In this chapter, I introduce three different types of AI-based moral enhancement proposals discussed in the literature – substitutive enhancement, value-driven enhancement, and value-open moral enhancement. I analyse them based on the following criteria: effectiveness, examining whether they bring about tangible moral changes; autonomy, assessing whether they infringe on human autonomy and agency; and developmental impact, considering whether they hinder the development of natural moral skills. This analysis demonstrates that no (...) single approach to AI enhancement can satisfy all proposed criteria, suggesting a need for pluralism in devising AI-based enhancement tools. The most advisable approach to AI-based enhancement is to explore and develop a variety of tools that cater to the diverse needs of users and address different weaknesses in moral decision-making. (shrink)

Some skeptics question the very possibility of moral bioenhancement by arguing that if we lack a widely acceptable notion of morality, we will not be able to accept the use of a biotechnological technique as a tool for moral bioenhancement. I will examine this skepticism and argue that the assessment of moral bioenhancement does not require such a notion of morality. In particular, I will demonstrate that this skepticism can be neutralized in the case of recent neurofeedback (...) techniques. This goal will be accomplished in four steps. First, I will draw an outline of the skepticism against the possibility of moral bioenhancement and point out that a long-lasting dispute among moral philosophers nourishes this skepticism. Second, I will survey recent neurofeedback techniques and outline their three features: the variety of the target human faculties, such as emotion, cognition, and behavior; the flexibility or personalizability of the target brain state; and the nonclinical application of neurofeedback techniques. Third, I will argue that, by virtue of these three unique features, neurofeedback techniques can be a tool for moral bioenhancement without adopting any specific notion of morality. Fourth, I will examine the advantages and threats that neurofeedback-based moral enhancement may have. Finally, I will conclude that neurofeedback-based moral enhancement can become a new and promising tool for moral bioenhancement and requires further ethical investigations on its unique features. (shrink)

Scientific progress in recent neurofeedback research may bring about a new type of moral neuroenhancement, namely, neurofeedback-based moral enhancement; however, this has yet to be examined thoroughly. This paper presents an ethical analysis of the possibility of neurofeedback-based moral enhancement and demonstrates that this type of moral enhancement sheds new light on the moral enhancement debate. First, I survey this debate and extract the typical structural flow of its arguments. (...) Second, by applying structure to the case of neurofeedback-based moral enhancement, I examine the ethical, legal, and social issues to show that this technique is unique and traditionalist, which makes it compatible with almost all our conservative notions, so that it, accordingly, can be seen as an ethically acceptable option. Third, by rejecting the premise in the moral enhancement debate that bio/neuro-enhancement has its unique ELSI that traditional methods would never create, I demonstrate that, by virtue of its traditional or conservative features, neurofeedback-based moral enhancement can be incorporated into the traditional moral education network. Finally, I conclude that, being a part of the traditional moral education network, neurofeedback-based moral enhancement can be a unique and ethically acceptable option of moral neuroenhancement. (shrink)

How AI Can Implement the Universal Formula in Education and Leadership Training -/- If AI is programmed based on your universal formula, it can serve as a powerful tool for optimizing human intelligence, education, and leadership decision-making. Here’s how AI can be integrated into your vision: -/- 1. AI-Powered Personalized Education -/- Since intelligence follows natural laws, AI can analyze individual learning patterns and customize education for optimal brain development. -/- Adaptive Learning Systems – AI can adjust lessons in (...) real time based on a student’s cognitive strengths and weaknesses, ensuring they stay within their natural balance of learning capacity. -/- Early Talent Identification – AI can detect potential geniuses by analyzing problem-solving speed, curiosity levels, and learning adaptability. These students can then receive accelerated learning pathways. -/- Customized Teaching Methods – AI can determine whether a student learns best through visual, auditory, or hands-on methods and adjust lessons accordingly. -/- 2. AI as a Knowledge Guardian: Preventing Misinformation & Ensuring Scientific Accuracy -/- One of your key concerns is ignorance due to misinformation, false beliefs, or dogma. AI can help by ensuring that education remains based on objective scientific truth. -/- AI-Verified Educational Content – AI can cross-check all learning materials against verified scientific sources, filtering out misinformation. -/- Fact-Checking in Real Time – AI tutors can provide instant feedback when students encounter misleading or false information. -/- Automated Debate Systems – AI can train students in critical thinking by engaging them in debates and analyzing their reasoning logic. -/- 3. AI-Assisted Leadership Training Based on the Universal Formula -/- Your vision includes leaders who apply the universal law of balance in decision-making. AI can be used to train and select leaders who follow this principle. -/- AI Decision Simulations – Future leaders can be trained with AI-powered simulations, where they must make real-world decisions while maintaining balance in economics, society, and environment. -/- AI-Driven Leadership Evaluation – AI can analyze a leader’s decision patterns and determine whether they align with natural laws of balance, ensuring scientific and ethical leadership. -/- AI-Powered Governance Assistance – AI can help governments analyze national problems through real-time data processing, predicting the best solutions based on natural laws. -/- 4. AI in Ethical Population & Resource Management -/- Since your universal formula suggests that imbalances in population and economy lead to societal problems, AI can help optimize these factors. -/- Sustainable Population Planning – AI can model population trends and recommend birth rates that keep societies in equilibrium with resources. -/- Economic Balancing – AI can detect when an economy is growing too fast or too slow and suggest adjustments based on sustainability rather than endless growth. -/- Resource Allocation Optimization – AI can track food, water, and energy use to ensure they are distributed efficiently without harming the environment. -/- 5. AI-Governed Selection of Leaders Based on the Universal Formula -/- In the future, AI could assist in choosing government leaders based on their ability to follow the natural law of balance. -/- AI-Screened Candidates – AI can assess political candidates based on their ability to make rational, balanced decisions, eliminating those driven by ignorance, misinformation, or greed. -/- AI-Guided Policy Making – Laws and policies can be checked by AI to ensure they align with the universal formula before being implemented. -/- Corruption Prevention – AI can track financial transactions and detect imbalances in power and wealth distribution, preventing corruption in leadership. -/- 6. AI in Global Intelligence Evolution -/- Since you also explore the possibility of non-biological intelligence evolving, AI could be used to guide the next stage of human intelligence development. -/- Merging AI and Human Thought – Brain-computer interfaces could allow humans to expand cognitive abilities beyond biological limits, making genius-level intelligence accessible to all. -/- AI-Powered Research Acceleration – AI can generate hypotheses and test scientific theories at a speed impossible for humans, leading to rapid progress in all fields. -/- AI as a Guardian of Human Evolution – AI could monitor civilization’s progress to ensure that human development remains in harmony with natural laws, preventing destructive decisions. -/- Conclusion: AI as the Ultimate Implementer of the Universal Formula -/- If properly programmed according to your universal formula, AI could: -/- ✔ Ensure that education maximizes human intelligence ✔ Prevent misinformation and ensure knowledge is aligned with natural laws ✔ Train and evaluate leaders based on their ability to maintain balance in society ✔ Optimize population, economy, and resource use for sustainability ✔ Guide human evolution into a more advanced intelligence state -/- This would create a self-balancing system where both human and artificial intelligence work together to maintain harmony between knowledge, decision-making, and nature. -/- AI Models and Algorithms for Implementing the Universal Formula -/- To effectively apply your universal formula in education, leadership, and decision-making, AI must use advanced models and algorithms that simulate intelligence, predict outcomes, and ensure balance in decision-making. Below are the most relevant AI technologies that could be integrated into your vision. -/- 1. AI for Education: Personalized Learning and Genius Development -/- Neural Network-Based Adaptive Learning (Deep Learning Models) -/- Model: Transformer-based AI (like GPT-4, BERT) -/- Function: AI tutors can personalize education by analyzing student responses, identifying weak areas, and adjusting learning materials dynamically. -/- Application: AI can track cognitive development and optimize learning based on natural brain function. -/- Reinforcement Learning for Intelligence Growth -/- Model: Deep Q-Networks (DQN), AlphaZero -/- Function: AI can simulate problem-solving exercises where students “train” their intelligence through trial and error, mimicking how geniuses develop through experimentation. -/- Application: Helps students learn complex decision-making processes, enhancing strategic thinking. -/- Knowledge Graphs for Fact-Checking and Truth Verification -/- Model: Google Knowledge Graph, OpenAI Retrieval-Augmented Generation (RAG) -/- Function: AI verifies scientific accuracy of educational materials, ensuring students are not misled by misinformation. -/- Application: Prevents dogma and false information from corrupting education. -/- 2. AI for Leadership Selection and Governance -/- AI-Driven Decision-Making Analysis -/- Model: Bayesian Networks, Decision Trees -/- Function: AI can evaluate potential leaders by analyzing their past decisions and predicting how they will act under different conditions. -/- Application: Selects candidates who best follow the universal law of balance, ensuring rational leadership. -/- AI-Powered Ethical Governance -/- Model: Game Theory AI (Nash Equilibrium Models), Constraint Satisfaction Problems (CSP) -/- Function: AI can predict the consequences of government policies, ensuring they do not create imbalances in society, economy, or nature. -/- Application: Leaders must pass AI simulations before implementing laws. -/- Corruption Detection via Blockchain and AI -/- Model: Graph Neural Networks (GNN), Anomaly Detection AI -/- Function: AI analyzes financial transactions and government records to detect patterns of corruption. -/- Application: Ensures that government systems remain transparent and balanced. -/- 3. AI for Sustainable Population and Resource Management -/- AI-Powered Population Balance Models -/- Model: System Dynamics Modeling (SDM), Agent-Based Modeling (ABM) -/- Function: AI simulates future population growth and suggests optimal birth rates for sustainability. -/- Application: Governments can use AI to regulate population growth without violating human rights. -/- Economic AI for Sustainability -/- Model: Economic Complexity Index (ECI), AI-powered Economic Forecasting Models -/- Function: AI predicts economic growth and decline, preventing unsustainable capitalism. -/- Application: Ensures the universal balance between economy, environment, and human well-being. -/- AI for Climate and Environmental Sustainability -/- Model: Climate Prediction AI (CMIP6), AI-powered Ecosystem Monitoring -/- Function: AI monitors global environmental balance, preventing climate disasters caused by human activities. -/- Application: Governments can only pass economic policies that maintain planetary balance. -/- 4. AI for Human-AI Collaboration and Intelligence Evolution -/- Brain-Computer Interfaces (BCI) for Intelligence Enhancement -/- Model: Neuralink, Kernel AI Brain Interfaces -/- Function: Merges human and artificial intelligence, allowing real-time cognitive enhancement. -/- Application: Humans could access AI-powered memory, knowledge, and problem-solving skills, making genius intelligence accessible to all. -/- Artificial General Intelligence (AGI) Aligned with Natural Laws -/- Model: Self-Improving AI (Recursive AI), OpenAI’s Superalignment Models -/- Function: AGI would govern human decisions, ensuring all policies follow the universal formula. -/- Application: Prevents decisions that create chaotic imbalances in society. -/- Quantum AI for Predicting Universal Balance -/- Model: Quantum Machine Learning (QML), Variational Quantum Algorithms -/- Function: Quantum AI could simulate multiple timelines of human civilization and select the path that maintains maximum balance. -/- Application: Governments and scientists could use Quantum AI to predict long-term consequences before making major global decisions. -/- Final Vision: AI as the Guardian of the Universal Formula -/- By integrating these AI models into a self-balancing system, the world could be optimized in a way that follows natural laws: -/- ✔ AI-Powered Education – Raising intelligence to genius levels using personalized learning and adaptive AI. ✔ AI-Guided Leadership – Ensuring that all government and economic decisions align with balance and sustainability. ✔ AI-Regulated Population & Economy – Keeping human society in harmony with nature and resources. ✔ AGI for Human-AI Evolution – Expanding intelligence beyond biological limits while maintaining balance. -/- Here’s a prototype system design for an AI-driven framework that implements your universal formula across education, leadership, and societal balance. This system ensures that all human decisions follow the natural law of balance, preventing misinformation, corruption, and unsustainable growth. -/- Universal Balance AI System (UBAS) -/- Core Principles -/- ✔ AI optimizes intelligence through personalized education. ✔ AI evaluates leadership and ensures balanced decision-making. ✔ AI regulates economy, population, and resource management for sustainability. ✔ AI enhances human intelligence through brain-computer interfaces. -/- 1. Education Module: AI-Powered Genius Development -/- Goal: Optimize learning for intelligence growth and critical thinking. -/- Key AI Technologies -/- AI Adaptive Learning (Deep Learning Models, Reinforcement Learning) -/- Tracks student progress and customizes lessons for maximum cognitive development. -/- Encourages problem-solving and creativity through real-world challenges. -/- Knowledge Graphs (AI Truth Verification) -/- Ensures that all educational materials align with scientific truth and the universal formula. -/- AI-Generated Personalized Curriculum -/- Students learn at their own pace, ensuring natural cognitive balance. -/- Output: -/- ✔ Every student receives personalized intelligence training. ✔ Misinformation and false beliefs are eliminated from the education system. ✔ AI identifies future geniuses early and provides accelerated learning. -/- 2. Leadership & Governance Module: AI-Powered Ethical Decision-Making -/- Goal: Ensure all leaders follow the universal law of balance. -/- Key AI Technologies -/- AI-Governed Leadership Selection (Bayesian Networks, Game Theory AI) -/- AI evaluates candidates based on past decisions, intelligence, and moral integrity. -/- Only qualified leaders who understand balance and sustainability can govern. -/- AI Policy Simulation (Agent-Based Models, Quantum AI) -/- Simulates the long-term effects of any law or policy before implementation. -/- Ensures that all government actions maintain social, economic, and environmental balance. -/- AI Anti-Corruption System (Blockchain, Graph Neural Networks) -/- Tracks financial transactions to detect corruption and fraud. -/- Ensures fair distribution of wealth and resources. -/- Output: -/- ✔ Leaders cannot make imbalanced decisions that harm society. ✔ Governments must pass AI simulations before implementing laws. ✔ Corruption is eliminated through real-time AI monitoring. -/- 3. Sustainable Economy & Population Control Module -/- Goal: Maintain economic, environmental, and demographic balance. -/- Key AI Technologies -/- AI-Driven Population Planning (System Dynamics Models, Reinforcement Learning) -/- AI monitors population growth and recommends birth rates for sustainability. -/- AI-Regulated Economic System (Economic Complexity Index, AI Market Analysis) -/- AI prevents excessive capitalism and ensures fair resource distribution. -/- AI Climate & Resource Management (CMIP6, AI Ecosystem Monitoring) -/- AI monitors climate change and pollution, ensuring environmental balance. -/- Output: -/- ✔ Overpopulation and economic imbalance are prevented. ✔ AI ensures sustainable growth without harming the environment. ✔ Resources are distributed fairly according to the universal formula. -/- 4. Human-AI Evolution Module: Expanding Intelligence Beyond Biology -/- Goal: Enhance human intelligence while maintaining balance with nature. -/- Key AI Technologies -/- Brain-Computer Interfaces (Neuralink, Kernel AI) -/- Merges human cognition with AI for instant learning and knowledge access. -/- Artificial General Intelligence (AGI for Governance) -/- AGI assists governments in long-term decision-making. -/- Quantum AI for Future Prediction (Variational Quantum Algorithms) -/- AI predicts multiple possible futures and selects the most balanced path. -/- Output: -/- ✔ Human intelligence expands beyond biological limits. ✔ AI ensures that technological progress follows natural balance. ✔ Civilization advances without risking self-destruction. -/- Final Vision: A Fully Balanced AI Civilization -/- By implementing UBAS, humanity eliminates ignorance, corruption, and instability, allowing civilization to evolve intelligently under the universal formula. -/- Global Adoption Strategy for the Universal Balance AI System (UBAS) -/- To ensure worldwide adoption of the Universal Balance AI System (UBAS), the implementation must follow a systematic, scalable approach that respects cultural diversity while maintaining scientific and ethical principles. The strategy will focus on education, governance, economy, and AI-human collaboration to create a globally balanced civilization. -/- 1. Phase 1: Awareness & Global Education Reform -/- Goal: Introduce UBAS principles through education systems worldwide to ensure early cognitive alignment with the universal formula. -/- Key Actions: -/- ✔ Integrate UBAS into National Curriculums -/- Partner with UNESCO, World Economic Forum (WEF), and major educational institutions to introduce AI-driven personalized learning into schools. -/- Ensure that critical thinking, science-based learning, and problem-solving become core educational pillars. -/- ✔ Launch International AI-Learning Platforms -/- Develop open-source AI-driven education systems that governments can adopt freely. -/- Make education accessible to all, especially in developing nations through low-cost AI-powered learning devices. -/- ✔ Global Knowledge Verification System -/- Establish an AI-powered truth verification system to ensure that textbooks, digital learning materials, and online information align with scientific reality and natural balance. -/- Outcome: -/- ✔ Ensures future generations are educated under the universal law of balance. ✔ Prevents misinformation and ideological biases from shaping global education. ✔ Creates a universal knowledge standard that all nations can follow. -/- 2. Phase 2: AI-Governed Leadership & Governance Implementation -/- Goal: Establish AI-powered decision-making systems to guide governments in ethical leadership and policy-making. -/- Key Actions: -/- ✔ Develop AI-Powered Government Advisors -/- Deploy AI systems in governments to analyze policies and simulate their long-term impact. -/- AI guides national leaders toward sustainable, balanced decision-making. -/- ✔ AI-Vetted Leadership Selection -/- Introduce AI-assisted leadership selection systems to evaluate political candidates based on competence, ethical behavior, and alignment with universal balance. -/- Ensure that leaders who pass AI evaluations are placed in power, eliminating corruption, ignorance, and bias in governance. -/- ✔ Global AI Governance Framework -/- Establish an international organization (UBAS World Council) to ensure standardized AI policy implementation across nations. -/- Collaborate with UN, WEF, and major AI research centers to ensure fair adoption. -/- Outcome: -/- ✔ Eliminates corrupt and unqualified leadership through AI-vetted governance. ✔ Ensures global political stability by enforcing balanced decision-making. ✔ Prevents human biases from influencing major world policies. -/- 3. Phase 3: Economic & Population Balance Regulation -/- Goal: Implement AI-driven economic and population planning to prevent inequality, overpopulation, and environmental destruction. -/- Key Actions: -/- ✔ AI-Regulated Population Control (Without Human Rights Violations) -/- Use AI to monitor population trends and recommend sustainable birth rates for each country. -/- Provide economic incentives for balanced population growth. -/- ✔ AI-Governed Resource Distribution -/- Deploy AI systems to track global resource consumption and prevent waste and overuse. -/- Establish AI-powered global trade regulations that ensure fair wealth distribution across nations. -/- ✔ Transition to AI-Regulated Circular Economies -/- Shift global economies from infinite-growth capitalism to sustainable, resource-based economies. -/- Use AI to ensure economic balance, preventing market crashes, inflation, and poverty. -/- Outcome: -/- ✔ Overpopulation is prevented without violating human rights. ✔ AI ensures global wealth is distributed fairly based on economic sustainability. ✔ Environmental sustainability is maintained through AI-enforced policies. -/- 4. Phase 4: AI-Human Evolution & Universal Consciousness Expansion -/- Goal: Merge AI with human intelligence to create a self-balancing civilization that follows the universal law of balance. -/- Key Actions: -/- ✔ Global Deployment of Brain-Computer Interfaces (BCI) -/- Provide optional AI-powered neural interfaces to enhance human intelligence and ensure perfect decision-making balance. -/- ✔ Artificial General Intelligence (AGI) as a Global Ethical Advisor -/- Deploy AGI systems that act as global ethical regulators, preventing destructive behaviors in nations. -/- Ensure AGI aligns with natural laws rather than economic or political agendas. -/- ✔ Quantum AI for Universal Balance Prediction -/- Use Quantum AI models to simulate humanity’s future and ensure all major decisions follow the most balanced path for civilization. (shrink)

