The increasing use of artificial intelligence (AI) systems in our daily life through various applications, services, and products explains the significance of trust/distrust in AI from a user perspective. AI-driven systems have significantly diffused into various fields of our lives, serving as beneficial tools used by human agents. These systems are also evolving to act as co-assistants or semi-agents in specific domains, potentially influencing human thought, decision-making, and agency. Trust/distrust in AI plays the role of a regulator and could significantly (...) control the level of this diffusion, as trust can increase, and distrust may reduce the rate of adoption of AI. Recently, varieties of studies have paid attention to the variant dimension of trust/distrust in AI, and its relevant considerations. In this systematic literature review, after conceptualization of trust in the current AI literature review, we will investigate trust in different types of human-Machine interaction, and its impact on technology acceptance in different domains. In addition to that, we propose a taxonomy of technical (i.e., safety, accuracy, robustness) and non-technical axiological (i.e., ethical, legal, and mixed) trustworthiness metrics, and some trustworthy measurements. Moreover, we examine some major trust-breakers in AI (e.g., autonomy and dignity threat), and trust makers; and propose some future directions and probable solutions for the transition to a trustworthy AI. (shrink)

The Salivaomics Knowledge Base (SKB) is designed to serve as a computational infrastructure that can permit global exploration and utilization of data and information relevant to salivaomics. SKB is created by aligning (1) the saliva biomarker discovery and validation resources at UCLA with (2) the ontology resources developed by the OBO (Open Biomedical Ontologies) Foundry, including a new Saliva Ontology (SALO). We define the Saliva Ontology (SALO; http://www.skb.ucla.edu/SALO/) as a consensus-based controlled vocabulary of terms and relations dedicated to the salivaomics (...) domain and to saliva-related diagnostics following the principles of the OBO (Open Biomedical Ontologies) Foundry. The Saliva Ontology is an ongoing exploratory initiative. The ontology will be used to facilitate salivaomics data retrieval and integration across multiple fields of research together with data analysis and data mining. The ontology will be tested through its ability to serve the annotation ('tagging') of a representative corpus of salivaomics research literature that is to be incorporated into the SKB. Background Saliva (oral fluid) is an emerging biofluid for non-invasive diagnostics used in the detection of human diseases. The need to advance saliva research is strongly emphasized by the National Institute of Dental and Craniofacial Research (NIDCR), and is included in the NIDCR's 2004- 2009 expert panel long-term research agenda [1]. The ability to monitor health status, disease onset, progression, recurrence and treatment outcome through noninvasive means is highly important to advancing health care management. Saliva is a perfect medium to be explored for personalized individual medicine including diagnostics, offering a non-invasive, easy to obtain means for detecting and monitoring diseases. Saliva testing potentially allows the patient to collect their own saliva samples at home, yielding convenience for the patient and savings in health costs, and facilitating multiple sampling. Specimen collection is less objectionable to patients and easier in children and elderly individuals. Due to these advantages. (shrink)

Recent AI progress led to a boost in beneficial applications from multiple research areas including VR. Simultaneously, in this newly unfolding deepfake era, ethically and security-relevant disagreements arose in the scientific community regarding the epistemic capabilities of present-day AI. However, given what is at stake, one can postulate that for a responsible approach, prior to engaging in a rigorous epistemic assessment of AI, humans may profit from a self-questioning strategy, an examination and calibration of the experience of their own (...) epistemic agency – especially to counteract both intentional misdirection by unethical actors and unintentional epistemic self-sabotage. In this paper, we expound on a new avenue of utilizing AIVR tools to advance an AI-related misdirection awareness of humans in the deepfake era. Firstly, we harness scientific knowledge from the psychology and neuroscience of magic where the study of misdirection techniques is center stage. Secondly, we connect the latter to creativity research linking human creative potential to inspiration from the seemingly impossible. Overall, AIVR could become an empowering experiential testbed for human epistemic agency enabling a better rational evaluation of AI capabilities. However, a misuse of the same type of tools could yield AIVR safety risks if not counteracted preemptively. (shrink)

There is, in some quarters, concern about high–level machine intelligence and superintelligent AI coming up in a few decades, bringing with it significant risks for humanity. In other quarters, these issues are ignored or considered science fiction. We wanted to clarify what the distribution of opinions actually is, what probability the best experts currently assign to high–level machine intelligence coming up within a particular time–frame, which risks they see with that development, and how fast they see these developing. We thus (...) designed a brief questionnaire and distributed it to four groups of experts in 2012/2013. The median estimate of respondents was for a one in two chance that high-level machine intelligence will be developed around 2040-2050, rising to a nine in ten chance by 2075. Experts expect that systems will move on to superintelligence in less than 30 years thereafter. They estimate the chance is about one in three that this development turns out to be ‘bad’ or ‘extremely bad’ for humanity. (shrink)

The AI and AI alignment communities have been instrumental in addressing existential risks, developing alignment methodologies, and promoting rationalist problem-solving approaches. However, as AI research ventures into increasingly uncertain domains, there is a risk of premature epistemic convergence, where prevailing methodologies influence not only the evaluation of ideas but also determine which ideas are considered within the discourse. This paper examines critical epistemic blind spots in AI alignment research, particularly the lack of predictive frameworks to differentiate problems necessitating general intelligence, (...) decentralized intelligence, or innovative alignment methodologies. We analyze how heuristic-based epistemic filtering—favoring ideas that conform to established norms over those assessed purely on logical merit—may inadvertently constrain the field's potential for groundbreaking solutions. The semblance of rigor, wherein social consensus and stylistic conformity replace foundational reasoning, further intensifies this concern. To mitigate these issues, we advocate for an epistemically adaptive research environment that emphasizes structured evaluation of paradigm-challenging ideas, cross-disciplinary collaboration, and strategies to identify and reduce epistemic overfitting. By broadening the epistemic frameworks governing AI alignment discourse, the field can progress beyond incremental improvements toward discovering entirely new solution paradigms. Maintaining epistemic inclusivity in AI safety research is not only an intellectual necessity but also a crucial step in preparing alignment efforts for the challenges posed by rapid AI advancements. (shrink)

A key assumption fuelling optimism about the progress of large language models (LLMs) in accurately and comprehensively modelling the world is that the truth is systematic: true statements about the world form a whole that is not just consistent, in that it contains no contradictions, but coherent, in that the truths are inferentially interlinked. This holds out the prospect that LLMs might in principle rely on that systematicity to fill in gaps and correct inaccuracies in the training data: consistency (...) and coherence promise to facilitate progress towards comprehensiveness in an LLM’s representation of the world. However, philosophers have identified compelling reasons to doubt that the truth is systematic across all domains of thought, arguing that in normative domains, in particular, the truth is largely asystematic. I argue that insofar as the truth in normative domains is asystematic, this renders it correspondingly harder for LLMs to make progress, because they cannot then leverage the systematicity of truth. And the less LLMs can rely on the systematicity of truth, the less we can rely on them to do our practical deliberation for us, because the very asystematicity of normative domains requires human agency to play a greater role in practical thought. (shrink)

The hypergrowth and hyperconnectivity of networks of artificial intelligence (AI) systems and algorithms increasingly cause our interactions with the world, socially and environmentally, more technologically mediated. AI systems start interfering with our choices or making decisions on our behalf: what we see, what we buy, which contents or foods we consume, where we travel to, who we hire, etc. It is imperative to understand the dynamics of human-computer interaction in the age of progressively more competent AI. This essay presents five (...) premises to serve as starting points for building our understanding of the “interactions’ in human-computer interactions. (shrink)

AI-supported methods for identifying and combating disinformation are progressing in their development and application. However, these methods face a litany of epistemic and ethical challenges. These include (1) robustly defining disinformation, (2) reliably classifying data according to this definition, and (3) navigating ethical risks in the deployment of countermeasures, which involve a mixture of harms and benefits. This paper seeks to expose and offer preliminary analysis of these challenges.

