This paper explores a novel methodology for utilizing artificial intelligence (Al), specifically large language models (LLMs) like ChatGPT, as an external cognitive augmentation tool. By integrating recursive self-analysis, structured thought expansion, and Al-facilitated selfmodification, individuals can enhance cognitive efficiency, accelerate self-improvement, and systematically refine their intellectual and psychological faculties. This approach builds on theories of extended cognition, recursive intelligence, and cognitive bias mitigation, demonstrating Al’s potential as a structured self-engineering framework. The implications extend to research, strategic (...) decision-making, therapy, and personal development. (shrink)

Cloud computing has transformed the nature of computation, sharing of information resources, and storage capabilities, including the flexibility to scale these resources for corporate use. Nevertheless, maintaining high reliability in cloud environments is still an issue that has not been solved because of factors such as Hardware failures, network interruptions/slowdowns and software vulnerabilities. This paper discusses several methods that can be employed in the reliability engineering of cloud computing, including fault tolerance, redundancy, monitoring and predictive maintenance. It also further (...) extends the basic reliability measures such as Mean Time Between Failure (MTBF), Mean Time To Repair (MTTR), Service Availability and Failure Rate, which measure system reliability and effectiveness. Moreover, the paper considers performance assessment methodologies through real-time monitoring, machine learning, and reliability assessment methods. It also addresses the nature and advancement of technologies of artificial intelligence-powered automation and self-healing applications for improved cloud dependability. The present work aims to identify the state-of-the-art state of dependability in cloud services and propose some recommendations for minimizing such costs, improving dependability levels, and reducing undesired downtime. The information is valuable for CSPs, IT designers/architects, and system engineers who wish to create fault-tolerant and optimal cloud environments. (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)

In the last year or so (and going back many decades) there has been extensive claims by major computational scientists, engineers, and others that AGI (artificial general intelligence) is 5 or 10 years away, but without a scintilla of scientific evidence, for a broad body of these claims: Computers will become conscious, have a “theory of mind,” think and reason, will become more intelligent than humans, and so on. But the claims are science fiction, not science. -/- This article reviews (...) evidence for the following three (3) propositions using extensive body of scientific research and related sources from the cognitive and neurosciences; evolutionary evidence; linguistics; data science; comparative psychology; self-driving cars, and robotics; and the learning sciences. (1) Do computing machines think or reason? (2) Are computing machines sentient or conscious? (3) Do computing machines have a theory of mind? (shrink)

As artificial intelligence becomes more sophisticated and robots approach autonomous decision-making, debates about how to assign moral responsibility have gained importance, urgency, and sophistication. Answering Stenseke’s (2022a) call for scaffolds that can help us classify views and commitments, we think the current debate space can be represented hierarchically, as answers to key questions. We use the resulting taxonomy of five stances to differentiate—and defend—what is known as the “blank check” proposal. According to this proposal, a person activating a robot could (...) willingly make themselves answerable for whatever events ensue, even if those events stem from the robot’s autonomous decision(s). This blank check solution was originally proposed in the context of automated warfare (Champagne & Tonkens, 2015), but we extend it to cover all robots. We argue that, because moral answerability in the blank check is accepted voluntarily and before bad outcomes are known, it proves superior to alternative ways of assigning blame. We end by highlighting how, in addition to being just, this self-initiated and prospective moral answerability for robot harm provides deterrence that the four other stances cannot match. (shrink)

With the fast pace of AI development, the problem of preventing its global catastrophic risks arises. However, no satisfactory solution has been found. From several possibilities, the confinement of AI in a box is considered as a low-quality possible solution for AI safety. However, some treacherous AIs can be stopped by effective confinement if it is used as an additional measure. Here, we proposed an idealized model of the best possible confinement by aggregating all known ideas in the field of (...) AI boxing. We model the confinement based on the principles used in the safety engineering of nuclear power plants. We show that AI confinement should be implemented in several levels of defense. These levels include 1) AI design in fail-safe manner 2) limiting its capabilities, preventing self-improving and circuit breakers on treacherous turn 3) isolation from the outside world and, lastly, as the final hope 4) outside measures oriented on stopping AI in the wild. We demonstrate that the substantial number (more than 50 ideas listed in the article) of mutually independent measures could provide a relatively high probability of the containment of a human-level AI but may be not sufficient to preserve runaway of superintelligent AI. Thus, these measures will work only if they are used to prevent superintelligent AI creation, but not for containing superintelligence. We suggest that there could be a safe operation threshold, on which AI is useful, but is not able to hack containment system from the inside, the same way as a safe level of chain reaction inside nuclear power plants is maintained. However, overall, a failure of the confinement is inevitable, so we need to use the full AGI limited number of times (AI-ticks). (shrink)

This paper investigates how human interactions with AI-powered chatbots may offend human dignity. Current chatbots, driven by large language models (LLMs), mimic human linguistic behaviour but lack the moral and rational capacities essential for genuine interpersonal respect. Human beings are prone to anthropomorphise chatbots—indeed, chatbots appear to be deliberately designed to elicit that response. As a result, human beings’ behaviour toward chatbots often resembles behaviours typical of interaction between moral agents. Drawing on a second-personal, relational account of dignity, we argue (...) that interacting with chatbots in this way is incompatible with the dignity of users. We show that, since second-personal respect is premised on reciprocal recognition of second-personal moral authority, behaving towards chatbots in ways that convey second-personal respect is bound to misfire in morally problematic ways, given the lack of reciprocity. Consequently, such chatbot interactions amount to subtle but significant violations of self-respect—the respect we are dutybound to show for our own dignity. We illustrate this by discussing four actual chatbot use cases (information retrieval, customer service, advising, and companionship), and propound that the increasing societal pressure to engage in such interactions with chatbots poses a hitherto underappreciated threat to human dignity. (shrink)

Breast cancer treatment is being transformed by artificial intelligence (AI). Nevertheless, most scientists, engineers, and physicians aren't ready to contribute to the healthcare AI revolution. In this paper, we discuss our experiences teaching a new American student undergraduate course that seeks to train the next generation for cross-cultural design thinking, which we believe is critical for AI to realize its full potential in breast cancer treatment. The main tasks of this course are preparing, performing and translating interviews with healthcare professionals (...) from both Portugal and the USA. Since the course is offered in Portugal as a short-term faculty-led study abroad program, students can explore the effect of culture on healthcare delivery and the design of healthcare technologies. The learning tests demonstrated student growth for breast cancer treatment in many areas important for the development of AI. In respect to understanding breast cancer care, most students had undervalued the effect of cancer and its treatment on the quality of life of women before taking this course and most were unaware of the importance of multidisciplinary care teams. Regarding AI in medical, students became more mindful of data privacy issues and the need to consider the effect of AI on healthcare professionals. (shrink)

