The Internet of Things (IoT) is transforming industries by enabling devices to gather and share data. However, IoT devices often face limitations in processing power, storage, and energy consumption, restricting their ability to make complex decisions in real time. To address these challenges, cloud-assisted edge AI combines the advantages of edge computing and cloud-powered machine learning models, enabling IoT devices to make intelligent decisions at the edge while leveraging cloud resources for more complex processing tasks. This paper (...) explores the integration of edge AI with cloud assistance, demonstrating how this hybrid approach enhances decision-making capabilities in IoT devices. The paper examines key concepts, the architecture of cloud-assisted edge AI systems, application areas, and the benefits of combining edge and cloud computing. Furthermore, challenges such as latency, data privacy, and integration issues are discussed, alongside future directions for this technology in IoT applications. (shrink)

In 2025, AI’s predictive surge—epitomized by GPT-5, Neuralink trials, and the EU AI Act—threatens to erase uncertainty from human life. This paper projects two futures from this trajectory: a “Certainty Cascade” by 2050, where saturation births “Certains”—efficient, doubt-free, yet stagnant—and an “Uncertainty Refusal,” where “Unknowers” resist, preserving risk and vitality. Extended to 2100 as a thought boundary, these scenarios test uncertainty’s role in essence, not intellect. Sartre’s freedom and Heidegger’s enframing judge the former as a loss of agency and wonder; (...) Kierkegaard’s leap and Camus’ rebellion frame the latter as humanity’s pulse. Grounded in data (e.g., 40% AI diagnostics, 70% trust), this departs from Bostrom’s superintelligence, offering “predictive saturation” as a new lens. It argues uncertainty’s erosion redefines us—philosophy must weigh adaptation’s cost and refusal’s promise. (shrink)

Principles of fairness and solidarity in AI ethics regularly overlap, creating obscurity in practice: acting in accordance with one can appear indistinguishable from deciding according to the rules of the other. However, there exist irregular cases where the two concepts split, and so reveal their disparate meanings and uses. This paper explores two cases in AI medical ethics – one that is irregular and the other more conventional – to fully distinguish fairness and solidarity. Then the distinction is applied to (...) the frequently cited COMPAS versus ProPublica dispute in judicial ethics. The application provides a broader model for settling contemporary and topical debates about fairness and solidarity. It also implies a deeper and disorienting truth about AI ethics principles and their justification. (shrink)

. In the evolving landscape of healthcare, the efficient processing of biomedical signals is critical for real-time diagnosis and personalized treatment. Conventional cloud-based AI systems for biomedical signal processing face challenges such as high latency, bandwidth consumption, and data privacy concerns. Edge computing, which brings data processing closer to the source, has emerged as a potential solution to these limitations. However, optimizing AI models for edge devices, which often have limited computational resources, remains a challenge. This paper proposes (...) an innovative approach to optimize AI models for biomedical signal processing by leveraging reinforcement learning (RL) techniques within edge computing environments. Reinforcement learning offers the potential to dynamically adapt model parameters based on real-time feedback from the edge devices, optimizing the trade-offs between model accuracy, resource utilization, and processing speed. The proposed system is designed to operate with high efficiency on edge devices, enabling faster signal processing while ensuring energy efficiency and maintaining diagnostic accuracy. A detailed review of existing literature highlights the benefits and challenges of edge computing in biomedical applications, the role of reinforcement learning in dynamic optimization, and the limitations of traditional AI models in constrained environments. The proposed system methodology integrates RL for model optimization, focusing on edge devices' unique constraints in terms of power, memory, and processing capacity. Simulation results demonstrate the improved efficiency and accuracy of biomedical signal processing with optimized AI models in edge computing setups, showing significant improvements in real-time diagnostic applications. (shrink)

The increasing complexity and demands of next-generation networks necessitate the integration of Artificial Intelligence (AI) to enhance their efficiency and security. This article explores advancements in AI-driven communication systems, focusing on optimizing network performance, ensuring robust security measures, and addressing the challenges of scalability and real-time adaptability. By analyzing case studies, emerging technologies, and recent research, this study highlights AI's transformative potential in redefining communication systems for future applications.

The field of AI has undergone a series of transformations, each marking a new phase of development. The initial phase emphasized curation of symbolic models which excelled in capturing reasoning but were fragile and not scalable. The next phase was characterized by machine learning models—most recently large language models (LLMs)—which were more robust and easier to scale but struggled with reasoning. Now, we are witnessing a return to symbolic models as complementing machine learning. Successes of LLMs contrast with their inscrutability, (...) inaccuracy, and hallucinations, which underwrite concerns over the reliability and trustworthiness of these systems, motivating investigations into commonsense reasoning, AI explainability, and formal verification techniques. Moreover, proper assessments of hybrid machine learning/symbolic systems require novel strategies to facilitate comparisons of performance and guide future AI progress. The EDGeS AAAI 2023 Spring symposium brought together researchers focusing on novel assessments and benchmarks for evaluating hybrid and artificial general intelligence (AGI) systems. This symposium revealed what was already suspected: research concerning evaluation of machine learning/symbolic hybrids and AGI is, unfortunately, lacking. Even so, the discussion was fruitful. (shrink)

