As artificial intelligence (AI) becomes increasingly integrated into critical sectors such as healthcare, finance, law enforcement, and education, the need for effective regulation and governance becomes more urgent. This paper explores current global approaches to AI governance, identifies major challenges including bias, accountability, and transparency, and compares frameworks from different countries and organizations. It evaluates both binding regulations and soft-law instruments, proposing a hybrid, adaptive governance model that balances innovation with ethical responsibility.

Deep learning has emerged as a transformative technology, enabling advancements in fields such as computer vision, natural language processing, and autonomous systems. Python, with its comprehensive libraries and frameworks, has become the primary language for developing deep learning models. This paper explores the latest advancements in Python-based deep learning, focusing on key frameworks, algorithms, and innovations. It also discusses the challenges associated with implementing deep learning solutions, such as computational cost, data quality, and model interpretability. Furthermore, it addresses sustainable approaches (...) to deep learning, emphasizing energy-efficient techniques, model optimization, and the adoption of green computing practices. By understanding these advancements and challenges, we can push forward towards more efficient and sustainable deep learning solutions using Python. (shrink)

Model-Driven Engineering (MDE) is a modern approach to software development that emphasizes the use of models as primary artifacts in the software design and development process. In MDE, models represent abstract versions of systems, serving as blueprints that can be automatically transformed into working code. This approach seeks to enhance software design, reduce the complexity of code generation, and improve maintainability. By focusing on high-level abstractions, MDE enables developers to design software systems more efficiently, automate repetitive tasks, and ensure consistency (...) across different system components. It fosters the development of platform-independent models, which can then be tailored for specific environments or target platforms using model transformations. This paper provides an indepth examination of Model-Driven Engineering, highlighting its applications in modern software development, particularly in enhancing the software design process and streamlining code generation. The research focuses on key MDE technologies, including Domain-Specific Languages (DSLs), metamodels, and model transformation techniques. It also examines how MDE contributes to the reduction of errors, the increase in productivity, and the facilitation of collaboration in large-scale software projects. The methodology section outlines the techniques used to implement MDE in real-world software development, with examples of successful case studies. Finally, the conclusion discusses the future potential of MDE, exploring emerging trends, challenges, and the ongoing evolution of this methodology. (shrink)

Advances in Artificial Intelligence (AI) have driven the evolution of reasoning in modern AI models, particularly with the development of Large Language Models (LLMs) and their "Think and Answer" paradigm. This paper explores the influence of human reinforcement on AI reasoning and its potential to enhance decision-making through dynamic human interaction. It analyzes the roots of bias and fairness in AI, arguing that these issues often stem from human data and reflect inherent human biases. The paper is structured as follows: (...) first, it frames reasoning as a mechanism for ethical reflection, grounded in dynamic learning and feedback loops; second, it discusses how scaling laws suggest that reinforcement learning (RL) can mitigate biases and promote fairness; third, it examines how RL allows models to view algorithmic bias and fairness as approximation problems. Through an experimental study, the paper demonstrates how AI models, empowered by RL, have successfully identified biases across various domains, including gender and socio-economic contexts, highlighting how reasoning improves algorithmic fairness. Ultimately, this work emphasizes the role of RL in mitigating algorithmic bias and enhancing fairness in AI systems. (shrink)

We live in a world that witnesses an ongoing war between an entitled audience and, for the purposes of this paper, comedians who are too afraid to be vocal in their acts. There is no better time to try and understand the journey of humor- how it has fared in history and how people have reacted to it over time. This paper focuses on the philosophical implications of a joke by trying to break down the concept of humor to its (...) fundamentals. (shrink)

The efficiency or the development of a bank can be plumbed by different measures like deposits, advances, working assets, incomes, expenditures, profits, no of assets, number of accounts and branches etc. The role of employees is also of great signification as each and every expression of a bank is directly affiliated to the attitude, motivation and work civilisation of the employees. so the parameters which are used to count the efficiency, should also incorporate the performance of their employees. In the (...) current study the employee performance reasoning of selected co.opt bank has been performed on the basis of 2 ratios. a. working funds employee b. operating profit employee ratio. In this research article researcher study the employee work productivity of BCC and AJMB bank. Dr. Nitin J. Dhamsaniya | Dr. Achyut C. Patel "A Comparative Study On Employee Productivity Of Amreli Jilla Madhyasth Sahkari Bank And The Baroda Central Cooperative Bank" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-1, December 2016. (shrink)

We propose a consciousness-first framework to coherently explain the contextual diversity of local experiences and the global unity of the phenomenal field. Utilizing category theory, we model experience as a coherent structure arising from the integration of context-dependent content. We introduce a category of Raw states (representing pre-semantic relational data) and a category of Sem states (representing meaningful experiential content). A faithful translation between these domains is established via an integrator functor, which maps raw relational structures to stable semantic categories. (...) These local semantic states are organized into a diagram indexed by a category of contexts, capturing the relationships and overlaps between distinct perspectives. We demonstrate that phenomenal unity can be rigorously defined as the colimit of this semantic diagram. Furthermore, a universal construction glues local contents together along their shared relational constraints, yielding a unified global structure that preserves perspectival differentiation without collapsing into a flat, undifferentiated whole. Ontologically, this formalism results in the following: rather than assuming a pre-existing physical spacetime reality that generates experience, it is possible to model the objective world as the invariant semantic core emerging from the coherence relations between plural phenomenal worlds. (shrink)