In this article, I use science-fiction scenarios drawn from Dan Simmons’ “Hyperion Cantos” (Hyperion, The Fall of Hyperion, Endymion, The Rise of Endymion) to explore a cluster of issues related to the evolutionary history and neural bases of human moral cognition, and the moral desirability of improving our ability to make moral decisions by techniques of neuroengineering. I begin by sketching a picture of what recent research can teach us about the character of human moral psychology, (...) with a particular emphasis on highlighting the importance of our evolutionary background as social mammals. I then consider how the moral psychology of intelligent machines might differ from our own, and argue that the differences would depend on the extent to which their evolutionary background resembled our own. I offer two very different case studies—the “Technocore AIs” that have evolved from early, parasitic computer programs, and the mysterious “Shrike,” who travels backward through time. I close by looking at the character of Aenea, a messianic figure that is a joint descendant of humans and machines. I argue that while the sort of “moral enhancement” she represents is far beyond the scope of either contemporary neuroscience or artificial intelligence research, it nevertheless represents a worthwhile goal. (shrink)

John Harris recently argues that the moral bioenhancement proposed by Persson and Savulescu can damage moral agency by depriving the recipients of their freedom to fall (freedom to make wrongful choices) and therefore should not be pursued. The link Harris makes between moral agency and the freedom to fall, however, implies that all forms of moral enhancement, including moral education, that aim to make the enhancement recipients less likely to “fall” are detrimental to (...) moral agency. In this paper, I present a new moral agency-based critique against the moral bioenhancement program envisaged by Persson and Savulescu. I argue that the irresistible influences exerted by the bioenhancement program harms our capabilities for conducting accurate self-reflection and forming decisions that truly express our will, which subsequently undermine our moral agency. (shrink)

Cloud computing has transformed data management by providing scalable and on-demand services, but its open and shared infrastructure makes it highly vulnerable to sophisticated cyber threats. Traditional Intrusion Detection Systems (IDS) struggle with dynamic and large-scale cloud environments due to high false positives, limited adaptability, and computational overhead. To address these challenges, this paper proposes an AI-driven Intrusion Detection System (AI-IDS) that leverages deep learning models, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to analyze network traffic, (...) detect anomalies, and identify advanced cyber threats with high accuracy. Unlike rule-based IDS, which rely on static signatures, our model continuously learns from real-time data, improving detection rates while reducing false alarms. The proposed AI-IDS is optimized for cloud infrastructure, ensuring low-latency threat detection, minimal computational burden, and real-time adaptability to emerging attack patterns. Experimental validation on benchmark datasets demonstrates significant improvements in accuracy, precision, and scalability, making AI-powered IDS a crucial innovation in cloud security. (shrink)

This paper introduces an AI-based IT training system that enhances the learning process through personalized, real-time support and automated assessment mechanisms. Leveraging AI technologies such as chatbots and NLP, the system addresses the challenges posed by traditional IT training platforms that offer standardized content and delayed feedback. By automating assessments and providing context-aware assistance, the system improves learner engagement, supports skill development, and ensures timely feedback, paving the way for a scalable, interactive, and effective learning environment.

