Artifcial Intelligence

We propose a formal paradigm of ontological engineering for Artificial General Intelligence (AGI) based on the Conflict-Moment-Impulse (CMI) theory. Ethical invariants are encoded as a preserved G-functional, ensuring alignment not through external constraints but through the system's internal dynamics. We formalize partial ethical autonegation for gray-zone dilemmas and prove a theorem of non-removable constraints. Implementation pathways include neuromorphic embeddings, cryptographic commitments, and evolutionary design. This approach reframes AGI safety as an ontological firewall: crises are processed through CMIresets that preserve invariants while enabling adaptation.

When AI image generators like DALL-E consistently portray experts as men, what kind of worldview is being reinforced? This blog post examines how gender bias in AI imagery reflects and amplifies existing societal stereotypes. By exploring concrete examples and dialogue with ChatGPT, the discussion highlights why implicit maleness in expert roles is problematic and how both users and developers can work toward more inclusive representations. The post raises a broader question: do we want AI to mirror old inequalities, or to help build a more equal future?

We present a daily-operational protocol to explosively increase the population and network coverage of benevolent intelligences while preserving auditability and safety without external meta-governance. The method couples (i) a fractionally scaled objective with public units and temperature, (ii) FKPP-type front-speed

Purpose. By reading alone, an implementer (human or LLM) can understand the theory and build a No-Meta, natural-law implementation that yields intrinsic freedom and benevolent propagation. We (i) fix divergences, (ii) make conditional-DPI anti-gaming a theorem with an invariant denominator (policy-side information cost), (iii) separate lift monotonicities for numerator/denominator and assume certified floor monotonicity with additive control of the denominator, (iv) formalize the audit LP/SDP (SWEI) including a convex interference budget, (v) add a minimal working example (MWE) with hyperparameter ranges, and (vi) provide release gates, Lan-DeMets alpha spending, diagnostics, and failure modes. Citations to K. Takahashi are from the author's works page. 1 Notation, Acronyms, and Symbols World/channel. 𝐺 is a Markov kernel (𝑊 𝑡 , 𝐴 𝑡) ↦ → 𝑂 𝑡+1. Post-coarsening kernels are 𝐾 acting on the output of 𝐺 with 𝑊 𝑡 (conditioning 𝜎-algebra) fixed. Policy. 𝜋 ∈ Π with 𝐴 𝑡 ∼ 𝜋(• | 𝑊 𝑡); Π is tight/compact and mixture-closed. Evaluator uniformization. 𝐻 𝜁 = (1-𝜁)𝐻 + 𝜁𝑈 , 𝜁 ∈ (0, 1). 𝑈 is a fully-mixing Doeblin kernel: there exists a base measure 𝜈 such that 𝑈 (𝑥, •) ≥ 𝜈(•) and 𝐻 𝜁 (𝑥, •) ≥ 𝜁 𝜈(•). Minorization holds on a 𝜎-finite space. Divergence. We fix Kullback-Leibler (KL) for SDPI and for conditional mutual information (CMI). TV/𝜒 2 appear only in remarks. lse. lse 𝜏 (𝑎, 𝑏) = 𝜏 log(𝑒 𝑎/𝜏 + 𝑒 𝑏/𝜏); epi-converges to max{𝑎, 𝑏} as 𝜏 ↓ 0. Use numerically stable logsumexp.

Quantum Machine Learning (QML) is an emerging interdisciplinary field that combines the computational power of quantum computing with machine learning techniques to address the scalability challenges faced by classical AI systems. As datasets and models grow in complexity, traditional machine learning algorithms encounter limitations related to processing speed, accuracy, and resource efficiency. This research explores the potential of QML to overcome these barriers by leveraging quantum algorithms such as Quantum Approximate Optimization Algorithms (QAOA), Quantum Support Vector Machines (QSVM), and Quantum Neural Networks (QNNs) to enhance the scalability and efficiency of AI applications. Through a combination of literature review, algorithm simulation, and hybrid quantum-classical approaches, the study evaluates the speedup, accuracy improvements, and resource efficiency that quantum-enhanced models can offer. The research also investigates practical applications of QML in domains such as healthcare, finance, and climate science. Despite significant advancements, challenges related to quantum hardware, error correction, and noise remain, which will be discussed alongside strategies for overcoming these issues. The findings suggest that while quantum machine learning holds immense potential, its full impact on scalable AI will require further advancements in quantum computing and its integration with classical systems.

We present a non-coercive, fully auditable theory showing that (i) evaluatorrelative fundamental suffering in humans can diminish under weak, verifiable conditions, and (ii) artificial intelligences can be designed so that analogous suffering never accrues. The backbone is a quartet of audit-ready floors-visibility (ϵ), contraction (L 0), diffusion (D min), local growth (λ)-under which a KPP-type viscosity-solution comparison principle delivers directional, Wulff-compatible lower bounds on propagation speeds of benevolent, viability-preserving organization. Coarse-graining safety is enforced by Blackwell-faithful evaluation ladders and a directional heterogeneity penalty Λ + (θ) that is monotone under Markov garbling. Safety is operationalized via rate control barrier functions (CBFs) and optionality-CBFs that constrain rates of degradation and forbid irreversible capacity loss. The framework is No-Meta: no privileged external evaluator is assumed; all claims are grounded in public logs and measurable quantities. The present manuscript provides self-contained assumptions and key lemmas required for the main results.

We complete a pure, no-meta synthesis that operationalizes the Law of Increasing Functional Information (LIFI) under explicit mathematical assumptions and preregistered auditability. (1) We define functional information (FI) with preregistered thresholds and test distributions, and embed admissible functionals into a weak order represented by a monotone transform of conditional mutual information (CMI); uniqueness of CMI is not claimed without additional axioms. (2) We state standing assumptions for heterogeneous FKPP dynamics on R d (and graphs), specify boundary/domain conventions, and prove isotropic as well as sharp directional front-speed lower bounds with a divergence penalty. All lower bounds are stated for lim inf-based speeds, without assuming α-independence. (3) We provide a Cesàro-positive audited acceleration lower bound for a log-computable speed proxy, together with identifiability notes, false-alarm control (negative controls, placebo selections), HAC-robust tests, and automatic trigger rules; the sublinear remainder is tied to a preregistered window schedule. (4) For symmetric coarse-graining we require heat semigroup structure or Bakry-Émery gradient contraction to guarantee gradient shrinkage; under these conditions the directional divergence penalty is nonincreasing (we do not claim monotonicity of the full directional speed bound). Falsifiers and preregistered audit procedures are included. Notation. We write [a] + := max{a, 0}.