In the era of big data and distributed ecosystems, understanding the origin, flow, and transformation of data across complex infrastructures is critical for ensuring transparency, accountability, and informed decision-making. As data-driven enterprises increasingly rely on hybrid cloud architectures, data lakes, and real-time pipelines, the complexity of tracking data movement and transformations grows exponentially. Traditional data lineage solutions, often based on static metadata extraction or rule-based approaches, are insufficient in dynamically evolving environments and fail to provide granular, context-aware insights. This research (...) introduces an AI-augmented, cognitive graph-based framework for autonomous data lineage, designed to enhance data traceability in large-scale and heterogeneous data ecosystems. The framework leverages cutting-edge machine learning (ML), natural language processing (NLP), and graph-based reasoning techniques to enable intelligent discovery, semantic interpretation, and continuous monitoring of data assets throughout their lifecycle. The core of the solution lies in the construction of a dynamic cognitive graph that represents relationships between datasets, processes, systems, and users, enriched with contextual annotations and temporal dimensions. Our architecture incorporates self-learning mechanisms, enabling adaptive lineage discovery and automated anomaly detection. By applying reinforcement learning and stream analytics, the framework not only maps data flows in real time but also evolves with system changes, schema variations, and business logic updates. It provides both forward and backward traceability, supports impact analysis, and enhances compliance auditing capabilities. Furthermore, our system is capable of processing both structured and unstructured metadata, employing advanced NLP models to extract implicit lineage information from data dictionaries, SQL queries, and documentation. The result is a holistic, intelligent, and scalable data lineage solution that reduces manual intervention, mitigates operational risks, and supports regulatory compliance frameworks such as GDPR, HIPAA, and SOX. Experimental evaluations conducted in hybrid cloud environments using tools such as Apache Kafka, Neo4j, Spark, and Python-based ML libraries demonstrate a significant improvement in lineage coverage, anomaly detection accuracy, and system scalability. Compared to conventional lineage tools, our AI-augmented framework delivers a 30% increase in traceability precision and a 40% reduction in manual effort required for lineage tracking and governance. This research lays the foundation for a new paradigm in data governance, where AI not only enhances observability but enables autonomous cognition within data infrastructure. The framework is poised to play a critical role in enabling data democratization, operational agility, and enterprise-wide data literacy. (shrink)

This article explores the transformative impact of artificial intelligence on deterrence theory, introducing 10 groundbreaking concepts that are reshaping global security dynamics. As traditional deterrence strategies face challenges in an increasingly complex and interconnected world, these innovative approaches leverage AI, complex systems theory, and emerging technologies to create more sophisticated and adaptive deterrence mechanisms. From Chaos Deterrence, which harnesses unpredictability, to Möbius Deterrence, which blurs the lines between offense and defense, these concepts represent a paradigm shift in conflict prevention and (...) strategic stability. The article examines each concept in detail, discussing their potential applications, historical precedents, and contemporary relevance. It also addresses the ethical implications, challenges, and potential risks associated with implementing these AI-driven deterrence strategies. By analyzing these cutting-edge concepts, the article provides valuable insights into the future of global security in the age of artificial intelligence. (shrink)

Hybrid Electric Vehicles (HEVs) are known for their ability to reduce carbon emissions and fuel consumption. However, managing the thermal aspects of HEVs, especially concerning their powertrains and battery systems, remains a significant challenge. Traditional cooling mechanisms often result in inefficiencies due to their static nature. This paper proposes an AI-based thermal management system designed to address these limitations by offering dynamic, adaptive thermal regulation for HEVs. The system integrates real-time data monitoring with AI algorithms to optimize the cooling process (...) based on vehicle operating conditions, thereby reducing energy wastage and enhancing overall vehicle performance. By employing machine learning models, the system can predict thermal anomalies and adjust cooling efforts accordingly, thereby preventing overheating, improving battery life, and minimizing wear on the electric motor and power electronics. The proposed AI-based thermal management system combines predictive analytics, data-driven decision-making, and advanced cooling methodologies to ensure optimal thermal regulation under varying driving conditions. This paper provides a comprehensive overview of existing thermal management technologies in HEVs, identifies current challenges, and presents the AI-enhanced system as a cutting-edge solution for improving HEV efficiency and reliability. (shrink)

"Genius AI" is an innovative SaaS platform poised to revolutionize the landscape of artificial intelligence tools. This dynamic platform integrates a diverse array of AI functionalities, including conversation generation, code generation, music generation, and video generation, all in one centralized hub. At its core, Smart AI harnesses cutting-edge machine learning algorithms and neural networks to empower users with unparalleled capabilities. The conversation generation feature enables seamless interaction with users through natural language processing, facilitating engaging and lifelike conversations. At its (...) core, Smart AI harnesses cutting-edge machine learning algorithms and neural networks to empower users with unparalleled capabilities. The conversation generation feature enables seamless interaction with users through natural language processing, facilitating engaging and lifelike conversations. At its core, Smart AI harnesses cutting-edge machine learning algorithms and neural networks to empower users with unparalleled capabilities. The conversation generation feature enables seamless interaction with users through natural language processing, facilitating engaging and lifelike conversations. For developers and coders, Smart AI offers a ground breaking code generation tool, streamlining the process of software development by automatically generating code snippets tailored to specific requirements and programming languages. Moreover, music aficionados can leverage Smart AI's advanced algorithms to compose original melodies, harmonies, and rhythms, personalized to suit any style or genre. Meanwhile, the video generation functionality empowers content creators with the ability to effortlessly produce high-quality videos, complete with customizable elements and visual effects. With its user-friendly interface and scalable architecture, Smart AI caters to a broad spectrum of industries and applications, from e-commerce and marketing to entertainment and education. Whether enhancing customer engagement, accelerating software development, or fostering artistic expression, Smart AI unlocks endless possibilities for innovation and creativity. Join the forefront of AI-driven transformation with Smart AI and redefine the way we interact with technology. (shrink)

In today's data-driven business environment, organizations increasingly rely on advanced analytics and decision support systems to gain a competitive edge. Azure AI and Machine Learning (ML) provide powerful tools for predictive analytics, enabling businesses to forecast trends, optimize processes, and make more informed decisions. By leveraging the capabilities of Microsoft Azure, businesses can integrate AI and ML into their decision-making processes, enhancing productivity and improving strategic outcomes. This paper explores how Azure's AI and ML tools can be applied to (...) predictive analytics and decision support systems, with a focus on real-time decision-making, risk mitigation, and operational efficiency. We will discuss the key Azure services like Azure Machine Learning, Azure Cognitive Services, and Azure Databricks, as well as real-world use cases that highlight their impact on business performance. The paper will also highlight the challenges associated with implementing these technologies and suggest best practices for organizations aiming to integrate AI and ML into their systems. (shrink)

