Our Team

Our team comprises experienced AI researchers and developers with expertise in probabilistic reasoning, Large Language Models (LLMs), and knowledge graph systems. The diverse skill set enables us to design and implement cutting-edge solutions integrating Probabilistic Logic Networks (PLN) with Atomspace and LLMs. Aasav Ravi (Project Lead): Extensive experience in AI and AGI development, specializing in integrating symbolic reasoning with neural systems. Leads the design, architecture, and implementation of PLN-enhanced LLM systems. Arifa Khan - Senior Researcher (PLN Specialist): Expert in probabilistic reasoning frameworks, focusing on the design and optimization of PLN for enriched knowledge graphs and dynamic decision-making systems. Knowledge Graph Developer: Skilled in Atomspace and hypergraph-based systems, with experience in optimizing graph structures for AI applications. Machine Learning Engineer: Proficient in LLM development and API integration, enabling seamless communication between LLMs and PLN-enhanced knowledge graphs. Data Scientist: Focused on validating the solution with real-world datasets, benchmarking performance, and ensuring scalability and robustness.

Company Name (if applicable)

Lasavo Labs

Project details

Longer Description: PLN Guidance for LLMs

The integration of Probabilistic Logic Networks (PLN) with Large Language Models (LLMs) represents a groundbreaking step in advancing AI capabilities. This proposal outlines an innovative approach to enhance LLM memory and reasoning systems by leveraging PLN for probabilistic guidance, enriching Atomspace knowledge graphs, and providing real-time decision-making capabilities. By addressing the limitations of traditional retrieval-augmented generation (RAG) methods, such as graphRAG, this project aims to significantly improve the functionality, scalability, and explainability of LLMs.

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Context and Background

Challenges with Current LLM Systems

Large Language Models have transformed natural language understanding and generation, but they face critical limitations:

1. Static Memory Systems: Existing methods, such as graphRAG, rely on static knowledge graphs, which do not adapt dynamically to new information.
2. Deterministic Reasoning: These systems lack probabilistic reasoning, making them less effective in handling uncertainty or multi-step reasoning tasks.
3. Scalability Issues: As the volume and complexity of data grow, retrieval systems become less efficient, leading to bottlenecks in performance.
4. Poor Explainability: Many LLM systems operate as black boxes, providing little transparency into their reasoning processes.

Role of PLN in Overcoming These Challenges

Probabilistic Logic Networks (PLN) offer a robust solution by:

• Introducing probabilistic reasoning for better decision-making under uncertainty.
• Dynamically updating knowledge graphs with probabilistically weighted relationships.
• Enabling explainable AI by tracing reasoning paths and providing justifications.

Atomspace Knowledge Graphs

Atomspace, a core component of the OpenCog Hyperon framework, is a hypergraph-based system that organizes knowledge into dynamic nodes and edges. PLN’s integration with Atomspace enhances these graphs by adding probabilistic relationships, enabling richer and more adaptable reasoning structures.

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Solution Overview

This project explores two primary approaches to integrating PLN with LLMs, each offering distinct advantages for memory retrieval and reasoning:

1. Batch Mode PLN on Atomspace Knowledge Graphs

PLN operates in batch mode to preprocess Atomspace graphs, enriching them with probabilistically weighted links and optimizing their structure.

• Features:

  • Adds probabilistic relationships to nodes and edges.
  • Identifies and strengthens meaningful connections in the graph.
  • Reduces irrelevant data, improving retrieval efficiency.

• Advantages:

  • Preprocessed graphs are faster to query, reducing computational costs.
  • Enriched graphs provide higher accuracy in LLM memory retrieval.
  • Scales effectively with larger datasets.

• Example Use Case: A healthcare application where medical ontologies are enriched with probabilistic relationships, enabling LLMs to retrieve accurate and relevant diagnostic suggestions.

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2. Real-Time PLN Queries on Atomspace

PLN performs live queries on Atomspace graphs, allowing LLMs to reason dynamically and adapt to new information in real time.

• Features:

  • Enables context-sensitive reasoning by querying the latest graph state.
  • Provides dynamic adaptability to changing environments or data.
  • Facilitates nuanced reasoning under uncertainty.

• Advantages:

  • Ideal for dynamic decision-making tasks, such as disaster response or financial predictions.
  • Enhances LLM reasoning capabilities by integrating real-time probabilistic updates.

• Example Use Case: A financial forecasting system where PLN dynamically queries updated market data in Atomspace to guide LLM-generated investment strategies.

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Key Benefits of PLN Integration

1. Enhanced Memory and Retrieval Accuracy: PLN-enriched knowledge graphs provide a richer structure for memory augmentation, leading to improved retrieval precision and relevance.

