implicated in diseases and controls major biological functions, but membranes are difficult to study experimentally due to their intrinsic disorder and complex phase behaviour. While MD simulations have been useful in understanding membrane systems, they require significant computational resources and often suffer from inaccuracies in model parameters. Here, we demonstrate how programmable interface for flexible implementation of data-driven and machine learning applications, and rapid access to simulation data through a graphical user interface, unlock possibilities beyond current MD simulation and experimental studies to understand cellular membranes. The proposed overlay databank concept can be further applied to other biomolecules, as well as in other fields where similar barriers hinder the AI revolution.

Technology with responsibility: Artificial Intelligence and its impacts on industry 4.0 and education

Technology is the most pressing and elusive goal of any nation for the development of the education system and the entire system. Artificial Intelligence is making many changes in the educational setup. It has both positive and negative impacts on education, a versatile approach plays an important role in the education sector with huge possibilities for new emerging trends in the learning and teaching process. The field of artificial intelligence is an area of science that creates and studies machines to stimulate human intelligence processes. As a result, the study objectives in this paper are designed to assist professionals, students, teachers as well as business experts. Firstly, it examines the important technological elements and characteristics of AI, which are vital for the fourth industrial revolution in the education field. Second, this article analyses the key achievements and different obstacles that make the AI revolution for education in Industry 4.0 so possible. AI changes the styles of teaching and learning presently. The study also looks at the possible influence of AI on education, such as how teachers' roles are evolving and the need for new skills in the industry. This study enhances the currently existing reservoir of knowledge and offers insights into the future implications of this fast-expanding technology by offering a complete analysis of AI's impact on Industry 4.0 and education. This is especially important in today's environment when the usage of artificial intelligence (AI) is becoming more common and has the potential to dramatically impact our society and economy.

Assessing radiologists' and radiographers' perceptions on artificial intelligence integration: opportunities and challenges.

The objective of this study was to evaluate radiologists' and radiographers' opinions and perspectives on artificial intelligence (AI) and its integration into the radiology department. Additionally, we investigated the most common challenges and barriers that radiologists and radiographers face when learning about AI. A nationwide, online descriptive cross-sectional survey was distributed to radiologists and radiographers working in hospitals and medical centres from May 29, 2023 to July 30, 2023. The questionnaire examined the participants' opinions, feelings, and predictions regarding AI and its applications in the radiology department. Descriptive statistics were used to report the participants' demographics and responses. Five-points Likert-scale data were reported using divergent stacked bar graphs to highlight any central tendencies. Responses were collected from 258 participants, revealing a positive attitude towards implementing AI. Both radiologists and radiographers predicted breast imaging would be the subspecialty most impacted by the AI revolution. MRI, mammography, and CT were identified as the primary modalities with significant importance in the field of AI application. The major barrier encountered by radiologists and radiographers when learning about AI was the lack of mentorship, guidance, and support from experts. Participants demonstrated a positive attitude towards learning about AI and implementing it in the radiology practice. However, radiologists and radiographers encounter several barriers when learning about AI, such as the absence of experienced professionals support and direction. Radiologists and radiographers reported several barriers to AI learning, with the most significant being the lack of mentorship and guidance from experts, followed by the lack of funding and investment in new technologies.

Revolution of artificial intelligence in antimicrobial, anti-biofilm, and anti-inflammatory techniques: Smart photo-sonodynamic appliance in the internet of dental things (IoDT)

Antimicrobial photo-sonodynamic therapy (aPSDT) is a promising treatment for microbial infections, but it faces challenges such as bacterial resistance, limited anti-inflammatory effects, and a doctor-centered approach. We hypothesize that the integration of artificial intelligence (AI), Internet of Dental Things (IoDT), and aPSDT to enhance the efficiency and effectiveness of aPSDT by focusing on four crucial aspects—patients' usage, antimicrobial efficacy, biofilm prevention, and inflammation reduction. By leveraging IoDT, patients can utilize specialized dental devices outside of the traditional clinic setting, enabling remote access to treatment information, monitoring disinfection and progress, receiving guidance, and making necessary adjustments remotely. This integration enhances convenience and efficiency for both patients and dental professionals. Additionally, AI plays a crucial role in combating antimicrobial resistance and improving the efficacy of aPSDT. Through data analysis, AI can predict the development of resistance, simulate microbial evolution, monitor treatment responses, recommend combination therapies, and personalize treatment plans. Furthermore, AI has the potential to augment the effectiveness of aPSDT in treating inflammation by employing image analysis, personalized treatment planning, real-time monitoring, and therapeutic discovery.