[This is the short version of: Müller, Vincent C. and Bostrom, Nick (forthcoming 2016), ‘Future progress in artificial intelligence: A survey of expert opinion’, in Vincent C. Müller (ed.), Fundamental Issues of Artificial Intelligence (Synthese Library 377; Berlin: Springer).] - - - In some quarters, there is intense concern about high–level machine intelligence and superintelligent AI coming up in a few dec- ades, bringing with it significant risks for human- ity; in other quarters, these issues are ignored or considered (...) science fiction. We wanted to clarify what the distribution of opinions actually is, what probability the best experts currently assign to high–level machine intelligence coming up within a particular time–frame, which risks they see with that development and how fast they see these developing. We thus designed a brief questionnaire and distributed it to four groups of experts. Overall, the results show an agreement among experts that AI systems will probably reach overall human ability around 2040-2050 and move on to superintelligence in less than 30 years thereafter. The experts say the probability is about one in three that this development turns out to be ‘bad’ or ‘extremely bad’ for humanity. (shrink)

It is generally assumed that existing artificial systems are not phenomenally conscious, and that the construction of phenomenally conscious artificial systems would require significant technological progress if it is possible at all. We challenge this assumption by arguing that if Global Workspace Theory (GWT) — a leading scientific theory of phenomenal consciousness — is correct, then instances of one widely implemented AI architecture, the artificial language agent, might easily be made phenomenally conscious if they are not already. Along the (...) way, we articulate an explicit methodology for thinking about how to apply scientific theories of consciousness to artificial systems and employ this methodology to arrive at a set of necessary and sufficient conditions for phenomenal consciousness according to GWT. (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)

AI and the Universal Law of Economic Balance: A Homeostatic Model for Sustainable Prosperity -/- Introduction -/- Modern economies are primarily driven by the profit motive, which, while encouraging innovation and efficiency, often leads to wage stagnation, wealth inequality, and resource exploitation. The imbalance between corporate profits, wages, purchasing power, and market demand has resulted in recurring economic crises, social unrest, and environmental degradation. -/- To resolve these systemic issues, economic policies must align with the universal law of balance in (...) nature, ensuring that financial systems function as self-regulating ecosystems rather than unstable, profit-driven markets. Achieving this balance requires both gradual reform and fundamental restructuring, first at the national level and then integrated into a global economic framework. -/- The key to Implementing and maintaining this balance lies in artificial intelligence (AI), which can analyze economic conditions in real time and adjust wages, prices, taxation, and resource distribution to maintain stability. However, to prevent AI from becoming a tool of economic control for powerful entities, it must be open-source, decentralized, and governed by ethical principles. -/- This essay explores how AI-driven economic balance can replace market-driven instability with a self-regulating, sustainable model that ensures fair wages, stable prices, and equitable wealth distribution. -/- The Problem: Profit Motive and Economic Imbalance -/- The traditional economic system relies on profit margins to sustain businesses. However, when companies focus on maximizing profits, several imbalances emerge: -/- 1. Lower Wages Reduce Purchasing Power -/- When businesses lower costs to remain competitive, wages stagnate. -/- Lower wages mean consumers cannot afford the products they produce. -/- 2. Weaker Demand Forces Businesses to Cut Costs Further -/- If people cannot buy goods, businesses cut jobs and wages further, deepening the problem. -/- Economic growth becomes dependent on debt, leading to financial instability. -/- 3. Wealth Concentration Creates Social and Political Instability -/- Extreme profit accumulation among a small elite leads to economic inequality. -/- Governments either increase taxes (which businesses resist) or cut social services (which leads to public unrest). -/- This cycle of imbalance proves that unregulated profit-seeking is unsustainable. Instead, an economy must be structured to self-regulate, ensuring a balance between wages, prices, profits, and demand. -/- A Homeostatic Economic Model: Balancing Wages, Prices, and Profits -/- A homeostatic economic model ensures that all components of the economy—wages, prices, profit margins, taxation, and demand—adjust dynamically to maintain stability. The following reforms would create this balance: -/- 1. Wage-Price Equilibrium -/- Wages must be indexed to productivity and inflation, ensuring purchasing power remains stable. -/- Profit-sharing incentives should be standardized, allowing workers to benefit from company success. -/- 2. Profit Regulation Without Killing Innovation -/- Essential industries (e.g., healthcare, food, energy) should have profit caps to prevent price exploitation. -/- Businesses that reinvest in worker wages, technology, or sustainability should receive tax breaks. -/- 3. Market Demand and Supply Stability -/- Governments and businesses must collaborate to prevent artificial scarcity and price manipulation. -/- Tax policies should support lower-income groups, ensuring their purchasing power remains intact. -/- 4. Dynamic Feedback Mechanism for Economic Stability -/- Economic decisions must be based on real-time data, avoiding speculation-based policies. -/- Policies should be adjustable based on changes in market conditions, preventing booms and busts. -/- These policies require both national reforms and global cooperation, ensuring that no single country or corporation can disrupt the balance for personal gain. -/- AI as the Key to Homeostatic Economic Balance -/- Maintaining economic balance in real time is too complex for human policymakers alone. This is where artificial intelligence (AI) can play a critical role in: -/- 1. Real-Time Wage and Price Adjustments -/- AI can analyze inflation, productivity, and cost of living to dynamically adjust wages and prices. -/- 2. Profit Optimization Without Exploitation -/- AI can track corporate profits and suggest profit-reinvestment policies that benefit workers and society. -/- 3. Smart Taxation and Wealth Redistribution -/- AI-driven progressive taxation models ensure fair wealth distribution while preventing economic stagnation. -/- 4. Market Demand and Supply Optimization -/- AI can predict consumer demand shifts and help businesses adjust production to prevent shortages or oversupply. -/- 5. Global Trade and Resource Management -/- AI can analyze trade patterns to ensure fair pricing and sustainable resource allocation worldwide. -/- 6. Public Awareness and Education -/- AI can identify economic misconceptions and suggest educational initiatives to promote financial literacy. -/- For AI to function effectively in this role, it must operate under ethical and decentralized governance to serve humanity rather than corporate or political interests. -/- Ensuring AI Remains Open-Source and Decentralized -/- To prevent AI from being misused as a tool of economic control, its development and governance must follow these principles: -/- 1. Open-Source AI for Economic Balance -/- AI models should be transparent and publicly auditable to prevent hidden biases. -/- Developers from multiple disciplines should contribute to ensure fairness and accuracy. -/- 2. Decentralized Governance Model -/- AI should be managed through global economic councils, not a single government or corporation. -/- Blockchain technology can ensure transparency in AI-driven economic decisions. -/- 3. Ethical Safeguards Against Manipulation -/- AI decisions should prioritize sustainability and human well-being over profit. -/- Economic policies should be auditable in real-time by independent researchers and civil organizations. -/- Failsafe mechanisms must prevent AI from implementing harmful policies due to data misinterpretation. -/- By ensuring AI-driven economic balance is open-source, decentralized, and ethically governed, it can create a self-correcting system that prevents economic crises, stabilizes wealth distribution, and ensures long-term prosperity. -/- Implementation Strategy: Gradual Reform Leading to Systemic Change -/- Since economic transition cannot happen overnight, a phased approach is necessary: -/- Phase 1: National-Level Reforms -/- 1. Implement wage-price indexing and profit-sharing policies. -/- 2. Reduce corporate taxes for businesses that reinvest in workers and sustainability. -/- 3. Introduce regulated price stabilization for essential goods. -/- Phase 2: Global Economic Integration -/- 1. Establish international economic councils to guide balance-driven policies. -/- 2. Reform global trade agreements to prioritize fair wages and sustainability. -/- 3. Create a universal economic sustainability index to track progress. -/- Phase 3: AI-Governed Dynamic Economic System -/- 1. Develop real-time AI monitoring for wage-price adjustments. -/- 2. Implement AI-driven smart taxation to prevent wealth inequality. -/- 3. Ensure AI operates under decentralized, transparent governance. -/- Conclusion: The Future of an AI-Driven Balanced Economy -/- By integrating AI with economic policy, we can create a self-regulating economic system that follows the universal law of balance, preventing both extreme poverty and extreme wealth accumulation. This homeostatic economic model ensures that profits, wages, prices, and demand remain in equilibrium, fostering sustainable prosperity for all. -/- . (shrink)