2. Improved Reasoning Capabilities: By enabling probabilistic reasoning, PLN enhances the ability of LLMs to handle complex queries and multi-step reasoning tasks.

3. Scalability and Efficiency: Batch-mode PLN reduces computational overhead, making it feasible to manage large-scale knowledge graphs while maintaining high performance.

4. Dynamic Adaptability: Real-time PLN queries allow LLMs to reason and adapt to new information dynamically, improving decision-making in rapidly changing environments.

5. Explainability and Trustworthiness: PLN’s probabilistic reasoning paths can be traced and justified, making AI systems more transparent and reliable, particularly in sensitive domains like healthcare and finance.

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Implementation Plan

1. Knowledge Graph Enrichment

• Use PLN to preprocess Atomspace graphs, adding probabilistic weights to relationships and refining node structures.
• Optimize graph organization to facilitate faster and more accurate retrieval.

2. Integration with LLMs

• Develop APIs to connect LLMs with PLN-enhanced Atomspace graphs.
• Implement mechanisms for LLMs to query enriched graphs in batch mode or real-time.

3. Real-Time Querying

• Build systems for PLN to perform dynamic queries on Atomspace graphs.
• Ensure LLMs can retrieve context-sensitive information and reason dynamically.

4. Testing and Validation

• Compare PLN-guided LLM systems with traditional graphRAG methods across key metrics, including retrieval accuracy, reasoning depth, and scalability.
• Evaluate performance in real-world scenarios, such as medical diagnostics and financial forecasting.

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Performance Metrics

1. Retrieval Accuracy: Measure improvements in the precision and relevance of information retrieved from PLN-enhanced knowledge graphs.

2. Reasoning Depth: Evaluate the ability of LLMs to handle complex, multi-step reasoning tasks with PLN guidance.

3. Efficiency: Compare retrieval and reasoning times for PLN-guided systems versus graphRAG.

4. Scalability: Test the solution's ability to handle larger knowledge graphs and increasing data complexity.

5. Explainability: Assess the transparency of reasoning paths and the clarity of probabilistic justifications.

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Use Cases

1. Healthcare Diagnostics: Enrich medical ontologies to improve diagnostic suggestions and transparency for healthcare applications.

2. Financial Forecasting: Enable dynamic reasoning for financial predictions, providing real-time investment insights.

3. Educational Tools: Build AI tutors that can reason probabilistically and explain their answers to complex student queries.

4. Disaster Management: Use real-time PLN queries to adapt decision-making in dynamic disaster response scenarios.

5. Knowledge Management: Enhance organizational knowledge bases with probabilistic structures for better data retrieval and decision-making.

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Expected Outcomes

1. Enhanced Knowledge Graphs: PLN-enriched Atomspace graphs with probabilistic relationships that improve memory and reasoning capabilities.

2. Improved LLM Performance: Demonstrable advancements in retrieval accuracy, reasoning depth, and adaptability.

3. Scalable Framework: A system capable of managing increasing data complexity while maintaining high efficiency.

4. Generalized Applicability: Frameworks and methodologies adaptable to various domains, including healthcare, finance, and education.

5. Explainable AI: Transparent reasoning processes that build trust and confidence in AI systems.

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Conclusion

The integration of Probabilistic Logic Networks with Large Language Models represents a transformative approach to addressing the limitations of existing memory and reasoning systems. By leveraging PLN’s probabilistic reasoning and Atomspace’s dynamic knowledge graphs, this solution offers a scalable, efficient, and explainable framework for enhancing AI capabilities. Through batch-mode graph enrichment and real-time querying, the proposed system sets a new standard for memory augmentation and reasoning in advanced AI applications, unlocking significant potential across multiple domains.

Links and references

https://github.com/trueagi-io/hyperon-pln
https://wiki.opencog.org/w/Atomspace
https://opencog.org/hyperon/
https://openai.com/research/gpt-4
https://arxiv.org/abs/0801.2089
https://huggingface.co/blog/rag
https://arxiv.org/abs/1902.05060

 

Additional videos

Here are additional videos with pasteable links for your reference:

1. Introduction to Atomspace Knowledge Graphs:
   https://www.youtube.com/watch?v=9cdBkPB66tg

2. Understanding Probabilistic Logic Networks (PLN):
   https://www.youtube.com/watch?v=yR4z3ocS3Nc

3. Retrieval-Augmented Generation (RAG) Overview:
   https://www.youtube.com/watch?v=kCc8FmEb1nY

4. Hyperon Framework Explained:
   https://www.youtube.com/watch?v=4cl2uFFry8s

5. Dynamic Reasoning in AI Systems:
   https://www.youtube.com/watch?v=U0s0f995w14