Nishant Nishant
Project OwnerThe Project Owner will oversee project execution, ensure alignment with goals, manage resources, communicate with stakeholders, and maintain quality control across all phases.
Our project aims to enhance the MOSES evolutionary algorithm by integrating Large Language Models (LLMs) within the Hyperon AGI framework. This integration will improve MOSES’s program generation, fitness modeling, and cross-domain learning capabilities, boosting adaptability and efficiency. By leveraging LLMs, we seek to optimize computational resources, achieve better fitness function estimations, and enable knowledge transfer across domains. This initiative contributes to the larger goal of advancing Artificial General Intelligence (AGI) within the SingularityNET ecosystem.
This RFP invites proposals to explore the integration of LLMs into the MOSES evolutionary algorithm. Researchers can pursue one of several approaches, including generation modeling, fitness function learning, fitness estimation, investigation into domain-independent “cognitively motivated” fitness functions, or propose new innovative ways to leverage LLMs to enhance MOSES's capabilities within the OpenCog Hyperon framework.
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This milestone focuses on aligning all project goals and establishing a comprehensive project plan. It involves finalizing project objectives, confirming timelines, and defining communication channels among stakeholders. This stage ensures that all team members are fully aware of the scope and deliverables, setting a solid foundation for the project's execution. We’ll also establish protocols for monitoring progress, risk management, and quality control, providing the team with clear guidelines for project success.
Key deliverables include a finalized project plan document, communication protocols, and a project management framework tailored for seamless execution and tracking. These deliverables will serve as the roadmap for the entire project, ensuring each phase remains on target and aligned with overall objectives.
$15,000 USD
Conduct a comprehensive analysis of the current MOSES framework to assess the feasibility of integrating LLM components. This milestone will involve studying MOSES’s existing capabilities, identifying potential integration points, and determining the scope for LLM optimization. A detailed requirements document will outline the necessary modifications and technical adjustments required for successful LLM integration.
The primary deliverable is a feasibility report detailing integration opportunities, technical challenges, and resource requirements. Additionally, a requirements document specifying LLM compatibility standards within MOSES will provide a blueprint for development in subsequent milestones.
$10,000 USD
Develop a comprehensive requirements document to guide the LLM integration process. This document will outline the specific technical requirements, compatibility needs, and expected modifications for MOSES to accommodate LLM-based modules. It will serve as a reference for development, ensuring alignment with the project’s objectives and technical feasibility.
The deliverable is a detailed requirements document that includes specifications for LLM integration, compatibility standards, and design considerations. This document will ensure that all stakeholders have a clear understanding of the technical scope and serve as a roadmap for the subsequent development phases.
$10,000 USD
This phase includes developing an initial prototype of the MOSES framework with basic LLM integration. The prototype will focus on program generation enhancements, providing a foundation for evaluating the effectiveness of LLM-enhanced components within MOSES. This prototype serves as a proof of concept, demonstrating potential improvements in program evolution and fitness function modeling.
The deliverable includes a functional prototype with documented LLM integration into MOSES’s generation modeling process. It will showcase initial enhancements in program evolution, and a brief report on the prototype’s performance will provide insights into areas for further optimization.
$10,000 USD
This milestone involves rigorous testing and validation of the prototype to ensure it meets project requirements. The team will conduct performance benchmarks, evaluate LLM-based program generation, and refine fitness modeling accuracy. Feedback from testing will guide adjustments to enhance efficiency and cross-domain learning capabilities within the MOSES framework.
Deliverables include a test report documenting prototype performance metrics, identified issues, and areas for improvement. A refined prototype with adjustments based on testing feedback will also be provided, focusing on optimizing LLM integration for better efficiency and accuracy.
$10,000 USD
Develop a detailed implementation plan for the full system rollout of LLM integration within MOSES. This plan will outline the steps, resources, and timeline required for deploying all modules, including generation modeling, cross-domain learning, and fitness estimation enhancements. This stage ensures the project’s operational readiness and provides a structured pathway for achieving final deliverables.
The deliverable is a comprehensive implementation plan that includes technical requirements, resource allocation, and a deployment timeline. This document will serve as a guide for the final integration phase, providing clarity on remaining tasks and preparing for the full rollout of LLM-enhanced capabilities.
$10,000 USD
Complete the integration of all LLM components into the MOSES framework, focusing on achieving full functionality and scalability. This phase will include final testing to ensure compatibility with Hyperon, performance benchmarking, and validation of cross-domain learning abilities. The system will be fully operational, ready for deployment within the SingularityNET ecosystem, and capable of contributing to AGI research and development.
The final deliverable is the completed and fully integrated MOSES framework with LLM-enhanced functionalities. A final project report will summarize the integration results, performance metrics, and insights gathered throughout the project, ensuring a smooth handover to SingularityNET for deployment and ongoing use. Total timeline will be 6 months.
$10,000 USD
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