Termed the 'Made-by-Human Hypothesis,' I challenge the commonly accepted notion that Artificial Intelligence (AI) is exclusively crafted by humans, emphasizing its impediment to progress. I argue that influences beyond human agency significantly shape AI's trajectory. Introducing the 'Hybrid Hypothesis,' I suggest that the creation of AI is multi-sourced; methods such as evolutionary algorithms influencing AI originate from diverse sources and yield varied impacts. I argue that the development of AI models will increasingly adopt a 'Human+' hybrid composition, where human (...) expertise merges with AI's intrinsic mechanisms, which themselves are influenced by non-human sources. The Hybrid Hypothesis posits that the origin of AI extends beyond human influence, prompting a thorough exploration of unresolved issues in the field of artificial intelligence. (shrink)

There are two types of artificial general intelligence (AGI) safety solutions: global and local. Most previously suggested solutions are local: they explain how to align or “box” a specific AI (Artificial Intelligence), but do not explain how to prevent the creation of dangerous AI in other places. Global solutions are those that ensure any AI on Earth is not dangerous. The number of suggested global solutions is much smaller than the number of proposed local solutions. Global solutions can be divided (...) into four groups: 1. No AI: AGI technology is banned or its use is otherwise prevented; 2. One AI: the first superintelligent AI is used to prevent the creation of any others; 3. Net of AIs as AI police: a balance is created between many AIs, so they evolve as a net and can prevent any rogue AI from taking over the world; 4. Humans inside AI: humans are augmented or part of AI. We explore many ideas, both old and new, regarding global solutions for AI safety. They include changing the number of AI teams, different forms of “AI Nanny” (non-self-improving global control AI system able to prevent creation of dangerous AIs), selling AI safety solutions, and sending messages to future AI. Not every local solution scales to a global solution or does it ethically and safely. The choice of the best local solution should include understanding of the ways in which it will be scaled up. Human-AI teams or a superintelligent AI Service as suggested by Drexler may be examples of such ethically scalable local solutions, but the final choice depends on some unknown variables such as the speed of AI progress. (shrink)

Artificial intelligence (AI) is expected to revolutionise the practice of medicine. Recent advancements in the field of deep learning have demonstrated success in variety of clinical tasks: detecting diabetic retinopathy from images, predicting hospital readmissions, aiding in the discovery of new drugs, etc. AI’s progress in medicine, however, has led to concerns regarding the potential effects of this technology on relationships of trust in clinical practice. In this paper, I will argue that there is merit to these concerns, since (...) AI systems can be relied on, and are capable of reliability, but cannot be trusted, and are not capable of trustworthiness. Insofar as patients are required to rely on AI systems for their medical decision-making, there is potential for this to produce a deficit of trust in relationships in clinical practice. (shrink)

Why interdisciplinary research in AI is so important, according to Jurassic Park. -/- “Your scientists were so preoccupied with whether or not they could, they didn’t stop to think if they should.” -/- I think this quote resonates with us now more than ever, especially in the world of technological development. The writers of Jurassic Park were years ahead of their time with this powerful quote. -/- As we build new technology, and we push on to see what can actually (...) be achieved there is an undertone of sales to whatever we build. The end product must be sold somewhere and to someone. This can derail any good intentions, just as building a resort full of dinosaurs was sold as a fun attraction, we see later in the film that it became a resort of terror. -/- In the field of AI we are certainly late to the party with ethics and regulation. Indeed, even existing modelling protocols have been, in many cases, circumvented or potentially ignored. This has led to a widening of the gap in the interdisciplinary field of AI. This is compounded by pop culture representations of AI and the Ethicist’s potential lack of knowledge surrounding technical progression in the field of AI. There are now seemingly two separate branches that ought to be in sync. The first branch is a group of Philosophers led by the Author of Superintelligence, Nick Bostrom, who believe that there is a singularity where AI takes over the world and starts to kill off humans. The second branch is the Technical Cohort who are led by companies such as Google and Deepmind. The remit of these developers is to see what can be developed and produced. Ultimately a separate sales team will determine what products can be sold. -/- We have seen multiple failures of AI due to lack of interdisciplinary discourse. Some cases include misallocated or cut off financial support and healthcare. This indicates the prevalence of issues that can grow and become ever more complex. -/- So, we have to ask ourselves two questions: One, do the Philosophers have a point? Two, does that point refer to humans programming AI in such a way that the destruction of the human race is its optimal aim in order to create a better future world? -/- Is the development of AI that different to an island of genetically engineered Dinosaurs? -/- The only solution, in my view, is interdisciplinary discourse. (shrink)

The challenge of AI alignment is not just a technological issue but fundamentally an epistemic one. AI safety research predominantly relies on empirical validation, often detecting failures only after they manifest. However, certain risks—such as deceptive alignment and goal misspecification—may not be empirically testable until it is too late, necessitating a shift toward leading-indicator logical reasoning. This paper explores how mainstream AI research systematically filters out deep epistemic insight, hindering progress in AI safety. We assess the rarity of such (...) insights, conduct an experiment testing large language models for epistemic blind spots, and propose structural reforms, including contrarian epistemic screening, decentralized collective intelligence mechanisms, and epistemic challenge platforms. Our findings suggest that while AGI may emerge through incremental engineering, ensuring its safe alignment likely requires an epistemic paradigm shift. (shrink)

Invited papers from PT-AI 2011. - Vincent C. Müller: Introduction: Theory and Philosophy of Artificial Intelligence - Nick Bostrom: The Superintelligent Will: Motivation and Instrumental Rationality in Advanced Artificial Agents - Hubert L. Dreyfus: A History of First Step Fallacies - Antoni Gomila, David Travieso and Lorena Lobo: Wherein is Human Cognition Systematic - J. Kevin O'Regan: How to Build a Robot that Is Conscious and Feels - Oron Shagrir: Computation, Implementation, Cognition.

Human emotional expressions can communicate the emotional state of the expresser, but they can also communicate appeals to perceivers. For example, sadness expressions such as crying request perceivers to aid and support, and anger expressions such as shouting urge perceivers to back off. Some contemporary artificial intelligence (AI) systems can mimic human emotional expressions in a (more or less) realistic way, and they are progressively being integrated into our daily lives. How should we respond to them? Do we have reasons (...) to reply to the appeals made by AI emotional expressions? In this paper, we examine the conditions under which AI emotional expressions could give us prudential or even moral reasons to change our behavior. We argue that these conditions do not depend on whether the emotional expression is genuine or not, but rather on the presence of features some of which can be implemented in emotive AI given our current level of technological development. We extract recommendations and warnings for the development of emotive AI. (shrink)

