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 →

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 →

The BI Counterpart to AI Infinite Context

This post maps the ideas in the recent paper Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention (Munkhdalai, T., Faruqui, M., & Gopal, S. 2024) to concepts from my book, Enterprise Intelligence, including the Tuple Correlation Web (TCW), Insight Space Graph (ISG), and cubespace as a semantic layer. In business intelligence (BI) and … Continue reading The BI Counterpart to AI Infinite Context →

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.

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.

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.