This paper explores the development and evaluation of physics-specific large-scale AI models, which we refer to as large physics models (LPMs). These models, based on foundation models such as large language models (LLMs) are tailored to address the unique demands of physics research. LPMs can function independently or as part of an integrated framework. This framework can incorporate specialized tools, including symbolic reasoning modules for mathematical manipulations, frameworks to analyse specific experimental and simulated data, and mechanisms for synthesizing insights from (...) physical theories and scientific literature. We begin by examining whether the physics community should actively develop and refine dedicated models, rather than relying solely on commercial LLMs. We then outline how LPMs can be realized through interdisciplinary collaboration among experts in physics, computer science, and philosophy of science. To integrate these models effectively, we identify three key pillars: Development, Evaluation, and Philosophical Reflection. Development focuses on constructing models capable of processing physics texts, mathematical formulations, and diverse physical data. Evaluation assesses accuracy and reliability through testing and benchmarking. Finally, Philosophical Reflection encompasses the analysis of broader implications of LLMs in physics, including their potential to generate new scientific understanding and what novel collaboration dynamics might arise in research. Inspired by the organizational structure of experimental collaborations in particle physics, we propose a similarly interdisciplinary and collaborative approach to building and refining large physics models. This roadmap provides specific objectives, defines pathways to achieve them, and identifies challenges that must be addressed to realise physics-specific large scale AI models. (shrink)

In recent years, the rise of generative models has significantly advanced the field of artificial intelligence, enabling the generation of highly realistic and contextually relevant outputs across a variety of modalities, such as text, images, and sound. However, the majority of generative models have traditionally focused on a single modality at a time, limiting their application potential. Multi-modal generative models, which integrate multiple modalities (text, image, sound), are emerging as a powerful solution to address this limitation. These models, by understanding (...) and generating across different forms of data, have the potential to revolutionize diverse fields such as media creation, human-computer interaction, and data-driven decision-making. Despite their promising capabilities, the integration of text, image, and sound within a unified framework presents several challenges. These include aligning representations across different modalities, handling heterogeneous data types, and optimizing training for simultaneous generation across modalities. Additionally, there are concerns regarding the ethical implications of multi- modal models, particularly around bias, misinformation, and content authenticity .This paper explores the current advancements and future directions of multi-modal generative models. It reviews key developments in text-to-image, text-to-sound, and image-to-sound generation, as well as the integrated models that combine these capabilities. We also discuss the methodologies used in training these models, the challenges encountered in aligning disparate modalities, and the impact of ethical concerns. Ultimately, we envision how the future of multi-modal generative models can reshape creative industries and enable more immersive and interactive AI applications. (shrink)

Water resource management is crucial for sustaining life, supporting agriculture, and promoting economic growth. With increasing water scarcity due to population growth, climate change, and pollution, it is essential to adopt sustainable water management strategies. This paper explores the role of data-driven strategies in improving water resource management. By using big data, machine learning, remote sensing, and predictive analytics, it is possible to optimize water distribution, predict water demand, and improve conservation efforts. The study examines various data sources and methodologies (...) for water management, highlighting successful case studies from around the world. The integration of data-driven approaches offers significant potential to address water scarcity challenges and create efficient, sustainable systems for water use. (shrink)

Machine learning (ML) models have become integral to decision-making processes across various sectors, including healthcare, finance, and criminal justice. However, these models often inherit and even amplify biases present in training data, leading to unfair outcomes for certain demographic groups. This paper critically examines the ethical implications of bias and fairness in ML models, exploring the sources of bias, its impact on marginalized communities, and the ethical challenges it poses. We review recent literature to identify common biases in ML systems, (...) such as racial, gender, and socioeconomic biases, and discuss the consequences of these biases in realworld applications. Furthermore, we evaluate existing fairness metrics and mitigation strategies, highlighting their strengths and limitations. The paper also discusses the role of transparency, accountability, and regulation in addressing these ethical concerns. Through this examination, we aim to provide a comprehensive understanding of the ethical dimensions of bias and fairness in ML models and propose pathways toward more equitable AI systems. (shrink)

With the growing need for data-driven decision-making, organizations are increasingly adopting Business Intelligence (BI) tools to visualize and analyze data. Power BI, Tableau, and Google Data Studio have emerged as popular platforms, each offering unique features tailored to different user needs and organizational capacities. This paper presents a comparative study of these tools based on usability, integration capabilities, performance, cost, and visualization power. The study aims to guide organizations in selecting the most appropriate BI tool based on strategic objectives and (...) resource constraints. (shrink)