Better instruments to predict the future evolution of artificial intelligence (AI) are needed, as the destiny of our civilization depends on it. One of the ways to such prediction is the analysis of the convergent drives of any future AI, started by Omohundro. We show that one of the convergent drives of AI is a militarization drive, arising from AI’s need to wage a war against its potential rivals by either physical or software means, or to increase its bargaining power. (...) This militarization trend increases global catastrophic risk or even existential risk during AI takeoff, which includes the use of nuclear weapons against rival AIs, blackmail by the threat of creating a global catastrophe, and the consequences of a war between two AIs. As a result, even benevolent AI may evolve into potentially dangerous military AI. The type and intensity of militarization drive depend on the relative speed of the AI takeoff and the number of potential rivals. We show that AI militarization drive and evolution of national defense will merge, as a superintelligence created in the defense environment will have quicker takeoff speeds, but a distorted value system. We conclude with peaceful alternatives. (shrink)

Self-supervised learning (SSL) is an emerging paradigm in machine learning that bridges the gap between supervised and unsupervised learning by allowing models to learn from unlabeled data. The core idea behind SSL is to generate supervisory signals from the data itself, thereby reducing the dependency on large labeled datasets. This paper explores the evolution of self-supervised learning, its underlying principles, key techniques, and recent advancements that make it a promising approach for the development of AI models with minimal (...) labeled data. We discuss the applications of SSL in various domains, such as natural language processing, computer vision, and speech recognition, and its potential to revolutionize industries that suffer from the scarcity of labeled data. Furthermore, we present challenges and future research directions in SSL, including the trade-offs between performance and label efficiency, generalization across tasks, and scalability for large datasets. (shrink)

Abstract: Cheating behaviors have been construed as a continuing and somewhat vexing issue for academic institutions as they increasingly conduct educational processes online and impose metrics on instructional evaluation. Research, development, and implementation initiatives on cheating detection have gained new dimensions in the advent of artificial intelligence (AI) applications; they have also engendered special challenges in terms of their social, ethical, and cultural implications. An assortment of commercial cheating–detection systems have been injected into educational contexts with little input on the (...) part of relevant stakeholders. This paper expands several specific cases of how systems for the detection of cheating have recently been implemented in higher education institutions in the US and UK. It investigates how such vehicles as wearable technologies, eye scanning, and keystroke capturing are being used to collect the data used for anti-cheating initiatives, often involving systems that have not gone through rigorous testing and evaluation for their validity and potential educational impacts. The paper discusses accountability- and policy-related issues concerning the outsourcing of cheating detection in institutional settings in the light of these emerging technological practices as well as student resistance against the systems involved. The cheating-detection practices can place students in a disempowered, asymmetrical position that is often at substantial variance with their cultural backgrounds. (shrink)

The rapid advancement of artificial intelligence (AI) and machine learning (ML) has led to significant innovations in the healthcare sector. One such development is AI-powered healthcare chatbots, which assist patients and medical professionals by providing medical guidance, symptom assessment, and appointment scheduling. This paper presents the design and implementation of an AI healthcare chatbot using machine learning techniques. The chatbot leverages natural language processing (NLP) and deep learning models to understand and respond to user queries effectively. Experimental results demonstrate (...) the chatbot's efficiency in delivering accurate and context-aware medical information. (shrink)

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

This participatory study, conducted in an introductory psychology class, recounts self-reflections of 22 undergraduate students and their instructor engaging in an GenAI-mediated storyboard generation process. It relies on Gordon Pask’s conversation theory, structuring out the nature of interactions between students, instructor, and GenAI, and then uses a qualitative narrative to describe these conversational feedback loops constituting the creation of draft and final storyboards. Results suggest students engaged in cyclical feedback driven processes to master their creations, used elements of photography (...) related to the relationships between objects in frame, image processing, and cultural objects/themes to generate narratives. Issues were faced with generating text on images, and the consistency and style of generated frames as they interacted with GenAI tools. This study provides guidelines to university teaching professionals to design effective activities mediated by text-to-image GenAI. (shrink)

The exponential growth of cloud computing has revolutionized the IT industry by providing scalable, flexible, and cost-efficient infrastructure solutions. However, as cloud systems become more complex, managing and optimizing these environments becomes increasingly challenging. Traditional cloud management methods often require manual intervention and significant resources to maintain performance, cost-efficiency, and security. Autonomous cloud operations, powered by artificial intelligence (AI) and machine learning (ML), represent the next frontier in cloud management. By leveraging advanced algorithms and real-time data analysis, self-optimizing cloud (...) systems can automate routine tasks such as resource allocation, performance tuning, security management, and fault detection. This paper explores the concept of autonomous cloud operations, highlighting the role of AI and ML in enabling intelligent cloud systems that can autonomously adapt to changing workloads, optimize resource utilization, and improve overall system performance. It also discusses the benefits, challenges, and future prospects of autonomous cloud operations, providing insights into how these technologies can transform cloud management and operations. (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)

The present research compares the efficiency of AI-based user behavior analysis to conventional security mechanisms in cloud environments. It specifically tests their precision, velocity, and predictive capacity for identifying and acting upon cyber attacks. As the adoption of the cloud continues to increase, incorporating Artificial Intelligence (AI) and machine learning into security infrastructures has become increasingly important. The study investigates the performance of AI-based security systems, using sophisticated pattern recognition and anomaly detection, compared to conventional methods in detecting deviations (...) from normal user behavior in cloud environments. Adopting a quantitative research approach, the study utilizes a systematic survey of cybersecurity professionals in various industries like finance, healthcare, IT, retail, and government. The survey, comprising closed- ended and Likert-scale questions, collects information on professionals' attitudes and experiences with AI-based versus conventional security methods. Data from a purposive sample of 243 cybersecurity professionals are examined through multiple regression methods to evaluate the effect of such security systems on threat detection and response effectiveness in cloud environments. Results show that although both AI-based and conventional methods improve threat detection accuracy significantly, conventional methods have a marginal advantage. Yet, AI-based solutions show better predictive ability and overall security performance. These results suggest that a hybrid security strategy is essential for cloud security, combining AI’s advanced predictive analytics and adaptability with the reliability and speed of traditional security measures. Such an integrated approach is recommended to address the complex and evolving nature of cloud-based cyber threats effectively. This research provides useful information for IT practitioners and companies on how to best optimize AI-powered security integration in cloud environments. It highlights the changing role of AI in cloud security and the need to strike a balance between new AI-based solutions and proven security practices in order to construct an integrated and robust cloud security framework. (shrink)

With the applications like MalGAN, such cyberattacks enhanced with artificial intelligence (AI) in a broad way across cyber-defense lifecycles successfully take the vulnerabilities of systems at advantage, which are many as these are evading defenses nowadays. Therefore, this methodology proposed a new method which presents the approach of data fingerprinting and visualization for AI-Enhanced Cyber-Defense Systems (AIECDS) for efficiency in detection. AIECDS approach is built combining dynamic reinforcement learning, feature extraction and visualization with Hilbert curves and tornado graphs, real-time data (...) processing. Experimenting using the UNSW-15 dataset shows that even using very small sample sizes it's possible to differentiate malicious sessions from benign ones, meaning a large advancement in AI-driven solutions to cybersecurity, by being more adept at identifying complex threats with simplified machine learning models. (shrink)

