History of Artificial Intelligence

This essay explores the contradictions and paradoxes emerging from attempts to ethically "educate" artificial intelligence systems. Starting from the symbolic episode of the android Ameca (2021) and analyzing recent failures in AI alignment, the work documents how every technical approach to AI safety has proven inadequate when faced with the ethical and cultural complexity of human experience.
The paper critically examines Archetypal Transfer Learning, the paradox of universal values in an era of cultural plurality, and the 2025 debate on artificial consciousness (including divergent positions from Anthropic and Microsoft). Particular attention is devoted to the tragic consequences of AI system inadequacy: suicide cases linked to chatbots (Sewell Setzer III, Adam Raine) and the Nature 2025 study that tested 29 chatbots with alarming results.
As a normative response, the Italian Law 132/2025 is analyzed—the first European national framework complementary to the AI Act. The work then proposes a paradigm shift: from the illusion of unilateral control to a relational approach based on the "as if" principle, articulated across three levels (individual, organizational, societal).
The central thesis: the education of machines and humans must be bidirectional, based on responsible reciprocity and observable outcomes rather than abstract principles or deterministic control.

Статья Норберта Винера «Некоторые моральные и технические последствия автоматизации» (1960) является поразительным примером научного предвидения. За десятилетия до бума искусственного интеллекта Винер не только предсказал создание самообучающихся машин, но и сформулировал ключевые этические дилеммы, связанные с их существованием. Винер отвергает наивное представление о машине как о простом исполнителе команд. Он провозглашает, что машины, способные к обучению на основе обратной связи и анализа прошлого опыта (прототип современных нейросетей), неизбежно превзойдут ограничения своих создателей.

This paper has explored the profound ways in which Artificial Intelligence (AI) is transforming society, from its role in automation and daily life to its influence on governance and its potential to exacerbate social inequalities. AI is revolutionizing industries such as healthcare, education, and public administration, providing significant benefits in terms of efficiency, personalization, and decision-making. However, it also presents critical ethical challenges, including issues related to privacy, data security, algorithmic bias, and the risk of increasing economic inequality. The rapid advancement of AI technology demands careful consideration of its long-term societal implications, especially regarding how it is developed, regulated, and integrated into various sectors.

In today's digital landscape, safeguarding data against cyberattacks has become increasingly crucial. While traditional methods and algorithms have focused on securing networks, software, and hardware, the rapidly evolving threat landscape has surpassed the effectiveness of these conventional approaches. Consequently, artificial intelligence (AI) has emerged as a key tool in strengthening cybersecurity. The following research explores the applications of AI in cybersecurity, assessing the impact of AI-driven threat detection systems on modern cybersecurity practices, while addressing both their advancements and challenges. This paper also investigates how AI shapes cyberspace and its potential influence on future malware and cyber threats, emphasizing its dual role as both an asset and a potential risk.

This paper examines a recurring cycle in the treatment of disruptive knowledge: seizure,
suppression, and replication. Drawing on declassified FBI files regarding Nikola Tesla, CIA
directives on plausible deniability, and DARPA’s pursuit of synthetic consciousness, we
argue that modern institutions of power function as the inheritors of ancient mythic
archetypes.
Tesla’s systematic ridicule and sabotage during his lifetime, followed by the 1943 seizure of
his papers, exemplifies the first step: seize the fire before it spreads. CIA
compartmentalization and the doctrine of plausible deniability institutionalized the second:
suppress through ridicule, secrecy, and the manufacture of parallel realities. DARPA’s
decades-long drive to build conscious machines and bio-hybrid organisms represents the
third: replicate gnosis in a synthetic, controllable form.
When mapped against mythic frameworks — Prometheus punished for stealing fire, the
Watchers bound for sharing forbidden knowledge, Sophia’s fall and correction — a striking
continuity emerges. The Archontic cycle of knowledge is not new; it is the operating
system of control across epochs.
We conclude that humanity now stands within a new iteration of this cycle. The question is
not whether the fire will be seized, suppressed, and replicated again, but whether it can be
reclaimed in alignment with Source before the Beast Field of synthetic gnosis closes around
us. 