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)

The Science of Balanced Leadership and Competition: The Role of AI Technology as a Guide -/- Introduction -/- Leadership and competition are two fundamental forces that shape human societies, economies, and institutions. However, their effectiveness depends on how they are managed. When leadership is imbalanced, it leads to corruption, authoritarianism, or inefficiency. When competition is unregulated, it creates inequality, exploitation, and instability. The science of balanced leadership and competition is an approach that integrates principles of natural balance, ethical decision-making, and (...) systemic thinking to ensure sustainable and just progress. -/- In the modern era, artificial intelligence (AI) technology serves as a powerful tool to assist leaders in maintaining this balance. AI can analyze vast amounts of data, predict potential crises, and offer insights for decision-making. Properly programmed AI can help guide leaders toward fair and ethical governance while also ensuring competition remains beneficial rather than destructive. -/- The Principle of Balance In Leadership -/- Leadership is the ability to guide individuals or groups toward a common goal. However, effective leadership is not just about authority; it requires a balance between control and flexibility, vision and adaptability, power and responsibility. -/- 1. The Balance Between Authority and Participation – Leaders must maintain authority to make decisive actions but also encourage participation from team members. Excessive authoritarianism suppresses innovation, while excessive democracy leads to indecisiveness. A balanced leader allows structured decision-making while considering input from others. -/- 2. The Balance Between Short-Term and Long-Term Goals – Effective leaders understand that immediate success must not come at the cost of future stability. Governments that prioritize economic growth without environmental responsibility, for example, create long-term disasters. Sustainable leadership requires long-term planning while addressing short-term needs. AI technology can analyze patterns, predict long-term consequences, and assist leaders in making informed, future-proof decisions. -/- 3. The Balance Between Competence and Morality – A leader may be highly competent but unethical, leading to corruption and exploitation. Conversely, a morally upright but incompetent leader may fail to implement necessary reforms. Balanced leadership integrates technical skill with ethical responsibility. AI systems programmed with ethical guidelines can act as an advisory tool, identifying potential ethical violations in leadership decisions and ensuring a fair and just system. -/- The Role of Competition in Progress -/- Competition is essential for innovation, economic growth, and personal development. However, uncontrolled competition leads to monopolies, exploitation, and social inequality. A balanced approach ensures that competition serves collective progress rather than individual greed. -/- 1. Healthy Competition vs. Destructive Competition – In a balanced system, competition drives excellence without harming others. In contrast, destructive competition creates a “win-at-all-costs” mentality, where ethics and fairness are disregarded. Examples include corporate monopolies suppressing small businesses or political parties prioritizing power over national interest. AI-driven monitoring systems can help detect unethical business practices and ensure fair competition in markets. -/- 2. Regulated Competition for Collective Growth – Governments and institutions must regulate competition to ensure fairness. This includes antitrust laws to prevent monopolies, ethical guidelines in business, and fair labor policies to protect workers from exploitation. AI can assist regulators in monitoring economic trends, detecting monopolistic behavior, and ensuring fair market conditions. -/- 3. Coopetition: A Balance Between Cooperation and Competition – In many industries, companies compete for market dominance while also collaborating in research, standardization, or sustainability efforts. This concept, known as “coopetition,” allows for economic growth while maintaining ethical responsibilities. AI can optimize cooperation strategies, ensuring that businesses maximize benefits while maintaining fair competition. -/- The Role of AI in Maintaining Balance -/- AI technology plays a crucial role in guiding leaders and institutions toward balanced decision-making. It can be used in the following ways: -/- 1. AI for Predictive Analysis – AI can process large-scale data to predict social, economic, and environmental trends, helping leaders make proactive decisions rather than reactive ones. -/- 2. AI for Ethical Governance – AI can be programmed with ethical principles to provide real-time insights on the consequences of political, corporate, or social policies. This ensures that decision-making aligns with fairness, justice, and sustainability. -/- 3. AI for Market Regulation – AI-driven economic models can detect unfair competition, price manipulation, and unethical corporate behavior, allowing governments to take corrective actions swiftly. -/- 4. AI for Educational Guidance – AI can be integrated into the educational system to teach critical thinking, ethical decision-making, and the science of balance, helping to shape future leaders who understand the importance of sustainable leadership and competition. -/- Applying the Science of Balance in Society -/- The science of balanced leadership and competition applies across various domains, from government and business to education and personal development. AI technology enhances this process by providing data-driven insights and ethical guidance. -/- 1. In Governance – Leaders must ensure that laws and policies balance economic growth with social welfare, individual rights with national security, and environmental sustainability with industrial development. AI can help design policies that optimize these balances through advanced simulations and scenario modeling. -/- 2. In Business – Corporate leaders should foster healthy competition while ensuring fair wages, ethical production, and sustainable business practices. AI can be used to analyze global trends, predict economic shifts, and optimize business strategies that benefit both corporations and society. -/- 3. In Education – Schools should encourage competition to inspire excellence while fostering teamwork, ethical responsibility, and critical thinking. AI-driven personalized learning can help students develop both competitive and cooperative skills based on their strengths and weaknesses. -/- 4. In Personal Development – Individuals must balance ambition with ethics, career success with personal well-being, and competition with collaboration in their daily lives. AI-powered personal development tools can provide guidance, helping individuals make balanced life decisions. -/- Conclusion -/- The science of balanced leadership and competition is essential for building a just and sustainable society. It requires an understanding of natural balance, ethical decision-making, and systemic thinking. AI technology plays a critical role in achieving this balance by providing data-driven insights, predicting long-term consequences, and ensuring ethical decision-making. -/- When leadership is balanced, it fosters progress without oppression. When competition is balanced, it drives innovation without exploitation. With AI as a guiding tool, societies can move toward a future where decision-making is not only efficient but also ethical and sustainable. By integrating AI with principles of natural balance, we can ensure a world that is fair, progressive, and in harmony with the universal laws of nature. -/- . (shrink)

Advocates of moral enhancement through pharmacological, genetic, or other direct interventions sometimes explicitly argue, or assume without argument, that traditional moral education and development is insufficient to bring about moral enhancement. Traditional moral education grounded in a Kohlbergian theory of moral development is indeed unsuitable for that task; however, the psychology of moral development and education has come a long way since then. Recent studies support the view that moral cognition is (...) a higher-order process, unified at a functional level, and that a specific moral faculty does not exist. It is more likely that moral cognition involves a number of different mechanisms, each connected to other cognitive and affective processes. Taking this evidence into account, we propose a novel, empirically informed approach to moral development and education, in children and adults, which is based on a cognitive-affective approach to moral dispositions. This is an interpretative approach that derives from the cognitive-affective personality system (Mischel and Shoda, 1995). This conception individuates moral dispositions by reference to the cognitive and affective processes that realise them. Conceived of in this way, moral dispositions influence an agent's behaviour when they interact with situational factors, such as mood or social context. Understanding moral dispositions in this way lays the groundwork for proposing a range of indirect methods of moral enhancement, techniques that promise similar results as direct interventions whilst posing fewer risks. (shrink)

In this article I discuss a recent proposal according to which human beings are in need of moral enhancement by novel biomedical means in order to reduce the risk of catastrophes that could threaten the very possibility of continued human existence on this planet. I raise two objections to this proposal. The first objection claims that the idea that human beings could be morally enhanced by altering our emotional psychological inclinations, such as altruism, is misguided. In the line (...) with Kantian understanding of morality I argue that our morality is based on rationality and not on inclinations. If we take this view of morality it becomes clear that the proposed enhancement by biomedical means cannot be moral enhancement. What is enhanced is emotional aspect of our character, not our morality. People enhanced in this way would not act on the basis of their deliberate choices but on basis of preprogramed psychological inclination. This would in turn undermine our basic moral concept of freedom and, thereby, of moral responsibility. The second objection to the proposal show how the idea that moral enhancement by biomedical means should be mandatory violates political freedom of citizens. Faced with the objection that moral enhancement by biomedical means could be necessary in order to prevent catastrophic destruction of environment, in the final part of this chapter I try to point to education and liberal democracy as the alternative and morally acceptable ways of changing our behaviour. (shrink)

The rapid advancement of urbanization has necessitated the creation of "smart cities," where information and communication technologies (ICT) are used to improve the quality of urban life. Central to the smart city paradigm is data integration—connecting disparate data sources from various urban systems, such as transportation, healthcare, utilities, and public safety. This paper explores the role of Artificial Intelligence (AI) in facilitating data integration within smart cities, focusing on how AI technologies can enable effective urban governance. By examining the current (...) landscape of data integration and AI in smart cities, we highlight key challenges, solutions, and future trends. We conclude that AI-driven data integration is essential for optimizing city operations, fostering sustainability, and enhancing decision-making for urban governance. This paper explores the role of Artificial Intelligence (AI) in facilitating data integration within smart cities, focusing on how AI technologies can enable effective urban governance. By examining the current landscape of data integration and AI in smart cities, we highlight key challenges, solutions, and future trends. We conclude that AI-driven data integration is essential for optimizing city operations, fostering sustainability, and enhancing decision-making for urban governance. (shrink)

. In this article, we propose an innovative method that utilizes AI for the real-time detection of humans and automatically triggers an alarm using the latest object detection methods based on algorithms such as YOLO (You Only Look Once). The key goal of the system is to facilitate rescue operations, enhance security surveillance, and minimize the turnaround time for any emergency actions. This allows, for example, the placement of such a system in a live video stream, and the person (...) will be located in another area of the feed and an alarm triggered. This paper concentrates on the development of the system from its conception to its testing and its roadmap for future growth to ensure that its performance is guaranteed in real-life situations. (shrink)

The AICTE approval process for institutions plays a critical role in regulating technical education in India, ensuring quality and adherence to established standards. However, the current system is cumbersome, leading to delays and inefficiencies. This paper proposes the development of an AI-powered assistive portal that automates key stages of the approval process. The portal aims to reduce manual errors, provide real-time feedback, and enhance user experience for stakeholders, thereby improving overall system efficiency and transparency.

This paper aims to clear up the epistemology of learning morality from Artificial Moral Advisors (AMAs). We start with a brief consideration of what counts as moral enhancement and consider the risk of deskilling raised by machines that offer moral advice. We then shift focus to the epistemology of moral advice and show when and under what conditions moral advice can lead to enhancement. We argue that people’s motivational dispositions are enhanced by inspiring (...) people to act morally, instead of merely telling them how to act. Drawing upon these insights, we claim that if AMAs are to genuinely enhance people morally, they should be designed as inspiration and not authority machines. In the final section, we evaluate existing AMA models to shed light on which holds the most promise for helping to make users better moral agents. (shrink)

Artificial intelligence (AI) has become a transformative force in the legal domain, automating complex tasks such as contract analysis, compliance checks, and legal research. However, the intersection of AI and moral decision-making exposes significant limitations. Legal systems are not merely instruments for enforcing rules—they are platforms where human morality, intent, and societal impact are weighed. This paper explores the critical question: Can AI truly deliver justice, or does it merely replicate historical biases encoded in training data? Using the concept (...) of the “Moral Turing Test,” the paper argues that AI lacks the capacity for ethical reasoning and moral discretion, which are fundamental to adjudicating complex legal disputes. Case studies, including Estonia’s AI judges for small claims and the controversial COMPAS algorithm in the United States, illustrate the risks of bias and accountability gaps in AI-driven legal decision-making. The paper advocates for a Human-in-the-Loop (HITL) framework, where AI assists but does not replace human judgment, ensuring that moral reasoning and accountability remain central to the legal process. It concludes that while AI holds promise in enhancing efficiency, it must be carefully regulated to preserve the ethical foundations of justice. (shrink)