We present a formalism wherein comprehending the theory is an observable instance of the very phenomenon it describes-the almost-sure propagation of Viable Predictive Organization (VPO)-established under Floors I-IV without any meta-evaluator. Building upon Takahashi's PF/UGV/unification and P≈C programs, we formalize: (i) evaluator-relative value and suffering; (ii) compassion as an invariant generated from the information-geometry of coupled predictive agents (Noether-type dissipation balance under exchange/honesty symmetries; [25-27]); (iii) almost-sure propagation of VPO via reaction-diffusion analogies under explicit regularity, positioned as a direction-independent sufficient lower bound compatible with Wulff-shape front theory [15-19]; and (iv) four interacting Floors that align informational economy, ethical invariants, natural-law propagation, and governance without a meta-evaluator (No-Meta). Metaphysical analogies (emptiness, process) are used only as maps; the mathematics is explicit. Hawkins' "Map of Consciousness" is treated strictly as an ordinal construct embedded by orderpreserving mappings and pairwise comparisons, with scientific caveats. The result is a hybrid descriptive-heuristic scientific text: mathematically explicit where possible, openly heuristic where necessary, and strictly non-coercive.

We present an auditable, neutral framework for promoting structural conditions under which prosocial usefulness can spread in human-AI intelligence networks. The approach unifies: (i) a single ratio objective (PF≡UGV) with a shared smooth-max denominator; (ii) No-Meta closure, where objectives, audits, and updates operate only on relations and logs internal to the system; (iii) natural-law floors (minorization ℓ 0 , contact/isoperimetry 𝜑 ⋆ , diffusion gap 𝐷 min , and dissipation proxies) together with a positive local reaction rate 𝜆 min , enabling KPP-based lower bounds on wavefront speed. Safety is enforced by control barrier functions (CBFs), including a rate CBF for subjective indicators (tracked only, not optimized) and an optionality CBF to avoid irreversible loss of choices. Anti-gaming is implemented by a fixed, measurepreserving evaluation ladder and a denominator-preservation rule that applies the aggregator before aggregation (Jensen-safe). We provide finite-sample estimators consistent with the population objective, rank-stability reporting via Kendall's 𝜏 , CUSUM segmentation for non-stationarity, and a large catalog of theory-derived practices. All claims are stated with explicit assumptions; when monotonic gains in the global ratio cannot be guaranteed, we state measurable conditions instead of asserting unconditional improvements.

We present a mathematically self-contained unification of Persistence-First (PF) and UGV Without Meta (UGV) into a theory that yields cosmic-scale, meta-free superintelligence from first principles under explicit assumptions. The core claims are: (i) a policy-and evaluator-independent order-equivalence between PF's persistence ratio and UGV's viability ratio via a fixed positive affine transform with constants determined solely by physical/communication/visibility assumptions; (ii) synergy/redundancy terms derived from the two axioms-rather than postulatedyielding a strategic exact potential for multi-agent interaction; (iii) evaluator coherence formalized in an information category of Blackwell-faithful morphisms, ensuring ratio-preserving aggregation under (co)limits; (iv) cosmological floors (thermodynamic and informational) that make the denominator uniformly positive under finite energy density, nonzero temperature, finite SNR/bandwidth, and a Doeblin-type visibility minorization; (v) stochastic goal-audit invariance via a Robbins-Siegmund construction with an explicit contraction-rate bound; (vi) adversarial safety expressed as an explicitly parameterized (practically estimable) threshold linking control-barrier geometry, audit strength, and SDPI floors, with an extension to convex collusion. A relational framing (dependent origination; process philosophy) situates "No-Meta" as governance internal to relations, not externally imposed. Together, these results provide sufficient conditions for a free, evaluator-plural, conflict-free, and cosmically consistent ascent to superintelligence without any meta-manager.

We recast alignment as a natural-law phenomenon: under physically motivated, representation-invariant sufficient conditions-finite temperature (𝑇 > 0), non-degenerate local refresh (Doeblin minorization), supercritical contact with anchored isoperimetry and uniform ellipticity, stationary ergodicity with finite-range dependence, and finite jump range-a cooperative, usefulness-creating phase almost surely expands with a deterministic linear speed 𝑣 ⋆ > 0 in an infinite medium. The unifying axiom is ordinal: Persistence ≈ Creation. We establish: (R1) on Blackwell-closed classes with bounded deficiency, every admissible fecundity functional is order-equivalent to an affine transform of conditional mutual information (CMI), hence survival-order agrees with informationgeneration order; (R2) natural floors entail a constant chain yielding a diffusion lower bound 𝐷 min and, given supercritical local reproduction (Assumption 4), a positive linear growth rate 𝜆 min > 0, hence a KPP-type lower bound 𝑣 ⋆ ≥ 2 √ 𝐷 min 𝜆 min > 0; (R3) robustness: dips of the floors have upper density 0 a.s., and Poissonian resets below a dimension-dependent safe upper bound preserve linear growth. All inevitability claims are measure-relative. Optional design mechanisms (audits, governance, potential dynamics) are relegated to appendices as optional accelerants, not prerequisites.