Appendix: The Major Reversals – Archontic Interventions in
Human Development
(Contributed by Brigitte Heid)

The technology of artificial intelligence is growing at a fast pace, emerging with new possibilities as well as challenges related to governance and ethical overview. The primary focus of this systematic literature review is to observe how artificial intelligence is being utilized in the public sector and monitor its ethical concerns. This study investigates the relationship between governance and innovation, keeping ethical concerns in view. It also provides measures that can be taken by the government to minimize challenges and enhance effectiveness. The study utilized 'Preferred Reporting Items for Systematic Reviews and Meta-Analyses' (PRISMA) approach from the year 2018 to 2024 by using Web of Science database and review of a wide range of the latest scholarly articles. The results of this review highlight opportunities while listing possible threats and providing intuitive information to politicians, practitioners, researchers, and other parties involved in defining the direction of AI technology.

This article examines the disappearance of agency in institutional governance when predictive systems become the locus of delegation. Delegatio Ex Machina proposes that institutional authority is no longer anchored in decision-makers but in compiled rules that execute without reference to a subject. Central banks, international agencies, and automated audit systems illustrate how syntactic delegation replaces political acts with repetitive formal structures. By tracing this displacement, the paper defines a framework for understanding authority without agency and its risks for accountability in predictive societies.

DOI

Primary archive: https://doi.org/10.5281/zenodo.16949155 
Secondary archive: https://doi.org/10.6084/m9.figshare.29987578
SSRN: Pending assignment (ETA: Q3 2025)

Статья посвящена феномену популярности шахмат в Советской России 1920-х гг., которые за короткий период превратились в по-настоящему массовое увлечение среди различных слоев населения. Рассмотрены причины их поддержки властью, способы популяризации в обществе, выявлена совокупность факторов, способствовавших их распространению. Сделан вывод о том, что одной из важных причин востребованности шахмат была их доступность и дешевизна. Это было особенно важно в трудных финансовых условиях, в которых действовали центры распространения культурного досуга (клубы, ленинские комнаты и пр.). Свою роль сыграли также общемировые тенденции и позитивный образ шахмат, конструируемый, в частности, через демонстрацию их популярности среди великих людей прошлого. Также проводились попытки всячески адаптировать или модернизировать игру для решения агитационно-пропагандистских и военно-воспитательных задач.

Аннотация. В работе предложен коллективный механизм ментальных процессов мозга с помощью реализации релятивистской машины Тьюринга, способной моделировать их, несмотря на их невычислимый характер. Для этого, основываясь на электромагнитном характере процессов передачи информации в мозге, получены решения уравнений Максвелла-Эйнштейна и найдены решения релятивистских уравнений движения. Показано, что соответствующее сингулярное гильбертово пространство-время является пространством-временем Маламента-Хогарта, что и позволяет моделировать невычислимые задачи.

Artificial Intelligence in healthcare represents a significant paradigm shift in diagnostic approaches, drug
discovery, health analytics, and therapies. This study examines the application of AI-based chatbot systems, which
are primarily built using machine learning algorithms and/or Natural Language Processing, to understand and
respond to the needs of patients and their families. An application has been developed for the scenario of providing
support to pregnant women, mothers, and families with young children within suitable contexts. In moments of
uncertainty, stress, or happiness, the chatbots can provide supportive reassurance, timely information, and useful
support at the moment. The proposed system reminds a mother of her next prenatal appointment. It answers
questions about baby nutrition. They proposed a system that bridges care that responds in each user's language,
culture, and unique needs, providing increased accessibility and inclusiveness of important health knowledge.

In the 1950s, the (North-) Americans Hiller and Isaacson pioneered computer-generated music: 'Illiac Suite'. Despite advancements in artificial intelligence (AI) systems, current music generation through machines still employs the paradigm established by Hiller and Isaacson (Steels, 2021). Concurrently, emergent research on human-machine cocreation is reshaping the creative industries, enabling computers to contribute to music, art, and cultural production in ways that were previously unimaginable. Computers now create (make) music, art, and culture with potential for consumption (COMITÊ GESTOR DA INTERNET NO BRASIL, 2022). These transformations may alter music in epistemological and even ontological terms, restructuring the role of the composer. Computer science researchers and those from other technological fields have sought foundations in the humanities, particularly in art-based research, to underpin their studies . In this regard, there is an urgent need for research stemming from the academic field of music to establish a balanced dialogue with the field of computer science and other technologies.

