Looking Inside the Function: From Neural Networks to Enterprise Intelligence

A fascinating piece of research from MIT CSAIL recently caught my attention: Sparks of Weight-Space Programming: LLMs Can Natively Read & Write Neural Network Weights, published November 2, 2026, by Rikiya Takehi, Jayden Teoh, Alex Zhang, and Omar Khattab. The researchers explored whether an advanced LLM, GPT-6 Astra, could examine the weights of a neural … Continue reading Looking Inside the Function: From Neural Networks to Enterprise Intelligence →

Abductive Calculation: Reasoning with Imperfect Information

Prologue Several recent blogs have developed different pieces of the same larger problem. That is, how an intelligence can reason and act when the information available to it is necessarily incomplete: In Even ASI Cannot Escape Imperfect Information, I argued that no intelligence, regardless of how capable it becomes, can possess a perfectly complete and … Continue reading Abductive Calculation: Reasoning with Imperfect Information →

Stories, Analogies, Metaphors—the Layer After LLMs

I learned today that Patrick Stewart passed away a couple of days ago, on Oct 28. So this blog also serves as a tribute to the actor who played one of my favorite characters. TL;DR: of the Blog: Human intelligence does more than accumulate facts. We package experience into stories, compress stories into references and … Continue reading Stories, Analogies, Metaphors—the Layer After LLMs →

Updating Decision Data Points: Why a Fast Semantic Layer Is Part of the Intelligence Loop

TL;DR When I wrote much of Enterprise Intelligence in 2023, the rapid rise of AI made it clear to me that enterprises needed to quickly start planning for something beyond dashboards and reports: an architecture capable of accumulating and using intelligence across the business. That opened the door wider for the Insight Space Graph (ISG), … Continue reading Updating Decision Data Points: Why a Fast Semantic Layer Is Part of the Intelligence Loop →

KPI-First Probing of the Tuple Correlation Web

Note: From the “deleted scenes” of my book, Enterprise Intelligence, Technics Publications, 2024. This addresses a layered approach to building a Tuple Correlation Web (TCW), which can be massive in its full, enterprise-scoped manifestation. The TCW is primarily an Enterprise Intelligence structure, but it also belongs within the larger idea of my new book, Semantic Webs of … Continue reading KPI-First Probing of the Tuple Correlation Web →

Insight Function Array: How the Insight Space Graph Notices, Remembers, and Connects

My 2024 book, Enterprise Intelligence, describes the Insight Space Graph (ISG), a graph database that records, maps, and connects the simple insights exposed through the everyday work of Business Intelligence (BI) analysts. Towards the goal of resolving business problems, analysts continually explore enterprise data through tools such as Tableau and Power BI, producing line graphs, … Continue reading Insight Function Array: How the Insight Space Graph Notices, Remembers, and Connects →

Trailer for My New Book: Semantic Webs of Meaning

Update August 25, 2026: The supplemental GitHub repo for the Semantic Webs of Meaning is live. The supplemental repo has these main purposes: Post material that is too down into the weeds for the book (ex. install instructions, full appendices, code samples, and tutorials) but is mentioned there. Enable me to expand upon and/or clarify … Continue reading Trailer for My New Book: Semantic Webs of Meaning →

Seeing the Hook through the Uncanny Valleys Encountered by an Enterprise

This blog describes the Time Molecules process for the analysis of adversarial situations. That includes frauds, competitive games such as Texas Hold'em Poker, and any other situation where imperfect information is the key asset. Beyond sheer skill, good adversaries rely on stealth, sleight-of-hand, misinformation/disinformation, information overload, mimicry (impersonation), etc. Adversaries paint a false picture for … Continue reading Seeing the Hook through the Uncanny Valleys Encountered by an Enterprise →

AI Agents, Context Engineering, and Time Molecules

Abstract AI agents are not just about producing answers—they are executing processes. If those processes emit events for each step, they can be studied the same way we study human and enterprise workflows. By capturing and analyzing these event streams, we can reconstruct the context behind agent decisions and understand how AI-driven systems actually operate … Continue reading AI Agents, Context Engineering, and Time Molecules →

The Products of System 2 – Prolog in the LLM Era – Spring Break 2026 Special

Abstract In The Assemblage of AI architecture, System 2 is modeled after the deliberate reasoning processes described in human cognition. But rather than focusing on psychology, this article examines what System 2 produces inside an AGI system. The outputs are not merely answers to questions but structured artifacts such as stories, plans, procedures, and models. … Continue reading The Products of System 2 – Prolog in the LLM Era – Spring Break 2026 Special →