Company Name (if applicable)

Project details

Our project focuses on integrating Large Language Models (LLMs) into the MOSES (Meta-Optimizing Semantic Evolutionary Search) framework, enhancing its program generation, fitness function modeling, and cross-domain learning capabilities. This initiative addresses key limitations within MOSES, such as adaptability, computational efficiency, and cross-domain knowledge transfer, aligning with the broader goal of advancing Artificial General Intelligence (AGI) within the SingularityNET ecosystem. The integration with LLMs will provide MOSES with the adaptability to navigate complex, evolving domains with higher precision and reduced computational costs.

Project Objectives

1. Enhanced Program Generation: By using LLMs to support or replace traditional Estimation of Distribution Algorithms (EDA), we aim to refine MOSES’s program generation process. LLMs, fine-tuned on MOSES-generated programs, will offer promising suggestions for program extensions, enhancing evolutionary outcomes.

2. Improved Fitness Modeling: LLMs will introduce dynamic adjustments to fitness function estimation, enabling MOSES to adapt more precisely to complex and evolving tasks. This approach promises to improve efficiency over traditional fitness estimation methods.

3. Cross-Domain Learning: Leveraging LLMs for fitness modeling across various domains (such as genomics and financial forecasting) will allow the MOSES framework to apply insights across problem spaces. This cross-domain transferability is pivotal in developing versatile and scalable AGI applications.

Vision

Our vision is to pioneer a new frontier in AI, where LLMs and evolutionary algorithms converge to create a highly adaptive, intelligent system. This integration of cognitive modeling with generative techniques will advance AGI within SingularityNET and establish a standard for program evolution in complex, dynamic environments.

Purpose of the Grant

The purpose of this grant is to enable the integration of LLMs into MOSES, focusing on three main areas: program generation, fitness evaluation, and cross-domain learning. This project will produce a scalable, efficient AGI framework within the Hyperon ecosystem, propelling SingularityNET’s objectives of creating transformative, decentralized AGI solutions.

Solution Overview

The integration of LLMs into the MOSES framework within the Hyperon ecosystem involves several key deliverables that will advance AGI development:

1. Generation Modeling: LLMs will be fine-tuned on MOSES program populations, enabling the generation of new candidate programs or extensions. This generative capability aims to streamline program evolution by replacing the traditional EDA component with advanced, LLM-based modeling.

2. Fitness Function Cross-Learning: By applying LLMs across different fitness functions from multiple domains, MOSES will achieve enhanced cross-domain learning. LLMs trained on data from domains like genomics and finance will abstract useful patterns, enriching MOSES’s problem-solving versatility.

3. Enhanced Fitness Estimation: LLMs will augment traditional fitness estimation with neural network-based predictions for candidate program evaluation. This improvement will reduce computational overhead and increase efficiency, critical for complex or large-scale AGI tasks.

4. Compatibility with MOSES and Hyperon: The solution will ensure seamless compatibility within the OpenCog Hyperon ecosystem, enhancing MOSES’s evolutionary capabilities by improving population fitness while minimizing required population sizes.

5. Reproducibility: Comprehensive documentation and structured code will support reproducibility, allowing other researchers to replicate our findings and contribute to AGI advancements.

6. Performance and Efficiency: Improved efficiency in program evolution and fitness evaluation will be demonstrated through benchmarking, showcasing the advantages of LLM integration over traditional methods.

7. Cross-Domain Learning: LLMs will be designed to transfer knowledge across various problem domains, advancing MOSES’s potential to generalize and adapt across fields.

8. Enhanced Probabilistic Modeling: The LLMs will either enhance or replace the existing EDA-based model within MOSES, improving predictive accuracy and enabling more efficient problem-solving across domains.

9. Dynamic Adaptation: By continuously adapting based on MOSES’s evolving population, LLMs will boost MOSES’s ability to handle complex, dynamic problem spaces.