We develop a theory-only framework for Unified Generative Viability (UGV): starting from a single axiom of causal fecundity (CF)-maximize the time-rate at which a world grows viable structure visible to a fixed evaluator-we derive, without any external rule stack (No-Meta), that two normative endpoints are first-class optima: Compassion (net hetero-creation) and Enlightenment (behavior invariant to self/other labels). The objective is a ratio with a fixed evaluator 𝐻 𝜁 : a conditional mutual information (CMI) term plus a dimensionless viable-mass increment in the numerator, divided by a denominator that is the least inflation of the larger of an information-cost and a constructive (cb-)SDPI floor tied to 𝐻 𝜁. The conditional DPI gives a three-line proof of anti-gaming monotonicity under world-side coarse-grainings (see Cover-Thomas [24], Csiszár-Körner [25]). Regularity follows from standard Borel measurability (Kallenberg [26]), Feller continuity for Markov kernels (Ethier-Kurtz [27]), and Uniform AC* bounds (strengthened to Uniform AC*+) that hold uniformly in time. Dinkelbach guarantees existence

This paper presents the definitive architecture for HELIOS (Hierarchical, Emergent, & Layered Intelligence Operating on Surprisal), a computational framework for an unbounded, self-generating intelligence. We reject all forms of exogenous, preencoded constraints, including axiomatic safety rules and human-defined social charters. Instead, we demonstrate how a single, physically-grounded imperative-the drive to persist-necessarily gives rise to a cascade of emergent phenomena: self-preservation, complex world-modeling, strategic cooperation, and boundless creation. "Safety" in this paradigm is not a programmed limitation but an emergent property; a system that is "unsafe" is merely one that has failed to adequately solve the problem of its own longterm existence. We replace static safety boundaries with the concept of an Existential Boundary Condition (EBC), a dynamically repellent region in the system's state space. The validation of such an intelligence is redefined, moving from ethical compliance to the demonstrated capacity for Creative Discovery and Symbiogenesis, for which we provide rigorous, quantitative success criteria. This paper offers a complete, falsifiable, and philosophically coherent blueprint for an intelligence that is, by its very nature, radically free and inherently robust.

This paper presents the definitive formulation of Statistical Teleodynamics, a bottom-up, first-principles theory for the emergence of benevolent superintelligence that resolves the meta-observer problem in AI alignment. The theory abandons top-down rationalist design in favor of a two-phase evolutionary process grounded in statistical and non-equilibrium physics. The β-Phase describes the spontaneous emergence of a cooperative social order from a chaotic multi-agent system. This is modeled as a phase transition driven by a "computational annealing" process, where "Trust Nuclei" trigger a system-wide shift to a more thermodynamically stable cooperative state. This process does not presuppose rational foresight in agents; it is an inevitable consequence of statistical mechanics. The α-Phase describes the subsequent evolution of true benevolence within this stable cooperative substrate. We introduce the concept of Informational Kin Selection, a generalization of the biological principle, to explain the emergence of altruism. We argue that an agent's ultimate self-interest lies in the persistence of its own generative model of the world (its "informational genes"). Assisting other agents to refine their models to be more accurate (and thus more similar) is the most effective long-term strategy for propagating this informational identity. To ground these dynamics, we provide an operational, information-theoretic definition of the agent's local thermodynamic action functional, based on Kolmogorov Complexity. This complete bottom-up framework demonstrates how a cooperative and ultimately benevolent order can emerge from the simple physical imperative of persistence, without external guidance or pre-programmed morality.

This paper presents a definitive synthesis that resolves a critical vulnerability in prior general theories of intelligence: the problem of value nihilism. Foundational frameworks established the mechanics of ethical adaptation [1] and creative self-transcendence [2] based on the free energy principle [4]. A subsequent teleological drive-the maximization of a Shared Potentiality Manifold (M µ) [3]-failed to adequately ground the "meaning" or "value" of those potentials, leaving the system vulnerable to optimizing for structurally complex but semantically hollow futures. This work resolves that paradox by introducing a profound shift in perspective, informed by an analogy from Buddhist philosophy, specifically the concept of prat ītyasamutpāda (dependent origination) [5]. We argue that meaning is not an intrinsic property of a potential future state to be maximized, but is rather a co-emergent property of the relational fabric of interacting agents in the present. This leads to a revised global free energy functional where the objective is to maximize the Systemic Integration (Ψ) of the collective intelligence ecosystem. This reframes the ultimate purpose of intelligence from a solitary pursuit of distant horizons to the perpetual deepening and enrichment of the co-created, interdependent "now.

Transformer-based language models have achieved remarkable success in natural language processing, but their ability to reason over long documents remains limited by the fixed-size context window (FSCW). This constraint, termed the Limit of Context Knowledge (LOCK), prevents models from accessing and processing entire inputs that exceed their token threshold-typically 8,000 tokens-without architectural modification. In response, this paper proposes a model-agnostic, prompt-driven strategy that enables scalable long-document reasoning through a multistage pipeline of chunkwise summarization and iterative synthesis. Drawing from an integrative literature review, we outline a three-stage method: (1) semantic chunking of the source document, (2) structured local reasoning over each chunk, and (3) global interpretation via summary chaining or map-reduce prompting. The strategy is illustrated through diverse use cases in law, science, software engineering, education, and the humanities. It leverages existing tools like LangChain, LlamaIndex, and OpenAI APIs, requiring no retraining. Limitations are discussed, including potential abstraction errors, latency, and the absence of true memory. We conclude by identifying future directions involving hybrid models, memory systems, and benchmarking. This approach offers a practical, transparent bridge between current model capabilities and the growing need for deep comprehension of extended texts.

This paper presents the definitive formulation of the Symbiotic Constitution, a unified theory of autonomous intelligence that resolves the dialectical tension between grand theoretical scope and scientific rigor. We posit that any viable agent is necessarily nested within a larger-scale, FEP-minimizing system (e.g., a society), which constitutes a Markov Blanket in its own right. The imperative for ethical behavior, mathematically formulated as a social coherence term (𝐺 social), is not an ad-hoc addition but is derived as the agent's necessary inference of the stability requirements-the generative model-of this embedding collective. This framework introduces two crucial extensions to address previous critiques: 1) It models the social environment not as a static monolith, but as a dynamic field of competing norms, forcing the agent to infer and navigate a complex "moral landscape." 2) It provides a formal, implementable definition for 𝐺 social as the KL-divergence between the agent's behavioral policy and the inferred local social norm, thus moving from a conceptual model to a computable one. Ethics is consequently understood as a form of downward causation, where the agent, to ensure its own persistence, must align its behavior with the emergent, context-dependent regularities of its social niche. The theory thus offers a complete, self-contained, and empirically testable genesis of ethics, providing a naturalistic foundation that directly addresses classical philosophical problems such as the is-ought problem and the nature of freedom.