As the integration of Artificial Intelligence (AI) and cloud computing transforms the insurance industry, it is undergoing a major breakthrough. With these technologies, insurers can modernize operations, improve the customer experience and make better decisions using real time data and predictive analytics. This paper aims to explore why AI and cloud play such critical roles in shifting insurance practice from legacy systems modernization, to data governance and regulatory compliance to workforce readiness. Today, world-class AI powered tools and cloud platforms such (...) as AWS and Snowflake provide hyper personalization, one-touch automated claims, fraud detection, etc. IoT data can be used to perform dynamic risk assessment for insurers and even to offer products that start with low code and no code solutions to make technology available across an organization. Edge computing, as well as sustainability across insurance products, shows that there is more innovation potential. Yet, these technologies cannot be fully integrated. However, the industry must figure out how to navigate evolving regulations, employ explainability in AI-driven decisions, and address vendor dependency issues. Governance frameworks need to be strengthened;the workforce needs to be upskilled, collaborate with InsurTech startups, and so on. The paper ends with a forward-looking view, offering potential expansion into underserved markets, more advanced predictive analytics, and better customer contact through omnichannel support via AI enabled risk coaching. Embracing these advances, insurers can chart a new course and lead the digital transformation era by providing more value to stakeholders in an evolving environment. (shrink)

The mind has always been a peculiar and elusive subject, sparking controversial theories throughout the history of philosophy. The initial theorization of the mind dates back to Orphism, which formulated a dualistic structure of soul and body (Johansen, 1999) [1], laying the foundation for Greek dualism, introspection, and the rise of metaphysical idealism. This ill-empirical stance, especially after Plato’s idea of forms, led to inaccessible theoretical concepts concerning the investigation of the relationship between body and mind. Although diverse theories provide (...) broader insights into such matters, they can also hinder progress if based on unsound methodological hypothesis. The present study aims to employ a rational reexamination of the potentiality of the concepts of mind, using abductive inspection to present a valid grasp through the lens of cognitive neuroscience, philosophy, and interdisciplinary fields. This paper speculates through empirical deduction by analyzing the literature and theories on the concept of mind and its possible implications for singularity via transhumanism. In the contemporary world, humankind is on the edge of singularity due to the exponential growth predicted by Moore’s law (Moore 1965) [2]. While many considered singularity an abstract akin to science fiction, significant projections indicate that the accelerationist change has already begun, irreversibly. This advancement occurs not only in the degree of semiconductors in electronics but also in the amplification of human cognition through external devices enabling compound information flow via the world wide web. However, the conjunction of robotics and AI may outdo humankind in the contest for sovereignty soon due to their advantageous architecture in process design, which increases the systemic risk of extinction for humankind. Achieving total control over AI is unrealistic, as it requires complete supervision over all computers and information processes across the world, rendering any regulatory efforts to foresee augmentation futile. Consequently, this paper proposes the adoption of a neuro-robotic approach, enriched with AI, and implementation through biomedical applications on the human body to augment human cognition and physicality. This study aims to raise awareness against existential risks, emphasizing that reliance solely on AI powered computers could lead to annihilation. It also aims to highlight that biological mind and body is lagging behind the modern science to compete against Artificial Intelligence. Moreover, the foundation of Artificial General Intelligence (AGI) is an epistemic foundation for cognitive machinery enhancement that cannot be reversed once it emergence become apparent, as with Pandora’s Box. Technological singularity (Shanahan, 2015) [3], the point where technology is intelligently engineered to engineer its own sets by compounding all possible implementations of practical and theoretical information, is appearing on the horizon. Even if there is no human agency left on the soil of Earth, there is still no turning back at that point. Humankind must actively hold the key to bend this alteration in a way that aligns with its desires. Therefore, we propose, future for humankind lies with transhumanism. (shrink)

Integrating machine learning (ML) into network management and orchestration has revolutionized edgebased networking paradigms, where real-time decision-making is critical. Traditional network management approaches often struggle with edge environments' dynamic and resource-constrained nature. By leveraging ML algorithms, networks at the edge can achieve enhanced efficiency, automation, and adaptability in areas such as traffic prediction, resource allocation, and anomaly detection (Wang et al., 2021). Supervised and unsupervised learning techniques facilitate proactive network optimization, reducing latency and improving quality of service (QoS) (...) (Li & Zhang, 2020). Furthermore, reinforcement learning (RL) models enable autonomous network orchestration, allowing edge devices to adapt intelligently to fluctuating workloads and environmental conditions (Patel et al., 2019). However, challenges such as data privacy, computational overhead, and model interpretability remain key concerns in deploying ML-driven network orchestration at the edge (Chen et al., 2022). This paper explores state-of-the-art advancements, key challenges, and future research directions in ML-based edge network management, highlighting its potential to drive the next generation of intelligent, self-optimizing networks. (shrink)

Serverless computing is changing the cloud application de- sign by removing the need to design, build, and manage infrastructure, and instead focusing on deploying code that can be elastic and rapid. However, while service meshes have recently been introduced to address the reliability of communication in microservices architectures, the grow- ing adoption of edge computing and multi-cloud strategies require new architectures that can cross different types of platforms. In this paper, we introduce a novel serverless mesh architecture that combines (...) server- less function platforms with a service mesh overlay that spans across clouds and edge sites. We give a brief overview of the serverless and service mesh technologies and review the related work in traditional cloud centric serverless, edge computing deployments and mesh-based microservice solutions. We then describe a framework where the server- less functions that are located in different cloud/edge environments are connected by a single service mesh that enables dynamic routing, inter- operability, and policy enforcement across the environments. The dia- grams show how the functions are deployed and invoked via the mesh. Simulated evaluation is described for video analytics, IoT sensor data in- gestion, and content delivery use cases, with the potential performance benefits shown in terms of end-to-end latency and throughput. Some of the key challenges of network latency, cold start delays, and data con- sistency issues in distributed serverless computing are identified, along with how the proposed architecture tackles or alleviates them. Last, fu- ture directions are outlined, which include AI/ML-based optimizations for function allocation and tuning, enhanced runtime portability via We- bAssembly (WASM), and open questions for secure, scalable serverless computing from edge to cloud. (shrink)