10. Transfer Learning: Implementing transfer learning within LLMs will enable MOSES to leverage previously learned insights across tasks, reducing retraining needs and improving the system's scalability.

Approach and Methodology

The project is structured around the integration of three primary LLM capabilities within the MOSES framework:

1. Generation Modeling with LLMs: We will fine-tune an LLM on MOSES-generated programs, allowing it to predict promising program extensions or new candidates. Clustering algorithms (e.g., K-Means) will be used to group similar program structures, enhancing cross-domain adaptability.

2. Cross-Domain Fitness Function Learning: LLMs will be trained to recognize patterns in fitness functions across diverse fields, establishing a foundation for cross-domain knowledge transfer. A dedicated module will abstract generalized patterns and feed them into MOSES for adaptive learning.

3. Enhanced Fitness Estimation: An LLM-based neural network will be developed to predict the fitness of candidate programs, leveraging historical data on program populations and fitness values to improve efficiency.

Technical Implementation

Our technical approach involves selecting appropriate programming languages and frameworks to ensure seamless integration and performance:

• Python: Due to its extensive AI and machine learning libraries (TensorFlow, PyTorch, Hugging Face Transformers), Python will be used for LLM-based module development.

• MeTTa and C++: These languages will be utilized for integration within MOSES and Hyperon, supporting evolutionary processes and modularity for effective LLM deployment.

• Modular Design: Each LLM component (generation modeling, fitness estimation, cross-learning) will be designed as a modular unit, allowing independent testing, optimization, and integration into MOSES.

• API Interface Development: We will create API interfaces to facilitate communication between MOSES and the LLM modules, ensuring efficient data transfer, fitness updates, and program suggestions.

• Benchmarking Strategy: Key performance metrics such as fitness scores, convergence time, program diversity, and computational efficiency will be established to compare traditional MOSES methods with the LLM-enhanced approach. Dynamic benchmarking will enable real-time performance tracking and continuous improvement.

Unique Selling Proposition

The integration of LLMs into MOSES within the OpenCog Hyperon ecosystem stands out due to:

1. Enhanced Evolutionary Search: The LLM-based approach replaces the traditional EDA with machine learning-driven methods, expediting MOSES’s search for optimal programs.

2. Cross-Domain Generalization: By training LLMs on multiple fitness functions, we enable MOSES to abstract patterns that transfer across problem domains, enhancing its versatility.

3. AI-Augmented Fitness Estimation: An LLM-based fitness estimator will streamline evaluations, reducing computational costs and improving accuracy for complex tasks.

4. LLM-Driven Cognitive Synergy and Knowledge Representation: The cognitive synergy achieved through LLM integration complements Hyperon’s structured knowledge framework, allowing MOSES to generate solutions that are contextually rich and adaptive.

Importance of the OpenCog Hyperon Ecosystem

The OpenCog Hyperon ecosystem is essential to realizing the benefits of this integration:

1. Modular and Scalable Architecture: Hyperon’s architecture allows for seamless integration of advanced LLM-based modules, ensuring scalability for future updates.

2. Interoperability with Other AI/ML Models: Hyperon’s interoperability facilitates integration with LLMs, neural networks, and machine learning frameworks, enhancing MOSES’s functionality through complementary tools.

3. Cognitive Synergy and Long-Term Adaptability: By combining LLM insights with Hyperon’s cognitive framework, the project supports a human-like approach to problem-solving, advancing AGI’s adaptability across domains.

4. Robustness and Knowledge Representation: Hyperon’s flexible knowledge representation enables MOSES to evolve nuanced solutions, leveraging LLM-generated insights for context-aware problem-solving.

5. Accelerated AGI Research and Innovation: Integrating LLMs within MOSES propels AGI research, fostering dynamic experimentation that brings SingularityNET closer to achieving adaptive, intelligent systems capable of cross-domain learning and rapid responsiveness.

Links and references

https://drive.google.com/file/d/15YHa25yHA4Vb51BMUisShuWGUAXRSltf/view?usp=sharing