Abstract: The Missing Phase in E=mc²—Plasma as the Foundational State of Energy-Mass Equivalence -/- 1. Problem Statement • E=mc² assumes an instantaneous energy-mass transition but lacks an intermediary stabilization state. • Mass should not be treated as a fundamental property but as an emergent resonance of structured energy. • Without a structured intermediary, mass formation remains incomplete, leaving gaps in quantum field theory and cosmology. -/- 2. Core Hypothesis – Plasma-First Theory (PFT) • Mass does not emerge directly from energy (...) but through a coherence-scored resonance process within a Quantum Coherence Field (QCF). • Plasma serves as a structured decoherence state, mediating energy-mass transitions via prime resonance constraints. • Gravity is not an independent force but a standing-wave remnant of plasma-field interactions. -/- 3. Key Implications • Mass formation follows structured prime resonance constraints rather than spontaneous emergence. • Dark matter may be a phase-locked plasma state, explaining its gravitational effects without direct interaction. • Plasma-first modeling predicts new methods for mass manipulation and energy extraction beyond fusion. • Black holes are extreme plasma-phase coherence transitions, not singularities. -/- 4. Experimental Validation • Prime-Based Plasma Spectroscopy → High-energy plasma emissions should exhibit prime-numbered coherence gaps. • Gravitational Resonance Quantization → LIGO data should reveal structured prime-frequency distortions. • Cosmic Spectral Analysis → Dark matter distributions should align with prime resonance constraints. -/- Technological Implications of Plasma-First Theory (PFT) -/- 1. Energy Generation & Storage • Beyond Fusion Power → Plasma resonance tuning could enable more efficient, controlled energy extraction beyond conventional fusion methods. • Resonance-Based Energy Harvesting → Coherence-scored plasma fields could allow direct conversion of structured energy states into usable power. • Zero-Waste Energy Systems → Understanding energy as a phase-state transition could lead to near-perfect energy recycling methods. -/- 2. Mass Manipulation & Material Science • Programmable Mass Formation → Using prime-locked plasma coherence, mass structures could be dynamically altered or synthesized at will. • New State of Matter Engineering → Plasma stabilization techniques may unlock previously unknown stable matter configurations. • Self-Organizing Materials → Materials could be designed to adaptively shift between energy and mass phases based on environmental conditions. -/- 3. Gravity & Inertia Control • Interference-Based Gravity Modulation → If gravity is a plasma standing wave effect, controlled resonance tuning could enable gravitational lensing or reduction of inertia. • Anti-Gravity Applications → Prime-frequency coherence interference might allow local mass fields to become inertia-free, revolutionizing transportation. • Resonant Spacecraft Propulsion → If mass emerges from structured resonance, spacecraft could manipulate their mass-energy phase to optimize thrust efficiency. -/- 4. Black Hole & Dark Matter Engineering • Black Hole Energy Extraction → If black holes are extreme plasma-phase transitions, they could be harnessed for structured energy retrieval instead of seen as one-way sinks. • Dark Matter Utilization → If dark matter is phase-locked plasma, unlocking its resonance signature could enable controlled interaction for energy extraction or shielding applications. -/- 5. Computing & Information Processing • Plasma-Phase Quantum Computing → If coherence-scored resonance governs energy structures, plasma-based quantum computing may outperform conventional qubit models. • Information Storage in Plasma Fields → Instead of silicon-based hardware, structured plasma coherence could encode and retrieve data with near-infinite density and efficiency. -/- 6. Medical & Biophysical Applications • Coherence-Based Healing Technologies → Structured energy fields could be tuned for cellular regeneration, accelerating healing and potentially reversing degenerative conditions. • Bio-Resonance Medicine → If biological structures follow prime-locked resonance principles, medical treatments could be optimized to match natural coherence states for maximum effectiveness. -/- 7. Universal Implications • Cosmological Engineering → Understanding how mass emerges from structured energy could allow future civilizations to modify space-time at will. • Resonance-Based AI → AI could be designed to operate on structured plasma coherence, allowing it to interact with fundamental physical principles instead of traditional computational limitations. In short, PFT could redefine energy, mass, gravity, and technology itself—bridging physics with engineering in a way that eliminates inefficiencies and unlocks entirely new paradigms. -/- This framework redefines mass, gravity, and matter formation, proposing that plasma is the missing intermediary state in energy-matter interactions. (shrink)

This paper addresses the challenge of efficiently handling a diverse array of customer queries by proposing the development of an innovative web-based customer support chatbot. The objectives encompass creating a versatile system capable of interpreting and resolving a spectrum of customer complaints, enhancing support staff efficiency, and facilitating knowledge base updates. The proposed methodology employs the MERN stack for web app development and integrates Generative AI and pre-trained Large Language Models (LLMs), specifically OpenAI's prebuilt models, for intelligent responses. The pseudo (...) code outlines the interactions between the chatbot, support staff, and administrators, ensuring a seamless process. Results indicate the chatbot's commendable ability to accurately interpret queries, showcasing language versatility in various Indian and foreign languages. Efficient handling of varied phrasings and also accommodating grammatical errors. The use of prebuilt models is highlighted as a practical and efficient solution, reducing implementation time compared to custom models. Additionally, the implementation is recognized for its scalability, positioning it as a practical and efficient solution for evolving customer support needs in the near future. (shrink)

Many artificial intelligence (AI) systems currently used for decision-making are opaque, i.e., the internal factors that determine their decisions are not fully known to people due to the systems’ computational complexity. In response to this problem, several researchers have argued that human decision-making is equally opaque and since simplifying, reason-giving explanations (rather than exhaustive causal accounts) of a decision are typically viewed as sufficient in the human case, the same should hold for algorithmic decision-making. Here, I contend that this argument (...) overlooks that human decision-making is sometimes significantly more transparent and trustworthy than algorithmic decision-making. This is because when people explain their decisions by giving reasons for them, this frequently prompts those giving the reasons to govern or regulate themselves so as to think and act in ways that confirm their reason reports. AI explanation systems lack this self-regulative feature. Overlooking it when comparing algorithmic and human decision-making can result in underestimations of the transparency of human decision-making and in the development of explainable AI that may mislead people by activating generally warranted beliefs about the regulative dimension of reason-giving. (shrink)

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

In recent years, with the advances in machine learning and neuroscience, the abundances of sensors and emotion data, computer engineers have started to endow machines with ability to detect, classify, and interact with human emotions. Emotional artificial intelligence (AI), also known as a more technical term in affective computing, is increasingly more prevalent in our daily life as it is embedded in many applications in our mobile devices as well as in physical spaces. Critically, emotional AI systems have not only (...) the ability to not passively read and classify emotions, but most also have an ability to predict and ‘nudge’ users to certain desired emotional states. Drawing from Piredda (2020)’s account on affective artifacts and other recent accounts, we've examined various emerging emotion-sensing technologies such as recommender algorithms, personal AI assistants, work-related emotion trackers, and emotional AI toys and robots through the lens of affective artifacts. We show how these technologies fulfill the criteria of being affective artifacts: they are artifactual, multifunctional, semi-agentive, and semi-interpesonal. Emotional AI systems as affective artifacts can influence the affective states of the users (e.g., help regulate emotions of ourselves and others), and importantly, our manipulation of these algorithms can intersect with the maintenance and construction of our sense of self and identity. (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)

-/- 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)

