Knowledge Graphs vs Prolog – Prolog’s Role in the LLM Era, Part 7

The integration of Prolog with large language models (LLMs) is explored, highlighting Prolog’s unique role in AI architecture alongside knowledge graphs (KGs). Prolog's ability to handle complex logical reasoning and rule-based systems is compared to the capabilities of KGs, emphasizing their complementary roles. KGs provide a scalable and semantically rich organizational framework, while Prolog excels in precise logical processing. Ultimately, their combined strengths enhance the robustness and intelligence of AI systems. Integration with LLMs adds broad, context-driven insights to create versatile AI systems capable of deep reasoning and broad understanding. The potential of combining Prolog, KGs, and LLMs for the future of AI is highlighted, emphasizing the benefits of leveraging their unique strengths.

Levels of Intelligence – Prolog’s Role in the LLM Era, Part 6

This episode of Prolog’s Role in the LLM Era explores parallels between human intelligence and Prolog, applying the concept to biological systems and artificial systems encoded in Prolog. It explores four levels of intelligence—simple, robust, iterative, and decoupled recognition and action—and relates them to biological and artificial recognition and decision-making processes, showcasing the potential power of using Prolog in AI systems.

Prolog and Business Intelligence – Prolog’s Role in the LLM Era, Part 5

In the latest installment of "Prolog’s Role in the LLM Era," several key topics were covered. These include integrating Prolog with traditional OLTP and OLAP databases, the concept of MetaFacts and MetaRules, and the development environment necessary for executing the exercises. The episode emphasizes the potential for creating dynamic and intelligent systems by combining Prolog with various data sources and advanced reasoning techniques. Through these integrations, real-time decision-making systems can be developed for optimized data processing and logic application, laying the groundwork for a distributed and scalable AI framework.

Prolog and ML Models – Prolog’s Role in the LLM Era, Part 4

The blog post discusses the integration of Prolog with large language models (LLMs) and its application in machine learning (ML). It explores the relationship between Prolog and ML models like decision trees, association rules, clustering, linear regression, and logistic regression. It also provides an example of transforming decision tree rules into Prolog using Python.

Playing with Prolog – Prolog’s Role in the LLM Era, Part 3

This blog series explores the synergy between Prolog's deterministic rules and Large Language Models (LLMs). Part 1 and 2 set the stage and discussed using Prolog alongside LLMs and Knowledge Graphs. A practical use case demonstrates how Prolog, aided by LLMs, can make meaningful contributions to AI systems. The series hopes to reignite interest in Prolog.

Prolog AI Agents – Prolog’s Role in the LLM Era, Part 2

Prolog, discussed in my "Prolog's Role in the LLM Era - Part 2" blog, offers transparent, clear rules for decision-making. This contrasts with the complexity of neural network models. Fusing fuzzy LLMs with Prolog's determinism creates a stable, reliable AI system. As technology advances, this partnership will optimize efficiency and reliability in decision-making systems.

Prolog’s Role in the LLM Era – Part 1

The content is a comprehensive exploration of the relationship between Prolog and LLMs (Large Language Models), detailing their differences, strengths, and potential synergies in the field of AI. It discusses the distinctive attributes of Prolog as a deterministic knowledge base and its contrast with LLMs, illustrating scenarios where each excels. The content also delves into the potential of ChatGPT to assist in authoring Prolog and creating logical, methodical systems. Additionally, it references the emergence of the GPT Store, as a manifestation of the original idea of "separation of logic and procedure". The overall tone of the content is informative, educational, and forward-thinking, and it serves as a valuable resource for understanding the intersection of traditional logic-based AI and modern LLMs.