Generative artificial intelligence (Gen AI) and machine learning (ML) technologies are changing the way the insurance industry looks, particularly by integrating Gen AI & ML technologies. As a leading platform for property and casualty insurance, Guidewire provides a perfect platform for deploying intelligent claims processing workflows that can dramatically improve efficiency, accuracy and customer satisfaction. The seamless integration of Gen AI and ML capabilities into Guidewire to autonomously process claims is explored in this paper. Insurers can use advanced models to (...) drive self-driving workflows that are capable of automating tasks like claims triage, fraud detection, damage assessment and settlement optimization. But most importantly, these technologies reduce operational costs, and reduce the human intervention involved such that claims can be handled faster and with more accuracy. In this work, we discuss what technical and strategic considerations need to be taken into account when implementing such workflows, such as data integration, model training, and ethical AI practices. This paper also demonstrates several real-world use cases, the challenges you in each, and the potential to scale these solutions across lines of business. With an eye on the future, autonomous claims processing enables insurers to find their next competitive edge by providing innovative solutions that alter the course of expectations of customers while qualifying to regulatory and compliance standards. Self-driving workflows powered by Gen AI and ML in claims are the future of insurance and their ability to transform the entire process of claims management. (shrink)

The semiconductor industry is the fulcrum of digital revolution in the contemporary era, driving cutting-edge technologies that characterize the world today as being interconnected. Increasing demands for smart, rapid, and efficient computing continue to drive semiconductor innovation to new frontiers of possibility. In the next decade, the world semiconductor market will see explosive growth driven by disruptive technologies like artificial intelligence (AI), autonomous cars, 5G networks, and the Internet of Things (IoT). Of these, AI semiconductors, or AI chips, are (...) proving to be a game-changer, offering peak processing and power efficiency for demanding machine learning and deep learning applications. AI chips represent a new frontier in microprocessor design, created to speed up AI computations with unprecedented speed and efficiency. Unlike regular processors, the chips have specialized architectures, including neural processing units (NPUs) and tensor processing units (TPUs), to maximize AI workloads. Their reach spans across industries, ranging from automobile innovation supporting autonomous capability to intelligent homes with intelligent automation, robotics revolutionizing manufacturing, and AI-driven healthcare innovations. As more industries rely on AI-driven decision-making, development of semiconductor technology will play a defining role in determining the future digital economy. This piece discusses the sudden surge of AI chips, in the wake of crucial technology innovations, competition among industries, and future trends that are shaping this new wave of semiconductor advancements. Additionally, it also points to the strategic significance of AI chips to revolutionize industries and fuel digital programs in the future. (shrink)

There are various aspects to Internet of Things security, such as guaranteeing the safety of both the devices and the Internet of Things networks to which they connect. Many other types of equipment, including industrial robots, smart grids, construction automation systems, entertainment gadgets, and many more, are included in this, despite the fact that they were not designed with network security in mind. When it comes to securing systems, networks, and data, IoT device security must be able to resist a (...) wide range of IoT security assaults. One of the most important issues in the field of data security is the creation of intrusion detection systems (IDSs) for the Internet of Things. Client devices (edge devices) in Federated Learning utilize local data to train the machine learning model, and then send the updated model parameters to a cloud server so that they may be aggregated (rather than raw data).This paper proposes a machine learning system that employs federated learning to detect intrusions in the IoT. The FedAVG algorithm is employed to aggregate models. Models are trained locally by nodes. The models are trained and validated using machine learning methods, including Random Forest, ID3, and Support Vector Machine. The NSL KDD data set is employed to undertake experiments. (shrink)

Abstract: Lotteries have long been a source of fascination and intrigue, offering the tantalizing prospect of unexpected fortunes. In this research paper, we delve into the world of lottery predictions, employing cutting-edge AI techniques to unlock the secrets of lottery outcomes. Our dataset, obtained from Kaggle, comprises historical lottery draws, and our goal is to develop predictive models that can anticipate future winning numbers. This study explores the use of deep learning and time series analysis to achieve this elusive (...) feat. Through rigorous experimentation and data-driven approaches, we seek to determine the viability of AI in the realm of lottery predictions. Our findings reveal both the promise and limitations of AI in this context, shedding light on the complexities of lottery data and the potential need for quantum computing as a last resort. (shrink)

Broadening access to both computational and educational resources is crit- ical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML leverages low-cost and globally (...) accessible hardware and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia and industry produced a four part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for global learners from a variety of backgrounds. It introduces real-world applications, ML algorithms, data-set engineering, and the ethi- cal considerations of these technologies through hands-on programming and deployment of TinyML applications in both the cloud and on their own microcontrollers. To facili- tate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project com- petition. We also open-sourced the course materials, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies. (shrink)

The exponential complexity of contemporary Life Insurance applications - shaped by stringent regulatory compliance requirements, dynamic customer - centric innovation, and expansive product diversification - has magnified the challenges of maintaining traditional test automation frameworks. These frameworks often falter under the weight of frequent application updates, evolving user interfaces (UI), and fluctuating backend integrations, leading to brittle test scripts and heightened maintenance costs. Self-healing test automation frameworks offer a cutting-edge, AI-driven solution to this paradigm. By leveraging advanced machine learning (...) (ML) algorithms and intelligent heuristics, these frameworks autonomously detect, diagnose, and remediate test failures caused by changes in application elements, workflows, or environments. Through real-time adaptation to UI modifications, API updates, and evolving test environments, self-healing frameworks ensure uninterrupted testing cycles with minimal manual intervention, significantly improving efficiency and scalability. This paper provides a comprehensive exploration of the architectural design and operational mechanisms of self healing test automation frameworks within the context of Life Insurance applications. Core focus areas include their ability to dynamically recalibrate element locators and adapt test scripts during runtime, thereby enhancing test coverage, optimizing maintenance workflows, and reducing the overall defect detection lifecycle. Through an in-depth analysis, the paper highlights the integration of AI-powered locator optimization engines, the deployment of predictive analytics for early anomaly detection, and the orchestration of continuous self-healing pipelines in CI/CD ecosystems. Detailed case studies from the Life Insurance domain illustrate tangible benefits such as accelerated regression cycles, substantial cost reductions, and improved system resilience. (shrink)

The paper continues my earlier Chat with OpenAI’s ChatGPT with a Focused LLM Experiment (FLEX). The idea is to conduct Large Language Model (LLM) based explorations of certain areas or concepts. The approach is based on crafting initial guiding prompts and then follow up with user prompts based on the LLMs’ responses. The goals include improving understanding of LLM capabilities and their limitations culminating in optimized prompts. The specific subjects explored as research subject matter include a) diagonalization techniques as practiced (...) by Cantor, Turing, Gödel, and the advances, such as the Forcing techniques introduced by Paul Cohen and later investigators, b) Knowledge Hierarchies & Mapping Exercises, c) Discussions of IJ Good’s Speculations Concerning the First Ultraintelligent Machine, AGI, and Super- intelligence. Results suggest variability between major models like ChatGPT-4, Llama-3, Cohere, Sonnet and Opus. Results also point to strong dependence on users’ preexisting knowledge and skill bases. The paper should be viewed as ’raw data’ rather than a polished authoritative reference. (shrink)