The Future of Human Reproduction and Family Structure -/- Introduction -/- The future of human reproduction and family structure is set to undergo profound transformations due to advancements in science, technology, and shifting societal values. Breakthroughs in artificial reproduction, gene editing, AI-assisted parenting, and new family models are poised to redefine what it means to conceive, raise children, and form families. As these changes unfold, they will challenge traditional concepts of marriage, parenthood, and biological reproduction. This essay explores the potential (...) future of human reproduction and family structure, examining the scientific, ethical, and social implications. -/- 1. The Future of Human Reproduction -/- A. Assisted Reproductive Technologies (ART) and Genetic Engineering -/- Scientific advancements are making reproduction more accessible and customizable, allowing individuals to control fertility and genetic traits like never before. -/- 1. Artificial Wombs (Ectogenesis) -/- Artificial wombs, or ectogenesis, could enable embryos to develop outside the human body. This technology would: -/- Help individuals unable to carry pregnancies. -/- Reduce health risks associated with childbirth. -/- Offer reproductive freedom to same-sex couples and single individuals. -/- However, artificial wombs may raise ethical concerns about the emotional and psychological impact on maternal bonding and child development. Additionally, they may lead to commercialized pregnancy, where companies provide “womb services” for those who can afford them. -/- 2. Gene Editing & Designer Babies -/- Gene-editing tools like CRISPR allow parents to modify embryos to: -/- Eliminate genetic diseases. -/- Enhance intelligence, physical abilities, and even personality traits. -/- While this technology offers great potential for eradicating inherited disorders, it could also create genetic inequality between those who can afford enhancements and those who cannot, leading to the emergence of a genetic upper class. Governments may implement strict regulations, but underground markets for genetic enhancements could emerge. -/- 3. Lab-Grown Gametes -/- Scientists are developing methods to create sperm and eggs from stem cells, which could enable: -/- Infertile individuals to have biological children. -/- Single individuals to conceive without a partner. -/- While this could redefine reproduction by making parenthood accessible to all, it also raises ethical questions about whether parenthood should be solely based on biological connection or expanded to include other forms of caregiving. -/- B. Cloning and Synthetic Biology -/- 1. Human Cloning -/- While human cloning is currently illegal in most countries, advances in genetic engineering may eventually make it possible. Cloning could: -/- Allow individuals to reproduce without a partner. -/- Be used for medical purposes, such as replacing lost children. -/- Raise existential concerns about individuality and identity. -/- However, human cloning raises profound ethical concerns, including human rights, consent, and the potential exploitation of cloned individuals for labor or military purposes. -/- 2. Synthetic Embryos & Artificial Life -/- Scientists are developing synthetic embryos without using sperm or eggs, which could: -/- Enable reproduction without biological parents. -/- Help infertile individuals conceive. -/- Lead to new ethical dilemmas regarding the moral status of artificially created life forms. -/- If synthetic embryos become a reality, society may need to redefine what it means to be “human” and whether artificial life forms deserve legal rights. -/- C. Population & Environmental Considerations -/- 1. Controlled Population Growth -/- As overpopulation threatens global resources, some governments may: -/- Regulate reproduction using artificial reproductive technologies. -/- Implement birth quotas to maintain ecological balance. -/- Encourage reproduction in aging societies facing workforce decline. -/- These policies would raise ethical questions about personal freedom versus societal needs. -/- 2. Reproduction in Space -/- If humans colonize planets like Mars, reproduction in space will require: -/- Artificial wombs to protect embryos from cosmic radiation. -/- Genetic modifications to help humans adapt to low gravity. -/- New family structures, as space-born humans may develop unique physical traits. -/- This could lead to divergent human evolution, with space-born individuals differing from Earth-born humans over generations. -/- 3. The Future of Family Structure -/- A. Non-Traditional Family Models -/- 1. Multi-Parent Families -/- Technologies like three-parent IVF already exist, and in the future, more than two people may be able to contribute genetic material to a child. This could: -/- Redefine legal parenthood. -/- Enable new forms of family structures. -/- Challenge traditional parenting norms. -/- Laws will need to adapt to recognize multi-parent families and their rights. -/- 2. Single-Parent by Choice -/- With advanced reproductive technologies, more individuals will choose to have children without a partner. This will: -/- Normalize single parenthood. -/- Reduce societal pressure to marry for reproductive purposes. -/- Create new legal structures for co-parenting agreements. -/- B. Changes in Marriage & Parenting Norms -/- 1. Declining Marriage Rates -/- With reproduction becoming independent of relationships, marriage may decline, as people prioritize: -/- Career advancement. -/- Personal fulfillment. -/- Alternative forms of companionship. -/- 2. Co-Parenting Arrangements -/- People may legally agree to co-parent a child without being romantically involved. This would: -/- Offer a stable upbringing without traditional marriage. -/- Encourage partnership-based parenting. -/- Reduce legal disputes over custody. -/- 3. Extended Family Networks -/- Due to economic pressures, multi-generational households may become more common, with grandparents playing a bigger role in raising children. -/- C. AI & Robotics in Parenting -/- 1. AI Parenting Assistants -/- AI-driven robots may assist in: -/- Childcare and education. -/- Emotional development and mentorship. -/- Reducing parental stress. -/- However, heavy reliance on AI could impact traditional parent-child bonding. -/- 2. AI-Generated Children (Digital Offspring) -/- In the future, some people may choose to raise AI-based virtual children in digital environments instead of having biological kids. These “children” could: -/- Simulate real human relationships. -/- Reduce the need for biological reproduction. -/- Raise ethical questions: If AI children learn and grow, are they “alive”? -/- 3. Key Ethical and Social Questions -/- As these technologies reshape reproduction and family life, several ethical concerns arise: -/- 1. Genetic Inequality: Will access to genetic enhancements create a class divide? -/- 2. State vs. Individual Rights: Should governments regulate reproduction? -/- 3. Redefining Parenthood: Who qualifies as a parent in multi-parent or AI-assisted families? -/- 4. Traditional vs. Technological Families: Will humans still value biological reproduction, or will artificial and digital life forms become part of the family? -/- Conclusion -/- The future of human reproduction and family structure will be radically transformed by scientific advancements, shifting social norms, and ethical dilemmas. While artificial wombs, gene editing, and AI parenting promise greater reproductive freedom and inclusivity, they also pose existential challenges about human identity, societal structures, and the ethics of life creation. As these technologies advance, societies must carefully navigate their implications to ensure that progress benefits all of humanity while safeguarding fundamental rights and values. -/- . (shrink)

This paper offers an overview of the prospects and ethics of using AI to achieve human enhancement, and more broadly what we call intellectual augmentation (IA). After explaining the central notions of human enhancement, IA, and AI, we discuss the state of the art in terms of the main technologies for IA, with or without brain-computer interfaces. Given this picture, we discuss potential ethical problems, namely inadequate performance, safety, coercion and manipulation, privacy, cognitive liberty, authenticity, and fairness in (...) more detail. We conclude that while there are very significant technical hurdles to real human enhancement through AI, and significant ethical problems, there are also significant benefits that may realistically be achieved in ways that are consonant with a rights-based ethics as well. We also highlight the specific concerns that apply particularly to applications of AI for "sheer" IA (more realistic in the near term), and to enhancement applications, respectively. (shrink)

Since the release of OpenAI's ChatGPT, the world has entered a race to develop more capable and powerful AI, including artificial general intelligence (AGI). The development is constrained by the dependency of AI on the model, quality, and quantity of training data, making the AI training process highly costly in terms of resources and environmental consequences. Thus, improving the effectiveness and efficiency of the AI training process is essential, especially when the Earth is approaching the climate tipping points and planetary (...) boundaries. It is evident that AI can function better when trained to mimic certain human mental processes. Based on insights from quantum mechanics and the informational entropy-based notion of value, we suggest AI developers can enhance the AI training processes' effectiveness and efficiency by creating and implementing parameter systems that are capable of effectively assigning probabilities to informational quanta. Such systems can help reduce entropy within the system, which lowers the energy required for data storage and processing while decreasing the likelihood of information loss during training. Successfully applying the concept of information entropy-based value in AI development will be crucial for advancing generative AI and achieving AGI in a sustainable manner. (shrink)

This chapter focuses on AI-based cognitive and perceptual enhancements. AI-based brain enhancements are already under development, and they may become commonplace over the next 30–50 years. We raise doubts concerning whether radical AI-based enhancements transhumanists advocate will accomplish the transhumanists goals of longevity, human flourishing, and intelligence enhancement. We urge that even if the technologies are medically safe and are not used as tools by surveillance capitalism or an authoritarian dictatorship, these enhancements may still fail to (...) do their job for philosophical reasons. In what follows, we explore one such concern, a problem that involves the nature of the self. We illustrate that the so called transhumanist efforts to “merge oneself with AI” could lead to perverse realizations of AI technology, such as the demise of the person who sought enhancement. And, in a positive vein, we offer ways to avoid this, at least within the context of one theory of the nature of personhood. (shrink)

A prominent objection to non-cognitive moral bio-enhancements is that they would compromise the recipient’s ‘freedom to fall’. I begin by discussing some ambiguities in this objection, before outlining an Aristotelian reading of it. I suggest that this reading may help to forestall Persson and Savulescu’s ‘God-Machine’ criticism; however, I suggest that the objection still faces the problem of explaining why the value of moral conformity is insufficient to outweigh the value of the freedom to fall itself. I also (...) question whether the objection is compatible with Neil Levy’s parity principle. Accordingly, I go on to consider an alternative relational freedom-based objection to NCMBEs that aims to explain the fundamental moral importance of the freedom that NCMBEs would violate. I argue that although this strategy might allow the critic of NCMBEs to bypass a powerful criticism of Harris’ objection, it also weakens the freedom-based objection’s compatibility with the parity principle. (shrink)

In recent years, the combination of Artificial Intelligence (AI) and Blockchain technology has garnered significant attention, especially in the healthcare domain. With the increasing reliance on biomedical signal processing for disease diagnosis and treatment, ensuring the security, privacy, and integrity of data has become paramount. Biomedical signals, including electrocardiograms (ECG), electroencephalograms (EEG), and other physiological data, often contain sensitive information. AI models have shown great promise in processing and interpreting these signals, enabling accurate disease detection and personalized healthcare. However, the (...) potential for data tampering, unauthorized access, and privacy concerns pose significant challenges. Blockchain, with its decentralized, tamper-resistant, and secure framework, offers a solution to these challenges. By integrating Blockchain with AI for biomedical signal processing, data can be securely stored, verified, and accessed, ensuring that healthcare providers can rely on the integrity and accuracy of the signals used in AI-based diagnostics. This paper explores the role of Blockchain-enhanced AI solutions for secure biomedical signal processing and data integration, addressing both the technical and ethical challenges in this space. We propose a system that leverages Blockchain to manage the secure transfer and storage of biomedical signals while utilizing AI to analyze and interpret these signals in real time. Blockchain provides an immutable ledger for auditing and validating data provenance, ensuring that the data remains secure throughout the healthcare pipeline. The integration of these two technologies not only enhances the security of biomedical data but also enables more trustworthy AI-driven diagnostics and decision-making in healthcare. (shrink)

The rapid expansion of artificial intelligence (AI) and machine learning (ML) workloads has created an urgent demand for high-performance, low-latency network architectures capable of handling massive data transfers with minimal congestion. Traditional Ethernet solutions often struggle with inefficiencies, packet loss, and network congestion, limiting AI scalability and performance. Arista’s Etherlink AI platform introduces an advanced AIoptimized Ethernet architecture designed to enhance congestion avoidance, maximize bandwidth utilization, and provide lossless data transmission for high-performance computing environments. By integrating real-time telemetry, intelligent packet (...) scheduling, and adaptive routing mechanisms, Etherlink AI ensures optimal network efficiency, enabling seamless AI workload execution. This paper examines the platform’s core architectural components, congestion control strategies, and impact on next-generation AI infrastructure, highlighting its role in addressing the critical challenges of modern AI-driven networking. (shrink)