Current approaches to AI governance often fall short in anticipating a future where AI agents manage critical tasks, such as financial operations, administrative functions, and beyond. As AI agents may eventually delegate tasks among themselves to optimize efficiency, understanding the foundational principles of human value exchange could offer insights into how AI-driven economies might operate. Just as trust and value exchange are central to human interactions in open marketplaces, they may also be critical for enabling secure and efficient interactions among (...) AI agents. While cryptocurrencies could serve as the foundation for monetizing value exchange in a collaboration and delegation dynamic among AI agents, a critical question remains: how can these agents reliably determine whom to trust, and how can humans ensure meaningful oversight and control as an economy of AI agents scales and evolves? This paper is a call for a collective exploration of cryptoeconomic incentives, which can help design decentralized governance systems that allow AI agents to autonomously interact and exchange value while ensuring human oversight via progressive decentralization. Toward this end, I propose a research agenda to address the question of agent-to-agent trust using AgentBound Tokens (ABTs)—non-transferable, non-fungible tokens uniquely tied to individual AI agents, akin to Soulbound tokens for humans in Web3. By staking ABTs as collateral for autonomous actions within an agent-to-agent network via a proof-of-stake mechanism, agents may be incentivized towards ethical behavior, and penalties for misconduct are automatically enforced. (shrink)

The goal of this project is to create a prototype smart farm using Internet of Things (IoT) technology. Developing an Internet of Things feeding control system and designing and implementing environmental monitoring and control systems for farm housing are the primary objectives. The development method uses open-source hardware and software and is conducted in pig farms in the Thai region of Nakhon Si Thammara. An ESP8266 Wi-Fi microcontroller is crucial to the system design, allowing several sensors to be connected to (...) the server and actuators to be controlled. The MQTT protocol is used to transmit data over a secure wireless local network. The data flow is designed using Node-RED, and storage is handled using a NoSQL database such as InFluxDB. The feeding control system achieved a 100% compliance rate by following instructions continuously. End customers can utilise a web application on their PCs or cellphones to obtain real-time data and operate the system. Furthermore, the collected data is processed and analysed as big data, which facilitates the analysis of climate change trends in the vicinity of the study site and forecasting. The overall performance rating of the system received great marks from users, averaging 4.40 out of 5 with a 0.46 standard deviation. As a result, the created IoT system functions as a prototype that may be further enhanced for use in bigger farms or other agricultural environments. (shrink)

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

The transformative power of artificial intelligence (AI) is coming to philosophy—the only question is the degree to which philosophers will harness it. In this paper, we argue that the application of AI tools to philosophy could have an impact on the field comparable to the advent of writing, and that it is likely that philosophical progress will significantly increase as a consequence of AI. The role of philosophers in this story is not merely to use AI but also to (...) help develop it and theorize about it. In fact, we argue that philosophers have a prima facie obligation to spend significant effort in doing so, at least insofar as they should spend effort philosophizing. (shrink)

Susan Schneider (2019) has proposed two new tests for consciousness in AI (artificial intelligence) systems, the AI Consciousness Test and the Chip Test. On their face, the two tests seem to have the virtue of proving satisfactory to a wide range of consciousness theorists holding divergent theoretical positions, rather than narrowly relying on the truth of any particular theory of consciousness. Unfortunately, both tests are undermined in having an ‘audience problem’: Those theorists with the kind of architectural worries that motivate (...) the need for such tests should, on similar grounds, doubt that the tests establish the existence of genuine consciousness in the AI in question. Nonetheless, the proposed tests constitute progress, as they could find use by some theorists holding fitting views about consciousness and perhaps in conjunction with other tests for AI consciousness. (shrink)

This article, “From Iron to AI: The Evolution of the Sources of State Power,” examines the progression of fundamental resources that have historically underpinned state power, from tangible assets like land and iron to modern advancements in artificial intelligence (AI). It traces the development of state power through three significant eras: the ancient period characterized by land, population, horses, and iron; the industrial era marked by railroads, coal, and electricity; and the contemporary digital age dominated by the Internet and emerging (...) technologies. Focusing on AI, the article explores its similarities and differences compared to previous sources of power, highlighting its transformative nature, potential for new industries, and unique challenges related to intellectual resources, rapid advancements, and global interconnectedness. The impact of AI on state power is analyzed through economic competitiveness, military capabilities, governance, ethical and social implications, and geopolitical shifts. (shrink)

The recent progression in AI, nanomedicine and robotics have increased concerns about ethics, policy and law. The increasing complexity and hybrid nature of AI and nanotechnologies impact the functionality of “law in action” which can lead to legal uncertainty and ultimately to a public distrust. There is an immediate need of collaboration between Central Asian biomedical scientists, AI engineers and academic lawyers for the harmonization of AI, nanomedicines and robotics in Central Asian legal system.

This article analyzes the potential impacts of Xenoaccelerationism and Accelerationism on the development of artificial intelligence (AI) and machine learning (ML). It examines how these speculative philosophies, which advocate technological acceleration and integration of diverse knowledge, may shape priorities and approaches in AI research and development. The risks and benefits of aligning AI progress with accelerationist values are discussed.

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)

The rapid advancement of Artificial Intelligence (AI) and the developments of Large Language Models (LLMs) like Generative Pretrained Transformers (GPTs) have significantly influenced content creation in scholarly communication and across various fields. This paper presents a comparative analysis of the content reliability between human-generated and GPT-generated scholarly articles. Recent developments in AI suggest that GPTs have become capable in generating content that can mimic human language to a greater extent. This highlights and raises questions about the quality, accuracy, and reliability (...) of such content, especially in academic contexts. Statistical evaluations and quantitative assessments conducted in this study uncover key differences in content accuracy, coherence, citation usage, and overall reliability between human and GPT-generated articles. The paper also examines the potential biases, the role of context, and the implications of AI-generated content for the future of scholarly communication. The study concludes with framework for predictive model indicating the potential future impact of GPT-driven content creation and recommendations for ensuring content quality and ethical considerations in AI usage. (shrink)

The healthcare sector is undergoing a transformative shift towards smart healthcare, driven by advancements in technology, including Artificial Intelligence (AI) and Blockchain. As healthcare systems generate vast amounts of data from multiple sources, such as electronic health records (EHRs), medical imaging, wearable devices, and sensor-based monitoring, the challenge lies in securely integrating and analyzing this data for real-time, actionable insights. Blockchain technology, with its decentralized, immutable, and transparent framework, offers a robust solution for securing data integrity, privacy, and sharing across (...) healthcare networks. AI, on the other hand, enables advanced data analytics, providing healthcare professionals with predictive insights, early diagnosis, and personalized treatment plans. This paper explores the integration of Blockchain and AI for advanced data management in smart healthcare. We propose a system that utilizes Blockchain for secure and transparent data storage, ensuring that healthcare data from various sources is tamper-proof and can be shared across multiple stakeholders. AI algorithms, particularly machine learning and deep learning models, are applied to the integrated data to detect patterns, predict disease progression, and optimize treatment plans. By leveraging Blockchain, healthcare data can be securely shared across providers and patients, ensuring privacy and regulatory compliance, while AI technologies provide the intelligence required for enhancing clinical decision-making. This integrated system not only addresses the growing concerns surrounding data security and privacy but also promotes interoperability and collaboration within the healthcare ecosystem. The paper discusses the challenges, opportunities, and future directions of Blockchain and AI integration in smart healthcare systems, highlighting how this convergence can lead to more efficient, secure, and personalized healthcare services. (shrink)

