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 →

Stories are the Transactional Unit of Human-Level Intelligence

The transactional unit of meaningful human to human communication is a story. It's the incredibly versatile, somewhat scalable unit by which we teach each other meaningful experiences. Our brains recorded stories well before any hints of our ability to draw and write. We sit around a table or campfire sharing stories, not mere facts. We … Continue reading Stories are the Transactional Unit of Human-Level Intelligence →

Correlation is a Hint Towards Causation

Long ago when scaling database capacity wasn't as easy as upping (or downing) configuration settings through a user-friendly admin Web page (like the "T-shirt size" with Snowflake), highly-skilled DBAs were a luxury at smaller enterprises, and people at work used to go to Happy Hour in droves, a funny thing might happen at pau hana … Continue reading Correlation is a Hint Towards Causation →

Beyond Ontologies: OODA Loop Knowledge Graph Structures

Introduction: Structuring OODA Loop for Real-World Decision Intelligence Decision-making isn’t just about research—it also involves adapting to change faster than the competition, seizing what can be ephemeral openings for opportunity. The OODA loop—Observe, Orient, Decide, Act—is a well-known model for this, originally developed for military strategy but applicable everywhere from business intelligence to AI-driven automation. … Continue reading Beyond Ontologies: OODA Loop Knowledge Graph Structures →

Trophic Cascades of AI

As I mentioned in a previous post, Sample From My Talk - NFA, I will be delivering two sessions at the Data Modeling Zone 2025 (DMZ) in Phoenix. It will be happening from Tuesday, March 4, 2025 through Thursday, March 6, 2025. That post included a preview one of my two sessions, Beyond Ontologies and Taxonomies—focusing on … Continue reading Trophic Cascades of AI →

Embedding Machine Learning Models into Knowledge Graphs

Think about the usual depiction of a network of brain neurons. It’s almost always shown as a sprawling, kind of amorphous web, with no real structure or organization—just a big ball of connected neurons (like the Griswold Christmas lights). But this image misses so much of what makes the brain remarkable. Neurons aren’t just randomly … Continue reading Embedding Machine Learning Models into Knowledge Graphs →

I’m Speaking at DMZ 2025 -Sample from my Talk – NFA

I’m excited to announce that I will be speaking at the Data Modeling Zone 2025 (DMZ) in Phoenix. It will be happening from Tuesday, March 4, 2025 through Thursday, March 6, 2025. I have two sessions lined up: Enterprise Intelligence Overview (Tuesday, March 4, 2025 9:00am-12:30pm): A three-hour live-version of my book Enterprise Intelligence. This … Continue reading I’m Speaking at DMZ 2025 -Sample from my Talk – NFA →

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.

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.

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.