Thinking Reliably and Creatively – Prolog in the LLM Era – Summer Vacation Special

Welcome to the Prolog in the LLM Era Summer Vacation Special! Starring … Prolog ... knowledge graphs ... ChatGPT o4-mini ... neuro-symbolic AI ... and our special guest ... Thinking Fast and Slow! In this episode (Part 11 of the series), I wish to address how neuro-symbolic AI relates to this series. After all, the series title, Prolog … Continue reading Thinking Reliably and Creatively – Prolog in the LLM Era – Summer Vacation Special →

Analogy and Curiosity-Driven Original Thinking

Original thinking doesn’t spring from a void. It’s the meticulous art of forging a path to a solution where none yet exists, of stitching together lessons from far-flung domains to carry us from where we are to where we wish to be. In everyday life, we rely on zero-sum games—win or lose, right or wrong—yet … Continue reading Analogy and Curiosity-Driven Original Thinking →

Thousands of Senses

In this mind-bogglingly complex world in which we live—countless moving parts fraught with imperfect information of many types—a versatile highly-functioning intelligence requires quick access to a wide variety of information in order to make intelligent decisions. It's not that all decisions should be made from that full breadth of information. It's that all decisions are … Continue reading Thousands of Senses →

From Data through Wisdom: The Case for Process-Aware Intelligence

My new book, Time Molecules, is available!! This is its launch-day blog (June 4, 2025)! Time Molecules is a book about bringing process awareness to business intelligence (BI). Traditional BI flattens reality into snapshots— scalar values, points of information— leaving us staring at dimensionally flattened shadows on a wall. But life unfolds over time, and … Continue reading From Data through Wisdom: The Case for Process-Aware Intelligence →

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 →

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.

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.

KPI Status Relationship Graph Revisited with LLMs

Introduction Back in 2006, I developed an ambitious idea that, though ahead of its time, now poses exciting possibilities in our data-driven era. The concept was simple yet potent: to construct a graph mapping various elements (such as database columns and parameters) used in formulas across Key Performance Indicator (KPI) statuses. The aim was to … Continue reading KPI Status Relationship Graph Revisited with LLMs →