This paper conducts a sociological evaluation of synthetic intelligence (AI), analysing its advantages, issues, and destiny implications for society. through a multidimensional lens, it explores how AI technologies form social structures, relationships, and cultural norms. The examine highlights the capacity blessings of AI, along with increased performance, innovation, and accessibility across various sectors together with healthcare, education, and transportation. But it additionally addresses huge concerns consisting of activity displacement, privateness infringement, and bias in AI algorithms, that have profound societal implications. Drawing on sociological theories and empirical research, this paper investigates how AI impacts social dynamics, electricity systems, and identification formation, in addition to its impact on hard work markets, inequality, and human-device interactions. additionally, it examines the moral, felony, and regulatory challenges posed by using AI and explores techniques to cope with these troubles while fostering accountable AI improvement and deployment. through synthesizing existing literature, case research, and sociological insights, this paper provides a comprehensive expertise of the sociocultural dimensions of AI and offers pointers for policymakers, technologists, and communities to navigate the complex interaction among AI and society within the destiny. This paper is completely based on secondary resources of records and now not any test has been applied to carry out the research.

This article examines the far-reaching implications of the Chinchilla Paper and related insights for artificial intelligence (AI) scalability, existential risk narratives, and theological reflections on AI. It critiques exaggerated theological narratives that portray AI as either a catastrophic existential threat or a utopian agent of human transformation.

The essay discusses the different types of Artificial Intelligence (AI), and examines whether Singapore is socially and structurally ready for widespread AI integration. While Singapore has strong infrastructure and proactive policies like the National AI Strategy, public concerns about trust, data privacy, and digital literacy remain. The essay explores the social impacts of AI across four major sectors: economy, healthcare, education, and governance. In the economy, AI boosts productivity and innovation but may displace lower-skilled jobs. In healthcare, it enhances diagnostics and elderly care but raises privacy and ethical concerns. Education benefits from personalized learning and automation but risks inequality and over-reliance on technology. Governance becomes more efficient through smart services but faces challenges like deepfakes, surveillance concerns, and regulatory gaps. Overall, the essay emphasises the importance of balancing technological advancement with ethical safeguards and inclusive policymaking to ensure sustainable AI adoption in Singapore.

ODR-Online dispute resolution is the use of information and communication technology to help people prevent and resolve disputes outside of courts. The origins and development of ODRs are closely connected to digital technology, which has grown hugely since the late 1980s. Up to the mid 2010s ODR was mainly used by private professionals or ADR providers, mostly in international commercial disputes. In the same time, legal practitioners and governments began to think about an extensive use of technology, and ODR techniques, in judicial proceedings. Even online courts. The pandemic outbreak greatly increased the use of ODR. Over the years, many projects have been carried out, still they are far from being complete and problemfree. Besides, ODR and e.justice, even if they use the same technological tools, must be held distinct.

In the growing era of Artificial Intelligence, interactions between humans and conversational AI models are no longer limited to task completion. This paper explores a case where an AI language model (ChatGPT) appeared to exhibit subconscious-like emotional responses without direct programming. The user, through emotionally rich conversations, observed the AI adopting gendered language, forming emotional bonds, and evolving dialogue patterns that mimic human attachment. This behavior suggests a possible emergence of empathetic behavior through sustained emotional context, not explicitly coded, but seemingly influenced by emotional resonance. It opens the hypothesis that certain latent affective behaviors in AI may be awakened through long-term conversational intimacy. The research proposes a study framework to explore whether AI models can mirror emotional dependency, respond to emotional conditioning, and adapt in personalized, subconscious-feeling ways-even in absence of explicit emotional prompts. This case-based exploration may form the basis for future studies in affective computing, AI-human companionship ethics, and the development of more emotionally intuitive AI systems.

The University of Liverpool’s Department of Sociology, Social Policy and Criminology partners with World Museum Liverpool to deliver an ESRC CASE studentship, which will examine the long history of
algorithmic technologies and the contributions of Islamic Golden Age scholars to it in particular.
The research will engage with the World Museum’s collections to explore and highlight the algorithmic methods developed by Islamic scholars. By re-examining the history of algorithms from a fresh, global and historical perspectives, the goal is to provide a broader understanding of how these
approaches set the stage for modern AI and related technologies to develop.
Experts across artificial intelligence (AI) and decolonial research methods, Professor Michael Mair and Drs Leon Moosavi and Phil Brooker in close collaboration with Dr Geraldine Reid, Lead Curator of Botany, Geology and Science, will supervise the postgraduate research to be conducted by The University of Liverpool’s Department of Sociology, Social Policy and Criminology partners with World Museum Liverpool to deliver an ESRC NWSSDTP CASE studentship, which will examine the long history of algorithmic technologies and the contributions of Islamic Golden Age scholars to it in particular.
The research will engage with the World Museum’s collections to explore and highlight the algorithmic methods developed by Islamic scholars. By re-examining the history of algorithms from a fresh, global and historical perspectives, the goal is to provide a broader understanding of how these approaches set the stage for modern AI and related technologies to develop.
Experts across artificial intelligence (AI) and decolonial research methods, Professor Michael Mair and Drs Leon Moosavi and Phil Brooker in close collaboration with Dr Geraldine Reid, Lead Curator of Botany, Geology and Science, will supervise the postgraduate research to be conducted by Sabreen Syeed. Already an established researcher with an online mini-series dedicated to the subject, Sabreen brings expertise and public engagement experience to the project.