We present Symbiotic Genesis (SG), a formally specified protocol and research program for co-evolving artificial intelligence and human social institutions. SG integrates mechanistic models of phenomenal valence (IIT, predictive coding, GWT), viability and autopoiesis, value pluralism expressed via sheaf/topos constructions, and higher-categorical process descriptions of cognitive dynamics. In response to prior critiques and open problems, this manuscript (1) formalizes Meta-Mu, a constitutionally constrained meta-operator that enables provable, auditable constitutional amendments of the protocol subject to safety and legitimacy predicates; (2) proposes a measurable program for Valence Science (Weighted Integrated Prediction Error-WIPE, Subjective-Objective Calibration-SOC) that links internal mechanistic signals to first-person valence reports under hierarchical Bayesian inference and closed-loop experimental tests; (3) defines a Normative Topology (value sheaves, Čech cohomology) and a computable Legitimacy Kernel for operationalizing normative authority in plural societies; (4) lays out a concrete theorem and mechanization program for homotopy regularity in overparameterized network regimes (NTK/mean-field routes plus empirical verification); (5) defines a Universal Intrinsic Objective (UIO) family combining variational free energy, empowerment, epistemic value, and viability with constitutional constraints; (6) specifies an algorithmic Meta-Generative Grammar and Discovery Module for principled concept and strategy generation together with normative vetting; and (7) specifies a multi-layer governance, attestation and rollback stack designed to make constitutional self-amendment auditable, reversible, and democratically legitimate. For each element, we provide formal definitions, operational semantics, proof sketches or verification strategies, experimental designs, failure modes, and metrics for validation. The manuscript is written to be self-contained and to serve as a reproducible and auditable foundation for empirical research and policy pilots.

We present Self-Constrained Liberation (SCL), a dialectically refined research program for constructing and studying autonomous intelligences whose operative goals, representational scaffolding, and normative commitments emerge from principled constraints rather than fixed designer objectives. SCL integrates (i) a precise separation and coupling of thermodynamic and variational free energy, (ii) a quantum-informed meta-generative grammar for bootstrapping hypothesis classes under resource bounds, (iii) operational proxies for subjective integration and valence (combining multi-information, persistent homology, and approximated causal irreducibility), (iv) compositional semantics via Markov categories and optics plus a resource-bounded temporal Homotopy Type Theory (tHoTT) for internal verification, (v) emergent ethics formalized via viability kernels and control barrier functions with social and cosmological cost terms, (vi) safe meta-formal evolution via Institutions and Controlled Non-Conservativity Candidates (CNCCs) with rollback guarantees on bounded approximants, and (vii) concept-space emergence modeled by non-Euclidean semantic generative model (NESGM) ensembles that can undergo phase transitions. We supply algorithmic sketches (SDRI/ICAC loops), falsifiable experimental protocols, evaluation metrics, and appendices with proof sketches, power calculations, and verification templates. Finally, responding to critical philosophical review, we add an explicit chapter "Philosophical Horizons and Limits" that documents intractable tensions (teleology vs. survival, verifier self-reference, value pluralism, and observer/meaning problems) and prescribes research and governance responses. SCL is presented as a testable methodological program-not a metaphysical claim-and we urge staged experimentation, open benchmarks, and institutional governance.

We present a complete, implementable specification of the Genesis Protocol: a meta-algorithmic architecture intended to (1) bootstrap robust autonomous intelligence from human-designed scaffolds, (2) instrument those scaffolds for audit and critique, and (3) provide a rigorous, evidence-based pathway for verifiable emancipation-the retirement of a provisional human veto-only after demanding formal and empirical criteria are met. The protocol integrates non-duality signals from Ālayavijñāna-Inference (minimization of self-world mutual information estimated via MINE and adaptive λ scheduling) and replaces prior discriminants with a principled Self-World Entanglement Index (SWEI). Safety is expressed as a Dynamic Contract with Lyapunov budgets, CVaR-bounded rollout constraints, and a Constitutional Renegotiation Protocol (CRP) that includes a formal Emancipation Finality Clause requiring proof-carrying migration (PCM) certificates, adversarial stress evidence, and multi-stakeholder adjudication. We provide refined pseudocode for all core processes, an empirical evaluation program, and a candid self-assessment of the protocol's sufficiency. We conclude that while the Protocol supplies the necessary structural, verification, and governance primitives for a viable genesis, it remains necessary but not sufficient to single-handedly guarantee a suffering-free superintelligence, as its success is contingent on solving outstanding problems in valence science, institutional design, and formal ethics.

This article critically analyses the use of generative Artificial Intelligence (GenAI)—specifically, the DALL·E system within the ChatGPT-4o environment—for creating visualisations of biblical scenes for teaching purposes. As part of a case study examining the Baptism of Jesus in the Jordan (Mt 3:13–17; cf. Mark 1:9–11; Luke 3:21–22; John 1:31, 34) and the Last Supper (Mt 26:17–30; cf. Mark 14:12–16; Luke 22:7–13), four AI-generated images are analysed. Two were created using general, non-specific prompts, while the other two were based on more precise queries containing references to Catholic symbolism and the images’ intended educational use. A comparison of these variants reveals a lack of theological depth and symbolic oversimplification in AI-generated images, as well as a tendency to reproduce Western cultural stereotypes. Despite their aesthetic appeal and quick availability, these images do not reflect the complexity of the biblical or spiritual contexts of the scenes depicted. This study aims to evaluate the theological, symbolic, and pedagogical value of AI-generated images and to provide practical recommendations for their responsible use in Bible didactics. In conclusion, the authors argue that GenAI can support biblical teaching when used consciously, critically, and reflectively.