The Artificial Intelligence Revolution: Transforming Robotics for Flexibility and Deformability

The Artificial Intelligence Revolution: Transforming Robotics for Flexibility and Deformability

Application of artificial intelligence and machine learning in drug repurposing.

The purpose of drug repurposing is to leverage previously approved drugs for a particular disease indication and apply them to another disease. It can be seen as a faster and more cost-effective approach to drug discovery and a powerful tool for achieving precision medicine. In addition, drug repurposing can be used to identify therapeutic candidates for rare diseases and phenotypic conditions with limited information on disease biology. Machine learningand artificial intelligence (AI) methodologies have enabled the construction of effective, data-driven repurposing pipelines by integrating and analyzing large-scale biomedical data. Recent technological advances, especially in heterogeneous network mining and natural language processing, have opened up exciting new opportunities and analytical strategies for drug repurposing. In this review, we first introduce the challenges in repurposing approaches and highlight some success stories, including those during the COVID-19 pandemic. Next, we review some existing computational frameworks in the literature, organized on the basis of the type of biomedical input data analyzed and the computational algorithms involved. In conclusion, we outline some exciting new directions that drug repurposing research may take, as pioneered by the generative AI revolution.

Industrial Revolution of Artificial Intelligence

The paper explores the challenges posed by the artificial intelligence (AI) industrial revolution and emphasizes the need for responsible and ethical handling of AI technology. It primarily focuses on issues related to data security, privacy, ethics, and societal consequences. The integration of AI into various aspects of society requires strict regulation to prevent unintentional biases, discrimination, and surveillance. Maintaining transparency, fairness, and accountability within AI systems is a challenging task. In addition to discussing these challenges, the paper identifies key research questions concerning the AI revolution's impact on ethics, privacy, workforce disruptions, and societal dynamics. These questions serve as a roadmap for future studies to better understand how AI affects individuals, organizations, and society as a whole. To investigate these challenges and opportunities, the paper outlines a comprehensive research methodology that combines qualitative and quantitative methods, including literature reviews, case studies, surveys, and expert interviews. This approach provides a well-rounded perspective on the AI revolution by synthesizing theoretical foundations, real-world examples, and insights from various stakeholders. The study presents findings highlighting the significant progress AI has brought in terms of automation, innovation, and productivity, enhancing human capabilities. However, it also underscores concerns related to privacy, ethics, and potential job displacement. The paper advocates for responsible AI development, emphasizing the importance of ethical guidelines, regulations, and transparency. These findings offer valuable insights for policymakers, industry experts, and researchers, helping them better understand and harness the potential of AI while addressing its risks and societal impacts.

Some issues of global problems in the changing system of values of the modern world

The article analyzes the peculiarities of global problems in the modern world system, their transformation, identification of more acute issues, taking into account the urgent issue of the security of each country, food security against the background of the growth of the total population of the planet. Issues of environmental protection and climate change. Modifications of global problems, taking into account wars and their consequences, along with the active phase of the Fourth Industrial Revolution, the development of artificial intelligence, which leads to the emergence of new professions, new forms of employment of the population, the development of humanity in general and, of course, affects the changes in the values of every society. The article analyzes the historical perspective of economic and social changes and the influence of technological processes, assesses the impact of the war on the economic and demographic components of society. It is noted that the results of the fourth industrial revolution, the emergence and development of artificial intelligence are a breakthrough for the survival of mankind. Artificial intelligence (AI) is rapidly transforming various industries, and environmental protection and climate change are no exception. The modern world transforms a person's way of life, forms of communication, changes the characteristics of relationships, views on the same things, manners of communication, and ultimately, values. In the articles of philosophers, economists, and psychologists who are engaged in researching the problems of forming the society of the future, the emergence of the technological structure of psychotechnologies and technologies related to moral responsibility is noted. Changes in the technological stages of society are accompanied by changes in society itself. Values influence people's desire to realize themselves and, accordingly, the sphere of employment. In the context of world transformations, there are changes in the values of society. Values can be divided into groups and everyone determines their priority (material, spiritual values (vital, moral, aesthetic), social, family values, the value of pleasure, health. Each person takes his place in society, which determines social values that depend from the environment, from needs, from goals, and they can change during life.Self-discovery is the deepest way of assessing one's values.