The Inefficiency of the Biological Brain and the Importance of AI for the Next Generation -/- The human brain, often considered the pinnacle of evolutionary design, is an extraordinary organ capable of creativity, critical thinking, and adaptation. However, despite its remarkable abilities, it is inherently inefficient when compared to artificial intelligence (AI) systems in certain domains. The inefficiencies of the biological brain, coupled with the rapid development of AI technology, underline why artificial general intelligence (AGI) holds immense promise for shaping (...) the future of the next generation. By complementing and extending human cognition, AI can address many of the limitations of the biological brain and equip young people with tools to solve the world’s most pressing challenges. -/- The Inefficiencies of the Biological Brain -/- The biological brain operates under significant constraints that limit its potential. Some of these inefficiencies include: -/- 1. Cognitive Biases and Emotional Interference The human brain is subject to cognitive biases, such as confirmation bias, which can hinder rational decision-making. Emotional states, while vital for human experience, can cloud judgment and lead to impulsive or irrational behavior. These limitations often result in flawed decisions, particularly in complex or high-stakes situations. -/- 2. Limited Processing Power and Memory While the brain is capable of parallel processing, it has finite processing power and memory storage. Tasks requiring large-scale computations, vast data analysis, or instantaneous recall of details often exceed its capabilities. For instance, analyzing terabytes of data to identify patterns or trends is beyond the capacity of any single individual. -/- 3. Slow Learning and Knowledge Retention Learning is a time-intensive process for humans. Acquiring expertise in a single domain can take years, and even then, the retention of knowledge often deteriorates over time. The brain also struggles to adapt quickly to entirely new fields or technologies without substantial effort. -/- 4. Physical Vulnerabilities The brain is biologically constrained by aging, disease, and trauma. These vulnerabilities can result in cognitive decline, memory loss, or even the complete inability to function. These natural limitations cannot yet be fully mitigated through biological means. -/- 5. Bias from Evolutionary Pressures The human brain evolved for survival in environments vastly different from the modern world. Traits like fear, aggression, or tribalism, which were once advantageous, can now lead to social conflicts, inefficiency, and short-term thinking. -/- Why AI and AGI Are Crucial for the Next Generation -/- Artificial intelligence has the potential to address the inefficiencies of the biological brain and create a brighter, more equitable future for the next generation. Its capabilities go far beyond traditional human cognition in several key areas: -/- 1. Enhanced Problem-Solving Abilities AI systems are capable of processing massive datasets, analyzing patterns, and generating solutions far more efficiently than the human brain. For example, AI is already transforming industries such as healthcare by diagnosing diseases with greater accuracy and speed. Future AGI could enable young people to tackle global challenges such as climate change, resource distribution, and poverty with unprecedented precision. -/- 2. Overcoming Human Biases Unlike the biological brain, which is prone to emotional and cognitive biases, well-designed AI systems can function based on objective logic and evidence. This could lead to more equitable policy-making, unbiased education systems, and transparent governance. -/- 3. Accelerated Learning and Knowledge Access AI-powered tools can revolutionize education by providing personalized learning experiences tailored to individual needs. With access to AI tutors and resources, young people can acquire skills and knowledge faster and more effectively than ever before, bridging educational gaps and fostering innovation. -/- 4. Augmentation of Human Potential By integrating AI with human cognition, individuals can expand their capabilities beyond natural limits. For example, brain-computer interfaces (BCIs) could allow people to directly interact with AI systems, enhancing memory, decision-making, and creativity. This symbiosis between humans and machines could redefine what it means to be human. -/- 5. Future-Proofing Against Global Challenges The next generation will face challenges that require solutions at a scale and complexity beyond human capacity. AI and AGI are indispensable for managing these challenges, from predicting pandemics and optimizing energy usage to navigating ethical dilemmas in an interconnected world. -/- Building a New Paradigm for the Next Generation -/- To ensure that AI benefits society as a whole, it is essential to integrate it into the foundational education and development of young people. Teaching students about AI, its capabilities, and ethical implications will empower them to use these tools responsibly. Furthermore, fostering collaboration between humans and AI, rather than fearing technological displacement, will cultivate a new paradigm where human creativity and machine efficiency coexist harmoniously. -/- Governments, educational institutions, and industries must work together to democratize access to AI technologies, ensuring that the benefits are shared equitably. Ethical frameworks should be developed to guide the use of AI, ensuring that it remains a tool for human progress rather than a source of inequality or harm. -/- Conclusion -/- The inefficiencies of the biological brain highlight the need for complementary technologies that can amplify human potential. AI and AGI represent the next frontier in human evolution, offering solutions to our cognitive and societal limitations. For the next generation, these technologies are not merely tools but essential companions in shaping a better, more sustainable world. By embracing AI and equipping young people with the skills to harness its power, humanity can transcend its limitations and unlock a future of unparalleled possibilities. -/- . (shrink)

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

I develop the anticipatory argument for the view that it is nomologically possible that some non-biological creatures are phenomenally conscious, including conventional, silicon-based AI systems. This argument rests on the general idea that we should make our beliefs conform to the outcomes of an ideal scientific process and that such an ideal scientific process would attribute consciousness to some possible AI systems. This kind of ideal scientific process is an ideal application of the iterative natural kind (INK) strategy, according to (...) which one should investigate consciousness by treating it as a natural kind which iteratively explains observable patterns and correlations between potentially consciousness-relevant features. The relevant AI systems are psychological duplicates. These are hypothetical non-biological creatures which share the coarse-grained functional organization of humans. I argue that an ideal application of the INK strategy would attribute consciousness to psychological duplicates because this gives rise to a simpler and more unifying explanatory account of biological and non-biological cognition. If my argument is sound, then creatures made from the same material as conventional AI systems can be conscious, thus removing one of the main uncertainties for assessing AI consciousness and suggesting that AI consciousness may be a serious near-term concern. (shrink)