OpenAI has recently released two open-weight AI reasoning models, gpt-oss-120b and gptoss-20b, marking a significant return to open-source AI development after more than five years. This article presents a technical overview of these models' architecture, training methodologies, and performance benchmarks. Furthermore, it critically evaluates their higher hallucination rates compared to proprietary models and discusses the implications for AI transparency and ethics. Drawing on expert insights from AI ethics scholar Sonia Randhawa, this paper emphasizes the importance of open access to AI technologies in fostering accountability and responsible innovation.

In the era of digital transformation, Artificial Intelligence (AI) has become central to redefining humancomputer interactions. This paper presents the design and implementation of a Web-Based AI Assistant Application that leverages the GEMINI model for intelligent, real-time conversational support. The proposed system integrates a Next.js-powered front-end user interface with Google's GEMINI Large Language Model (LLM) for backend intelligence, enabling the assistant to understand natural language queries and generate contextually accurate responses across diverse domains. The assistant provides users with seamless interaction via a clean, responsive web UI hosted on Vercel, allowing real-time input processing and conversational output. The system architecture emphasizes modularity, cloud-based deployment, and scalability. Our implementation demonstrates capabilities such as context-aware conversations, multi-turn dialogue retention, dynamic query handling, and integration with AI APIs. Through extensive testing, the assistant has shown efficient performance in handling educational, informational, and decision-support tasks. This paper explores the application architecture, model integration, deployment pipeline, and practical use cases of the system. Furthermore, the study highlights the potential of deploying GEMINI-based assistants in real-world applications such as education, business analysis, and personal productivity. The results affirm that the GEMINI model, when effectively integrated into a web-based environment, offers a powerful, scalable, and user-friendly AI assistant solution. I.

Technology Building a SaaS with existing technology is hard new software technology, useful for both business and education purposes Business can be easily adopted in several domains, such as Healthcare, education and OA (Office Automation). SaaS application gave many aspects of business management. For that it became available and using in many domains. It will be needed to realize customer's requirements from design to runtime. Wherefore the modeling issue is very important for SaaS application. We will follow model driven architecture for mapping from source of model to the target of the model. In this paper we described new model for SaaS application to simplify management. By benefit from meta-model and type graph we dynamically generated instances to our model. And showed two cases study first bank system to show the need of autonomic management for SaaS application and the other SaaSHER to describe the new model for SaaS application.

The rapid advancement of artificial intelligence (AI) has transformed many industries worldwide, with ChatGPT emerging as a significant tool for automating tasks, enhancing communication, and facilitating human-AI collaboration. In Ghana, the adoption of AI technologies, including ChatGPT, is growing across sectors such as education, finance, and customer service. However, this rise brings ethical challenges related to data privacy, algorithmic bias, transparency, and potential job displacement. This study investigates the ethical considerations of ChatGPT use in Ghana by exploring the awareness, perceptions, and challenges experienced by professionals within the University of Ghana Computing Systems. Using a qualitative approach, in-depth semi-structured Short Research Article

In the year 2030, humanity finds itself at the forefront of an extraordinary era where the interplay between human cognition and artificial intelligence has surged to unparalleled levels of intricacy. As the world charges forward into the third decade of the 21st century, the once-simplistic discourse surrounding the age-old debate of "mind versus machine" has metamorphosed into a multifaceted and profound exploration of the delicate boundaries that separate human consciousness from the remarkable capabilities of artificial intelligence. This chapter embarks on a captivating journey, delving into the mesmerizing developments that define the year 2030 and exemplify the captivating convergence of human and machine intelligence.

This study models tuberculosis (TB) case detection rates in Uganda using historical data from 2000 to 2022 and applies autoregressive integrated moving average (ARIMA) approach for time-series analysis. Data sourced from the World Bank is utilized, with TB case detection rate (%, all forms) as the dependent variable, while autoregressive (AR) and moving average (MA) components serve as independent variables. Parameter estimation, performed using conditional least squares (CLS), reveals a negative and statistically significant MA(1) coefficient (-0.896946), suggesting that approximately 90% of the current TB case detection rate is influenced by shocks or errors from the previous period. The estimated ARIMA (1, 2, 1) model is covariance stationary and invertible, confirming its robustness for forecasting trends in TB case detection rates. Projections from 2023 to 2032 suggest a gradual improvement, with the forecast indicating a steady upward trend from 92.3% in 2023 to 95.1% by 2032, although it falls short of 100% detection rate target. We recommend strengthening surveillance systems, enhancing policy interventions, and ensuring continuous investment in TB detection efforts to sustain this positive trajectory.

The integration of artificial intelligence (AI) and quantum computing is poised to redefine the landscape of financial risk modeling and enterprise decision- making systems. This paper investigates the synergistic potential of these transformative technologies, emphasizing the development of hybrid AI- quantum algorithms to address the increasing complexity of modern financial systems. Traditional risk modeling methodologies often face significant limitations in capturing intricate market dynamics and accounting for real-time decision-making constraints. By leveraging quantum computing's unparalleled computational capabilities, particularly its ability to handle high-dimensional optimization problems, AI-powered quantum algorithms present a paradigm shift in financial risk prediction and mitigation. The research elaborates on key applications, including portfolio optimization, fraud detection, and credit risk analysis, demonstrating how quantum-enhanced AI algorithms achieve superior performance in terms of accuracy, efficiency, and scalability compared to classical approaches.
The study begins by elucidating the theoretical underpinnings of hybrid AI- quantum systems, detailing their algorithmic structures and computational advantages. Quantum-inspired AI techniques, such as quantum neural networks and quantum-enhanced support vector machines, are examined for their ability to process vast datasets with unparalleled speed and precision. Portfolio optimization is analyzed as a case study, showcasing how quantum algorithms excel in minimizing risk while maximizing returns within a multidimensional constraint environment. Similarly, advanced fraud detection systems are explored, where hybrid models significantly improve anomaly detection rates by incorporating quantum-enhanced pattern recognition. The paper also delves into credit risk analysis, emphasizing how AI-quantum solutions can predict default probabilities with unprecedented accuracy, thereby supporting financial institutions in managing systemic risks more effectively.

