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’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.

AI Winter? Bad or Good Timing for My Book?

The author recounts the challenges of pitching a product during the dot-com bubble burst, paralleling it with current skepticism towards AI. Despite the doubts, the book "Enterprise Intelligence" argues that AI, though not without flaws, can greatly enhance enterprise analytics. It emphasizes a balanced approach and offers practical knowledge for leveraging AI in business.

Analytics Maturity Levels and “Enterprise Intelligence”

This blog revisits the traditional "analytics maturity model," providing a new perspective from the book "Enterprise Intelligence." The four analytics maturity levels: Descriptive, Diagnostic, Predictive, and Prescriptive, are explained along with their impact in the AI era. The blog emphasizes the importance of understanding these levels and their role in enterprise intelligence.

Enterprise Intelligence: Integrating BI, Data Mesh, Knowledge Graphs, Knowledge Workers, and LLMs

In today's rapidly evolving data analytics landscape turbo-charged by AI, businesses require an informationally-scalable infrastructure. "Enterprise Intelligence" offers a comprehensive framework integrating modern advancements like data mesh, knowledge graphs, and Large Language Models. The book addresses governance issues, providing a robust foundation for decision-making in an AI-driven world.

Exploring the Higher Levels of Bloom’s Taxonomy

Expanding BI Analyst Horizons with AI In the landscape of Business Intelligence (BI), the integration of AI and advanced reasoning in the enterprise is essential for staying afloat. To fully integrate with AI, traditional BI analysts, accustomed to working with empirical data and generating reports, must now expand their skills to include deeper analysis, evaluation, … Continue reading Exploring the Higher Levels of Bloom’s Taxonomy →

Release Date for my Book, “Enterprise Intelligence”

The book "Enterprise Intelligence" by Technics Publications, set to release on June 21, 2024, advocates for integrating AI with Business Intelligence to enhance enterprise performance. It emphasizes the importance of critical/creative thinking alongside AI and details a framework for Augmented Enterprise Intelligence. The book explores the merge of BI data and advanced AI to build adaptable and thriving enterprises in a rapidly evolving business landscape.