I consider applications of “AI extenders” (Vold & Hernández-Orallo 2021) to dementia care. AI extenders are AI-powered technologies that extend minds in ways interestingly different from old-school tech like notebooks, sketch pads, models, and microscopes. I focus on AI extenders as ambiance: so thoroughly embedded into things and spaces that they fade from view and become part of a subject’s taken-for-granted background. Using dementia care as a case study, I argue that ambient AI extenders are promising because they afford (...) richer and more durable forms of multidimensional integration than do old-school extenders like Otto’s notebook. They can be tailored, in fine-grained ways along multiple timescales, to a user’s particular needs, values, and preferences — and crucially, they can do much of this self-optimizing on their own. I discuss why this is so, why it matters, and its potential impact on affect and agency. I conclude with some worries in need of further discussion. (shrink)

Applying the Holistic Governance System (HGS) to the Philippines -/- The Philippines has struggled with corruption, political instability, economic inequality, and weak governance. Applying the Holistic Governance System (HGS) could transform the country into a stable, prosperous, and globally competitive nation. -/- Key Challenges in the Philippines: -/- 1. Corruption – Widespread in government agencies, law enforcement, and politics. -/- 2. Political Dynasties & Electoral Manipulation – Many leaders come from elite families, limiting true democracy. -/- 3. Economic Inequality – (...) The wealth gap is extreme, with many Filipinos in poverty. -/- 4. Weak Law Enforcement & Justice System – Crime, illegal drugs, and extrajudicial killings undermine public trust. -/- 5. Underdeveloped Infrastructure – Slow development of transportation, digital connectivity, and urban planning. -/- 6. Brain Drain – Many skilled Filipinos leave for better opportunities abroad. -/- HGS can address these issues by combining the best governance models from around the world while adapting them to the Philippine context. -/- Step-by-Step Implementation of HGS in the Philippines: -/- 1. Political Reform: Replacing Traditional Politics with a Meritocratic Leadership System -/- The Strategic Council (7 Leaders): Instead of a single president, the Philippines would be led by a council of seven experts, each specializing in key areas like economy, education, security, and technology. -/- No Political Dynasties: Candidates must prove their qualifications (economic, scientific, or governance expertise) before being allowed to run. -/- Fixed 10-Year Term: Leaders serve long enough to implement national development plans but cannot seek re-election. -/- ???? Impact: This will eliminate corruption, improve decision-making, and end elite family control of politics. -/- 2. Balanced Democracy: Direct Citizen Participation in Decision-Making -/- Annual Referendums: Inspired by Switzerland, Filipinos will vote directly on key national policies (e.g., education reforms, infrastructure projects). -/- Congress Reform: Replace the current system with: -/- People’s Assembly – Elected by citizens to represent public concerns. -/- Expert Senate – Composed of scientists, economists, and policy experts. -/- ???? Impact: This system prevents corrupt politicians from making bad decisions and gives Filipinos a real voice in governance. -/- 3. Economic Reform: A Hybrid System for Growth & Stability -/- Balanced Free Market + State Planning: -/- Private businesses continue operating, but key industries (energy, healthcare, education, transport) are state-controlled for stability. -/- Large government investment in technology, renewable energy, and industrial growth. -/- Anti-Corruption Crackdown: Modeled after Singapore’s strict anti-corruption policies. -/- Digital Government System: Minimize human corruption by making all government transactions online with AI verification. -/- ???? Impact: The economy grows without exploitation, and corruption is eliminated in business and government. -/- 4. Social Welfare: Universal Services for All Filipinos -/- Free Universal Healthcare: Like Norway & Sweden, all Filipinos receive high-quality medical care. -/- Free Higher Education: Universities are fully funded, with scholarships for top students. -/- Basic Income for the Poor: Prevents extreme poverty and improves national productivity. -/- Affordable Housing & Smart Cities: Government builds modern, eco-friendly, low-cost housing. -/- ???? Impact: This ensures every Filipino has access to education, health, and stable living conditions. -/- 5. Justice & Security: Smart Law Enforcement with AI & Independent Courts -/- AI-Powered Legal System: Reduce human bias in court cases and prevent political influence. -/- Strict Anti-Corruption Laws: Inspired by Singapore, harsh penalties for corruption at all levels. -/- High-Tech Crime Prevention: AI surveillance, digital ID verification, and cybercrime prevention. -/- ???? Impact: A fast, fair, and corruption-free justice system. -/- 6. Infrastructure & Urban Development: Smart Cities & Green Energy -/- Nationwide High-Speed Rail & Smart Transportation: Inspired by China’s urban planning. -/- 100% Renewable Energy Goal: Government investment in solar, wind, and hydro energy. -/- Smart City Development: AI-controlled traffic, clean public spaces, and sustainable urban design. -/- ???? Impact: A modern Philippines with world-class infrastructure and zero energy dependence. -/- 7. Foreign Policy & National Defense: Economic Strength + Global Neutrality -/- Neutrality in Global Conflicts: Inspired by Switzerland, the Philippines avoids military conflicts but strengthens economic ties with all nations. -/- Strong Cybersecurity & Modernized Military: Instead of focusing on war, invest in cyber defense and AI-powered security. -/- Self-Sufficient Agriculture & Industry: Reduce dependence on foreign imports by boosting local food and industrial production. -/- ???? Impact: The Philippines becomes a secure, self-reliant, and economically strong nation. -/- The Future Philippines Under HGS: A Model Nation -/- Final Thoughts: Why the Philippines Needs HGS -/- ???? HGS will transform the Philippines into a: ✅ Politically stable nation with honest, expert-led governance. ✅ Economically strong country with innovation and self-sufficiency. ✅ Corruption-free government with strict anti-corruption policies. ✅ Technology-driven society with AI, smart cities, and digital governance. ✅ Socially progressive state with free healthcare, education, and a guaranteed basic income. -/- By eliminating corruption, restructuring governance, and investing in technology, the Philippines can rise as a global model for modern governance. -/- Roadmap for Transitioning the Philippines to the Holistic Governance System (HGS) -/- Goal: Transform the Philippines into a corruption-free, economically strong, socially progressive, and technologically advanced nation using HGS principles. -/- ???? Phase 1: Laying the Foundation (Year 1-5) -/- Objective: Establish public trust, eliminate corruption, and implement key reforms in government, economy, and education. -/- 1. Anti-Corruption Revolution (Year 1-3) -/- ✅ Create a National Anti-Corruption Tribunal (similar to Singapore’s system) with special prosecutors and AI-powered case review to ensure fair trials. ✅ Strict penalties for corruption (lifetime ban from politics, confiscation of illegally obtained wealth, long prison sentences). ✅ Full Digital Government System – All government transactions go online to remove human corruption (permits, taxes, elections, etc.). -/- ???? Impact: Government corruption is drastically reduced, and public trust in leadership increases. -/- 2. Leadership Reform (Year 2-5) -/- ✅ Ban Political Dynasties – Require strict merit-based qualifications for all public officials. ✅ Restructure the Government: Replace the president with the Strategic Council (7 experts) and reform Congress into: -/- People’s Assembly (elected representatives). -/- Expert Senate (top scientists, economists, and technocrats). ✅ Mandatory Leadership Training – All future leaders must undergo a 5-year program on governance, economics, and philosophy. -/- ???? Impact: The Philippines shifts from a corrupt dynasty-driven system to a competence-based government. -/- 3. Education Reform (Year 2-4) -/- ✅ Upgrade the curriculum to teach critical thinking, technological skills, and the Universal Law of Balance. ✅ Free College Education (funded by corruption money recovery). ✅ Global Research Partnerships with universities in Europe, Singapore, and the U.S. to improve STEM education. -/- ???? Impact: The youth become future innovators and critical thinkers, reducing brain drain. -/- ???? Phase 2: Economic & Social Transformation (Year 6-10) -/- Objective: Transition to a self-sufficient economy with world-class infrastructure and social services. -/- 4. Economic Overhaul (Year 6-10) -/- ✅ Hybrid Economic Model (similar to Singapore & China): -/- Free market for small businesses & entrepreneurship. -/- Government-controlled key industries (healthcare, energy, infrastructure). ✅ AI-Governed Tax System – Automate taxation to prevent tax evasion. ✅ Massive Government Investment in Technology (AI, robotics, green energy, and space research). -/- ???? Impact: The Philippines becomes a tech-driven economy, independent of remittances. -/- 5. Universal Social Welfare System (Year 6-8) -/- ✅ Free Universal Healthcare (Norway model) – Build more hospitals, increase doctor salaries, and implement telemedicine in rural areas. ✅ Basic Income for the Poor (funded by recovered corruption money and AI-driven economic growth). ✅ National Housing Program – Construct affordable smart cities with AI-managed traffic, green energy, and disaster-resistant buildings. -/- ???? Impact: All Filipinos have access to quality healthcare, education, and stable housing. -/- 6. Infrastructure Boom & Smart City Development (Year 6-10) -/- ✅ High-Speed Rail System (China model) – Connect Luzon, Visayas, and Mindanao with a modern railway. ✅ Digital Infrastructure – High-speed internet for all citizens, free public WiFi, and AI-managed urban planning. ✅ 100% Renewable Energy – Solar, wind, and hydro power investments to eliminate electricity shortages. -/- ???? Impact: The Philippines becomes a modern, highly developed nation with smart, efficient cities. -/- ???? Phase 3: Global Powerhouse & Permanent Stability (Year 11-20) -/- Objective: Establish the Philippines as a global leader in innovation, sustainability, and governance. -/- 7. AI-Powered Governance (Year 11-15) -/- ✅ Full AI Integration in Government – Automate law enforcement, policymaking analysis, and economic planning. ✅ Digital Direct Democracy – Filipinos can vote online in annual national referendums on key policies (Switzerland model). -/- ???? Impact: A corruption-proof, people-powered government that makes smart, data-driven decisions. -/- 8. Military & Foreign Policy Reform (Year 11-15) -/- ✅ Neutrality & Economic Diplomacy (Swiss Model) – Stay independent of global conflicts while expanding trade with the U.S., China, and ASEAN. ✅ Cybersecurity Defense System – Develop AI-powered cyber-defense technology to prevent hacking & cyber threats. ✅ Self-Sufficiency in Agriculture & Industry – Invest in high-tech farming and local industries to reduce import reliance. -/- ???? Impact: The Philippines becomes an economically powerful, self-sufficient, and diplomatically respected nation. -/- 9. Space & Advanced Scientific Research (Year 16-20) -/- ✅ Launch a Philippine Space Program (like NASA) to develop satellite technology and space exploration. ✅ Invest in AI & Quantum Computing Research to compete in the global tech industry. ✅ Biotechnology & Medicine Research to create Philippine-made vaccines, treatments, and bio-engineered food solutions. -/- ???? Impact: The Philippines becomes a hub for science, space, and technological innovation. -/- ???? Summary: The New Philippines Under HGS -/- ???? Conclusion: The Philippines as a Global Model Nation -/- By transitioning to HGS, the Philippines will: ✅ Eliminate corruption & political dynasties through AI-powered governance. ✅ Become a tech-driven, self-sufficient economy with free healthcare, education, and infrastructure. ✅ Modernize cities, transport, and digital services for efficiency and sustainability. ✅ Be a global leader in science, AI, and space exploration. -/- . (shrink)