This book reports on the results of the third edition of the premier conference in the field of philosophy of artificial intelligence, PT-AI 2017, held on November 4 - 5, 2017 at the University of Leeds, UK. It covers: advanced knowledge on key AI concepts, including complexity, computation, creativity, embodiment, representation and superintelligence; cutting-edge ethical issues, such as the AI impact on human dignity and society, responsibilities and rights of machines, as well as AI threats to humanity and AI (...) safety; and cutting-edge developments in techniques to achieve AI, including machine learning, neural networks, dynamical systems. The book also discusses important applications of AI, including big data analytics, expert systems, cognitive architectures, and robotics. It offers a timely, yet very comprehensive snapshot of what is going on in the field of AI, especially at the interfaces between philosophy, cognitive science, ethics and computing. (shrink)

The global shift to remote work has greatly increased the potential targets for cyberattacks, demanding stronger security measures than what Virtual Private Networks (VPNs) can provide. VPNs provide a bottom-line layer of security but don’t address the changing complexity of the distributed world of work. In this paper, we discuss advanced security frameworks and best practices to protect data and identity in a purely remote work setting, focusing on threat mitigation. The discussed key strategies include Zero Trust Architecture (ZTA), Secure (...) Access Service Edge (SASE), Endpoint Detection and Response (EDR), and multi-factor authentication (MFA). The paper also points out that employee cybersecurity training, risk assessments and integration with AI-driven threat intelligence systems. These approaches are demonstrated through case studies showing how organizations have navigated the shift to a remote-first setup. Using these cutting-edge strategies, enterprises can avoid the risk of remote work by continuing to be productive and secure. (shrink)

: As organizations continue to adopt cloud computing for its numerous advantages—such as scalability, cost-efficiency, and flexibility—the need to secure digital assets within the cloud has never been more critical. Cloud security faces unique challenges due to the distributed and multi-tenant nature of cloud services. Cyber-attacks, data breaches, and misconfigurations are among the prominent risks that threaten the integrity, confidentiality, and availability of cloud-based data and applications. In this paper, we explore CloudGuard, an advanced security framework designed to protect organizations' (...) digital assets in the cloud. This fortress security approach focuses on implementing multiple layers of protection through cutting-edge technologies, such as encryption, identity and access management (IAM), threat intelligence, and security automation. We also highlight the integration of AI and Machine Learning to improve threat detection and response times, and blockchain's potential in ensuring data integrity and privacy. Through a comprehensive analysis, this paper outlines practical strategies for organizations to adopt CloudGuard and strengthen their security posture against evolving cyber threats in the digital world. (shrink)

Cloud computing has revolutionized the way businesses and individuals access and utilize computing resources. With its rapid adoption over the last few decades, it has become the backbone of many IT services across industries. This paper explores the evolution of cloud computing, highlighting key trends and identifying future directions that will shape the landscape. It discusses the evolution from traditional on-premise systems to the cloud-first approach, current advancements such as multi-cloud and hybrid cloud environments, and the growing integration of artificial (...) intelligence (AI) and machine learning (ML) into cloud services. Furthermore, the paper examines the challenges organizations face, including security concerns and regulatory compliance. Finally, the future directions of cloud computing are explored, focusing on edge computing, quantum cloud services, and the potential for deeper AI integration. (shrink)

This essay argues that Huey Newton’s philosophical explanation of US empire fills an epistemological gap in our thinking that provides us with a basis for understanding the emergence and operational application of predictive policing, Big Data, cutting-edge surveillance programs, and semi-autonomous weapons by US military and policing apparati to maintain control over racialized populations historically and in the (still ongoing) Global War on Terror today – a phenomenon that Black Studies scholars and Black philosophers alike have yet to demonstrate (...) the conceptual acumen to explain. Newton’s ability to explain the conceptual foundation of US empire alongside his prediction of the emergence and application of AI and other Big Data technologies towards the repression of dissidents, criminals, terrorists, and other racialized groups abroad gives intercommunalism the explanatory power to lay the basis for a genealogical account of Black repression that also advances our understanding of how the US as a liberal democratic regime engages in enemy target acquisition (the construction of racialized groups as threats internally and externally) to rationalize the brutal repression of these populations by military-police agencies whose operational application of new AI and computing-surveillance technologies and semi-autonomous weapons are conditioned by the intensity of their racialized dehumanization of the enemy. (shrink)

Abstract: Early detection of breast cancer is vital for improving patient outcomes and reducing mortality rates. Technological advancements have significantly enhanced the accuracy and efficiency of screening methods. This paper explores recent innovations in early detection, focusing on the evolution of digital mammography, the benefits of 3D mammography (tomosynthesis), and the application of advanced imaging techniques such as molecular imaging and MRI. It also examines the role of artificial intelligence (AI) in diagnostic tools, showing how machine learning algorithms are improving (...) imaging analysis and reducing diagnostic errors. Furthermore, the paper discusses advancements in genetic screening, liquid biopsies, and biomarkers, emphasizing their potential in identifying high-risk individuals and supporting personalized treatment plans. The paper provides a forward-looking perspective on the future of breast cancer detection, addressing both the opportunities and challenges associated with the adoption of these cutting-edge technologies to improve patient outcomes. (shrink)