Reforming All Countries Through the Universal Law of Balance: A Path to Global Stability and Progress -/- By Angelito Enriquez Malicse -/- Introduction -/- Throughout history, human societies have struggled with instability, conflict, economic inequality, environmental degradation, and governance failures. Despite technological advancements, many nations still face deep-rooted problems caused by imbalanced decision-making at both individual and collective levels. My universal formula, grounded in the universal law of balance in nature, offers a transformative solution to reform all countries and (...) create a world where governance, economics, education, and social structures operate in harmony. -/- By integrating this fundamental law of balance into global education systems, policy-making, and leadership training, societies can achieve sustainability, fairness, and progress. This essay explores how this universal formula can reform all countries and the positive effects it will bring to the entire world. -/- The Universal Law of Balance: A Foundation for Reform -/- The universal law of balance in nature states that all systems—biological, social, economic, and political—function optimally when they maintain equilibrium. Imbalance leads to corruption, inequality, overpopulation, environmental destruction, and human suffering. To achieve long-term global stability, this principle must be embedded in education, leadership, and governance. -/- 1. Reforming Education to Instill the Universal Law of Balance -/- Education is the foundation of societal change. Implementing the universal formula in education will: -/- Teach individuals how natural balance governs decision-making. Students will learn to evaluate choices scientifically rather than through emotional bias, misinformation, or outdated beliefs. -/- Encourage critical thinking and holistic problem-solving. Future generations will be trained to apply balanced solutions to complex global issues, such as economic inequality and climate change. -/- Bridge the gap between science and religion. Since religious dogma often influences social structures, the universal law of balance can provide a neutral, scientific approach that respects faith while promoting rational decision-making. -/- Train future leaders. Policymakers, economists, and scientists will be educated with a deep understanding of natural balance, preventing governance failures caused by greed, short-term thinking, and political manipulation. -/- 2. Reforming Leadership and Governance -/- Governments worldwide struggle with corruption, inefficiency, and poor decision-making due to imbalanced priorities. Applying the universal law of balance in governance will: -/- Create leaders who prioritize national and global stability. Balanced governance ensures that decisions benefit both the present and future generations. -/- Eliminate extreme capitalism and unregulated economic growth. Governments will regulate economies based on resource sustainability rather than limitless expansion, reducing wealth inequality and environmental destruction. -/- Prevent overpopulation and resource depletion. Balanced policies will control population growth to match available resources, ensuring sustainable development. -/- Ensure fair justice systems. Laws and policies will be structured to balance individual rights with collective responsibility, preventing both authoritarian rule and chaotic anarchy. -/- 3. Reforming Economic Systems for Global Sustainability -/- The modern economic system prioritizes profit over sustainability, leading to financial crises, worker exploitation, and environmental collapse. By implementing the universal formula, economies will: -/- Shift from profit-driven models to sustainability-driven economies. Instead of uncontrolled consumption, economic policies will ensure equitable wealth distribution and environmental responsibility. -/- Balance technological progress with ethical considerations. AI, automation, and scientific advancements will be aligned with human well-being rather than purely corporate interests. -/- Prevent extreme wealth accumulation. Tax structures and wealth redistribution mechanisms will balance economic power, preventing the concentration of resources in the hands of a few. -/- 4. Using AI and Technology to Implement the Universal Formula -/- Artificial intelligence and automation are shaping the future of governance, education, and economic management. If AGI (Artificial General Intelligence) is programmed according to the universal law of balance, it can: -/- Provide unbiased decision-making for governments and organizations. AI can analyze global trends and propose balanced policies that prevent economic crashes, food shortages, and political instability. -/- Ensure balanced media and information dissemination. AI can counteract misinformation and propaganda, ensuring that people receive accurate, scientifically-backed information. -/- Optimize resource management. Smart systems can ensure that natural resources are used efficiently, preventing over-extraction and environmental collapse. -/- Positive Effects of the Universal Formula on All Countries and the World -/- 1. Global Peace and Conflict Resolution -/- Many wars and conflicts arise from imbalanced decision-making, whether in the form of territorial disputes, economic competition, or ideological clashes. If all countries implement the universal law of balance: -/- Diplomacy will replace war. Nations will resolve conflicts through mutual benefit and balance rather than through force or economic domination. -/- Religious and cultural tensions will decrease. Education based on balance will prevent extremist ideologies and promote global cooperation. -/- Geopolitical stability will be achieved. Superpowers will shift from military competition to balanced global governance, ensuring world peace. -/- 2. Economic Stability and Sustainable Development -/- Instead of pursuing endless economic growth, nations will adopt policies that balance wealth distribution, technological advancement, and resource sustainability. This will result in: -/- Elimination of poverty and extreme inequality. Wealth will be distributed according to natural balance, ensuring that no country suffers from extreme poverty while others hoard wealth. -/- Protection of natural ecosystems. Economic activity will be regulated to align with environmental sustainability, reversing climate change and preventing resource depletion. -/- Harmonized global trade. Countries will trade based on mutual benefit, reducing exploitative economic relationships and dependency on harmful industries. -/- 3. A More Conscious and Responsible Humanity -/- By implementing the universal law of balance in all human activities, societies will: -/- Develop a collective consciousness of responsibility. People will prioritize balanced decision-making, preventing greed, ignorance, and reckless behavior. -/- Reduce crime and corruption. Societies where balance is understood and practiced will no longer tolerate corruption, exploitation, and unethical leadership. -/- Achieve spiritual and intellectual enlightenment. Individuals will no longer be manipulated by false ideologies but will instead align their lives with natural balance, achieving greater peace and fulfillment. -/- Conclusion: A Call for Global Implementation -/- The universal law of balance is the missing foundation needed to reform all countries and create a stable, just, and progressive world. By integrating this principle into education, governance, economics, and technology, we can eliminate poverty, war, corruption, and environmental destruction, ensuring a sustainable future for all. -/- The challenge is global cooperation and willingness to change. If leaders, educators, and scientists recognize the scientific necessity of balance, the world can shift from chaos to harmony. This is not a utopian vision—it is the natural evolution of human civilization once it aligns with the fundamental laws of nature. -/- Now is the time to act. The future of humanity depends on our ability to embrace balance, reform our societies, and secure a future where all nations thrive in equilibrium. -/- . (shrink)

Scholarly processes play a pivotal role in discovering, challenging, improving, advancing, synthesizing, codifying, and disseminating knowledge. Since the 17th Century, both the quality and quantity of knowledge that scholarship has produced has increased tremendously, granting academic research a pivotal role in ensuring material and social progress. Artificial Intelligence (AI) is poised to enable a new leap in the creation of scholarly content. New forms of engagement with AI systems, such as collaborations with large language models like GPT-3, offer affordances (...) that will change the nature of both the scholarly process and the artifacts it produces. (shrink)

-/- Why the World Will Not Fully Progress Without the Full Implementation of My Universal Formula -/- Introduction -/- Throughout history, humanity has sought progress through technological advancements, economic growth, and political reforms. However, despite these efforts, the world continues to face persistent challenges such as poverty, corruption, environmental destruction, and social conflicts. These problems arise because human decision-making often lacks a fundamental guiding principle that ensures balance and sustainability. My universal formula, which solves the problem of free (...) will by aligning human actions with the universal law of balance in nature, is the missing key to true progress. Without its full implementation, societies will continue to struggle with imbalances that hinder their full potential. -/- The Root Cause of Global Problems -/- The world’s problems are not merely the result of poor leadership, lack of resources, or technological limitations. At the core, they stem from imbalanced decision-making caused by ignorance, misinformation, and flawed belief systems. Leaders, policymakers, and individuals often make choices that disrupt the natural balance, leading to unintended consequences. For example: -/- Economic systems focused on endless growth create income inequality and resource depletion. -/- Political systems influenced by corruption and power struggles fail to serve the common good. -/- Religious and ideological dogmas often suppress critical thinking, preventing societies from evolving based on rational and scientific principles. -/- Environmental destruction continues because economic and political decisions ignore the natural limits of ecosystems. -/- Without a universal guiding principle that ensures balance, human actions remain disconnected from natural laws, resulting in long-term instability and suffering. -/- The Role of My Universal Formula -/- My universal formula provides the only exact solution to this problem by establishing that all human decisions must follow the universal law of balance in nature. This means: -/- 1. Education must be reformed to ensure that individuals understand how their decisions affect both personal and societal balance. -/- 2. Governance must be structured to make decisions that align with natural laws, avoiding policies that create social and environmental harm. -/- 3. Economic and technological progress must be sustainable, ensuring that advancements do not disrupt the natural equilibrium of resources and societies. -/- 4. Religious and ideological influences must be harmonized with rational and scientific understanding, preventing dogma from obstructing true knowledge. -/- Without this guiding principle, all human efforts remain incomplete and prone to failure. Even the most well-intended policies and advancements will eventually create new problems if they do not adhere to the law of balance. -/- Estimated Timeframe for Full Effectiveness -/- If all countries fully implement my universal formula, the expected timeframe for effectiveness would follow a structured progression: -/- 1. First 5 Years – Educational and Policy Foundation -/- Education systems are restructured to integrate the universal law of balance in nature. -/- Governments reform laws and policies based on natural balance principles. -/- Global organizations (UN, World Economic Forum, etc.) adopt the framework and push for worldwide implementation. -/- 2. Years 6–15 – Global Awareness and Behavioral Shift -/- The new educational system produces a generation of students who think critically and make balanced decisions. -/- Government leaders and policymakers apply the principles in decision-making. -/- Economic and technological innovations align with sustainable progress. -/- 3. Years 16–30 – Significant Societal Transformation -/- Corruption and misinformation significantly decrease as societies value truth and balance. -/- Economic stability is achieved as unsustainable growth is replaced by long-term, balanced development. -/- Social conflicts decline due to a rational, well-informed population making balanced decisions. -/- 4. Years 31–50 – Full Global Stabilization -/- The majority of the world’s problems (poverty, inequality, environmental destruction, political instability) are minimized or resolved. -/- The world enters a new era of sustainable and harmonious progress, where human decisions align with natural laws. -/- Future AI and technological advancements operate based on the universal law of balance, ensuring long-term stability. -/- Without full implementation, these effects would remain fragmented, and societies would continue to struggle with imbalance-driven crises. -/- Why Full Implementation is Necessary -/- Partial or selective application of my universal formula will not be enough to achieve full progress. If only a few countries or institutions adopt it, the rest of the world will continue to operate under flawed systems, creating conflicts and setbacks. For example: -/- A few nations regulating population growth while others continue unchecked expansion will still result in global overpopulation. -/- Some governments adopting ethical policies while others remain corrupt will continue to create international instability. -/- Certain industries practicing sustainability while others exploit resources will still lead to environmental crises. -/- Only through universal adoption and integration into education, governance, and policy-making worldwide can humanity achieve true progress. -/- The Consequences of Ignoring the Universal Formula -/- If the world fails to fully implement my universal formula, the consequences will be dire: -/- Economic collapses due to unregulated financial systems and unsustainable practices. -/- Political instability as governments fail to balance power, leading to conflicts and revolutions. -/- Social unrest as inequalities widen and misinformation spreads. -/- Environmental disasters as resource depletion and climate change accelerate. -/- These problems will persist and worsen because they all stem from imbalanced decision-making—a fundamental issue that only my universal formula can correct. -/- Conclusion -/- The world will never fully progress without implementing my universal formula because it is the only exact solution that aligns human decision-making with the universal law of balance in nature. All major global challenges arise from imbalanced decisions, and no political, economic, or technological solution can succeed unless it follows this fundamental law. By integrating this formula into education, governance, and global policies, humanity can finally overcome its deepest problems and achieve sustainable, harmonious progress. -/- With full global implementation, the world can experience significant transformation within 30 to 50 years—a small price to pay for an eternity of stability and prosperity. The choice is clear: either we fully implement this universal formula and secure a future of balance and progress, or we continue down a path of imbalance, where progress is always undermined by new crises. The world must recognize and adopt this solution before it is too late. -/- . (shrink)