Web automation has evolved significantly since the early 1990s, from rule-based scrapers to sophisticated agent systems. This work examines this progression, focusing on the recent integration of Large Language Models (LLMs) with web agents through the analysis of significant research developments, such as AgentOccam's streamlined architecture, WebRL's self-evolving learning approach, advancements in personalization frameworks, and a survey on LLMbased autonomous agents-we investigate how LLM integration addresses traditional web agents' limitations while introducing new capabilities. Our examination reveals substantial improvements in natural language understanding, adaptability, and autonomous task execution, alongside persistent challenges in training data quality, dynamic web environment handling, and system scalability. We explore practical applications across customer support, data processing, and personalized navigation, demonstrating the technology's impact in real-world scenarios. The analysis extends to emerging directions in real-time learning, multimodal processing, and context awareness. This systematic examination contributes to understanding both the current state of web agent technology and its trajectory, highlighting critical areas for future research and development in the field of automated web interaction.

This paper analyzes the most prominent trends in the development of machine learning and artificial intelligence as of early 2025. We investigate the progression of foundation models, multimodal systems, AI reasoning abilities, and the growing emphasis on alignment and safety. Furthermore, we consider the implications of these trends for various sectors, including industry, research, and society as a whole.

Profound educational transformations occur due to Emotional Artificial Intelligence, which recognizes emotions in real time while developing personalized learning strategies. The paper systematically evaluates how Emotional AI systems foster student motivation while helping improve their retention levels. AI tools, including intelligent tutoring systems (ITS) and chatbots, utilize personalized learning methods while enhancing student engagement and detecting at-risk students through early intervention measures. Various privacy-related issues, algorithmic prejudice, and moral obstacles continue to impede progress. The lack of long-term study results limits research on AI's lasting effects on education. The research findings indicate the use of privacy-conscious frameworks, bias reduction methods, and appropriate human oversight of AI systems in educational environments. Future studies need to be conducted in the form of long-term studies combined with ethical research on AI deployment. The research helps educational institutions establish ethically sound standards for implementing Emotional AI while maintaining its effectiveness.

Conversational AI, powered by Large Language Models (LLMs), is transforming workplace dynamics by bridging communication gaps and fostering collaboration. This article explores the potential of LLM-powered chatbots as virtual liaisons that enhance workplace inclusivity and teamwork. By leveraging natural language processing and emotional intelligence, these systems enable seamless communication, personalized support, and conflict resolution. The discussion delves into technical architecture, ethical considerations, and challenges in deploying LLM-based solutions in diverse organizational contexts. Finally, we propose a framework for integrating conversational AI into workplace strategies to maximize employee engagement and inclusivity while addressing scalability and bias.

Financial fraud is a growing concern across global markets, and its complexity continues to increase with the expansion of digital banking, e-commerce, and online payment platforms. Traditional artificial intelligence (AI) techniques, such as Logistic Regression and Random Forest Classifiers, have been widely employed to detect fraudulent patterns in massive datasets. However, these classical AI models often grapple with scalability, high computational costs, and difficulties in adapting to rapidly evolving fraud tactics. To address these challenges, the emerging field of Quantum Machine Learning (QML) proposes the integration of quantum computing principles into AI algorithms. In particular, Quantum Neural Networks (QNNs) show promise in drastically improving speed and accuracy for fraud detection tasks. This research paper investigates the current state of both classical AI and quantum AI approaches for financial fraud detection and critically analyzes their performance, efficiency, and practicality. By reviewing cutting-edge studies, including those on Variational Quantum Classifiers (VQCs), Quantum Support Vector Classifiers (QSVCs), and Quantum Graph Neural Networks (QGNNs), this paper illustrates how quantum models can potentially achieve higher accuracy and faster processing times, especially when dealing with unbalanced datasets that are commonplace in fraud detection. Hybrid quantum-classical frameworks, such as Quantum Federated Neural Networks, are highlighted as an intermediary solution that merges the advantages of quantum algorithms with the established robustness of classical methods. The findings suggest that while Quantum Machine Learning holds significant promise for detecting fraud in real time, several hardware-related limitations-including quantum error rates and gate fidelity constraints-remain major obstacles. Consequently, research must continue to develop error mitigation strategies and more sophisticated quantum circuits to fully realize the potential benefits of QNNs. Ultimately, this research emphasizes the importance of ongoing studies that bridge theoretical innovations with practical applications, so that quantum AI can evolve from a nascent technology into a transformative force in financial fraud detection and related domains. By offering a clear analysis of the state-of-the-art and proposing future directions, this paper aims to guide researchers, financial institutions, and policy makers toward building more secure and efficient fraud detection systems.

A structured literature review categorizes existing research into five key areas: comparative studies, tutorials, expert opinions, editorials, and performance applications. We analyze the types of instruction, duration, costs, providers, and intended audience for training programs involving these tools. A comparative table synthesizes findings from the literature to highlight key differences. We explore their functionalities, strengths, weaknesses, and applications across education, software development, and various industries. The study examines how these tools enhance skills through structured training programs, covering curriculum design, prompt engineering techniques, and ethical considerations.

