This paper introduces a preliminary concept aimed at achieving Artificial General Intelligence (AGI) by leveraging a novel approach rooted in two key aspects. Firstly, we present the General Intelligent Network (GIN) paradigm, which integrates information entropy principles with a generative network, reminiscent of Generative Adversarial Networks (GANs). Within the GIN network, original multimodal information is encoded as low information entropy hidden state representations (HPPs). These HPPs serve as efficient carriers of contextual information, enabling reverse parsing by contextually relevant generative networks to reconstruct observable information. Secondly, we propose a Generalized Machine Learning Operating System (GML System) to facilitate the seamless integration of the GIN paradigm into the AGI framework. The GML system comprises three fundamental components: an Observable Processor (AOP) responsible for real-time processing of observable information, an HPP Storage System for the efficient retention of low entropy hidden state representations, and a Multimodal Implicit Sensing/Execution Network designed to handle diverse sensory inputs and execute corresponding actions. By combining the GIN paradigm and GML system, our approach aims to create a holistic AGI system capable of encoding, processing, and reconstructing information in a manner akin to human-like intelligence. The synergy of information entropy principles and generative networks, along with the orchestrated functioning of the GML system, presents a promising avenue towards achieving advanced cognitive capabilities in artificial systems. This preliminary concept lays the groundwork for further exploration and refinement in the pursuit of true brain-like intelligence in machines.
Who dominates the destiny of the world, humans or artificial intelligence (AI)? This question strikes at the very heart of contemporary humanity's existential anxieties about its future. If we want to seriously consider whether or not unfriendly AI 'neurons' pose any threat to human civilisation and humanity's continual existence and evolution in the Universe, we need to know as much as possible about the Universe in which we find ourselves, our place in it, and what cognition, consciousness and mentality really are. How might we combine philosophical, cognitive science and technological perspectives, to explore the evolving relationship between humans and AI, in order to engage and address the questions at the core of this human-AI complex, namely the future of civilisation-what will it look like, who can claim to be our successors, towards what goals and ends? The evolution and development of human cognition as well as the emergence of AI can help us define these potential paths of future development. Where do we stand today, in relation to our own history and development and to the possibilities that artificial intelligence can offer us? The essay explores the ethical, social and existential questions that arise from the increasing automation of artificial intelligence and how it relates to the story of humanity, from its origins to its contemporary cultural expression. It also underscores the significance of holistic approaches to apprehending and addressing the risks that come with AI development. Such approaches should combine findings from various fields namely philosophy, morality, psyche and technology so as to manage a complicated set of problems. To sum up, this summary highlights the critical necessity for sophisticated viewpoints that go beyond simple man versus machine divisions. It is rather proposing a situation where humans use AI as an instrument for improving collective happiness and ensuring responsible management over technological advances and the larger life system.
 This paper introduces a novel robot parallel evolution design algorithm , leveraging the concept of a module network, to optimize the learning process of collision avoidance, approach, and wall switching behaviors in evolutionary robots. The proposed algorithm is validated and tested, demonstrating its efficacy in enabling evolutionary robots to autonomously exhibit behaviors such as collision avoidance, movement, repli-cation, and attack. The learning methodology focuses on refining the neural network-based strategies for collision avoidance, approach, and wall switching behaviors. The evolutionary robots, operating in a simulated environment, show-case the ability to adapt and enhance their performance over time. The simulation environment includes randomly generated rectangular obstacles with varying side lengths, strategically placed to represent real-world challenges. Additionally, the environment features randomly scattered approach targets, serving as goals for the robots. The modular design of the neural network allows for the integration of fundamental behaviors such as collision avoidance and approach, enabling a progressive enhancement of the robot’s capabilities. As the neural network evolves, the robots demonstrate an increasingly sophisticated ability to navigate their surroundings, avoid obstacles, approach targets, and adapt to dynamic scenarios. Through extensive simulations, the proposed algorithm proves effective in training evolutionary robots to navigate complex environments autonomously. The study contributes to the field of evolutionary robotics by presenting a modular neural network approach that enables the gradual acquisition and integration of diverse behaviors, showcasing the potential for autonomous and adaptive robotic systems in dynamic and challenging environments.Â
 In this paper, we delve into the historical and enduring algebraic conundrum known as the Two Couriers Problem, originally posed by the French mathematician Clairaut in 1746. Over the centuries, this problem has persisted, finding its way into numerous textbooks, journals, and mathematical discussions. One of the remarkable aspects of the Two Couriers Problem is its inherent connection to division by zero, a mathematical operation that has intrigued scholars for generations. Division by zero, a concept laden with complexity and ambiguity, has sparked diverse mathematical approaches. Conventional mathematics regards division by zero as an indeterminate or undefined result. However, alternative methodologies have emerged over time. Transmathematics defines division by zero as either nullity or explicitly positive or negative infinity, offering a different perspective. Saitoh simplifies division by zero as zero, challenging traditional conventions, while Barukˇci´c explores the possibility of defining it as either unity or explicitly positive or implicitly negative infinity. Amidst these varied approaches, the central question persists: which method offers the most effective solution to the enigma of division by zero? To answer this question, we propose utilizing the Two Couriers Problem as an objective benchmark. By subjecting these different mathematical approaches to this historical problem, we aim to rigorously evaluate their efficacy and determine which one stands out as the most viable solution. This paper seeks to unravel the complexities of division by zero through a systematic analysis, utilizing the Two Couriers Problem as a guiding light. By doing so, we endeavor to shed new insights on this age-old mathematical puzzle and contribute valuable perspectives to the ongoing discourse surrounding division by zero and its diverse interpretationÂ