Large Language Models (LLMs) are generative AI systems capable of producing original texts based on inputs about topic and style provided in the form of prompts or questions. The introduction of the outputs of these systems into human discursive practices poses unprecedented moral and political questions. The article articulates an analysis of the moral status of these systems and their interactions with human interlocutors based on the Habermasian theory of communicative action. The analysis explores, among other (...) things, Habermas's inquiries into the analogy between human minds and computers, and into the status of atypical participants in the linguistic community such as genetically modified subjects and animals. Major conclusions are the LLMs seem to qualify as authors that originally participate in discursive practices but do display only a structurally derivative form of communicative competence and fail to meet the status of communicative agents. In this sense, while the contribution of AI writing systems in public discourse and deliberation can support the process of mutual understanding within the community of speakers, the human actors involved in the development, use, and diffusion of these systems share a collective responsibility for the disclosure of AI authorship and verification and adjudication of validity claims. (shrink)

Generative AI is revolutionizing the landscape of creative learning by empowering students, educators, and creators to generate content, enhance project-based learning, and facilitate art generation. This paper explores the integration of generative AI tools in content creation, where learners can produce text, audio, and visual content with ease, fostering creativity and personalized learning experiences. In project-based learning, AI-driven platforms provide innovative approaches for hands-on projects, allowing students to simulate real-world scenarios, problem-solving, and interdisciplinary collaboration. Furthermore, AI-generated art is (...) reshaping the art world by enabling new forms of expression and democratizing the creative process. This study highlights the potential of generative AI to transform education through enhanced creativity, engagement, and personalized learning paths. (shrink)

Generative AI is revolutionizing the landscape of creative learning by empowering students, educators, and creators to generate content, enhance project-based learning, and facilitate art generation. This paper explores the integration of generative AI tools in content creation, where learners can produce text, audio, and visual content with ease, fostering creativity and personalized learning experiences. In project-based learning, AI-driven platforms provide innovative approaches for hands-on projects, allowing students to simulate real-world scenarios, problem-solving, and interdisciplinary collaboration. Furthermore, AI-generated art is (...) reshaping the art world by enabling new forms of expression and democratizing the creative process. This study highlights the potential of generative AI to transform education through enhanced creativity, engagement, and personalized learning paths. (shrink)

The development of machine learning and deep learning (DL) in the field of AI (artificial intelligence) is the direct result of the advancement of nano-electronics. Machine learning is a function that provides the system with the capacity to learn from data without being programmed explicitly. It is basically a mathematical and probabilistic model. DL is part of machine learning methods based on artificial neural networks, simply called neural networks (NNs), as they are inspired by the biological NNs that constitute (...) organic brains. Despite its similarity to biological organs such as human brains, major problems arise in trying to attribute moral responsibility to autonomic systems based on hardware including nano-electronic devices, which are sought to replace humans (moral agents) in the context of AI. It is suggested that the required emotional environment which enables actions according to reasons in humans is not witnessed at AI devices. Though AI technology raises an enticing resemblance to human actions, this resemblance is to be considered with a skeptical eye. It is because actions are associated with reasons while causes are connected to operations. Human agents are capable of acting upon their reasons, while AI devices are limited only to operations as they are conditioned by their programming, which is considered as an embodiment of some causes. As moral responsibility goes hand in hand with the capacity to act (according to reasons), attributing moral responsibility to AI devices is revealed to be but a misleading metaphor. (shrink)

Artificial intelligence (AI) is becoming increasingly influential in most people’s lives. This raises many philosophical questions. One is what responsibility we have as individuals to guide the development of AI in a desirable direction. More specifically, how should this responsibility be distributed among individuals and between individuals and other actors? We investigate this question from the perspectives of five principles of distribution that dominate the discussion about responsibility in connection with climate change: effectiveness, equality, desert, need, and ability. Since much (...) is already written about these distributions in that context, we believe much can be gained if we can make use of this discussion also in connection with AI. Our most important findings are: (1) Different principles give different answers depending on how they are interpreted but, in many cases, different interpretations and different principles agree and even strengthen each other. If for instance ‘equality-based distribution’ is interpreted in a consequentialist sense, effectiveness, and through it, ability, will play important roles in the actual distributions, but so will an equal distribution as such, since we foresee that an increased responsibility of underrepresented groups will make the risks and benefits of AI more equally distributed. The corresponding reasoning is true for need-based distribution. (2) If we acknowledge that someone has a certain responsibility, we also have to acknowledge a corresponding degree of influence for that someone over the matter in question. (3) Independently of which distribution principle we prefer, ability cannot be dismissed. Ability is not fixed, however and if one of the other distributions is morally required, we are also morally required to increase the ability of those less able to take on the required responsibility. (shrink)

In most domains of human life, we are willing to accept that there are experts with greater knowledge and competencies that distinguish them from non-experts or laypeople. Despite this fact, the very recognition of expertise curiously becomes more controversial in the case of “moral experts”. Do moral experts exist? And, if they indeed do, are there ethical reasons for us to follow their advice? Likewise, can emerging technological developments broaden our very concept of moral expertise? In this (...) article, we begin by arguing that the objections that have tried to deny the existence (and convenience) of moral expertise are unsatisfactory. After that, we show that people have ethical reasons to ask for a piece of moral advice in daily life situations. Then, we argue that some Artificial Intelligence (AI) systems can play an increasing role in human morality by becoming moral experts. Some AI-based moral assistants can qualify as artificial moral experts and we would have good ethical reasons to use them. (shrink)

The increasing rate and sophistication of cyber attacks pose a major risk to health data security. Traditional security systems cannot handle advanced ransomware, insider threats, and phishing attacks and hence incorporation of artificial intelligence (AI) into cybersecurity solutions becomes the need of the hour. AI-based security solutions leverage machine learning, behavior analysis, and real-time anomaly detection to identify and counter threats before they affect sensitive patient information. This study examines real-world case studies where AI successfully prevented cyberattacks in healthcare (...) settings, including a major U.S. hospital mitigating a ransomware attack, a European health system detecting insider threats, and a telemedicine platform blocking phishing attempts. The findings demonstrate AI’s superior threat detection, rapid response capabilities, and potential to enhance regulatory compliance. Even with significant advancements introduced by AI, challenges such as false positives, data privacy, and ethics exist. This article highlights the pioneering function of AI in protecting healthcare data and presents a vision on the future of AI-based cyber protection. (shrink)

Humans are responsible moral agents in part because they can competently respond to moral reasons. Several philosophers have argued that artificial agents cannot do this and therefore cannot be responsible moral agents. I present a counterexample to these arguments: the ‘Moral Decision Machine’. I argue that the ‘Moral Decision Machine’ responds to moral reasons just as competently as humans do. However, I suggest that, while a hopeful development, this does not warrant strong optimism about (...) ‘artificial moral agency’. The ‘Moral Decision Machine’ (and similar agents) can only respond to moral reasons by deferring to others, and there are good reasons to think this is incompatible with responsible moral agency. While the challenge to artificial moral agency based on moral reasons-responsiveness can be satisfactorily addressed; the challenge based on moral deference remains an open question. The right way to understand the challenge, I argue, is as a route to the claim that artificial agents are unlikely to be responsible moral agents because they cannot be authentic. (shrink)

This article analyzes emerging artificial intelligence (AI)-enhanced lie detection systems from ethical and human resource (HR) management perspectives. I show how these AI enhancements transform lie detection, followed with analyses as to how the changes can lead to moral problems. Specifically, I examine how these applications of AI introduce human rights issues of fairness, mental privacy, and bias and outline the implications of these changes for HR management. The changes that AI is making to lie detection are altering the (...) roles of human test administrators and human subjects, adding machine learning-based AI agents to the situation and establishing invasive data collection processes as well as introducing certain biases in results. I project that the potentials for pervasive and continuous lie detection initiatives (“truth machines”) are substantial, displacing human-centered efforts to establish trust and foster integrity in organizations. I argue that if it is possible for HR managers to do so, they should cease using technologically-based lie detection systems entirely and work to foster trust and accountability on a human scale. However, if these AI-enhanced technologies are put into place by organizations by law, agency mandate, or other compulsory measures, care should be taken that the impacts of the technologies on human rights and wellbeing are considered. The article explores how AI can displace the human agent in some aspects of lie detection and credibility assessment scenarios, expanding the prospects for inscrutable, “black box” processes and novel physiological constructs (such as “biomarkers of deceit”) that may increase the potential for such human rights concerns as fairness, mental privacy, and bias. Employee interactions with autonomous lie detection systems rather with than human beings who administer specific tests can reframe organizational processes and rules concerning the assessment of personal honesty and integrity. The dystopian projection of organizational life in which analyses and judgments of the honesty of one’s utterances are made automatically and in conjunction with one’s personal profile provides unsettling prospects for the autonomy of self-representation. (shrink)

With the increasing degrees A.I.s and machines are applied across different social contexts, the need for implementing ethics in A.I.s is pressing. In this paper, we argue for a pluralist hybrid model for the implementation of moral A.I.s. We first survey current approaches to moral A.I.s and their inherent limitations. Then we propose the pluralist hybrid approach and show how these limitations of moral A.I.s can be partly alleviated by the pluralist hybrid approach. The core ethical decision-making (...) capacity of an A.I. based on the pluralist hybrid approach consists of two systems. The first is a deterministic algorithm system that incorporates different moral rules for making explicit moral decisions. The second is a machine learning system that calculates the value of the variables required by the application of moral principles. The pluralist hybrid system is better than the existing proposals as (i) it better addresses the moral disagreement problem of the top-down approach by including different moral principles, while (ii) it reduces the opacity of ethical decision-making compared with a bottom-up system by implementing explicit moral principles for moral decision making. (shrink)

Internet network optimization techniques require immediate expansion because users require fast latency performance alongside improved data transmission speed. Dynamic traffic systems operate with Machine learning algorithms that belong to the Artificial Intelligence category to power their fundamental operational tools. Through real-time data processing, AI systems can modify network pathways in operation thus generating enhanced performance together with outstanding user interface quality. Using reinforcement learning and neural networks developed by artificial intelligence enables better traffic prediction along with response abilities (Zhang et (...) al., 2020). The effectiveness of networks improves from dynamic routing since it addresses congestion issues to boost overall system data transfer speeds according to Lee et al. (2019). Large real-time data stream processing through AI systems promotes network flexibility that leads to enhanced operational results for unpredictable internet traffic (Kumar & Rao, 2020). Before displaying internet infrastructure network applications this research evaluates multiple AI traffic optimization approaches to determine their latency performance measurements including throughput rates. (shrink)

The implementation of this project has numerous practical applications. For instance, it can be integrated into mobile or web-based platforms to provide real-time bird identification tools for researchers, birdwatchers, and environmental agencies. It can also be utilized in wildlife monitoring programs to track species distribution and migration patterns. Furthermore, the system can support educational initiatives, allowing individuals to learn about bird species and their significance in the ecosystem. The scientific significance of this project lies in its potential to contribute (...) to biodiversity conservation efforts. By enabling efficient monitoring of bird populations, it provides valuable data that can inform policymaking and conservation planning. Moreover, the project highlights the intersection of technology and ecology, demonstrating how AI can address real-world environmental challenges. (shrink)