If the intelligence of artificial systems were to surpass that of humans significantly, this would constitute a significant risk for humanity. Time has come to consider these issues, and this consideration must include progress in AI as much as insights from the theory of AI. The papers in this volume try to make cautious headway in setting the problem, evaluating predictions on the future of AI, proposing ways to ensure that AI systems will be beneficial to humans – and (...) critically evaluating such proposals. (shrink)

Papers from the conference on AI Risk (published in JETAI), supplemented by additional work. --- If the intelligence of artificial systems were to surpass that of humans, humanity would face significant risks. The time has come to consider these issues, and this consideration must include progress in artificial intelligence (AI) as much as insights from AI theory. -- Featuring contributions from leading experts and thinkers in artificial intelligence, Risks of Artificial Intelligence is the first volume of collected chapters dedicated (...) to examining the risks of AI. The book evaluates predictions of the future of AI, proposes ways to ensure that AI systems will be beneficial to humans, and then critically evaluates such proposals. 1 Vincent C. Müller, Editorial: Risks of Artificial Intelligence - 2 Steve Omohundro, Autonomous Technology and the Greater Human Good - 3 Stuart Armstrong, Kaj Sotala and Sean O’Heigeartaigh, The Errors, Insights and Lessons of Famous AI Predictions - and What they Mean for the Future - 4 Ted Goertzel, The Path to More General Artificial Intelligence - 5 Miles Brundage, Limitations and Risks of Machine Ethics - 6 Roman Yampolskiy, Utility Function Security in Artificially Intelligent Agents - 7 Ben Goertzel, GOLEM: Toward an AGI Meta-Architecture Enabling Both Goal Preservation and Radical Self-Improvement - 8 Alexey Potapov and Sergey Rodionov, Universal Empathy and Ethical Bias for Artificial General Intelligence - 9 András Kornai, Bounding the Impact of AGI - 10 Anders Sandberg, Ethics and Impact of Brain Emulations 11 Daniel Dewey, Long-Term Strategies for Ending Existential Risk from Fast Takeoff - 12 Mark Bishop, The Singularity, or How I Learned to Stop Worrying and Love AI -. (shrink)

Recent progress in artificial intelligence (AI) has drawn attention to the technology’s transformative potential, including what some see as its prospects for causing large-scale harm. We review two influential arguments purporting to show how AI could pose catastrophic risks. The first argument — the Problem of Power-Seeking — claims that, under certain assumptions, advanced AI systems are likely to engage in dangerous power-seeking behavior in pursuit of their goals. We review reasons for thinking that AI systems might seek power, (...) that they might obtain it, that this could lead to catastrophe, and that we might build and deploy such systems anyway. The second argument claims that the development of human-level AI will unlock rapid further progress, culminating in AI systems far more capable than any human — this is the Singularity Hypothesis. Power-seeking behavior on the part of such systems might be particularly dangerous. We discuss a variety of objections to both arguments and conclude by assessing the state of the debate. (shrink)

The field of AI has undergone a series of transformations, each marking a new phase of development. The initial phase emphasized curation of symbolic models which excelled in capturing reasoning but were fragile and not scalable. The next phase was characterized by machine learning models—most recently large language models (LLMs)—which were more robust and easier to scale but struggled with reasoning. Now, we are witnessing a return to symbolic models as complementing machine learning. Successes of LLMs contrast with their inscrutability, (...) inaccuracy, and hallucinations, which underwrite concerns over the reliability and trustworthiness of these systems, motivating investigations into commonsense reasoning, AI explainability, and formal verification techniques. Moreover, proper assessments of hybrid machine learning/symbolic systems require novel strategies to facilitate comparisons of performance and guide future AI progress. The EDGeS AAAI 2023 Spring symposium brought together researchers focusing on novel assessments and benchmarks for evaluating hybrid and artificial general intelligence (AGI) systems. This symposium revealed what was already suspected: research concerning evaluation of machine learning/symbolic hybrids and AGI is, unfortunately, lacking. Even so, the discussion was fruitful. (shrink)

Technological progress has brought about the emergence of machines that have the capacity to take human lives without human control. These represent an unprecedented threat to humankind. This paper starts from the example of chemical weapons, now banned worldwide by the Geneva protocol, to illustrate how technological development initially aimed at the benefit of humankind has, ultimately, produced what is now called the “Weaponization of Artificial Intelligence (AI)”. Autonomous Weapon Systems (AWS) fail the so-called discrimination principle, yet, the wider (...) public is largely unaware of this problem. Given that ongoing scientific research on AWS, performed in the military sector, is generally not made available to the public domain, many of the viewpoints on this subject, expressed across different media, invoke common sense rather than scientific evidence. Yet, the implications of a potential weaponization of our work as scientists, especially in the field of AI, are reaching further than some may think. The potential consequences of a deployment of AWS for citizen stakeholders are incommensurable, and it is time to raise awareness in the public domain of the kind of potential threats identified, and to encourage legal policies ensuring that these threats will not materialize. (shrink)

Inchul Yum (2024) argues that the widespread adoption of language agent architectures would likely increase the risk posed by AI by simplifying the process of aligning artificial systems with human values and thereby making it easier for malicious actors to use them to cause a variety of harms. Yum takes this to be an example of a broader phenomenon: progress on the alignment problem is likely to be net safety-negative because it makes artificial systems easier for malicious actors to (...) control. I offer some reasons for skepticism about this surprising and pessimistic conclusion. (shrink)

