Our Team

Our team unites academic depth with AI development expertise. Dr. Joseph Iyanda offers scientific research. Victor Asuquo leads in AI/ML, specializing in NLP and vision. Obongofon Udombat excels in data science and GenAI. Godspower Ufot is our frontend web specialist. Ubongabasi Jerome provides backend engineering. Daniel Udo adds NLP proficiency for personalized feedback systems. This blend of skills ensures robust AGI motivation framework development.

Company Name (if applicable)

Project details

Our solution is a modular and extensible framework for integrating various motivational systems into AGI architectures, enhanced by a dedicated "Ethical Modulation Layer" focused on cognitive preservation. This framework will:

• Core Modular Motivation Framework: Provide a flexible foundation to incorporate diverse motivational structures, supporting both human-inspired and potentially "alien digital" drives.
• Ethical Modulation Layer (Neuro-Mathematically Informed): This is the core innovation, designed based on:
• Neuroscience Principles: Incorporating findings on memory formation, consolidation, the importance of effortful processing ("desirable difficulties"), and the negative impact of distractions on learning.
• Mathematical Models: Drawing inspiration from our previously developed differential equation models that illustrate the dynamics of memory interaction with AI, to conceptually guide the design and potential evaluation of the ethical rules.
• Ethical Guidelines: Translating our previously defined guidelines (emphasizing critical thinking, promoting active learning, and using AI as a supplement) into concrete rules and incentives within the modulation layer.
• Integration Mechanisms: Design clear interfaces for integrating this framework with key AGI architecture components, particularly within the Hyperon framework (ECAN, DAS, MeTTa), to influence attention allocation, knowledge management, and behavior generation.

 Functional Requirements:

Must Haves:

• Core motivational framework design: Develop a modular, extensible framework usable with Hyperon components.
• Ethical Modulation for Cognitive Preservation: Implement a layer of rules and incentives specifically designed to promote human cognitive engagement and prevent over-reliance on the AGI.
• Detailed Use Cases: Provide coded demonstrations in at least two scenarios:
• Chatbot System: An interaction model that encourages user recall, critical thinking, and active learning.
• Virtual Agent (e.g., Metaverse): A virtual agent motivated to guide users in a way that promotes cognitive engagement and memory retention within the virtual environment.
• Efficiency of Execution: Design the framework to be performant for real-time interactions.
• Scalability and Adaptability: Consider scalability to more complex AGI systems.

Should Haves:

• Support for Human-like and Alien Motivations: Design the core framework to accommodate diverse motivational structures.
• Real-time Adaptability: Enable the ethical modulation layer to dynamically adjust its influence based on user interaction patterns and potential signs of over-reliance.
• Conceptual Link to Memory Dynamics: While a full integration might be later, the initial design should conceptually align with the principles illustrated by our mathematical models of memory interaction with AI.
• Customizable Ethical Rules: Allow for configuration and adaptation of the ethical modulation rules.

Could Haves:

• Cross-Domain Applicability: Provide conceptual guidance on applying the framework across different AGI applications.
• Integration Considerations for Sub-Symbolic Systems: Briefly explore potential integration with sub-symbolic motivational approaches.

Won't Have (Yet):

• Complex emotional simulation.
• Autonomous ethical rule generation (the framework will adhere to predefined ethical principles).

 Non-Functional Requirements:

• Conceptual Depth/Rigor: Ground the framework in well-reasoned neuroscience and cognitive science principles.
• Ethical Alignment: Deeply embed ethical considerations for human cognitive well-being into the core design.
• Scalability and Adaptability: Design for future expansion and modification as AGI technology evolves.
• Practical Use Cases: Provide clear and demonstrable examples of the framework in action.
• Roadmap: Suggest a pathway for ongoing refinement and integration with future AGI components.
• Transparency and Challenges: Identify potential limitations and challenges in applying the framework.

Project Milestones and Deliverables:

We propose the following milestones:

• Milestone 1: Thorough Research Plan and Framework Design (20% of grant):
• Deliverables: Detailed research plan outlining our approach, agile breakdown of tasks with timeline, and comprehensive framework design document including the architecture of the core motivation system and the ethical modulation layer (detailing the neuroscience principles and initial ethical rules).
• Milestone 2: Initial Framework Implementation and Preliminary Testing (40% of grant):
• Deliverables: Draft implementation of the core motivation framework and the ethical modulation layer, demonstrating the conceptual underpinnings of the RFP. Includes preliminary testing results and analysis against initial benchmarks in simplified chatbot and virtual agent scenarios.
• Milestone 3: Final Materials and Demonstration (40% of grant):
• Deliverables: Final report with performance analysis, complete code repository, demonstration of the framework in the detailed use cases (chatbot and virtual agent), comprehensive documentation, recommendations for generalization and extension, and any relevant websites or demonstrations.

