Putting the "I" Back into BI
Welcome to the Prolog in the LLM Era Spring Break Special! Starring ... decoupled recognition and action ... event streaming ... correlation doesn't imply causation ... and our special guest star ... Deductive Time Travel!!! Notes before diving in: Please see Part 1 in the series for background on Prolog if you're not familiar with … Continue reading Closer to Causation – Prolog in the LLM Era – Spring Break Special
This is a sneak peek of my upcoming book, Time Molecules: The BI Side of Process Mining and Systems Thinking—to be published in the May-June 2025 timeframe. I’ve long been fascinated by systems thinking, ever since reading The Fifth Discipline, Peter Senge, when it was first released. The idea that complex systems behave in ways … Continue reading Sneak Peek at My New Book—Time Molecules
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
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
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
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
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.
This blog advocates investing time in the foundational book "Enterprise Intelligence," emphasizing its painless approach to developing an integrated understanding of business intelligence. It elaborates on the book's themes, including BI's role in transformative AI, the Insight Space Graph, and the Tuple Correlation Web. The book is detailed and offers a 25% discount for readers.
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.
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.
Soft Coded Logic 