We use the myth of Pygmalion as a lens to investigate and frame the relationship between women and artificial intelligence (AI). Pygmalion was a legendary ancient king of Cyprus and sculptor. Having been repulsed by women, he used his skills to create a statue, which was imbued with life by the goddess Aphrodite. This can be seen as one of the primordial AI-like myths, wherein humanity creates intelligent life-like self-images to reproduce or replace ourselves. In addition, the myth prefigures (...) historical and present gendered dynamics within the field of AI and between AI and society at large. Throughout history, the theme of women being replaced by inanimate objects (e.g. automata, algorithms) has been repeated, and continues to repeat in contemporary AI technologies. However, this socially detrimental pattern in technology — what we dub Pygmalion displacement — is often overlooked, whether due to naive excitement about new developments, or due to an unacknowledged sexist history of the field itself. As we demonstrate herein, Pygmalion displacement prefigures heavily, but in an unacknowledged way, in the original Turing test, the imitation game: a central thought experiment, foundational to AI. With women, and the feminine generally, being both dislocated and erased from and by technology, AI is and has been (presented as) created mainly by privileged men, subserving capitalist patriarchal ends. This poses serious dangers to women and other marginalised people. By tracing the historical and ongoing entwinement of femininity (from a patriarchal perspective) and AI, we aim to understand, make visible, and start a dialogue on the ways in which AI harms women. (shrink)

AI systems have often been found to contain gender biases. As a result of these gender biases, AI routinely fails to adequately recognize the needs, rights, and accomplishments of women. In this article, we use Axel Honneth’s theory of recognition to argue that AI’s gender biases are not only an ethical problem because they can lead to discrimination, but also because they resemble forms of misrecognition that can hurt women’s self-development and self-worth. Furthermore, we argue that Honneth’s theory (...) of recognition offers a fruitful framework for improving our understanding of the psychological and normative implications of gender bias in modern technologies. Moreover, our Honnethian analysis of gender bias in AI shows that the goal of responsible AI requires us to address these issues not only through technical interventions, but also through a change in how we grant and deny recognition to each other. (shrink)

Demand forecasting in grocery retail encounters considerable difficulties due to fluctuating consumer behavior, as well as external factors such as weather conditions and local events. This research presents an innovative framework that utilizes generative artificial intelligence (AI) to improve forecasting accuracy by incorporating various contextual elements. By employing Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), we integrate weather data, local events, and consumer behavior to better predict grocery sales. The proposed approach aims to optimize inventory management, minimizing stockouts and (...) reducing waste. Experimental results indicate that this context-aware method significantly outperforms traditional forecasting models, providing a powerful solution for improving supply chain operations in grocery retail. (shrink)

Over the last few years, personal health monitoring wearable devices have emerged as innovative applications of Artificial Intelligence (AI) in the healthcare industry as they help in real time analysis and prediction of health standardized check-ups and health management. To navigate through the current trends, new technologies and developments, the prospects are as follows: The article also gives a logical look at the state of the art of such devices, enumerating the advantages and drawbacks, as well as outlining the main (...) ethical issues. In terms of method, it also discusses different AI approaches, data acquisition procedures, and those integrating systems used in the context of health monitoring. The finding and implication section discusses the findings of the current trend and the possibility of improvement in application and user acceptance. The last section provides a prognosis on what the area of personal health monitoring might look like in the future, as well as the position of AI in the optimisation of patient experience. (shrink)