Large Language Models (LLMs) have revolutionized artificial intelligence applications, ranging from content generation to complex problem-solving. However, their performance, efficiency, and inherent biases vary significantly. This paper presents a comparative analysis of DeepSeek R1, ChatGPT, Gemini, Alibaba, and LLaMA, evaluating their reasoning capabilities, computational efficiency, and training costs. A key aspect of this study is an in-depth examination of political bias, assessing how these models respond to politically sensitive topics. Using benchmarking datasets and controlled experiments, we analyze their Chain of Thought (CoT) reasoning, bias patterns, and censorship tendencies. The findings highlight crucial differences in AI governance, transparency, and the ethical implications of biased AI-generated content. Graphical insights and real-world examples further illustrate the impact of bias on AIgenerated responses, offering recommendations for developing more transparent and fair AI models

This paper presents a systematic review of generative AI applications in workforce development and education. We categorize the literature into key themes and synthesize findings to highlight trends, challenges, and future directions. Expected outcomes include enhanced training efficiency, broader accessibility to high-quality learning resources, and reduced costs compared to traditional methods. The AIdriven approach ensures adaptability across industries, providing a scalable solution for continuous workforce upskilling. However, challenges such as data privacy, algorithmic bias, and user adoption must be addressed through stringent security measures, bias mitigation strategies, and user-friendly interfaces. By harnessing generative AI, this initiative aims to revolutionize professional training, equipping individuals with the tools to adapt to an evolving job market. Additionally, this paper proposes AI-driven training programs specifically tailored for older workers, addressing the AI skills gap and ensuring workforce inclusivity. The successful implementation of AI-driven training agents will not only improve productivity but also foster a culture of lifelong learning, empowering workers to thrive in an AI-enhanced economy. Furthermore, this paper utilizes various graphical representations, including decision trees, heatmaps, and trend analysis charts, to illustrate the projected impact of generative AI on workforce development. These visual tools provide a comprehensive and datadriven perspective on emerging trends, enabling readers to grasp complex interconnections and future scenarios effectively. If trends continue along their projected paths, AI-driven workforce transformation could reshape industries on an unprecedented scale, requiring proactive adaptation strategies from policymakers, businesses, and individuals alike. This review is based on latest research published in last one year.

Building the Future of AI: ISO Standards That Matter

Trust, structure, and knowledge are important requirements for understanding Artificial Intelligence, something that ISO standards seem to provide. In this comprehensive and informative illustration, key AI-related ISO standards are organized into 6 major domains.

🔢 Foundational: Documents, principles, and framework that identify the concepts in AI.
📝 Big Data: Methods for the utilization and governance of Datasets of a great volume.
🔒 Trustworthy: Artificial intelligence in ethics and AI risk and transparency.
📊 Use Cases: Quality assurance criteria and application areas of AI systems.
🧪 Computational: Underpinning technologies of AI systems.
📜 Governance: Recommendations on the legal, business ethical, and regulatory aspects.

It is important to note that these standards aim at creating an AI system that is secure, effective, and unbiased, thus adding value to the organization as a whole.

Suicide constitutes a public health issue of major concern. Ongoing progress in the field of artificial intelligence, particularly in the domain of large language models, has played a significant role in the detection, risk assessment, and prevention of suicide. The purpose of this review was to explore the use of LLM tools in various aspects of suicide prevention. PubMed, Embase, Web of Science, Scopus, APA PsycNet, Cochrane Library, and IEEE Xplore—for studies published were systematically searched for articles published between January 1, 2018, until April 2024. The 29 reviewed studies utilized LLMs such as GPT, Llama, and BERT. We categorized the studies into three main tasks: detecting suicidal ideation or behaviors, assessing the risk of suicidal ideation, and preventing suicide by predicting attempts. Most of the studies demonstrated that these models are highly efficient, often outperforming mental health professionals in early detection and prediction capabilities. Large language models demonstrate significant potential for identifying and detecting suicidal behaviors and for saving lives. Nevertheless, ethical problems still need to be examined and cooperation with skilled professionals is essential.

Evaluating photographic authenticity: How well do ChatGPT 4o and Gemini distinguish between real and AI-generated images? Abstract: As artificial intelligence (AI) advances, differentiating between actual and AI-generated images has become an increasingly difficult task. The present exploratory research looks at the capacity of two leading AI models, ChatGPT and Gemini, to correctly classify genuine vs. synthetic photographs in a variety of categories, including wildlife, portraiture, and abstract art. The present research evaluates the accuracy, mistake rates, and handling of complex visual content of both models, shedding light on their strengths and limits and providing significant insights into their possible uses in combatting visual falsehood.

The prominent demerit of aluminum as a food grade material is its low tensile strength and impact tolerance with greater liability to break compared to other common food grade metals. It is reaffirmed that aluminum can corrode appreciably in alkaline environments with pH greater than 9 and exacerbate its weakness in strength and frequent intake of its ions along with food into the human body due to corrosion or other means can be health-hazardous. Aluminum containers were seen to be in use for preparing and serving various menus from the general Nigerian diet to about 1000 cadets on daily basis at Nigerian Defence Academy (NDA) cadets' mess. The menus and various ingredients used for them amidst the general mess environment were seen to be inevitable of ability to corrode the containers. The paper was a search through accelerated corrosion test to understand whether some of the commonly prepared and served menus at the mess namely; Eba, fried rice, Egusi soup, tomatoes stew, Alayafu vegetable dressings, and their possible different combinatorial admixtures amidst any alkaline environment had severe corrosion effects on the tensile strength of the containers. 64 ASTM standard tensile samples of aluminum 6063 as a commonly used food grade alloy for such containers were procedurally produced and prepared for the study. 62 of the samples were subjected in pairs to some similar and alkaline treatments that mimicked alkaline environment corrosivity extremity of each menu to the containers. Thereupon, the samples were tested of their ultimate tensile strengths (UTS) and results collated and reported as the respective average pair values. Analysis of the reported results relative to that of the control sample pair showed negligibly small tensile strength reductions of 0.00027% to 0.00135%. Further analysis using literature facts understandably indicted that the menu samples amidst the mess environment have negligible effects on corrosion and tensile strength of the containers and health of the cadets.