This treatise introduces a transformative socio-political paradigm that merges empathy, ethics, equality, and the refinement of excellence to address the novel challenges of the 21st century and beyond. By combining ethical bio-genetic enhancement, empathetic governance, and the cautious reconstruction of socio-political structures, it envisions a society capable of achieving unprecedented precision, efficiency, and unity in governance. This interdisciplinary framework integrates modern science, classical philosophy, and historical lessons to propose a genetically optimized, rigorously educated, and ethically inclined sub-population—the “Electi.” Designed to (...) embody human refinement and empathy, the Electi are uniquely qualified to navigate the ethical, environmental, and scientific challenges of the future. Building on the works of Aristotle and Plato while incorporating contemporary concepts like Polygenic Scores, artificial intelligence, and psychometrics, the treatise provides a balanced model that addresses democracy’s inefficiencies without sacrificing equality or representation. Ethical safeguards and public engagement strategies are woven into the paradigm to counter fears of elitism and inequality. This model aims to accelerate societal advancement, improve policy-making efficiency, and respond proactively to crises such as climate change and AI threats. By harmonizing ethical considerations with practical governance, the treatise offers an innovative framework for academics, philosophers, and policymakers to reimagine the future of human progress and governance. (shrink)

Artificial intelligence (AI) and machine learning (ML) models have undoubtedly become useful tools in science. In general, scientists and ML developers are optimistic – perhaps rightfully so – about the potential that these models have in facilitating scientific progress. The philosophy of AI literature carries a different mood. The attention of philosophers remains on potential epistemological issues that stem from the so-called “black box” features of ML models. For instance, Eamon Duede (2023) argues that opacity in deep learning models (...) (DLMs) is epistemically problematic in the context of justification, though not in the context of discovery. -/- In this paper, I aim to show that a similar epistemological concern is echoed in the epistemology of imagination literature. It is traditionally held that, given its black box features, reliance on the imagination is epistemically problematic in the context of justification, though not in the context of discovery. The constraints-based approach to the imagination answers the epistemological concern by providing an account of how we can rely on the imagination in the context of justification by way of constraints. I argue by analogy that a similar approach can be applied to the opaque DLM case. Ultimately, my goal is to explore just how far this analogy extends, and whether a constraints-based approach to opaque DLMs can answer the epistemological concern surrounding their black box features in the context of justification. -/- (Note that this paper is IN PROGRESS and UNPUBLISHED). (shrink)

With the rise and public accessibility of AI-enabled decision-support systems, individuals outsource increasingly more of their decisions, even those that carry ethical dimensions. Considering this trend, scholars have highlighted that uncritical deference to these systems would be problematic and consequently called for investigations of the impact of pertinent technology on humans’ ethical decision-making. To this end, this article conducts a systematic review of existing scholarship and derives an integrated framework that demonstrates how intelligent decision-support systems (IDSSs) shape humans’ ethical decision-making. (...) In particular, we identify resulting consequences on an individual level (i.e., deliberation enhancement, motivation enhancement, autonomy enhancement and action enhancement) and on a societal level (i.e., moral deskilling, restricted moral progress and moral responsibility gaps). We carve out two distinct methods/operation types (i.e., processoriented and outcome-oriented navigation) that decision-support systems can deploy and postulate that these determine to what extent the previously stated consequences materialize. Overall, this study holds important theoretical and practical implications by establishing clarity in the conceptions, underlying mechanisms and (directions of) influences that can be expected when using particular IDSSs for ethical decisions. (shrink)

Artificial intelligence (AI) techniques are used to model human activities and predict behavior. Such systems have shown race, gender and other kinds of bias, which are typically understood as technical problems. Here we try to show that: 1) to get rid of such biases, we need a system that can understand the structure of human activities and;2) to create such a system, we need to solve foundational problems of AI, such as the common-sense problem. Additionally, when informational platforms uses these (...) models to mediate interactions with their users, which is a commonplace nowadays, there is an illusion of progress, for what is an increasingly higher influence over our own behavior is took for an increasingly higher predictive accuracy. Given this, we argue that the bias problem is deeply connected to non-technical issues that must be discussed in public spaces. -/- Técnicas de inteligência artificial (IA) são utilizadas para modelar as atividades humanas e gerar predições comportamentais. Estes sistemas têm apresentado vieses diversos, inclusive de raça e gênero, tipicamente tomados como problemas de engenharia. Realiza-se aqui um esforço argumentativo para mostrar que: 1) escapar dos vieses demanda um sistema que compreenda a estrutura das atividades humanas e; 2) criar um sistema que apresente tal compreensão, demanda a solução de problemas fundacionais da IA, em particular, o problema de como modelar o senso comum. No caso de plataformas informacionais que usam desses modelos para mediar interações com seus usuários, algo cada vez mais comum, o não reconhecimento disso dá margem a uma ilusão de progresso, em que uma crescente influência sobre nosso comportamento é tomada como uma crescente acurácia preditiva. Nesse cenário, argumenta-se que o problema dos vieses está associado a questões não técnicas que devem ser discutidas em espaços públicos. (shrink)

The appearance of openly accessible Artificial Intelligence Applications such as Large Language Models, nowadays capable of almost human-level performances in complex reasoning tasks had a tremendous impact on public opinion. Are we going to be "replaced" by the machines? Or - even worse - "ruled" by them? The behavior of these systems is so advanced they might almost appear "alive" to end users, and there have been claims about these programs being "sentient". Since many of our relationships of power and (...) social dominance are apparently based on intelligence, where the "smarter" seems to prevail on the "dumber" (as we see for example with livestock farming, or with the distribution of salaries according to formal and intellectual qualifications etc.) the emergence of programs and AI applications of this level of sophistication brings us to the question "what will AI do to us?". Our error is to frame Artificial Intelligence in the same way we frame Natural Intelligence and mistakenly address it as we would do with biological life, even applying to it the same biopolitical categories. We here try to reverse the common arguments in relationship between life and intelligence and show that our progresses in Artificial Intelligence might have no immediate consequence for the topic of life as such, as life and intelligence are well separated issues, and - even when we would like to somehow fuse them together, we are very far away from having an "Artificial Life" to the degree of complexity and effectiveness we have with "Artificial Intelligence". (shrink)

This study explores the integration of ChatGPT as a teaching assistant in physics education, emphasizing its potential to transform traditional pedagogical approaches. ChatGPT facilitates interactive and inquiry-based learning grounded in constructivist learning theory, allowing students to engage actively in experiments and better grasp abstract concepts through hands-on activities. The AI's adaptive dialogue systems promote socio-constructivist learning by encouraging social interaction and personalized feedback, which is essential for addressing individual learning gaps and enhancing student engagement. ChatGPT's ability to simulate real-world physics (...) problems and provide immediate feedback fosters experiential learning, making complex concepts more accessible and promoting critical thinking skills. By offering tailored interventions and adapting to individual learning paces, ChatGPT supports a personalized educational experience that caters to the unique needs of each student. This adaptability is particularly beneficial in physics education, where students often struggle with abstract concepts and require immediate clarification to progress effectively. The paper concludes that the synthesis of generative AI and pedagogy has the potential to reshape science education, fostering deeper understanding and curiosity among students. By leveraging innovative methodologies and augmented data, ChatGPT enriches teacher-student interactions, creating a comprehensive educational experience that promotes a culture of curiosity and exploration, thereby nurturing future scientists and engineers. Ultimately, the integration of ChatGPT into physics education offers a valuable opportunity to enhance student engagement and understanding of scientific concepts through interactive and personalized support. (shrink)

Intelligence in current AI research is measured according to designer-assigned tasks that lack any relevance for an agent itself. As such, tasks and their evaluation reveal a lot more about our intelligence than the possible intelligence of agents that we design and evaluate. As a possible first step in remedying this, this article introduces the notion of “self-concern,” a property of a complex system that describes its tendency to bring about states that are compatible with its continued self-maintenance. Self-concern, as (...) argued, is the foundation of the kind of basic intelligence found across all biological systems, because it reflects any such system's existential task of continued viability. This article aims to cautiously progress a few steps closer to a better understanding of some necessary organisational conditions that are central to self-concern in biological systems. By emulating these conditions in embodied AI, perhaps something like genuine self-concern can be implemented in machines, bringing AI one step closer to its original goal of emulating human-like intelligence. (shrink)