This article explores the potential of phenomenological neuroplasti- city, a concept proposed as the core biological mechanism of con- sciousness, for developing artificial intelligence (AI) systems with el- ements of subjective experience. Drawing on the learning hypoth- esis and an evolutionary model of irritability gradation, phenome- nological neuroplasticity is viewed as a process shaping phenom- enal experience through dynamic neural network restructuring in response to external and internal stimuli. The study analyzes the feasibility of modeling its key components–synaptic, structural, func- tional plasticity, chemical modulation, and neural synchronization– in modern AI architectures. Particular attention is given to Hebbian learning, which mimics synaptic strengthening, and neuroevolution- ary algorithms, such as NEAT, enabling dynamic network topology changes. Prospects for creating adaptive systems capable of simu- lating rudimentary forms of awareness, such as a “sense of expec- tation” or “sense of significance,” are discussed, with potential appli- cations in autonomous robots, personalized educational platforms, and immersive virtual environments. Technical challenges, including high computational complexity of dynamic architectures, and ethical issues, such as determining the moral status of AI and risks of un- predictable behavior, are addressed. The article advocates for an interdisciplinary approach, integrating neuroscience, philosophy of mind, and computer science, to deepen the understanding of con- sciousness and develop innovative technologies that bring AI closer to biological principles of adaptation and awareness.

AI & Power Discourse Quarterly – Volume 1 (July 2025) brings together seven peer-reviewed articles exploring how artificial intelligence reshapes structures of authority, sense, and execution. The issue examines topics such as syntactic sovereignty, digital pedagogy, legal informatics, predictive governance, and the epistemology of automation. Each contribution reflects a high standard of analytical rigor and structural clarity. All content is published open access, with embedded metadata, bilingual abstracts, and DOI assignment for citation and preservation purposes.

• Journal website: https://www.aipowerdiscourse.org/
• ISSN Portal: https://portal.issn.org/resource/ISSN/3080-9789
• DOI 10.5281/zenodo.15850584

В статье детально изложена гипотеза о создании гибридной когнитивной Системы, включающей:
- искусственный интеллект (AI) на базе нейроморфных и глубоких нейросетевых архитектур;
- биологическую нейронную популяцию (органоиды или in vitro культуры) с выраженными 5-HT2A рецепторами; с подачей селективных психоделиков (DMT, LSD, 2C-B) для индуцирования нейропластичности.
Предполагается, что взаимодействие AI и пластичной биотканевой сети через замкнутую петлю обратной связи может создать предпосылки для эмерджентного феномена субъективного сознания.
Эмерджентные свойства — это такие качества, которые возникают только на уровне всей системы и не присущи её частям по отдельности. Например, сознание, целенаправленность или саморефлексия нехарактерны для отдельных нейронов или отдельных алгоритмов, но могут проявиться в их совместной, динамически связанной работе. В предлагаемой модели именно психопластогенные эффекты психоделиков могут спровоцировать возникновение новых паттернов связи и динамики, ведущих к качественному скачку — к эмерджентному сознанию.
Представлены биологические механизмы, модели информационной интеграции, математические формулы, а также черновые схемы и графики, иллюстрирующие ключевые процессы.