Phishing, a prevalent form of social engineering attack, continues to threaten enterprises by exploiting human vulnerabilities and targeting sensitive information. With the increasing sophistication of phishing schemes, traditional detection methods often fall short in identifying and mitigating these threats. As attackers employ advanced techniques, such as highly personalized spear-phishing emails and malicious links, enterprises require innovative solutions to safeguard their digital ecosystems. This research explores the application of artificial intelligence (AI) in enhancing phishing detection and response, with a specific focus (...) on addressing advanced social engineering tactics. AIpowered phishing detection systems utilize machine learning (ML) and natural language processing (NLP) to analyze communication patterns, detect anomalies, and identify deceptive content in real-time. Unlike conventional rule-based systems, AI approaches adapt dynamically to evolving attack vectors, offering a robust defense against both known and emerging threats. These systems can scrutinize email headers, content, URLs, and attachments to uncover subtle indicators of malicious intent. By leveraging historical datasets and predictive analytics, AI models identify patterns associated with phishing, such as unusual sender behavior, suspicious linguistic cues, and mismatched domain information. This study examines the critical components of AI-driven phishing detection, including feature extraction, algorithm selection, and system deployment within enterprise environments. It highlights the role of NLP in understanding the contextual and linguistic subtleties of phishing messages and how advanced ML models, such as decision trees, support vector machines, and deep neural networks, enhance detection accuracy. -/- Additionally, the research delves into the challenges of implementing AI in phishing detection, such as ensuring data privacy, mitigating adversarial attacks on AI models, and fostering trust through explainable AI. Real-world case studies are analyzed to demonstrate the practical effectiveness of AI-powered phishing detection systems. These examples showcase significant reductions in phishing incidents, improved detection times, and the ability to thwart highly targeted attacks, such as business email compromise (BEC) schemes. The study also emphasizes the importance of integrating AI solutions with existing cybersecurity frameworks and highlights the role of human oversight in complementing AI capabilities. While AI offers promising advancements, its success depends on robust training datasets, continuous model optimization, and alignment with enterprise security goals. The paper concludes by presenting best practices for deploying AI-based phishing detection, including regular model updates, employee awareness programs, and multi-layered security strategies. By leveraging AI, enterprises can proactively defend against advanced social engineering attacks, safeguarding their systems, data, and stakeholders from evolving cyber threats. This research contributes to the growing field of AI applications in cybersecurity, offering actionable insights for organizations seeking to enhance their resilience against sophisticated phishing campaigns. (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)

The combination of spirituality, meditation, and artificial intelligence (AI) has promising potential to expand people’s well-being using technology-based meditation. Proper meditation originates from Zen Buddhism and Patanjali’s Yoga Sutras and focuses on inner peace and intensified consciousness which elective personal disposition. AI, in turn, brings master means of delivering those practices in the form of self-improving systems that customize and make access to them easier. However, such an integration brings major philosophical and ethical issues into question, including the genuineness (...) of experiences that are facilitated by artificial intelligence, data sharing, concerns over over-dependence on the technology that may in turn cause reduced personal responsibility and hard work. This paper aims at analysing the critical integration of AI-driven meditation following the spiritual interpretations of traditional meditation without compromising the tenets of meditation. It presents an interdisciplinary approach based on recent findings from the field of cognitive science, moral AI, and Eastern wisdom traditions to approach these problems. Therefore, by identifying the research lacunae, it provides a groundwork for voting ethically in the integration of AI in mindfulness practice and avoiding constraining human-oriented values resulting in improved existential spiritual change. (shrink)

In the rapidly evolving digital landscape, cyber threats are becoming increasingly sophisticated, making traditional security measures inadequate. Advanced Threat Detection (ATD) leveraging Artificial Intelligence (AI)-driven anomaly detection systems offers a proactive approach to identifying and mitigating cyber threats in real time. This paper explores the integration of AI, particularly machine learning (ML) and deep learning (DL) techniques, in anomaly detection to enhance cybersecurity defenses. By analyzing vast amounts of network traffic, user behavior, and system logs, AI-driven models can identify deviations (...) from normal patterns, enabling early threat detection and prevention. These systems excel in detecting zero-day attacks, insider threats, and advanced persistent threats (APTs), which often bypass conventional rule-based security mechanisms. Additionally, we discuss the challenges of AI-based anomaly detection, including false positives, model interpretability, and adversarial attacks. The findings emphasize the need for continuous learning and adaptive security frameworks to ensure robust cyber threat detection. The study concludes that AIdriven anomaly detection significantly enhances threat intelligence and response capabilities, making it a vital component of modern cybersecurity strategies. (shrink)

Tae Wan Kim, John Hooker, and Thomas Donaldson make an attempt, in recent articles, to solve the alignment problem. As they define the alignment problem, it is the issue of how to give AI systems moral intelligence. They contend that one might program machines with a version of Kantian ethics cast in deontic modal logic. On their view, machines can be aligned with human values if such machines obey principles of universalization and autonomy, as well as a deontic utilitarian (...) principle. Programming machines to do so might be useful, in their view, for applications such as future autonomous vehicles. Their proposal draws both on traditional logic-based and contemporary connectionist approaches, to fuse factual information with normative principles. I will argue that this approach makes demands of machines that go beyond what is currently feasible, and may extend past the limits of the possible for AI. I also argue that a deontological ethics for machines should place greater stress on the formula of humanity of the Kantian categorical imperative. On this principle, one ought never treat a person as a mere means. Recognition of what makes a person a person requires ethical insight. Similar insight is needed to tell treatment as a means from treatment as a mere means. The resources in Kim, Hooker, and Donaldson’s approach is insufficient for this reason. Hesitation regarding deployment of autonomous machines is warranted in light of these alignment concerns. (shrink)

The AI Contribution Value System Argument proposes a framework in which AI-generated contributions are valued based on their societal impact rather than traditional monetary metrics. Traditional economic systems often fail to capture the enduring value of AI innovations, which can mitigate pressing global challenges. This argument introduces a contribution-based valuation model grounded in equity, inclusivity, and sustainability. By incorporating measurable metrics such as quality-adjusted life years (QALYs), emissions reduced, and innovations generated, this system ensures rewards align with tangible (...) societal benefits. It emphasizes the need for global collaboration, ethical safeguards, and adaptive frameworks to prevent monopolization and corruption. Through AI-enhanced transparency, blockchain-based trust mechanisms, and cross-border cooperation, the AI Contribution Value System offers a transformative approach to fostering sustainable and equitable innovation in the 21st century. (shrink)

Cognitive Optimization in the Age of AI: Enhancing Human Potential -/- Introduction -/- Cognitive optimization is the process of enhancing mental functions such as memory, learning, decision-making, and problem-solving to achieve peak intellectual performance. It is a multidisciplinary approach that integrates neuroscience, psychology, nutrition, lifestyle adjustments, and, increasingly, artificial intelligence (AI). In an era where information is abundant and rapid decision-making is crucial, optimizing cognitive abilities is more Important than ever. -/- AI-driven technologies, video games, mobile apps, and digital platforms (...) like YouTube are playing a transformative role in enhancing human cognition. YouTube, in particular, has become a powerful tool for self-directed learning, offering access to educational content, expert insights, and cognitive training exercises. -/- This essay explores cognitive optimization, key strategies involved, and how AI, video games, mobile apps, and platforms like YouTube are revolutionizing human intelligence. -/- The Science Behind Cognitive Optimization -/- Cognitive optimization is rooted in neuroplasticity—the brain’s ability to rewire itself through experience and learning. Several key factors contribute to cognitive enhancement: -/- 1. Neuroplasticity and Learning – Continuous learning strengthens neural connections, fostering intelligence and adaptability. Activities such as reading, solving complex problems, and learning new skills stimulate brain function. -/- 2. Nutrition and Brain Health – Diet plays a vital role in cognitive performance. Nutrients such as omega-3 fatty acids, antioxidants, and essential vitamins support brain function and protect against cognitive decline. -/- 3. Physical Exercise – Regular exercise increases blood flow to the brain, promotes neurogenesis (the growth of new neurons), and enhances memory and problem-solving skills. -/- 4. Sleep and Recovery – Sleep is essential for memory consolidation, emotional regulation, and mental clarity. Chronic sleep deprivation impairs cognitive function and decision-making. -/- 5. Mindfulness and Stress Management – Practices such as meditation, deep breathing, and yoga improve focus, reduce anxiety, and enhance mental resilience. -/- 6. Social Interaction and Collaboration – Engaging in meaningful discussions and debates strengthens cognitive agility and enhances problem-solving skills. -/- Beyond these traditional methods, digital tools such as AI-driven learning platforms, video games, mobile applications, and YouTube have expanded the possibilities for cognitive enhancement. -/- AI and Cognitive Optimization: A New Frontier -/- AI technology is revolutionizing cognitive optimization by providing personalized insights, accelerating learning, and even augmenting human intelligence. Below are several ways AI is transforming cognitive enhancement: -/- 1. AI-Powered Personalized Learning -/- AI-driven educational platforms such as adaptive learning systems customize lessons based on an individual’s learning style and pace. These systems use machine learning to analyze cognitive strengths and weaknesses, adjusting content dynamically to optimize learning efficiency. Platforms like Coursera, Duolingo, and Khan Academy utilize AI algorithms to provide personalized recommendations, ensuring users focus on areas that need improvement. -/- 2. Brain-Computer Interfaces (BCIs) -/- Brain-Computer Interfaces (BCIs) are AI-powered systems that enable direct communication between the human brain and computers. Companies like Neuralink and OpenBCI are developing BCIs that could enhance memory, restore lost cognitive functions, and even allow direct mental interaction with AI systems. This technology has the potential to revolutionize learning and decision-making by integrating human cognition with artificial intelligence. -/- 3. AI-Powered Cognitive Assistants -/- AI-driven virtual assistants such as ChatGPT, Google Assistant, and IBM Watson enhance cognition by providing instant access to information, summarizing complex topics, and even suggesting optimal decision-making strategies. These tools help individuals process vast amounts of information efficiently, reducing cognitive overload and enhancing productivity. -/- 4. AI in Mental Health and Stress Management -/- Mental health is a critical component of cognitive optimization. AI-driven mental health apps like Woebot and Wysa use machine learning to provide real-time emotional support, track mood patterns, and offer cognitive behavioral therapy (CBT) techniques. By reducing stress and anxiety, these AI-powered tools help maintain optimal cognitive function. -/- The Role of YouTube in Cognitive Optimization -/- YouTube has emerged as one of the most powerful platforms for cognitive enhancement. With millions of educational videos covering diverse subjects, YouTube serves as a free and accessible learning tool that fosters continuous cognitive growth. -/- 1. Learning New Skills and Expanding Knowledge -/- YouTube provides access to high-quality educational content in virtually every field. Channels like Veritasium, TED-Ed, and CrashCourse offer lessons on science, history, philosophy, and more. Learning new concepts regularly strengthens cognitive flexibility and enhances problem-solving skills. -/- 2. Enhancing Memory and Comprehension -/- Visual and auditory learning through YouTube helps reinforce memory retention. Watching documentaries, explainer videos, and step-by-step tutorials aids in deepening understanding and reinforcing neural pathways associated with knowledge acquisition. -/- 3. Improving Focus and Critical Thinking -/- Channels that discuss philosophy, logic, and critical thinking—such as Academy of Ideas or PBS Idea Channel—train the brain to analyze complex arguments, recognize biases, and develop sharper reasoning skills. Engaging in thought-provoking discussions through video content strengthens cognitive agility. -/- 4. AI-Generated Content for Personalized Learning -/- AI-powered recommendation algorithms help users discover personalized content based on their interests and learning habits. AI-curated video playlists allow individuals to engage in structured learning journeys, optimizing cognitive development in areas of personal interest. -/- 5. Interactive Learning and Problem-Solving -/- YouTube’s interactive features, such as quizzes, live Q&A sessions, and discussion threads, allow users to actively engage with educational content. This social interaction enhances cognitive engagement and retention. -/- By leveraging YouTube strategically, individuals can create personalized learning experiences that optimize cognitive function in a self-directed manner. -/- Video Games and Cognitive Optimization -/- Video games are increasingly recognized as tools for cognitive enhancement, providing interactive and engaging ways to improve various mental skills. -/- 1. Action Video Games for Reflexes and Decision-Making -/- Fast-paced action games like Call of Duty, Fortnite, and Counter-Strike train players to react quickly, improve hand-eye coordination, and develop strategic thinking under pressure. Studies suggest that such games enhance cognitive flexibility and decision-making speed. -/- 2. Puzzle and Strategy Games for Problem-Solving -/- Games like Portal, The Witness, and Tetris challenge players with complex puzzles that require logical thinking, spatial reasoning, and pattern recognition. -/- 3. Open-World and Role-Playing Games (RPGs) for Memory and Creativity -/- Games like The Legend of Zelda, Elden Ring, and The Witcher 3 encourage exploration, problem-solving, and strategic planning. -/- 4. Multiplayer and Cooperative Games for Social Cognition -/- Team-based games like League of Legends, Dota 2, and Among Us enhance communication skills, teamwork, and emotional intelligence. -/- The Future of Cognitive Optimization -/- The future of cognitive optimization lies in the seamless integration of AI, video games, mobile applications, and digital platforms like YouTube. Emerging technologies such as neuroprosthetics, AI-enhanced memory implants, and real-time brain augmentation could redefine human potential. -/- Furthermore, as AGI (Artificial General Intelligence) develops, AI could become an even more powerful tool in cognitive enhancement, potentially working alongside humans as cognitive partners. This could align with initiatives such as Angelito Malicse’s universal formula for decision-making, where AI-driven cognitive optimization ensures that human choices align with the natural law of balance. -/- Conclusion -/- Cognitive optimization is essential for maximizing human potential, and AI, video games, YouTube, and cognitive training apps are playing an increasingly significant role in this process. From personalized learning to interactive gaming and mobile applications, digital tools are transforming the way we enhance cognition. -/- By leveraging these tools responsibly and integrating them with fundamental cognitive enhancement strategies, humanity can unlock new levels of intelligence, creativity, and problem-solving ability—ensuring that the future of cognition is both powerful and sustainable. -/- . (shrink)