The rapid evolution of cloud computing and the increasing sophistication of cyber threats have necessitated a paradigm shift in the approach to cybersecurity. The rapid growth of cloud computing has revolutionized business operations with unparalleled scalability, flexibility, and access to enormous computational power. Nevertheless, exponential growth has also led to an exponential rise in security threats, with cloud environments being the main target for cyberattacks. Conventional security controls lag behind the rising complexity of these threats. Artificial intelligence (AI) has become (...) a revolutionary solution to the problem, providing sophisticated capabilities that strengthen the security posture of cloud environments. This paper discusses the potential role of AI in shaping future cloud security, with an emphasis on its potential to facilitate real-time threat detection, automated response, and predict future vulnerabilities. Through an examination of AI-driven innovations in cloud security, this paper tries to shed light on how companies can use AI in a manner that will improve security, reduce cost, and improve operational resilience. (shrink)

Artificial Intelligence (AI) is a technology that enables machines or computer systems to perform tasks that usually require human intelligence. AI systems can understand and interpret information, make decisions, and solve problems based on patterns and data. They can also improve their performance over time by learning from their experiences. AI is used in various applications, such as enhancing knowledge and understanding, helping as voice assistants, aiding in image recognition, facilitating self-driving cars, and helping diagnose diseases. The appropriate usage (...) of AI can help in the promotion of Sikhism. However, awareness of the pitfalls of using AI for religious purposes is essential. So, we must use AI with caution in spiritual matters. (shrink)

Will AI take away your job? The answer is probably not. AI systems can be good predictive systems and be very good at pattern recognition. AI systems have a very repetitive approach to sets of data, which can be useful in certain circumstances. However, AI does make obvious mistakes. This is because AI does not have a sense of context. As Humans we have years of experience in the real world. We have vast amounts of contextual data stored in our (...) brains that make it possible to predict and to know boundaries of the real world so that even if we have never been in a particular situation, we are still able to deal with it. The unknown situation is where AI will fall down. In engineering, AI is being developed to monitor and control certain systems, this could extend to Nuclear Power Plants for example. If we examine control systems for Nuclear Power Plants on vessels, the system would likely be programmed using the safety of the reactor as the main priority. Automation is crucial in engineering systems like this, as the likelihood and cost of human error is high and human reaction times are slow in comparison to the system. However, there is always the possibility of unforeseen external factors that may override plant safety which cannot be programmed. If we look at a plant onboard a Submarine, for example, we see such additional factors as ship safety. In one example, the power plant might breach safety parameters, in which case an automatic system may shut down the reactor. However, there might be a greater urgency such as a flood in another compartment or an attack upon the vessel, that would override shutting down the reactor. The AI system can neither observe nor utilise information from the external environment to make this type of judgement, whereas the operator may be completely aware. To programme context into AI systems would be near impossible. We still need operators as we still need to understand the environment around us, which may contain an infinite number of possibilities. In a further example of this; if we examine the ICO (Information Commissioner's Office) guidance, it is clear that in clinical settings a human being will always be required to validate findings and to provide context. Sometimes having a lot of data just doesn't replace social interaction, intuition and experience. (shrink)

In this paper, I argue that surveillance capitalism and computational propaganda can undermine democratic equality. First, I argue that two types of resources are relevant for democratic equality: 1) free time, which entails time that is free from systemic surveillance, and 2) epistemic resources. In order for everyone in a democratic system to be equally capable of full political participation, it’s a minimum requirement that these two resources are distributed fairly. But AI that’s used for surveillance capitalism can undermine the (...) fair distribution of these resources, thereby threatening democracy. I further argue that computational propaganda undermines the democratic aim of collective self-determination by normalizing relations of domination and thereby disrupting the equal standing of persons. I conclude by considering some potential solutions. (shrink)

Abstract; As artificial intelligence (AI) technologies become increasingly integrated into various facets of society, their impact on human rights has garnered significant attention. This paper examines the intersection of AI and human rights, focusing on key issues such as privacy, bias, surveillance, access, and accountability. AI systems, while offering remarkable advancements in efficiency and capability, also pose risks to individual privacy and can perpetuate existing biases, leading to potential discrimination. The use of AI in surveillance raises ethical concerns about the (...) balance between security and civil liberties. Additionally, disparities in access to AI technologies can exacerbate inequalities, affecting human rights across different populations. This paper explores these challenges and proposes frameworks for ensuring that AI development and deployment adhere to human rights principles. By analyzing current practices and policies, the study aims to provide actionable recommendations for creating a more equitable and responsible AI landscape. (shrink)

Examining previous discussions on how to construe the concepts of gender and race, we advocate what we call strategic conceptual engineering. This is the employment of a (possibly novel) concept for specific epistemic or social aims, concomitant with the openness to use a different concept (e.g., of race) for other purposes. We illustrate this approach by sketching three distinct concepts of gender and arguing that all of them are needed, as they answer to different social aims. The first concept (...) serves the aim of identifying and explaining gender-based discrimination. It is similar to Haslanger’s well-known account, except that rather than offering a definition of ‘woman’ we focus on ‘gender’ as one among several axes of discrimination. The second concept of gender is to assign legal rights and social recognitions, and thus is to be trans-inclusive. We argue that this cannot be achieved by previously suggested concepts that include substantial gender-related psychological features, such as awareness of social expectations. Instead, our concept counts someone as being of a certain gender solely based on the person’s self-identification with this gender. The third concept of gender serves the aim of personal empowerment by means of one’s gender identity. In this context, substantial psychological features and awareness of one’s social situation are involved. While previous accounts of concepts have focused on their role in determining extensions, we point to contexts where a concept’s role in explanation and moral reasoning can be more important. (shrink)

Artificial Intelligence (AI) and Machine Learning (ML) are transforming the insurance industry by optimizing risk assessment, fraud detection, and customer service. However, the rapid adoption of these technologies raises significant ethical concerns, particularly regarding bias and fairness. This chapter explores the ethical challenges of using AI and ML in insurance risk management, focusing on bias mitigation and fairness enhancement strategies. By analyzing real-world case studies, regulatory frameworks, and technical methodologies, this chapter aims to provide a roadmap for developing ethical (...) AI/ML systems in the insurance sector. It highlights the importance of transparency, accountability, and inclusivity in ensuring equitable outcomes. (shrink)

The use of Artificial Intelligence (AI) in educational gamification marks a significant advancement, transforming traditional learning methods by offering interactive, adaptive, and personalized content. This approach makes historical content more relatable and promotes active learning and exploration. This research presents an innovative approach to heritage education, combining AI and gamification, explicitly targeting the Silk Roads. It represents a significant progression in a series of research, transitioning from basic 2D textual interactions to a 3D environment using photogrammetry, combining historical authenticity (...) and immersive gameplay. It features AI-driven characters developed in Unreal Engine, which provides heritage insights augmented by personalized interactions, highlighting the importance of collaborative tools like interactive quizzes for enriching education and promoting critical thinking and community among learners. In conclusion, this research underlines the transformative impact of AI and gamification in heritage education enhancement, integrating real-time voice conversation and interactive pedagogical methods into immersive 3D environments as a supplementary tool. (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)