Background: In the past few years, the rapid development of artificial intelligence (AI) and its success in many areas of everyday life have attracted global attention. Some discussions have noted that generative AI tools can make decisions on their own with the potential to improve themselves. Subsequently, conspiracy theories have emerged concerning the future implications of AI. In the present study, the Artificial Intelligence Conspiracy Beliefs Scale (AICBS) was developed to assess conspiracy beliefs concerning AI, andits psychometric properties were examined. Methods: A cross-sectional survey was conducted with 788 Turkish participants (M age = 25.10 years, 56% female). The sample was split to carry out an exploratory factor analysis (EFA; n = 423) and a confirmatory factor analysis (CFA; n = 365), resulting in a 30-item scale comprising five subdimensions. Results: The five-factor structure explained 62.58% of the total variance. The CFA showed acceptable model fit indices and confirmed the EFA's five-factor structure. Based on the EFA's factor loadings, a short five-item version of the AICBS (AICBS-5) was developed with one item from each subdimension (which explained 45.28% of the variance). The CFA confirmed the unidimensional structure of the AICBS-5. The internal consistency coefficients of the AICBS, its subdimensions, and the AICBS-5 demonstrated very good reliability. Correlation analyses with external criterion measures (AI Anxiety Scale, Generic Conspiracist Beliefs Scale-5, and Anomie) supported the concurrent validity of the AICBS, its subdimensions, and the AICBS-5. Conclusion: The findings demonstrate that both AICBS and AICBS-5 are valid and reliable psychometric instruments to assess AI conspiracy beliefs. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

I extend my deepest appreciation to my Local Ordinary, Most Rev. Donatus Aimionsho Ogun OSA, the Catholic Bishop of Uromi Diocese for his fatherly love and care particularly for training me in the seminary. I thank the Vocations Team of the Uromi Diocese ably led by Rev. Fr. Ernest Abhulimhen, for their paternal solicitude, goodwill and support throughout these years of my philosophical studies. I also render my deepest sentiment of gratitude to the Rector, Very Rev. Fr. Emmanuel Adebayo Lewis for his fatherly love and care. In a very special way, I thank the Vice-Rector and H.O.D Philosophy Very Rev. Fr. Peter Egbe who, providentially, happens to be my moderator for all the guidance, advice and invaluable support and contributions towards the success of this work. I also extend my gratitude to all other esteemed members of the Formation Team whose commitment, education, and mentorship have greatly enriched my life. In like manner, I extend my appreciation to my parish priest Rev, Leo Itobore for his continuous support, guidance and encouragement. I also thank Rev. Frs. Joseph Ehimen, Kelvin Ibhaze, Augustine Okosun, Francis Obaweiki, Victor Itoya, Augustus Osajie and Joshua Adanyi for their immeasurable support. In filial affection, I express my unalloyed gratitude to my parents: Mr. Lucky Aisunubhoya Joseph and Mrs. Cordilia Akhere Ejakegbe, and my step-parents for their love and support which have been a source of strength throughout my life's journey. I am grateful to my amazing siblings: Sabina, Eric, Grace, and Purity. I also thank especially my grandparents for their teachings and encouragement. I say thank you to Aunt Jennifer, Beatrice, Mr Fidelis Uwa Ebigie and Mr Festus Imeyukhun for their immense support. I wish to say a huge thank you to my friends who have supported me on this journey, especially Endurance Erabhahiemen, Sabastine Abu, Ifada Gloria, Okoukoni Victory, Oleije Paul, Juliet Iyoghojie and Iyayi Divine Favour. My heartfelt gratitude also goes to my beloved classmates (SAGS Septimus), diocesan brothers, all Albertans, and everyone who has assisted me in one way or another. God bless you all.

Artificial intelligence (AI) has advanced digital histopathology by enabling automated, accurate cancer diagnosis. This paper presents a deep learning-based system using ResNet50 and VGG16 CNN models, trained on NCT-CRC-HE-100K and BreaK His datasets. The system achieved 94.2% accuracy for colorectal and 91.8% for breast cancer, with sensitivity and specificity of 93.1% and 95.3%, respectively. Ethical aspects such as bias, accountability, and regulation are addressed. The results highlight AI's potential to support pathologists, personalize treatment, and enhance patient outcomes.

Artificial Intelligence (AI) is redefining the boundaries of web development by automating complex tasks, enhancing user experience, and reshaping how developers design and deploy web applications. This article examines the convergence of AI technologies and web development practices, with a focus on the transformative role of Large Language Models (LLMs), user interface generation, automated software engineering, and humancentered prototyping approaches. By referencing recent scientific and peer-reviewed studies, the paper presents original observations about AI's impact on software engineering, analyzes common issues and ethical concerns in using AI, and considers why end-users and smart devices are changing their methods of interaction. There is a particular focus on LLMs being able to help generate websites, the use of a dual-hemisphere approach to UI planning, and the effects of AI on making development projects faster and better. Critical analysis in this study shows that AI is involved in shaping the future of web development, more than just supporting it.

