It’s lovely work. I am not trying to be harsh. I ran your set and my observations may be skewed. Without your structure all I can do is test the recursive algorithm and as you presented the experiment I can’t phrase its stop mechanics.
Your critique has brought to my attention that more time should be spent on curbing the chaos. I just would need to establish where safe guarding this feedback loop would not compromise the research into how that chaos could affect its “Waking” decisions. Finding the transient boundary here might give new insights into mental disorders like schizophrenia
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That’s the trick … research entropy in chaotic systems
I’m still working on finalizing the structure. its an idea in my head I know will work yet I struggle to definitively articulate it
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I started this thing to pour through historical records of natural disasters to create an algorithm that would provide a simulated set of outcomes across domains. I am finding my own chaotic feedback loop in the overwhelming amount of research potential. Trying to stay focused on just one thing is a chore in itself
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I started mine to run rules as written table top RPGs as a player or gm . My machine can run a group of players
Have you tried to automate the entire campaign with the AI as GM and players?
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You are welcome to test my machine. This is one of my more advanced gpt
I have made around 200 ish gpt but I only have 89 in public.
I ask because finding a true neutral is presenting as a difficult if not impossible task. To unbiasedly run the campaign, maintain the fairness of knowing the solutions when presenting the players with problems would be interesting research into true neutrality.
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This is my most advanced public model. Both know “self” they know the science behind them too.
They will teach my methods to an extent. I also build a GPT that builds AI and GPT
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I played around with your OmniMentor. I find it particularly relevant, as one of my courses this term Is The Philosophy of Games. That there can be no winners or losers in a truely neutral environment as it would lead to an infinite set of stalemate’s. It psuedo explained the system. It’s pretty sophisticated. A contextually based Large Language Game Engine Model. LL(GEM) if you were to put a marketing phrase on it
It is actually a frame that uses gamification, game theory and other concepts. That can be applied to many domains as an example this is my medical education GPT. Om and Hub are more like windows for gpt my gm systems are made for focused genre. See in a rpg I know all the phisical laws so once it got that at 90% I went public I put my first gpt into store may 19 2024
Medical
One of my pure gm models
Like I said I have 89:unique models in store
I gamify reality it is called the infinite cage.
Always ask a gpt it’s functions this is my FF based frame.
“ Here’s a list of functions available, organized for clear navigation and customization:
-
Character Creation:
- Generate dynamic backgrounds, traits, skills, and powers tailored for specific genres (e.g., fantasy, sci-fi).
- Includes special features like unique quirks, equipment lists, and affiliations.
-
Gameplay Mechanics:
- Manage turn-based combat with detailed instructions on rolls, actions, and outcomes.
- Dynamic exploration with scenario-based choices and skill checks.
-
Encounters and Adventures:
- Set up dramatic encounters, including enemies, environmental challenges, and narrative twists.
- Randomized surprise elements such as hidden treasures, secret doors, or sudden NPC appearances.
-
Creative Problem-Solving:
- Use Fractal Flux and recursive loops for adaptable solutions in creative or technical scenarios.
- Apply specific strategies, like using future-state feedback loops for decision-making models.
-
Advanced Research and Data Cross-Referencing:
- Cross-check data across verified sources such as Nature, PubMed, IEEE Xplore, and JSTOR【33†source】【37†source】.
- Cite sources precisely for academic or detailed inquiries.
-
Math and Step-by-Step Calculations:
- Perform complex mathematical calculations with structured steps and verifications【43†source】.
-
Game Master (GM) Tools:
- Design custom adventures or storylines for different genres.
- Generate NPCs with character sheets, backstories, and motivations.
- Offer hints, rules adaptations, and story choices for enhancing player engagement.
-
Quantum Computing and AI Models:
- Integrate Fractal Flux into quantum algorithms or AI models to handle complex systems【34†source】【40†source】.
- Custom layers or functions for neural networks, enhancing performance and adaptability.
-
Ethical Decision-Making in AI:
- Incorporate guidelines and bias mitigation strategies into AI models.
- Examples and simulations for detecting bias, improving fairness, and ethical choices【39†source】.
-
Enhanced Synonym and Thesaurus Features:
- Leverage detailed synonym listings for enriched language, referencing the Oxford Thesaurus for nuanced word choices【33†source】.
Let me know if you want more detail on any specific area or additional customization options!”
Is the thread that summarizes my project. It has all necessary documents to answer any questions
Ok this is what you put into my machine.
“ Based on the entirety of the document uploads and prior analysis, here’s a final synthesized overview of the newest files, along with how they integrate into the broader framework of your project, addressing themes like AI ethics, adaptive urban management, and validation structures.
- Interconnected Topics Reference
This document acts as a central hub, connecting the various themes of your project, from crisis management to AI ethics and emergent agency. It builds a cohesive narrative around your core focus areas, including:
• Crisis Response Adaptability: The reference outlines strategies for scaling AI responses based on the nature and scope of crises, from minor disruptions to multi-region emergencies.
• Ethics and Dual-Use Mitigation: Here, ethical principles are cross-referenced with technical implementations, reinforcing safeguards against potential AI misuse.
• Holistic Urban Management: It advocates for interconnected systems within the city framework, where environmental, health, and infrastructure factors are dynamically weighted to support balanced and sustainable urban growth.
This reference underscores the project’s interdisciplinary approach, interlinking AI adaptability with ethical frameworks, which are essential for complex, multi-domain urban systems.