With the progress of artificial intelligence, the digitalization of the lifeworld, and the reduction of the mind to neuronal processes, the human being appears more and more as a product of data and algorithms. Thus, we conceive ourselves “in the image of our machines,” and conversely, we elevate our machines and our brains to new subjects. At the same time, demands for an enhancement of human nature culminate in transhumanist visions of taking human evolution to a new stage. Against this (...) self-reification of the human being, the present book defends a humanism of embodiment: our corporeality, vitality, and embodied freedom are the foundations of a self-determined existence, which uses the new technologies only as means instead of submitting to them. The book offers an array of interventions directed against a reductionist naturalism in various areas of science and society. As an alternative, it offers an embodied and enactive account of the human person: we are neither pure minds nor brains, but primarily embodied, living beings in relation with others. This general concept is applied to issues such as artificial intelligence (AI), transhumanism and enhancement, virtual reality, neuroscience, embodied freedom, psychiatry, and finally to the accelerating dynamics of current society which lead to an increasing disembodiment of our everyday life. The book thus applies cutting-edge concepts of embodiment and enactivism to current scientific, technological, and cultural tendencies that will crucially influence our society’s development in the twenty-first century. (shrink)

In this article, I review textual evidence demonstrating that James and Dewey incorporated Kant’s ideas, even while criticizing him. I specifically argue that the pragmatic evolution of the Kantian a priori carried out by James and Dewey is a transition from the mental to the bodily. I further argue that the parallels between pragmatists and Kant, along with the transition from the mental to bodily, relate to scientific contexts in which all developed their outlooks. Though historically grounded, my ultimate goal (...) is to show that pragmatism and by extension Kantianism mesh with and indeed contribute to cutting edge ideas in fields ranging from neuropathology to robotics and AI to cognitive science, whether in the form of Gibsonian theory or enactivism. (shrink)

Autonomous agricultural equipment represents a transformative innovation, combining cutting-edge technologies such as artificial intelligence (AI), robotics, and the Internet of Things (IoT). These machines address challenges such as labor shortages, environmental sustainability, and rising food demand. This paper explores the concept, key technologies, applications, and benefits of autonomous farming equipment. A comprehensive literature review highlights current advancements and challenges in the field. An experimental study evaluates the efficiency and impact of autonomous machinery in farming, providing results that demonstrate their (...) potential. (shrink)

Computer vision, or image recognition, analyses and interprets visual data in real-world scenarios like images and videos. AI and ML research focusses on object, scene, action, and feature identification because of its usefulness in image processing. Neural networks and deep learning have improved image recognition systems significantly in recent years. Early image recognition used template matching to identify objects. A photo is compared to a stored template using similarity measures like correlation to get the best match. There are several constraints, (...) especially with distorted, scaled, or noisy images. Template matching is computationally expensive. CNNs are the most common deep learning architecture for computer vision. CNNs automatically learn visual input hierarchies. They include pooling, convolutional, and fully connected layers. Regional patterns like shapes, textures, and edges in the input image are filtered by convolutional layers. Pooling layers reduce feature map spatial dimensions, making feature extraction consistent and robust. Thick layers, or fully linked layers, classify or regress using learning features. CNN training requires a big labelled dataset. This research article introduces a deep learning model for the classification and recognition of images. The input data for this model is the Images Data Set. The GenNet Algorithm is employed to derive critical features. The accuracy of classification results is enhanced through the use of preprocessing and feature extraction. The classification model is generated using Convolution Neural Network, AlexNet, ResNet, and VGG. (shrink)

Property and Casualty (P&C) insurers are adopting multi-cloud environments as a strategic imperative because of their increasing data volumes and complexities in regulatory compliance, customer expectations and technological advancements. This paper discusses how Snowflake’s cloud-agnostic, unified platform enables insurers to create resilient, efficient, and compliant multi cloud data strategies. Using Snowflake’s elastic scalability, real-time analytics, secure data sharing and seamless cloud interoperability, insurers can optimize claims processing, augment fraud detection, and support customer engagement. The study offers core design principles for (...) multi cloud data architectures and sheds light on problems in data fragmentation, latency, and security risks. It points to how Snowflake enables data resilience, including data replication, encryption, GDPR, and CCPA compliance. Details are provided. Practical implementation strategies of phased data migration ETL pipelines and integration with existing enterprise system to ensure a smooth deployment. Snowflake is seen as a clear win through a cost-benefit analysis with metrics like RTO and RPO that define how well Snowflake performs disaster recovery and business continuity. Additionally, the paper also looks at the emergence of tech like AI, blockchain and edge computing which could additionally bolster multi-cloud strategies even further. Finally, Snowflake enables the journey for P&C insurers through cloud ecosystem pitfalls, enabling operational resilience, regulatory compliance, along competitive advantage in a data-driven industry. (shrink)

In the dynamic landscape of modern software development, the integration of Quality Assurance (QA) with advanced analytics and metrics is redefining the paradigms of software quality engineering. This paper delves into the strategic role of QA metrics and analytics in enabling data driven decisions, which foster a proactive and predictive approach to quality management. Traditional QA processes, often plagued by subjective assessments and reactive defect handling, are being replaced by evidence-based frameworks that utilize cutting-edge technologies such as machine learning (...) (ML), artificial intelligence (AI), and real-time dashboards. Key performance indicators (KPIs) like Defect Removal Efficiency (DRE), Mean Time to Repair (MTTR), and automation coverage provide a granular understanding of the development pipeline. Predictive analytics models, integrated within CI/CD pipelines, leverage historical defect trends and code complexity metrics to forecast potential failure points, optimize resource allocation, and reduce time-to-market. Furthermore, prescriptive analytics equips QA teams with actionable insights, recommending remediation paths and improving decision-making agility. This paper underscores the transformative potential of QA analytics in driving efficiency and reliability across software ecosystems. It also highlights challenges, such as overcoming data silos, ensuring cross-platform compatibility, and addressing skill gaps in QA teams. The study presents a comprehensive metrics framework, explores state-of-the-art tools and methodologies, and includes a case study demonstrating a 40% reduction in production defects using advanced analytics. Finally, the paper proposes future directions, including ethical QA analytics, real-time quality dashboards, and deeper integration with DevSecOps workflows. By adopting these innovations, organizations can align QA objectives with business goals, achieving enhanced customer satisfaction, minimized defect leakage, and optimized development cycles. This shift represents not merely an enhancement of existing practices but a fundamental evolution of the QA discipline, positioning it as a critical driver of technological and organizational excellence. (shrink)