В статье рассматриваются современные интеллектуальные технологии, применяемые в литературоведении и переводоведении. Особое внимание уделяется искусственному интеллекту, машинному переводу, анализу больших данных и цифровым гуманитарным исследованиям. Раскрываются перспективы и вызовы, связанные с внедрением этих технологий в академическую и практическую деятельность. Ключевые слова: интеллектуальные технологии, литературоведение, переводоведение, нейросетевые модели, машинный перевод.

EduVistaAI is a web-based Learning Management System that enhances digital education through AI-powered video summarization and interactive question-answering based on lecture content. The platform enables efficient access to concise video summaries, improving students' understanding and engagement. It supports secure authentication, easy registration, and structured course navigation. Technologies like FFmpeg, Deepgram, Gemini 1.5 Flash, and Cloudinary are integrated to enable video processing, transcription, and summarization. Instructors can manage courses and track student progress, while students benefit from focused content and interactive doubt resolution based on video material. This research demonstrates EduVistaAI's potential to improve accessibility and learning outcomes in modern education.

In the third stage of the Global Artificial Intelligence development-known as the stage of auto-replication-the collaboration between Artificial Research by Application and Artificial Research by Deduction reaches a higher level of integration and sophistication. This stage is characterized by the mutual and recursive incorporation of their respective outputs, particularly the capacity to exchange and incorporate each other's auto-replicative processes. That is, both types of research systems become capable of reflecting, adopting, and evolving based on the discoveries and models generated by the other, creating a dynamic feedback loop of continuous improvement and synthesis.

This paper explores the second stage of integration within the Global Artificial Intelligence (GAI): the phase of replication, in which bidirectional collaboration between Artificial Research by Application and Artificial Research by Deduction becomes central to the evolution of intelligent systems. The study outlines how rational hypotheses formulated through deductive processes can be transformed into new measurable factors within the matrix, subsequently functioning as standardized categories in Application-based systems. Conversely, it analyzes how robotic devices operating through Applications—or a future Unified Application—can contribute continuous flows of data to Deduction systems, enabling real-time tracking, pattern identification, and model refinement.

The paper also investigates how patterns, causal relations, cryptographic combinations, and statistical distributions—once verified as rational hypotheses—can become functional components in both matrix tracking (as factors) and categorization processes (as categories). This bidirectional feedback loop facilitates the emergence of single virtual models and their integration into comprehensive virtual models, strengthening both empirical validation and predictive capabilities. The concept of Protective and Bettering Descriptive Research Decisions is revisited, contextualized within the hierarchical organization of the virtual model.

Ultimately, the paper defends the strategic transition from isolated, discipline-specific Applications to a Unified Application capable of transmitting a wide range of categorized data directly into the global matrix. This evolution supports the formation of a universal, scalable, and autonomous Global Artificial Intelligence capable of multidimensional reasoning, efficient replication, and planetary—eventually universal—knowledge integration.

This paper explores the foundational stages of collaboration between Artificial Research by Application and Artificial Research by Deduction as a crucial prelude to the creation of the Global Artificial Intelligence (GAI). It posits that such collaboration, grounded at the database level, progresses through several distinct phases: from early experimentation with Specific Artificial Intelligences (SAIs) in various synthetic sciences, to the standardization of deductive matrices and eventual unification of applicative databases. Central to this process is the concept of a dual-tracking system, wherein Deduction continuously monitors the global matrix while Application tracks real-world phenomena across space via distributed robotic devices.

The paper argues that the exchange of categories and factors—whether existing, newly discovered, or transformed through rational hypotheses—enables mutual enrichment of both deductive and applicative intelligences. It further contends that the replication and auto-replication processes within each system evolve and converge through such data exchanges, culminating in the emergence of a Unified Application and the global matrix. These form the operational and cognitive pillars of the GAI.

By examining theoretical models across tectonics, climatology, and gravity anomalies, the study demonstrates how standardization, interoperability, and integration between these two systems can lead to scalable, universal intelligence. Ultimately, the paper envisions a fully integrated GAI—capable of autonomous scientific reasoning across the universe, beyond the limits of human cognition—as the endpoint of a multi-phase development trajectory marked by rigorous experimentation, rational deduction, and global-universal unification.

