Nishant Nishant
Project OwnerResponsible for overseeing project execution, ensuring alignment with objectives, managing resources, coordinating with stakeholders, and guiding the team to achieve milestones effectively.
This project explores Evolutionary Methods (EMs), particularly the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), as a novel approach to optimize Deep Neural Networks (DNNs) and transformer models, addressing limitations in traditional training techniques like backpropagation. By evolving both the weights and architecture of neural networks, this project aims to enhance scalability and performance, especially within decentralized Artificial General Intelligence (AGI) frameworks like Hyperon. The integration with Hyperon’s Atomspace will enable robust cognitive capabilities for dynamic, large-scale applications.
Explore and demonstrate the use of evolutionary methods (EMs) for training various DNNs including transformer networks. Such exploration could include using EMs to determine model node weights, and/or using EMs to evolve DNN/LLM architectures. Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is an example of one very promising evolutionary method among others.
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This initial phase focuses on finalizing project specifications, establishing the overall architecture, and setting up the foundational elements needed for the project’s execution. Activities will include defining sprint plans, outlining the Hyperon integration framework, and creating a detailed architecture blueprint. This phase is critical to ensure alignment among all team members and establish a solid foundation for efficient workflow and progress tracking throughout the project.
Key deliverables will include finalized project documentation detailing technical and functional specifications, a project roadmap with milestone schedules, and a setup of initial development environments. Additionally, the Hyperon framework will be prepared for integration, ensuring compatibility with the evolutionary DNNs. All team members will be onboarded, and project management tools will be configured to streamline communication and collaboration.
$10,000 USD
This phase involves the development and implementation of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. Key tasks include initializing data ingestion pipelines, configuring preprocessing stages, and building the foundational weight optimization module. This milestone is aimed at establishing the core evolutionary engine, which will be rigorously tested using baseline datasets to validate its effectiveness and performance.
Deliverables for this milestone will include a functional CMA-ES module, integrated data ingestion and preprocessing pipelines, and a baseline dataset for testing purposes. This will also encompass initial testing results, documenting the algorithm’s performance and preliminary benchmarking against traditional training techniques, setting a benchmark for iterative improvements in subsequent milestones.
$10,000 USD
This milestone focuses on implementing the architecture evolution module, allowing for the modification of DNN structures, such as layer configuration and node connectivity, using EMs. Integration with Hyperon Atomspace will enable cognitive module interactions, facilitating modularity and adaptability within the framework. The team will conduct benchmarking experiments and comparative analyses against traditional DNNs, refining the evolutionary strategies based on real-world performance data.
Deliverables include a fully integrated architecture evolution module within Hyperon, updated CMA-ES strategy configurations based on benchmarking results, and detailed comparative analysis reports. The milestone will also yield insights into areas where evolutionary strategies excel, which will guide further optimizations and highlight potential application areas for evolved DNNs within Hyperon.
$10,000 USD
In this final phase, the project will undergo comprehensive testing and performance evaluation to confirm the robustness and scalability of the integrated DNN models within Hyperon Atomspace. Documentation and reports will be prepared, detailing findings, methodologies, and insights gained throughout the project. This phase also includes the final integration, ensuring operational readiness and alignment with project objectives.
Final deliverables will include fully integrated and optimized DNN models within Hyperon, a set of extensive performance evaluation reports, and comprehensive project documentation. The documentation will cover the entire process from algorithm development to integration, providing transparency and replicability for future research. Finalized reports will also be shared with the DeepFunding community to support knowledge sharing and foster further innovation in decentralized AGI. Total time taken will be 6 months.
$10,000 USD
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