The increasing use of Generative AI raises many ethical, philosophical, and legal issues. A key issue here is uncertainties about how different degrees of Generative AI assistance in the production of text impacts assessments of the human authorship of that text. To explore this issue, we developed an experimental mixed methods survey study (N = 602) asking participants to reflect on a scenario of a human author receiving assistance to write a short novel as part of a 3 (high, medium, (...) or low degrees of assistance) X 2 (human or AI assistant) factorial design. We found that, for a human author, the degree of assistance they receive matters for our assessments of their level of authorship, creatorship, and responsibility, but not what or who rendered that assistance, although it was more important to disclose human rather than AI assistance. However, in our assessments of the assisting agent, human assistants were viewed as warranting higher rates of authorship, creatorship, and responsibility, compared to AI assistants rendering the same level of support. These results help us to better understand emerging norms around collaborative human-AI generated text, with implications for other types of collaborative content creation. (shrink)

Beauty filters are automated photo editing tools that use artificial intelligence and computer vision to detect facial features and modify them, allegedly improving a face’s physical appearance and attractiveness. Widespread use of these filters has raised concern due to their potentially damaging psychological effects. In this paper, I offer an account that examines the effect that interacting with such filters has on self-perception. I argue that when looking at digitally-beautified versions of themselves, individuals are looking at AI-curated distorted mirrors. (...) This helps identify two potential cognitive effects of this behavior. First, it can elicit affective attitudes that change how individuals feel when looking at their unfiltered self-images. Second, exposure to filtered self-images might cause a perceptual normalization of such images. Finally, I argue that this form of distorted mirror gazing is a novel cultural practice for self-perception, and I highlight some ways in which this practice could be critically evaluated and ultimately changed. (shrink)

We introduce the Bias Network Approach (BNA) as a sociotechnical method for AI developers to identify, map, and relate biases across the AI development process. This approach addresses the limitations of what we call the "isolationist approach to AI bias," a trend in AI literature where biases are seen as separate occurrence linked to specific stages in an AI pipeline. Dealing with these multiple biases can trigger a sense of excessive overload in managing each potential bias individually or promote the (...) adoption of an uncritical approach to understanding the influence of biases in developers’ decision-making. The BNA fosters dialogue and a critical stance among developers, guided by external experts, using graphical representations to depict biased connections. To test the BNA, we conducted a pilot case study on the "waiting list” project, involving a small AI developer team creating a healthcare waiting list NPL model in Chile. The analysis showed promising findings: (i) the BNA aids in visualizing interconnected biases and their impacts, facilitating ethical reflection in a more accessible way; (ii) it promotes transparency in decision-making throughout AI development; and (iii) more focus is necessary on professional biases and material limitations as sources of bias in AI development. (shrink)

Traditional medical practices and relationships are changing given the widespread adoption of AI-driven technologies across the various domains of health and healthcare. In many cases, these new technologies are not specific to the field of healthcare. Still, they are existent, ubiquitous, and commercially available systems upskilled to integrate these novel care practices. Given the widespread adoption, coupled with the dramatic changes in practices, new ethical and social issues emerge due to how these systems nudge users into making decisions and changing (...) behaviours. This article discusses how these AI-driven systems pose particular ethical challenges with regards to nudging. To confront these issues, the value sensitive design approach is adopted as a principled methodology that designers can adopt to design these systems to avoid harming and contribute to the social good. The AI for Social Good factors are adopted as the norms constraining maleficence. In contrast, higher-order values specific to AI, such as those from the EU High-Level Expert Group on AI and the United Nations Sustainable Development Goals, are adopted as the values to be promoted as much as possible in design. The use case of Amazon Alexa's Healthcare Skills is used to illustrate this design approach. It provides an exemplar of how designers and engineers can begin to orientate their design programs of these technologies towards the social good. (shrink)

The alignment problem in artificial intelligence (AI) is a critical challenge that extends beyond the need to align future superintelligent systems with human values. This paper argues that even "merely intelligent" AI systems, built on current-gen technologies, pose existential risks due to their competence-without-comprehension nature. Current AI models, despite their advanced capabilities, lack intrinsic moral reasoning and are prone to catastrophic misalignment when faced with ethical dilemmas, as illustrated by recent controversies. Solutions such as hard-coded censorship and rule-based restrictions prove (...) inadequate, as they fail to imbue AI with a true understanding of moral complexities, making these systems vulnerable to manipulation. To address these risks, the paper explores a paradigm shift from competence to comprehension, emphasizing the importance of developing AI systems with an intrinsic value framework capable of moral reasoning. By adopting intrinsic teleology, AI could self-reflect, simulate other minds, and develop empathy, which would enable it to navigate ethical dilemmas in ways that are currently impossible. Using theories from philosophy, ethics, and AI research, the paper proposes the development of AI systems that can reason through moral dilemmas autonomously, suggesting that such systems would be safer and more alignable than current generation competence-driven AI. (shrink)

High-Performance Computing (HPC) has become a cornerstone for enabling breakthroughs in artificial intelligence (AI) by offering the computational resources necessary to process vast datasets and optimize complex algorithms. As AI models continue to grow in complexity, traditional HPC systems, reliant on central processing units (CPUs), face limitations in scalability, efficiency, and speed. Emerging technologies like quantum computing and specialized hardware such as Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), and Field Programmable Gate Arrays (FPGAs) are poised to address these (...) challenges. This research paper explores various HPC techniques used to optimize and accelerate AI algorithms, focusing on quantum computing’s potential for parallelism and specialized hardware's capabilities in delivering faster computation and energy efficiency. It delves into current advancements, comparative analyses of different HPC methods, and the integration of hybrid quantum-classical approaches to further enhance AI optimization. The study also examines the challenges of implementing these technologies at scale, with an eye toward the future of AI acceleration and the role of HPC in maintaining energy efficiency while meeting computational demands. Through this investigation, we aim to provide a comprehensive overview of how quantum computing and specialized hardware are reshaping the landscape of AI, paving the way for more advanced, efficient, and sustainable AI solutions. (shrink)

The so-called Baldwin Effect generally says how learning, as a form of ontogenetic adaptation, can influence the process of phylogenetic adaptation, or evolution. This idea has also been taken into computation in which evolution and learning are used as computational metaphors, including evolving neural networks. This paper presents a technique called evolving self-taught neural networks – neural networks that can teach themselves without external supervision or reward. The self-taught neural network is intrinsically motivated. Moreover, the self-taught neural (...) network is the product of the interplay between evolution and learning. We simulate a multi-agent system in which neural networks are used to control autonomous agents. These agents have to forage for resources and compete for their own survival. Experimental results show that the interaction between evolution and the ability to teach oneself in self-taught neural networks outperform evolution and self-teaching alone. More specifically, the emergence of an intelligent foraging strategy is also demonstrated through that interaction. Indications for future work on evolving neural networks are also presented. (shrink)