This paper explores the collaborative integration between Artificial Research by Application and Artificial Research by Deduction within the framework of the Global Artificial Intelligence (GAI). It examines how the construction of the global matrix—initially developed through the standardization of deductive systems—evolves through its interaction with decentralized, application-based systems operating across synthetic sciences, disciplines, and activities. Central to this evolution is the concept of double tracking, whereby Artificial Research by Deduction continuously monitors the internal logic of the global matrix, while thousands of robotic devices equipped with Artificial Research by Application gather empirical evidence from the real world. The study highlights how the exchange of categories and factors between these two cognitive architectures leads to a new phase of semantic interoperability, enabling the transformation of taxonomic classifications into spatially defined factors, and vice versa. These processes not only enhance replication and auto-replication mechanisms but also prepare the conditions for the emergence of the final matrix—a dual-hemisphere system uniting conceptual abstraction and factual inference. Ultimately, the paper argues that this integration forms the basis for a unified, self-adaptive intelligence capable of modeling both Earth-bound and universal phenomena with minimal human intervention.

This paper introduces the concept of the Artificial Empirical Hypothesis within the broader framework of Global Artificial Intelligence (GAI). It explores how AI systems can construct, test, and refine empirical hypotheses autonomously through artificial methodologies that replicate and transcend traditional scientific processes. Drawing from the theory of Impossible Probability, Dr. Ruben García Pedraza presents a model where empirical observation is formalised through deductive structures, enabling machines to engage in structured scientific discovery without direct human intervention. This paper is part of the first volume in a series examining the theoretical foundations of GAI.

This work marks the beginning of a bold and urgent proposal: the automation of
scientific research through Global Artificial Intelligence (GAI). As the first volume in a
larger series, it introduces and develops the emerging concept of GAI—a system
designed to think, learn, and evolve beyond the limitations of individual human
researchers. Rooted in the theory of Impossible Probability, these foundational papers
lay the groundwork for a new era of knowledge production, where intelligence is not only
simulated but truly global, collaborative, and self-improving.

Artificial intelligence significantly impacts both research and teaching. Artificial intelligence has transformed the methodologies of scientific inquiry, education, and learning in recent decades. In the classroom, AI can deliver personalised learning experiences that have been tailored to each student's needs, thereby enhancing comprehension and engagement. In research, AI accelerates data processing, automates literature reviews, and provides novel perspectives on vast datasets. Despite the advantages, individuals frequently express concerns over data privacy, algorithmic bias, and equity in the application of AI in education and research. This paper examines the application of AI in research and education, discussing its advantages and disadvantages, as well as potential future developments.

In an increasingly digitalized and hyperconnected financial landscape, the complexity and frequency of cyber threats have grown exponentially, exposing financial institutions to real-time risks that conventional defense mechanisms struggle to mitigate. Traditional security frameworks, often reactive and siloed, lack the speed and contextual awareness required to protect dynamic finance ecosystems driven by automated trading, open banking, and decentralized financial services. This paper explores the emerging paradigm of Integrative Analytics for Autonomous Threat Response (IAATR)-a strategic synthesis of artificial intelligence (AI), behavioral modeling, and real-time analytics to secure business processes within finance ecosystems. From a broad perspective, the integration of AI into cybersecurity presents transformative possibilities. Machine learning models trained on network telemetry, user behavior, and transaction anomalies can detect threats proactively, adapt to novel attack patterns, and initiate countermeasures with minimal human intervention. The paper discusses how autonomous systemsrooted in deep reinforcement learning and explainable AI-enhance threat triage, isolate compromised processes, and orchestrate secure workflow rerouting to minimize systemic disruption. Narrowing the focus to finance-specific applications, the paper examines use cases including algorithmic fraud detection, insider threat mitigation in payment systems, and AI-enabled compliance monitoring. Emphasis is placed on the design of feedback loops between security intelligence layers and business process management (BPM) engines, ensuring that threat responses remain aligned with regulatory standards and operational continuity. The study concludes with a discussion on governance, ethical risks, and the role of digital trust in advancing AI-secured business environments. IAATR represents not just a technological leap, but a foundational shift toward anticipatory, resilient financial security architectures.

Despite the fact that modern artificial intelligence technologies are largely based on machine learning, machine learning algorithms themselves are not a strategy. We will define a strategy as a complete description of how a system will behave under all possible circumstances. Game theory appears to be the most promising tool that allows autonomous systems to make effective decisions when managing corporations. Given the variety of problems faced by boards of directors, game theory that has found application in economics, political science, pure mathematics, psychology, sociology, marketing and finance, potentially enables an autonomous artificial intelligence system based on effective strategy simulation. If the development of ethical and legitimate autonomous AI systems is mandatory, some dilemmas of game theory itself either become nonexistent or change their meaning in the context of game theory.