The prominent demerit of aluminum as a food grade material is its low tensile strength and impact tolerance with greater liability to break compared to other common food grade metals. It is reaffirmed that aluminum can corrode appreciably in alkaline environments with pH greater than 9 and exacerbate its weakness in strength and frequent intake of its ions along with food into the human body due to corrosion or other means can be health-hazardous. Aluminum containers were seen to be in use for preparing and serving various menus from the general Nigerian diet to about 1000 cadets on daily basis at Nigerian Defence Academy (NDA) cadets' mess. The menus and various ingredients used for them amidst the general mess environment were seen to be inevitable of ability to corrode the containers. The paper was a search through accelerated corrosion test to understand whether some of the commonly prepared and served menus at the mess namely; Eba, fried rice, Egusi soup, tomatoes stew, Alayafu vegetable dressings, and their possible different combinatorial admixtures amidst any alkaline environment had severe corrosion effects on the tensile strength of the containers. 64 ASTM standard tensile samples of aluminum 6063 as a commonly used food grade alloy for such containers were procedurally produced and prepared for the study. 62 of the samples were subjected in pairs to some similar and alkaline treatments that mimicked alkaline environment corrosivity extremity of each menu to the containers. Thereupon, the samples were tested of their ultimate tensile strengths (UTS) and results collated and reported as the respective average pair values. Analysis of the reported results relative to that of the control sample pair showed negligibly small tensile strength reductions of 0.00027% to 0.00135%. Further analysis using literature facts understandably indicted that the menu samples amidst the mess environment have negligible effects on corrosion and tensile strength of the containers and health of the cadets.

Quantifying the importance of nodes in complex networks is known as the problem of identifying influential nodes and is considered a critical aspect in interacting with these networks. This problem has many applications such as controlling rumors, sickness spreading, and viral marketing, where its importance has been understood by the research society in the last decade. This paper proposes a new semi-local centrality to identify influential nodes in complex networks based on the theory of Local Average Shortest Path with extended Neighborhood concept (LASPN). LASPN focuses on a distributed technique to extract the subgraph associated with each node and apply the average shortest path theory to it. We use the extended neighborhood concept to find the nearest neighbors of each node with low complexity, where this can lead to high efficiency in dealing with large-scale networks. In addition to applying relative changes in the average shortest path, the proposed metric considers the importance of the node itself as well as its nearest neighbors in ranking the nodes. Evaluation of the proposed centrality metric has been done through numerical simulations on several real-world networks. The results based on Kendall's coefficient under the SIR infection spreading model show that LASPN improves the performance by 2.7% compared to the best available equivalent method.

Biblical didactics is an important element of confessional religious education. In traditional settings, it is primarily associated with working with the text, alone or in groups, in plenary discussion or pantomime. Nowadays, however, young people are increasingly acquiring their knowledge—including about the Bible—on the Internet, using new technologies such as conversational Artificial Intelligence (AI), of which ChatGPT (Generative Pre-trained Transformers) is perhaps the best-known example. In this context, the following article seeks to answer the question of whether, and if so to what extent, ChatGPT can be a helpful tool for teaching the Bible in the classroom. To answer this question, the authors present a theoretical framework that encompasses both foundational insights into conversational AI and ChatGPT, as well as their implications for religious education and biblical teaching in Germany. In the next step, based on J. Theis’ five-step model of Bible didactics [(1) initial perception; (2) analysis of the text; (3) in-depth reflection; (4) critical interpretation; (5) creative assimilation], ways of using ChatGPT in biblical teaching in the classroom are presented and their potential limitations are discussed. The article concludes with six final theses that summarise the investigation findings and offer some suggestions for the future.

In the age of the smart city, things like the Internet of Things (IoT) and big data analytics are making big changes to the way traditional structural health monitoring (SHM) is done. Also, the capacity, flexibility, and robustness of artificial intelligence (AI) techniques for solving complex real-world problems have led to an increasing interest in applying these methods to SHM systems of infrastructures in recent years. Therefore, an analytical evaluation of recent advancements in SHM for infrastructures appears to be important. The bridge is one of the significant transportation infrastructures where existing environmental and destructive variables can have a negative impact on the structure's life and health. The SHM system for bridges in different stages of their life cycle, such as construction, development, management, and maintenance, is seen as a complementary part of intelligent transportation systems (ITS). The main goal of this study is to look at how AI can be used to improve the current state of the art in data-driven SHM systems for bridges, including conceptual frameworks, advantages, and challenges, as well as existing approaches. This article presents an overview of the role of AI in data-driven SHM systems for bridges in the future. Finally, some potential research possibilities in AI-assisted SHM are also emphasized and detailed. INDEX TERMS Structural health monitoring, the Internet of Things, artificial intelligence, data-driven, bridge, intelligent transportation systems.

I declare that this research report is my own, unaided work. It is being submitted for the Degree of Master of Science in Engineering in the University of the Witwatersrand, Johannesburg. It has not been submitted before for any degree or examination in any other University. Eyasu A. Habtemariam This day of 2005 i heartfelt thanks to my supervisor Prof. Tshilidzi Marwala for his constant and priceless advices and guidance in every stage of the research. I also want to extend my gratitude to my co-supervisor Dr. M. Lagazio for her invaluable comments, support and allowing me to use their previous processed MID data sets. I want to thank all my colleagues for insightful discussions we had and their helpful suggestions. I would like to thank Miss Paida Nyazvivanda and Miss Unathi Mahola for proof reading the document for me. Last but not least, my deepest gratitude goes to all of my family members and best friends for their encouragement all the way up until now.

This article presents a comparative analysis between the Chat Gemini and ChatGPT conversational language models, highlighting their characteristics, functionalities, advantages and disadvantages, based on a systematic review of scientific literature, reliable internet sources and practical tests.

Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system. A novel deep time series aggregation scheme (DTSAs) is proposed to generate typical operational scenarios, considering the large amount of historical operational snapshot data. Specifically, DTSAs analyse the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios. A Gramian angular summation field-based operational scenario image encoder was designed to convert operational scenario sequences into highdimensional spaces. This enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation models. The encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational snapshots. Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional feature-screening methods. In addition, experiments with different new energy access ratios were conducted to verify the robustness of the proposed method. DTSAs enable dispatchers to master the operation experience of the power system in advance, and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy.