Tag: prolog

Conditional Trade-Off Graphs – Prolog in the LLM Era – AI 3rd Anniversary Special

Skip Intro. 🎉 Welcome to the AI “Go-to-Market” 3rd Anniversary Special!! 🎉 Starring ... 🌐 The Semantic Web ⚙️ Event Processing 📊 Machine Learning 🌀 Vibe Coding 🦕 Prolog … and your host … 🤖 ChatGPT!!! Following is ChatGPT 5's self-written, unedited, introduction monologue—in a Johnny Carson style. Please do keep reading because this blog … Continue reading Conditional Trade-Off Graphs – Prolog in the LLM Era – AI 3rd Anniversary Special →

Prolog Strategy Map – Prolog in the LLM Era – Holiday Season Special

Welcome to the Prolog in the LLM Era Holiday Season Special! Starring … pyswip … SWI-Prolog … the Semantic Web … data mesh … and … special guest star, ChatGPT! Notes: This blog is best consumed by first reading at least Part 1 of the series (preferably the first 3 parts): Prolog in the LLM Era – Part 1. This blog is really Part … Continue reading Prolog Strategy Map – Prolog in the LLM Era – Holiday Season Special →

Deductive Time Travel – Prolog in the LLM Era – Thanksgiving Special

The content discusses the significance of historical context in learning and decision-making, emphasizing the value of Prolog as a tool to understand complex logical rules over time. It explores how expert knowledge and decision-making evolve, and how modern technology can facilitate the integration of historical insights into artificial intelligence, enabling enriched decision-making today.

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.

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.