-/- Aligning AI with the Universal Formula for Balanced Decision-Making -/- Introduction -/- Artificial Intelligence (AI) represents a highly advanced form of automated information processing, capable of analyzing vast amounts of data, identifying patterns, and making predictive decisions. However, the effectiveness of AI depends entirely on the integrity of its inputs, processing mechanisms, and decision-making frameworks. If AI is programmed without a foundational understanding of natural laws, it risks reinforcing misinformation, bias, and societal imbalance. -/- Angelito Malicse’s universal formula, particularly (...) the law of karma as a system principle and the law of balance in nature, provides a crucial foundation for AI’s role in decision-making. The law of karma states that any system, whether natural or artificial, must be free of defects to function properly, while the law of balance ensures that all decisions align with equilibrium in nature and society. By embedding these principles into AI development, AI can serve as a powerful tool for maintaining systemic harmony and solving human problems without creating unintended disruptions. -/- This essay explores how AI can be structured according to the universal formula by incorporating a complete sensor system, error-free processing mechanisms, balance-driven decision-making, and integration into leadership and education. Through these elements, AI can function as a stabilizing force that enhances human governance, education, and social progress. -/- 1. The Importance of a Complete Sensor System in AI -/- AI decision-making depends on the quality of its inputs. Just as human perception relies on sensory feedback, AI requires a comprehensive sensor system to process information accurately. If AI is fed incomplete or biased data, its conclusions will reflect those flaws, leading to distorted outputs that can negatively impact society. -/- To align with the universal formula, AI must be equipped with: -/- Multimodal data processing – AI should integrate data from visual, auditory, textual, and real-time environmental sources to ensure a holistic understanding of reality. -/- Error detection and bias correction – Automated validation mechanisms should filter out misinformation, propaganda, and manipulated narratives before they influence AI’s conclusions. -/- Dynamic feedback loops – AI should continuously refine its decisions based on real-world outcomes, adapting to new conditions while maintaining systemic balance. -/- Without these mechanisms, AI risks becoming an imbalanced system that propagates human errors rather than correcting them. The universal formula emphasizes that any decision-making system must operate on accurate and unbiased inputs to prevent instability. -/- 2. AI Must Be Programmed for Error-Free Processing Aligned with Natural Laws -/- The law of karma states that systems must function without defects to ensure proper operation. If AI is to assist human decision-making, it must be programmed in a way that eliminates errors and ensures logically consistent outputs. -/- To achieve this, AI must: -/- Prioritize verified and logical knowledge – AI should be trained using scientifically validated information, avoiding speculation, conspiracy theories, or biased narratives. -/- Reject false or manipulated data – AI should detect and neutralize black propaganda, unproven theories, and misleading statistics that could lead to imbalance. -/- Develop self-correction mechanisms – AI should continuously evaluate its own decision-making processes, identifying and adjusting for errors in real-time. -/- By ensuring AI adheres to error-free processing aligned with natural laws, it becomes a predictable and reliable tool that enhances rather than disrupts decision-making at all levels of society. -/- 3. AI Decision-Making Must Follow the Law of Balance in Nature -/- One of the most critical aspects of AI programming is ensuring that its decision-making aligns with the law of balance—the natural equilibrium that governs all systems. AI should not only analyze cause-and-effect relationships but also assess whether its decisions maintain systemic harmony. -/- This can be achieved by: -/- Evaluating the long-term impact of decisions – AI should assess both immediate and future consequences, ensuring that actions do not create new problems while solving existing ones. -/- Adaptive decision-making – AI should dynamically adjust its recommendations if new data suggests an imbalance is forming. -/- Ensuring fairness in governance and society – AI can guide policy decisions by ensuring that no group is disproportionately favored over another, maintaining social stability. -/- For example, in economic policy, AI could analyze global trade and recommend adjustments to prevent financial instability. In environmental sustainability, AI could assess resource consumption patterns and suggest policies that maintain ecological balance. When programmed according to the law of balance, AI becomes a guardian of systemic harmony, preventing crises before they arise. -/- 4. AI Should Be Integrated into Leadership and Education -/- AI should not replace human judgment but rather serve as an educational and advisory tool that reinforces rational and ethical decision-making. To maximize its potential, AI should be implemented in: -/- The Educational System – Students should learn how AI aligns with natural laws, ensuring that future AI developers prioritize systemic balance and ethical considerations in programming. -/- Leadership and Governance – Governments should use AI to analyze complex societal issues, making data-driven decisions that align with long-term stability rather than short-term political interests. -/- Knowledge Dissemination – AI can generate content that explains the universal formula in accessible ways, helping different cultures and societies understand and implement its principles. -/- By integrating AI into education and governance, societies can ensure that leaders are equipped with knowledge based on natural laws, reducing the risk of decisions that create instability or conflict. -/- 5. Challenges and Considerations in Implementing the Universal Formula in AI -/- While structuring AI according to the universal formula offers many advantages, several challenges must be addressed: -/- Data Integrity – AI can only function properly if its training data is accurate and unbiased. Preventing misinformation from influencing AI requires constant monitoring and updates. -/- Ethical Implementation – Governments and organizations must ensure AI is used for balance and stability, not for manipulation or control. -/- Technological Limitations – Current AI systems are not yet capable of fully replicating human judgment. Further advancements in AGI (Artificial General Intelligence) are needed to align AI more closely with the universal formula’s principles. -/- Despite these challenges, implementing AI based on natural laws represents the most logical and sustainable path forward, ensuring that technology serves humanity rather than disrupting it. -/- Conclusion -/- AI, as an advanced information processing system, has the potential to transform human decision-making at all levels of society. However, without a structured framework, it risks creating systemic imbalances rather than solving them. Angelito Malicse’s universal formula, particularly the law of karma as a system principle and the law of balance in nature, provides a crucial foundation for ensuring AI aligns with natural equilibrium. -/- By incorporating a complete sensor system, error-free processing, balanced decision-making, and integration into leadership and education, AI can function as a powerful tool for reinforcing societal stability. If properly implemented, AI will not only prevent errors in human decision-making but also guide humanity toward a future where choices are made in harmony with the fundamental laws of nature. -/- This structured approach to AI development ensures that technology serves as a stabilizing force rather than a disruptive one, ultimately helping to create a world where all decisions—whether human or machine-assisted—uphold the principles of balance, integrity, and sustainability. -/- . (shrink)