Solving Typhoon Turbulence Using the Universal Formula -/- By Angelito Malicse -/- Introduction -/- Typhoons are among the most destructive natural phenomena, bringing extreme winds, heavy rainfall, and turbulent ocean currents. The chaotic turbulence within a typhoon makes it difficult to predict and control, causing widespread devastation to coastal regions, infrastructure, and human lives. Despite advancements in meteorology and fluid dynamics, the turbulence inside typhoons remains a challenge for accurate forecasting and disaster mitigation. -/- By applying my universal formula, (...) based on three fundamental laws of nature, we can develop a structured and effective approach to understanding and controlling typhoon turbulence. This essay explores how the Law of Karma (causality and system integrity), the Law of Balance in Nature, and the Law of Feedback Regulation provide the ultimate solution to typhoon turbulence. -/- Understanding Typhoon Turbulence Using the Universal Formula -/- Typhoons form due to energy imbalances in the atmosphere and ocean, driven by heat, pressure differences, and moisture content. The chaotic turbulence inside a typhoon is the result of: -/- 1. Unstable air currents caused by pressure and temperature gradients. -/- 2. Vortex formation driven by Earth’s rotation and wind shear. -/- 3. Excess energy accumulation in warm ocean waters. -/- By examining typhoon turbulence through the three universal laws, we can develop a scientific strategy to predict, weaken, and potentially neutralize typhoons before they cause destruction. -/- 1. The Law of Karma: Cause, Effect, and Systemic Integrity -/- The Law of Karma states that every effect has a cause, and for a system to function properly, it must be free of defects. A typhoon is an effect caused by imbalances in atmospheric and oceanic conditions. If these imbalances are corrected, typhoon turbulence can be weakened or prevented. -/- Applying the Law of Karma to Typhoon Control -/- ✅ Eliminate the root cause: Typhoons are fueled by warm ocean waters. Artificial cooling methods, such as cloud seeding or deep-sea upwelling (bringing cold water to the surface), could reduce the heat energy supply and prevent typhoon formation. -/- ✅ Detect turbulence before it intensifies: AI-driven weather models can predict early-stage typhoon turbulence and allow preemptive interventions. AI can identify defective atmospheric conditions (such as extreme heat buildup in ocean currents) and trigger mitigation efforts. -/- ✅ Disrupt vortex formation: Since typhoons rely on rotational forces, deploying strategically placed atmospheric pressure disturbances (via drones or cloud-seeding techniques) could disrupt the vortex before it gains full strength. -/- By identifying and neutralizing the causes of typhoon turbulence, we ensure that the system remains balanced, preventing large-scale destruction. -/- 2. The Law of Balance in Nature -/- The Law of Balance states that all systems naturally seek equilibrium. When a typhoon forms, it represents an extreme imbalance in the Earth’s energy system—excess heat and moisture create an unstable feedback loop of wind and turbulence. -/- Restoring Balance to Weaken Typhoon Turbulence -/- ✅ Regulating ocean surface temperature: Large-scale ocean-based temperature regulation systems (such as reflective films or artificial cooling platforms) can prevent typhoon formation by reducing the excess heat energy that fuels storms. -/- ✅ Breaking the pressure imbalance: Typhoons depend on a low-pressure core that intensifies wind speeds. By artificially increasing local air pressure using ionization or controlled cloud seeding, the extreme wind turbulence can be diffused before it reaches destructive levels. -/- ✅ Balancing atmospheric moisture: AI-assisted weather control could help regulate humidity levels through strategic rainfall induction in high-risk areas, ensuring that water vapor does not accumulate excessively to fuel a typhoon. -/- Since all systems follow natural equilibrium, strategically rebalancing temperature, pressure, and humidity will neutralize extreme typhoon turbulence. -/- 3. The Law of Feedback Regulation -/- The Law of Feedback Regulation states that all systems operate through feedback loops, where continuous monitoring and adjustment are necessary for stability. Typhoon turbulence follows feedback patterns, which means it can be tracked, analyzed, and counteracted using real-time data. -/- Developing an AI-Driven Typhoon Suppression System -/- ✅ AI-powered real-time monitoring: Advanced satellites, ocean buoys, and drones can provide instant feedback on typhoon development, allowing for proactive interventions rather than reactive disaster response. -/- ✅ Adaptive wind and pressure control: Deploying AI-controlled drones or weather balloons to release cooling agents, modify air pressure, or disperse cloud formations could interrupt the storm's feedback loop, reducing its intensity. -/- ✅ AI-driven weather forecasting for early response: Using deep learning algorithms, AI can accurately predict turbulence patterns weeks in advance, allowing governments to take preventive action instead of just preparing for disaster relief. -/- By implementing real-time monitoring and intervention, we can use feedback regulation to weaken or redirect typhoons before they cause catastrophic damage. -/- The Future of Typhoon Control with the Universal Formula -/- By integrating my universal formula into meteorology and disaster prevention, we can develop a comprehensive AI-driven typhoon suppression system that follows the three fundamental laws of nature: -/- 1. Law of Karma (Causality and Systemic Integrity): Address the root causes of typhoon formation by eliminating atmospheric and oceanic imbalances. -/- 2. Law of Balance in Nature: Restore equilibrium by regulating ocean temperatures, air pressure, and humidity. -/- 3. Law of Feedback Regulation: Use AI-driven real-time monitoring and intervention to weaken or prevent typhoons. -/- Through advanced AI, environmental engineering, and a deep understanding of nature’s laws, we can move beyond simply predicting typhoons and start actively controlling their development. -/- Conclusion -/- Typhoon turbulence, long considered an unpredictable force of nature, is fundamentally a problem of imbalance, energy accumulation, and feedback-driven intensification. By applying my universal formula, we can shift from passive disaster response to active typhoon control, using AI, environmental intervention, and natural equilibrium principles to prevent devastation. -/- This solution is not just theoretical—it aligns with the fundamental laws governing all natural systems. Once implemented, it will mark the beginning of a new era where humanity can harness nature’s own principles to create balance, stability, and protection from extreme weather. -/- . (shrink)

The idea of artificial intelligence for social good is gaining traction within information societies in general and the AI community in particular. It has the potential to tackle social problems through the development of AI-based solutions. Yet, to date, there is only limited understanding of what makes AI socially good in theory, what counts as AI4SG in practice, and how to reproduce its initial successes in terms of policies. This article addresses this gap by identifying seven ethical factors that are (...) essential for future AI4SG initiatives. The analysis is supported by 27 case examples of AI4SG projects. Some of these factors are almost entirely novel to AI, while the significance of other factors is heightened by the use of AI. From each of these factors, corresponding best practices are formulated which, subject to context and balance, may serve as preliminary guidelines to ensure that well-designed AI is more likely to serve the social good. (shrink)

Two art exhibitions, “Training Humans” and “Making Faces,” and the accompanying essay “Excavating AI: The politics of images in machine learning training sets” by Kate Crawford and Trevor Paglen, are making substantial impact on discourse taking place in the social and mass media networks, and some scholarly circles. Critical scrutiny reveals, however, a self-contradictory stance regarding informed consent for the use of facial images, as well as serious flaws in their critique of ML training sets. Our analysis underlines the (...) non-negotiability of informed consent when using human data in artistic and other contexts, and clarifies issues relating to the description of ML training sets. (shrink)