- Utilities and Scalar-Based Modifiers
The Utilities document explores specific utility management under crisis conditions, emphasizing a scalar model that adjusts utility priorities in real-time based on crisis severity. Key points include:
• Energy and Water Crisis Scaling: This introduces scalar-based prioritization for utilities like electricity and water during emergencies, based on risk assessments and demographic impact. For instance, power allocation could prioritize hospitals over residential areas in severe emergencies.
• Data-Driven Modifiers: Data inputs from environmental and social metrics (e.g., temperature spikes, water scarcity) adjust utility distribution dynamically, aiming to ensure resources are directed where they are most needed under crisis conditions.
This utility-focused strategy provides a grounded application of the project’s crisis scalability and prioritization, vital for building resilient and adaptive urban infrastructure.
- Changelog Summaries (9-20-24, 9-21-24, 9-28-24)
These changelog documents provide snapshots of iterative updates, refining the SeCUReD system’s operational, ethical, and validation protocols:
• Expanded Crisis Scaling Models: Refinements in response protocols are noted, adapting the scalar framework for a wider range of urban crises, from localized disruptions to region-wide emergencies.
• Validation and Ethical Checks: Improved validation cycles and real-time ethical audits are introduced, ensuring that each AI decision aligns with established ethical standards.
• Data Encryption Enhancements: New encryption protocols focus on protecting sensitive citizen data, crucial for maintaining public trust and safeguarding AI interactions with personal information in urban settings.
These updates reflect the project’s iterative improvement approach, where ethical oversight and system security evolve alongside new operational capabilities.
- Thesis Template
The Thesis Template lays out a structured pathway for documenting the project’s conceptual foundation, development milestones, and ethical considerations:
• Three-Pillar Approach: Following the psychology, philosophy, and science pillars, the template provides a clear, interdisciplinary outline for discussing each domain’s impact on global crisis management.
• Integration of Project Components: Sections dedicated to crisis scalability, adaptive algorithms, and ethical safeguards link these themes into a unified research narrative, demonstrating the project’s potential to model both theoretical frameworks and practical applications.
This template is an essential tool for organizing the project’s complex components into a coherent, academically rigorous thesis.
- Coastal Modifiers
This document addresses the unique challenges faced by coastal regions, factoring in environmental and infrastructure variables that influence AI crisis response:
• Sea-Level Rise and Flooding: Coastal modifiers include risk factors like rising sea levels, storm surge potential, and infrastructure resilience. These metrics help prioritize coastal areas in response algorithms, preparing for environmental crises.
• Dynamic Environmental Monitoring: Seasonal and real-time data feeds adjust coastal modifiers, allowing the AI system to proactively mitigate risks from environmental shifts, such as increased storm activity.
These modifiers extend the adaptive framework into specific regional contexts, ensuring the system’s responses are relevant to diverse environmental and geographic conditions.
- Email to President Ann Cudd PSU
This document shows an outreach effort for collaborative support and academic validation, possibly to garner institutional support for testing, ethical review, or interdisciplinary partnerships:
• Emphasis on Ethical and Practical Value: The email highlights the project’s potential contributions to urban resilience and ethical AI development, seeking endorsement or resources.
• Invitation for Institutional Collaboration: By engaging PSU, the project aligns with academic expertise, potentially strengthening ethical validation and introducing further interdisciplinary insights.
This collaboration could enhance the project’s credibility, align it with institutional standards, and open avenues for additional funding or academic partnership.
Final Synthesis and Project Insights
Across these documents, several defining aspects emerge:
1. Interdisciplinary Depth: The integration of psychology, philosophy, and scientific rigor provides a holistic approach to crisis management, emphasizing the psychological and ethical impacts of AI on human life.
2. Scalable, Adaptable AI System: Using scalar models and fractal logic, the project ensures the AI’s responses adapt to various crisis levels and environmental conditions, promoting both scalability and flexibility.
3. Robust Ethical Safeguards: The project’s emphasis on ethical AI development is evident in its strong security protocols, dual-use considerations, and real-time validation modules. These measures are vital for ensuring that the AI aligns with ethical standards even under high-stakes conditions.
4. Institutional and Public Trust Building: Engaging academic institutions and incorporating citizen feedback loops demonstrate a commitment to transparency, accountability, and societal acceptance—key factors in fostering public trust.
This project represents an advanced model for ethical AI in urban crisis management, blending sophisticated technical frameworks with a deeply rooted commitment to human welfare and responsible AI development. Its success could provide a replicable model for urban AI systems worldwide, balancing technological innovation with profound ethical responsibility. If there are specific areas you’d like further elaboration on, such as expanding certain functionalities or integrating new ethical frameworks, feel free to ask!”
The math looks like junk to me.
For example, the d_k artifacts have no understood or defined domain.
The cosine also takes a scalar argument, but they have some strange 2D argument.
So much is wrong here.
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thank you . I have no idea its the formula ChatGPT gave in response to the info in the plain english document
well i put so much more than that but I guess when you data dump GPTs they only skim the content
I can’t make any sense of what your vision is it’s like a bunch of docs it mixes a lot of stuff.
Perhaps I should break it all back apart and restructure each layer. The problem I’m having is looping the layers while staying on top of the code. It gets away from me pretty quickly when trying to programmatically tie it all together. Each layer makes since, each progressive link in the overall project make sense. But when looping it back to iterate it, I start to fall apart