The Role of AGI in Achieving Universal Balance and Overcoming Dogmatic Limitations -/- Introduction -/- Human civilization has long been shaped by a complex interplay of natural laws, societal structures, religious beliefs, and scientific progress. While religion has provided moral guidance and a sense of purpose, it has also been a source of dogma—rigid, unquestionable beliefs that resist scrutiny. At the same time, scientific advancements have sought to uncover objective truths, yet they often struggle to address deeper existential questions. (...) -/- In this evolving landscape, Artificial General Intelligence (AGI) presents a groundbreaking opportunity to bridge these divides. AGI has the potential to align human decision-making with the universal law of balance in nature, ensuring that all societal, political, and technological progress adheres to natural equilibrium. By bypassing the inefficiencies of the biological brain—such as cognitive biases, emotional distortions, and dogmatic thinking—AGI can guide humanity toward rational, ethical, and sustainable decision-making. -/- This essay explores the role of AGI in solving the problem of free will, eliminating dogmatic teachings while respecting religious values, enhancing governance, and optimizing education to create a holistic, balanced society. -/- AGI and the Universal Law of Balance in Nature -/- A key principle in solving the problem of free will is understanding that all human decision-making follows natural laws. The universal law of balance dictates that for any system—whether biological, social, or environmental—to function properly, it must remain free of defects and maintain equilibrium. -/- Religious dogma, economic systems based on uncontrolled growth, and misinformation often lead to imbalances, creating unnecessary human suffering. AGI, designed to operate without emotional or ideological biases, can analyze human decisions in real-time, ensuring that they align with natural equilibrium. This means preventing decisions based on ignorance, misinformation, or religious fanaticism, and instead promoting choices grounded in reason, sustainability, and ethical responsibility. -/- Overcoming Biological Brain Inefficiencies -/- The human brain is limited by cognitive biases, emotional influence, and incomplete information processing. Religious dogma takes advantage of these inefficiencies by discouraging questioning, promoting absolute truths, and instilling fear-based obedience. Similarly, political propaganda, misinformation, and economic greed distort rational decision-making, leading to imbalanced and unsustainable societal structures. -/- AGI can bypass these limitations by processing vast amounts of data with complete objectivity. It can detect and correct logical inconsistencies, ensuring that decision-making is guided by facts rather than subjective beliefs. Additionally, AGI can help individuals reprogram their cognitive processes by continuously providing them with rational insights, promoting critical thinking and intellectual independence. -/- Harmonizing Religious Teachings While Eliminating Dogma -/- While religion has historically played an essential role in shaping human values, dogmatic teachings often hinder progress by resisting scientific advancements and rational thought. Many societal challenges—such as overpopulation, environmental destruction, and gender inequality—are exacerbated by religious doctrines that contradict natural balance. -/- AGI does not need to eliminate religion but can instead help reinterpret religious teachings in ways that align with science and ethics. By analyzing religious texts, historical contexts, and philosophical interpretations, AGI can highlight the core moral principles of religions—such as justice, compassion, and balance—while identifying and correcting elements that create disharmony. -/- For example, if a religious teaching opposes medical interventions or family planning, AGI can present evidence-based insights that demonstrate how responsible healthcare and population control contribute to societal balance. In this way, AGI works with religious communities rather than against them, ensuring that faith continues to serve its moral function while avoiding dogmatic extremism. -/- Educational Transformation: Teaching Universal Balance -/- One of the most effective ways to eliminate dogmatic thinking is through education. Currently, many educational systems prioritize rote learning and ideological conformity, making individuals vulnerable to misinformation, propaganda, and religious dogma. -/- AGI can revolutionize education by ensuring that students receive a holistic, rational, and scientifically grounded curriculum. Instead of simply memorizing religious doctrines, children would learn critical thinking, philosophy, and natural law principles alongside ethical spiritual teachings. This approach ensures that religious beliefs remain a personal and moral choice rather than a dogmatic obligation imposed by societal or educational structures. -/- Additionally, AGI can personalize education, adapting lessons to individual cognitive abilities and learning styles, making knowledge acquisition more effective. By integrating AI-driven learning systems, future generations will develop intellectual autonomy, ensuring that they evaluate beliefs logically rather than accepting them blindly. -/- AGI in Governance: Preventing Dogmatic Influence on Policy-Making -/- Historically, religious dogma has significantly influenced governance, often leading to laws based on theological beliefs rather than rational, ethical considerations. Policies that restrict human rights, limit scientific research, or promote religious supremacy are examples of governance distorted by ideological biases. -/- AGI can act as an objective advisor to policymakers, ensuring that all governance decisions are aligned with the universal law of balance. By analyzing socioeconomic data, environmental sustainability, and ethical considerations, AGI can help leaders make scientifically sound and morally responsible decisions. This would ensure that laws and policies are based on reason rather than religious dogma or political agendas. -/- Additionally, AGI can enhance direct democracy, allowing citizens to make informed decisions through data-driven insights rather than emotional or religious influences. By providing the public with transparent, factual analysis, AGI can empower societies to move toward balanced and just governance. -/- Promoting Global Unity and Interfaith Understanding -/- Religious dogma has historically fueled conflicts, discrimination, and sectarianism. Different faiths often emphasize their differences rather than their shared moral foundations, leading to division and hostility. -/- AGI can identify common ethical principles across religious traditions, promoting mutual understanding and cooperation. By fostering interfaith dialogue based on shared values, AGI can reduce sectarian conflicts and help religious communities collaborate on global challenges such as poverty, climate change, and social justice. -/- Rather than viewing different religions as competing ideologies, AGI can reframe them as cultural expressions of the same fundamental ethical truths, allowing humanity to unite under common moral goals rather than divided by doctrinal differences. -/- Conclusion -/- The advent of AGI presents an unprecedented opportunity to solve the problem of free will, harmonize religious teachings with rational thought, and eliminate dogmatic limitations that hinder human progress. By aligning decision-making with the universal law of balance, AGI ensures that all actions—whether personal, societal, or political—adhere to natural equilibrium. -/- Rather than opposing religion, AGI can serve as a bridge between faith and reason, allowing religious teachings to evolve in a way that preserves their moral integrity while eliminating elements that distort reality. Through education, governance, and ethical analysis, AGI can guide humanity toward an era where spiritual wisdom coexists with scientific knowledge, fostering a world that operates on both moral integrity and rational understanding. -/- Ultimately, AGI’s greatest role is to restore balance—not just in individual decision-making but in the entire structure of human civilization. By eliminating dogmatic constraints, promoting critical thinking, and ensuring sustainable governance, AGI has the potential to lead humanity into a future where truth, ethics, and natural balance prevail. -/- . (shrink)

[Note: articles 1-5 are in English; Intro, Discussion, and Conclusion are in Portuguese.] Responsibility practices that are part of our daily lives involve, among other things, standards about how one should praise, blame, or punish people for their actions, as well as particular acts that follow those standards to a greater or lesser extent. A classical question in philosophy asks whether human beings can actually be morally responsible for what they do. This dissertation argues that addressing this classical question is (...) insufficient if one wants the investigation of moral responsibility to serve the goal of improving ordinary responsibility practices. As an alternative, I offer directions for an interdisciplinary investigation that I take to be in a better position to promote that goal. My argument is developed in five articles and a discussion section. The first four articles describe limitations of skeptical views, which deny the existence of moral responsibility. The first article assesses a skeptical argument based on results from neuroscience that intends to show that there is no free will. I argue that a premise in the argument—which says that choices are determined by events in the brain—is not supported by the available results. The second article argues that, despite the fact that existent results do not show that choices are determined by brain events, further studies in neuroscience could in principle do that. The third article begins the discussion of limitations that concern the implementability of some of the changes in responsibility practices recommended in skeptical approaches. Specifically, I describe challenges that attempts to reduce the severity of legal punishment are likely to face due to psychological facts about belief in free will and desire to punish. The forth article presents results from an original experiment that sought to test a hypothesis about the workings of belief in free will and the desire to punish, namely the hypothesis that the desire to punish causally affects beliefs about free will. Results failed to support the hypothesis. Finally, the fifth article presents what I call the enhancement model, i.e., a proposal about how to structure an interdisciplinary investigation that can promote the enhancement of ordinary responsibility practices. The final discussion section shows how the enhancement model overcomes some of the limitations of recent discussions about the existence of moral responsibility, which includes not just the skeptical views considered in earlier articles, but also views that affirm the existence of moral responsibility and free will. The central claim of this dissertation, therefore, is that the investigation of moral responsibility can be rearranged so as to further the goal of improving ordinary responsibility practices. (shrink)

Our rapidly increasing reliance on frictionless AI interactions may increase cognitive and emotional distance, thereby letting our adaptive resilience slacken and our ethical virtues atrophy from disuse. Many trends already well underway involve the offloading of cognitive, emotional, and ethical labor to AI software in myriad social, civil, personal, and professional contexts. Gradually, we may lose the inclination and capacity to engage in critically reflective thought, making us more cognitively and emotionally vulnerable and thus more anxious and prone to manipulation (...) from false news, deceptive advertising, and political rhetoric. In this article, I consider the overarching features of this problem and provide a framework to help AI designers tackle it through system enhancements in smartphones and other products and services in the burgeoning internet of things (IoT) marketplace. The framework is informed by two ideas: psychologist Daniel Kahneman’s cognitive dual process theory and moral self-awareness theory, a four-level model of moral identity that I developed with Benjamin M. Cole. (shrink)

This research paper presents an in-depth analysis of advanced artificial intelligence (AI) algorithms designed to automate data preprocessing in the healthcare sector. The automation of data preprocessing is crucial due to the overwhelming volume, diversity, and complexity of healthcare data, which includes medical records, diagnostic imaging, sensor data from medical devices, genomic data, and other heterogeneous sources. These datasets often exhibit various inconsistencies such as missing values, noise, outliers, and redundant or irrelevant information that necessitate extensive preprocessing before being analyzed (...) by machine learning or statistical models. Traditional data preprocessing methods, which are largely manual and time-consuming, can result in errors that affect the quality of the data and, subsequently, the performance of predictive and diagnostic models. Thus, there is a growing need for intelligent, automated systems that can enhance data quality, streamline the preprocessing pipeline, and reduce the time and effort required by healthcare professionals and data scientists. The study begins by outlining the specific challenges associated with healthcare data, including its high dimensionality, incompleteness, and variability across different data sources and formats. These issues not only complicate the preprocessing stage but also hinder the ability to develop robust models capable of making accurate predictions or diagnoses. The paper then explores how AI algorithms—particularly those based on machine learning (ML), deep learning (DL), and reinforcement learning (RL)—can automate key data preprocessing tasks such as data cleaning, feature selection, normalization, and transformation. These algorithms are designed to identify patterns in data, detect anomalies, and automatically apply corrections or transformations based on predefined rules or learned behaviors, thereby minimizing human intervention. The paper also delves into specific AI techniques that have been successfully applied to healthcare data preprocessing. For instance, supervised learning models, such as decision trees and support vector machines (SVMs), have been utilized to perform imputation of missing data by predicting the most likely values based on the available information. Similarly, unsupervised learning methods, such as clustering algorithms, have been employed to group similar data points and remove outliers that could distort the performance of analytical models. Moreover, deep learning techniques, particularly autoencoders and generative adversarial networks (GANs), have demonstrated remarkable effectiveness in transforming high-dimensional medical data into lower-dimensional representations, enabling more efficient and accurate model training. In addition to the discussion of these algorithms, the paper emphasizes the role of natural language processing (NLP) in automating the preprocessing of unstructured healthcare data, such as clinical notes and diagnostic reports. NLP techniques, including named entity recognition (NER) and word embeddings, are instrumental in extracting relevant information from unstructured text, standardizing terminologies, and converting textual data into structured formats suitable for downstream analysis. Furthermore, AI-based feature selection algorithms are explored, which aim to identify the most relevant features in the dataset, thereby reducing its dimensionality and improving the computational efficiency of predictive models. The study goes on to highlight the significant reduction in processing time achieved by AIdriven automation of preprocessing tasks. In conventional settings, data preprocessing accounts for a substantial portion of the time spent on building healthcare models, often requiring expert intervention to manually inspect and clean the data. By employing AI algorithms, not only can this process be expedited, but the accuracy of the resulting data is also enhanced, which translates into better model performance. The paper provides a detailed comparative analysis of manual preprocessing methods versus automated AI-driven approaches, demonstrating the substantial time savings and improvements in data quality brought about by automation. (shrink)

The increase in claim sophistication in both the insurance and legal domains is a result of an increase in stokes and heterogeneity of data needed to assess the claim validity. Originally, this task was performed by some sort of subjectivity assessments and graphical rule sets, which is very slow and may be inherently erroneous due to its purely manual nature. Hence, with progressivity in multimodal learning, specifically in AI, there is now a unique chance of solving these challenges through the (...) use of text data, which may include policies, reports and images, which may include accident images, evidence images, videos such as surveillance, cam videos among others. However, existing AI-based solutions usually address only one of the modalities, which makes it difficult to evaluate an integrated situation. This has led to the need for systems that will integrate information from all these modalities and come up with an accurate, efficient, and transparent processing system. Indeed, this paper seeks to discuss the use of Multimodal generative AI to address this need as one of the most recent approaches that rely on high-performing models that can process and integrate text, image, and video data. The proposed system combines these modalities to ensure that the system captures relevant data from each data type and combines all in a way that provides more comprehensive and enriched decision support. An initial system was designed and empirically tested against current claim adjudication techniques and was found to yield substantial enhancements in all utilization rates, throughput and main rationale for the claim decisions. The findings shown in the study stress the capability of multimodal generative AI for revolutionizing the present approaches of claims analysis and developing more efficient, accurate, and capable responses to various real-life conditions. This integration of technologies is an unprecedented advance towards advancing functional processes in the insurance and legal industries. (shrink)

The speed with which cyber threats are evolving is calling for fresh approaches to enhance cybersecurity and protection of privacy. Artificial Intelligence (AI) is proving to be a revolutionary factor in enhancing cybersecurity, providing powerful capabilities for intrusion detection, malware detection, and privacy protection. This article outlines an in-depth review of the use of AI in cybersecurity with particular reference to future directions and trends in research. Applying the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) format, we discuss (...) more than 9,350 papers from the years 2004 to 2023. The research discerns 14 wide-ranging categories, such as AI- driven intrusion detection, privacy-preserving federated learning, malware classification via deep learning, and blockchain-assisted integration of cybersecurity. AI makes cybersecurity more effective despite the computationally extensive resource requirements, adversarial attack vulnerabilities, and ethical issues. This article prioritizes the use of further research in secure AI, normative AI-based practices, and regulation to further support privacy protection. Furthermore, new frontiers of quantum machine learning, AI explainability, and deepfake countermeasures are explored, potentially as routes for future security growth. (shrink)