 Budget:

Budget Allocation:

• Milestone 1: Thorough Research Plan and Framework Design (20% of $28,700 = $5,740 USD)
• Personnel (Research & Design): $4,000 USD (Covers team member time dedicated to in-depth research, literature review, framework conceptualization, architecture design, and documentation.)
• Tools & Resources: $740 USD (Includes access to relevant academic databases, research papers, collaboration software, and potentially initial prototyping tools for conceptual visualization.)
• Contingency: $1,000 USD (A small buffer for unforeseen research costs or minor adjustments to the design phase.)
• Milestone 2: Initial Framework Implementation and Preliminary Testing (40% of $28,700 = $11,480 USD)
• Personnel (Development & Testing): $8,000 USD (Covers team member time for coding the core motivation framework, implementing the ethical modulation layer, and conducting initial testing in the defined use cases.)
• Computational Resources: $1,480 USD (Includes costs for accessing necessary computing infrastructure, cloud services for prototyping, and potential API access for integration testing.)
• Software & Libraries: $1,000 USD (Allocation for specific software licenses or libraries that might be required for the implementation and testing phases.)
• Contingency: $1,000 USD (Buffer for potential development complexities or additional testing requirements.)
• Milestone 3: Final Materials and Demonstration (40% of $28,700 = $11,480 USD)
• Personnel (Final Development, Documentation & Demonstration): $7,500 USD (Covers time for finalizing the implementation, creating comprehensive documentation, preparing demonstration materials, and writing the final report.)
• Platform & Hosting (Demonstration): $980 USD (Costs associated with hosting the demonstration (if web-based), video creation for showcasing the framework, or any necessary presentation tools.)
• Open-Source Contribution & Community Engagement: $1,000 USD (Allocation for packaging the code for open-source release, creating community-facing documentation, and potential outreach efforts.)
• Contingency: $2,000 USD (A larger buffer for potential final stage complexities, report revisions, or unexpected demonstration requirements.)

Total: $5,740 + $11,480 + $11,480 = $28,700 USD

 Impact on Hyperon and Future Research:

This foundational work will provide an integral framework for motivation within Hyperon, specifically designed with ethical considerations for human cognition. It will directly interact with:

• ECAN: Influencing attention allocation based on motivational priorities and ethical constraints related to user cognitive engagement.
• DAS: Storing and managing motivational states, ethical rules, and their impact on AGI behavior.
• MeTTa Language: The framework's logic and ethical rules can be expressed and managed through MeTTa, allowing for flexible and complex motivation-driven behaviors.

This project will establish a crucial base for continued research into ethically aligned AGI motivation, paving the way for future advancements in human-AI interaction and the development of truly benevolent general intelligence.

Conclusion and Call to Action:

We believe that "Ethical AGI Motivation for Cognitive Preservation" is a timely and critical research direction that aligns perfectly with the goals of Deep Funding and the mission of SingularityNET. Our team's unique blend of expertise, coupled with our proven ability to build practical AI tools, positions us strongly to deliver a valuable and impactful contribution to the development of responsible and beneficial AGI. We are eager to engage with the community, incorporate feedback, and embark on this important endeavor.

Open Source Licensing

We will adopt a Custom License Model.

Our custom license will be based on the Apache License 2.0 but with specific additions to ensure the ethical use of the "Ethical AGI Motivation for Cognitive Preservation" framework. These additions will:

1. Require Attribution: Clearly mandate attribution to our team and the project.
2. Promote Benevolent Use: Include a clause encouraging the use of the framework in alignment with benevolent AGI principles, particularly concerning the preservation and enhancement of human cognitive abilities. While not legally binding in a strict sense, this clause will serve as a clear ethical guideline for users.
3. Discourage Malicious Applications: Explicitly discourage the use of the framework in applications demonstrably intended to exploit or diminish human cognitive capabilities.

Background & Experience

AI-Powered Education Research: Investigation into Generative AI's role in high school assessments, enhancing self-assessment, metacognition, and feedback mechanisms across learning paradigms.

AI Lab: Development of a platform with AI tools for various applications (ideation, image/video generation, programming, data analysis, education, and task automation), coupled with a prompt engineering resource and educational materials.

GenAI Cognitive Impact Research: Examination of the ethical use of AI by students and professionals, with a focus on mitigating negative effects and promoting productivity.

African Language AI Development: Development of AI models for African languages, including Pidgin sentiment analysis and speech-to-text error reduction.

Enterprise-Level Microservice Applications: Design and development of enterprise-level microservice applications.

Model Performance Optimization: Optimization of model performance, resulting in a significant improvement in recommendation accuracy.

Links and references

https://github.com/RaterX/klinchainx

https://figshare.com/articles/thesis/_b_EXPLORING_THE_IMPACT_OF_GENERATIVE_AI_ON_ASSESSMENT_AS_LEARNING_AaL_ASSESSMENT_OF_LEARNING_AoL_AND_ASSESSMENT_FOR_LEARNING_AfL_IN_HIGH_SCHOOL_b_/28328909/1?file=52077026

https://ai.ilabs.world/

https://docs.google.com/document/d/18hTD41tfcEh0cjuhUUR6QVoL14BNtlTGhU4k2TmHy28/edit?usp=sharing

https://github.com/Victorasuquo

https://www.linkedin.com/in/obongofon-udombat-a30414146/

github.com/ubyjerome

www.myeasyschool.com.ng/

Describe the particulars.

An advisor and former client reffered us.