"The Neo-Five Virtues Path: Humanistic Evolution of Chinese Enterprises" delves into the creative transformation of traditional Chinese wisdom in modern society. Mr. He Liqun proposes the "Yiti-Wuchang-Wuyong" theoretical framework (abbr. YT-WC-WY; One Unity with Five Cardinal Norms and Five Practical Manifestations), centered on Confucian principles of Virtue (De), Righteousness (Yi), Propriety (Li), Wisdom (Zhi), and Frugality (Jian), manifested through Love (Ai), Trust (Xin), Respect (Jing), Learning (Xue), and Conservation (Sheng). This constructs a synergistic development framework spanning corporate governance, technological ethics, (...) and ecological civilization. Through vivid case studies of enterprises like Gree, BYD, Huawei, and Haier, the book deciphers the unique code of China’s modernization path—from CATL’s green battery revolution to Hongxing Erke’s goodwill economy, from Haier’s "Every Employee as an Entrepreneur" model to Ant Forest’s inclusive design. It demonstrates how traditional wisdom empowers technological innovation, reshapes business ethics, and drives sustainable development. The work further explores cutting-edge issues such as metaverse governance and AI ethics, proposing "Digital Five Constants" solutions to anchor human values amid technological upheavals. More than a corporate management guide, this book embodies a profound dialogue between Chinese civilization and the digital age, offering Eastern wisdom for global sustainable development. (shrink)

The advent of autonomous vehicles (AVs) is catalyzing a paradigm shift within the automotive industry, with far-reaching implications for various sectors, particularly life insurance. As AVs significantly diminish accident rates and fatalities, life insurers must recalibrate their risk assessment methodologies, pricing strategies, and claims management processes to align with these transformative changes. This paper delves into the potential impact of AV integration on life insurance, highlighting the evolution of conventional risk models, the emergence of novel risk vectors such as cybersecurity, (...) and the infusion of cutting-edge, data-driven techniques in underwriting. Additionally, it proposes a robust framework for insurers to refine their business strategies, ensuring adaptability in the face of rapid technological advancements brought about by AVs. The discussion extends to the regulatory and ethical challenges insurers must navigate when incorporating AV-derived data into their risk evaluation paradigms, emphasizing the need for a balance between innovation and responsibility. (shrink)

The advent of autonomous vehicles (AVs) is catalyzing a paradigm shift within the automotive industry, with far-reaching implications for various sectors, particularly life insurance. As AVs significantly diminish accident rates and fatalities, life insurers must recalibrate their risk assessment methodologies, pricing strategies, and claims management processes to align with these transformative changes. This paper delves into the potential impact of AV integration on life insurance, highlighting the evolution of conventional risk models, the emergence of novel risk vectors such as cybersecurity, (...) and the infusion of cutting-edge, data-driven techniques in underwriting. Additionally, it proposes a robust framework for insurers to refine their business strategies, ensuring adaptability in the face of rapid technological advancements brought about by AVs. The discussion extends to the regulatory and ethical challenges insurers must navigate when incorporating AV-derived data into their risk evaluation paradigms, emphasizing the need for a balance between innovation and responsibility. (shrink)

The internet has made the world more linked than ever before. While taking advantage of this online transition, cybercriminals target flaws in online systems, networks, and infrastructure. Businesses, government organizations, people, and communities all across the world, particularly in African countries, are all severely impacted on an economic and social level. Many African countries focused more on developing secure electricity and internet networks; yet, cybersecurity usually receives less attention than it should. One of Africa's major issues is the lack of (...) adequate digital security infrastructure, which has harmed businesses, governmental institutions, and individual communities more than it has helped. The majority of African countries operate without cybersecurity measures in place to combat cyberattacks. Only a few examples of today's cyber risks include digital extortion, business email intrusion, data breaches, online fraud, ransomware, and phishing, and new types of cybercrime are always developing. Due to the advent of new technology, cybercriminals have become more organized and quicker in their attacks and alliance creation. To maintain a secure digital environment for all internet users, this study focused on the challenges, solutions, and need for African countries to improve their online safety by tackling cybercrime, online fraud, and cybersecurity concerns. The objective of this study is to offer practical and long-term answers to the problems posed by cybercrime, with a continuing emphasis on enhancing online safety. It will assess the effectiveness of cutting-edge cybersecurity measures, legislative frameworks, and cross-border cooperative efforts, as well as potential areas for improvement. Additionally, the study will examine cutting-edge methods like blockchain technology, machine learning, and other cutting-edge methods that could improve our using digital defences to stop cybercrime. (shrink)

Since security threats in IoT-enabled smart cities may not appear clear and present to detection mechanisms, efforts have been made to use artificial intelligence methods for anomaly detection. Anomaly detection has been performed using unsupervised learning approaches (Autoencoders, GANs, One Class SVMs) in turn, with these instances considered security threats. In addition, an element for patches and traffic redirection in real time is included in the framework. Results show that the AI detection in general has much more security resilience, decreasing (...) possible attack vectors. This makes the integration of various features like AI, blockchain, and IDS for a solid IoT security a must. Since security threats in IoT-enabled smart cities may not appear clear and present to detection mechanisms, efforts have been made to use artificial intelligence methods for anomaly detection. Anomaly detection has been performed using unsupervised learning approaches (Autoencoders, GANs, One-Class SVMs) in turn, with these instances considered security threats. In addition, an element for patches and traffic redirection in real time is included in the framework. Results show that the AI detection in general has much more security resilience, decreasing possible attack vectors. This makes the integration of various features like AI, blockchain, and IDS for a solid IoT security a must. (shrink)

Bostrom's Simulation Argument has become synonymous with simulation theory, despite the existence of alternative frameworks that also suggest our universe may be a computer simulation—frameworks that do not require the assumption of posthumans. Further, Bostrom's work has spurred discussions around the potential destruction or termination of our universe, prompting existential anxieties and fueling extensive philosophical speculation. We evaluate the arguments of Greene, Turchin, Batin, Denkenberger, and Yampolskiy, alongside the counterarguments of Braddon-Mitchell, Latham, and Edge. We conclude that Bostrom's Simulation (...) Argument has been philosophically extended far beyond its original scope. As a contrasting model, we present Campbell's consciousness-based My Big TOE (MBT) Theory. MBT requires fewer assumptions, excludes posthuman entities, reduces the likelihood of simulation termination to near zero, includes a clear cosmogony and telos, and frames existence as an information-entropy-reduction system designed to evolve the consciousnesses of its participants—rather than the existential risk research project framework implicitly assumed in Bostrom's model. (shrink)

