aasavravi1234
Project OwnerA seasoned AI researcher -extensive experience in evolutionary algorithms, large language models, and neural-symbolic AI integration, enhance frameworks-MOSES and Hyperon, contribution in advanced AGI
Our proposal aims to integrate Large Language Models (LLMs) into the MOSES framework to enhance its evolutionary algorithm capabilities. By leveraging LLMs, we will improve program generation modeling, cross-domain learning of fitness functions, and efficient fitness estimation. Our approach includes fine-tuning LLMs on MOSES program populations, abstracting patterns across diverse domains, and developing neural networks for fitness evaluation. The integration will reduce computational overhead, enhance program evolution, and provide a robust foundation for AGI development 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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Develop the foundational components for integrating LLMs into MOSES. This includes fine-tuning LLMs for program generation, designing initial neural fitness estimation models, and setting up compatibility with the MOSES framework.
Fine-tuned LLM model for program generation. Prototype of the neural fitness estimation component. Documentation of initial setup and integration process.
$20,000 USD
Successful integration of a fine-tuned LLM with MOSES, with basic program generation demonstrated. Neural fitness estimation prototype achieves at least 70% accuracy in fitness predictions on test datasets.
Develop the hybrid evaluation system combining LLM-based estimators with traditional MOSES evaluation. Optimize switching mechanisms for dynamic evaluation based on uncertainty thresholds.
Functional hybrid evaluation system integrated with MOSES. Switching mechanism implementation. Performance comparison reports between hybrid and baseline methods.
$30,000 USD
Hybrid evaluation system demonstrates at least a 20% reduction in computational costs compared to traditional methods while maintaining comparable accuracy.
Conduct large-scale validation and benchmarking of the LLM-integrated MOSES framework across multiple domains (e.g., genomics, financial prediction). Publish findings and refine the system based on results.
Performance benchmarks and comparative reports. Reproducible codebase with comprehensive documentation. Peer-reviewed publication submission.
$30,000 USD
Validation shows a minimum 30% improvement in program evolution efficiency and cross-domain adaptability. Codebase is publicly available and validated by at least two independent researchers.
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