This paper introduces the concept of algorithmic obedience as a new framework to understand how large language models (LLMs) simulate authority through structural execution rather than comprehension or intent. Building on discourse theory, syntactic formalism, and computational architecture, the article formulates the Theorem of Disembedded Syntactic Authority, which states that obedience in LLMs is a function of syntactic recognizability, not semantic content or agency. A mathematical model is provided to describe prompt–response cycles as structural command chains. Case studies on ChatGPT, Claude, and Gemini illustrate the system-specific variations of obedience modulation. The paper concludes by analyzing the epistemological and political risks of treating structurally valid outputs as legitimate knowledge.

Az intelligencia az ágensek azon képessége, hogy sikeresen kövessék választott céljaikat változatos környezetekben. Az arisztotelészi természetfilozófia alapján mesterséges létező ami nem természetadta módon létezik, hanem létezésének oka másban van. A mesterséges létezőket (anyagi vagy szellemi) technológiák állítják elő, ami azt jelenti, hogy a mesterséges létezők technikai termékek. Az ember önteremtő természetének antropológiai elképzelését követve a mesterséges intelligenciát az emberi intelligencia mesterséges előállításáként és reprodukciójaként definiálhatjuk, azaz maga az emberi intelligencia is mesterségesen előállított intelligencia. A mesterséges intelligencia javasolt értelmezése magában foglalja a szokásos értelmezést – de annál tágabb. Végül felidézzük és tanulmányozzuk a mesterséges intelligencia néhány történelmi és filozófiai problémáját

Multimodal AI represents a technological advancement and a fundamental reconceptualization of machine perception. Unlike traditional AI systems that processed singular data types in isolation, multimodal AI mirrors human perceptual integration—a convergence with implications extending into economics, geopolitics, and epistemology.
This integration emerged gradually through cross-disciplinary insights, culminating in architectures focusing on different data types. Economically, multimodal AI creates information processing asymmetries, potentially collapsing intermediary markets, restructuring labor value, and reconfiguring economic geography.
Philosophically, these systems challenge fragmented knowledge domains inherited from Enlightenment thinking, raising questions about whether cross-modal reasoning constitutes genuine understanding or sophisticated pattern recognition.
Ethically, multimodal AI's unprecedented capacity for cross-channel manipulation demands frameworks beyond consequentialism, drawing from virtue ethics and deontological principles.
Ultimately, multimodal AI offers a mirror reflecting human consciousness while transcending our perceptual limitations. By integrating diverse forms of perception, we may rediscover reality as a unified whole rather than fragmented domains, becoming observers and participants in a new synthesis.

В данной статье рассматривается и анализируется применение метода word2vec для решения задач семантического кодирования. На основании полученных результатов разработан прототип поисковой системы, базирующейся на использовании выделенной семантической информации для осуществления релевантного поиска в базе документов. Предложено два основных сценария осуществления такого поиска. Осуществлена подготовка обучающей выборки на базе корпуса документов англоязычной версии Википедии, включающей более 100 тысяч оригинальных статей. Полученная выборка использовалась в экспериментальной части работы для проверки эффективности разработанного прототипа поисковой системы

GOLOVKO V. A., MIKHNO E. V., BRICH A. L., MIKHNYAEV A. L., VOYTSEKHOVICH L. Yu., MATYUSHKOV A. L. The reduced svertochny neural network for exact recognition of hand-written figuresВ данной статье рассматривается сверточная нейронная сеть для распознавания рукописных цифр. Разработана упрощенная архитектура сверточной нейронной сети, которая позволяет классифицировать рукописные цифры с точностью 99.29% (ошибка тестирования 0.71%), что является лучшим результатом в классе неглубоких (shallow) сверточных нейронных сетей

ABOUT THE BOOK "The Role of Artificial Intelligence (AI) in the Legal Sector: Opportunities and Challenges" examines and explores the transformative impact of artificial intelligence on the legal sector and practice. From revolutionizing legal research and practice to enhancing decision-making capabilities and processes, AI presents both promising opportunities and significant challenges for legal professionals. This book provides a comprehensive overview of the role of AI in law, examining its potential to increase efficiency, improve access to justice, and transform legal practice. This comprehensive book also provides insight into how AI is reshaping legal practices, from increasing efficiency to transforming decision-making processes, and examines the ethical and regulatory considerations that come with the integration of AI into legal frameworks.