This paper aims to answer a basic question: is it possible to forge democratic citizenship through various online tools that are already available? To answer this question, I introduce the conception of digital political identities, i.e., the ways in which online environments contribute to creating, maintaining, and changing political identities. Because the well-functioning of democracy rests on citizens with the ability to make informed decisions, vote, and engage in public deliberation, this paper is looking for new and innovative online tools (...) for participating in meaningful online deliberation, acquiring accurate information in the digital space, and making informed voting decisions. By introducing the conception of digital political identities and linking it to online tools that can improve democracy and citizen engagement, I aim to make further progress in cutting edge research on the relationship between digital technologies and democracy. In a nutshell, I am mainly concerned with proposing and defending a normative framework for the use of various online tools that could foster digital democracy. (shrink)

The Salivaomics Knowledge Base (SKB) is designed to serve as a computational infrastructure that can permit global exploration and utilization of data and information relevant to salivaomics. SKB is created by aligning (1) the saliva biomarker discovery and validation resources at UCLA with (2) the ontology resources developed by the OBO (Open Biomedical Ontologies) Foundry, including a new Saliva Ontology (SALO). We define the Saliva Ontology (SALO; http://www.skb.ucla.edu/SALO/) as a consensus-based controlled vocabulary of terms and relations dedicated to the salivaomics (...) domain and to saliva-related diagnostics following the principles of the OBO (Open Biomedical Ontologies) Foundry. The Saliva Ontology is an ongoing exploratory initiative. The ontology will be used to facilitate salivaomics data retrieval and integration across multiple fields of research together with data analysis and data mining. The ontology will be tested through its ability to serve the annotation ('tagging') of a representative corpus of salivaomics research literature that is to be incorporated into the SKB. Background Saliva (oral fluid) is an emerging biofluid for non-invasive diagnostics used in the detection of human diseases. The need to advance saliva research is strongly emphasized by the National Institute of Dental and Craniofacial Research (NIDCR), and is included in the NIDCR's 2004- 2009 expert panel long-term research agenda [1]. The ability to monitor health status, disease onset, progression, recurrence and treatment outcome through noninvasive means is highly important to advancing health care management. Saliva is a perfect medium to be explored for personalized individual medicine including diagnostics, offering a non-invasive, easy to obtain means for detecting and monitoring diseases. Saliva testing potentially allows the patient to collect their own saliva samples at home, yielding convenience for the patient and savings in health costs, and facilitating multiple sampling. Specimen collection is less objectionable to patients and easier in children and elderly individuals. Due to these advantages. (shrink)

There is a need recognized by the National Institute of Dental & Craniofacial Research and the National Cancer Institute to advance basic, translational and clinical saliva research. The goal of the Salivaomics Knowledge Base (SKB) is to create a data management system and web resource constructed to support human salivaomics research. To maximize the utility of the SKB for retrieval, integration and analysis of data, we have developed the Saliva Ontology and SDxMart. This article reviews the informatics advances in saliva (...) diagnostics made possible by the Saliva Ontology and SDxMart. (shrink)

We describe the rationale for an application ontology covering the domain of human body fluids that is designed to facilitate representation, reuse, sharing and integration of diagnostic, physiological, and biochemical data, We briefly review the Blood Ontology (BLO), Saliva Ontology (SALO) and Kidney and Urinary Pathway Ontology (KUPO) initiatives. We discuss the methods employed in each, and address the project of using them as starting point for a unified body fluids ontology resource. We conclude with a description of how the (...) body fluids ontology initiative may provide support to basic and translational science. (shrink)

On 24th February 2025, Justin Mike (United States) wrote a review for Wild Wise Weird as follows: “Wild Wise Weird celebrates the wild spirit, the wisdom gained from unusual experiences, and the eccentric qualities that define us as individuals, thereby capturing the beauty of embracing authenticity. It serves as a reminder that being unique is something to be proud of, not just acceptable.” The review made me feel really relevant to the content of the book. I was curious what AI (...) would think of Mike’s review, so I asked Gemini “what do you think of the review?”. (shrink)

This article proposes a unified solution to the mind-body problem, grounded in the philosophical framework of Ethical Empirical Rationalism. By presenting a mathematical model of the mind-body interaction, we oƯer a dynamic feedback loop that resolves the traditional dualistic separation between mind and body. At the core of our model is the concept of essence—an eternal, metaphysical truth that sustains both the mind and body. Through coupled diƯerential equations, we demonstrate how the mind and body are two expressions of the (...) same essence, interacting in a continuous and harmonious relationship. This approach provides a new perspective on the mind-body relationship, grounded in both empirical evidence and metaphysical insight. Keywords: Mind-body problem, Unmoved Mover, Essence, Unified Theory, Feedback Loop, Ethical Empirical Rationalism, Mathematical Modeling. (shrink)

Hiện nay, chế độ sở hữu ở Việt Nam đã có những đổi mới cơ bản, nhưng vẫn còn những khác biệt rất lớn so với chế độ sở hữu ở các nền kinh tế thị trường hiện đại. Trong cơ cấu của chế độ sở hữu ở Việt Nam, tỷ trọng của sở hữu nhà nước còn quá lớn; kinh tế nhà nước giữ vai trò chủ đạo… Chính những khác biệt này đã làm cho nền kinh tế thị (...) trường Việt Nam bị méo mó và giảm lợi thế cạnh tranh quốc gia. Do vậy, công cuộc đổi mới và hoàn thiện chế độ sở hữu ở Việt Nam phải hướng tới việc khắc phục những hạn chế trên và đạt tới sức cạnh tranh ngang hàng với các nền kinh tế thị trường hiện đại khác. (shrink)

Climate change is affecting communities across the globe, yet those most vulnerable to its impacts are often the last to receive the financial assistance they need. A recent critical review by Venner, García-Lamarca, and Olazabal (2024) examines how climate adaptation finance—funding intended to help societies adjust to the impacts of climate change—is distributed. The findings are concerning: despite repeated global commitments to prioritize those most at risk, adaptation finance tends to benefit the most powerful and well-resourced actors rather than the (...) communities that need it most. (shrink)