Alan Morrison @AlanMorrison

By contrast, only a few hundred also provide the deterministic reasoning and precision description necessary to trusted agentic computing. graphrag.info/2026/10/12/don…

How to avoid getting taken again: Ask questions like this one: Does the vendor cover the whole AI picture? Probabilistic methods get you partway. Almost all of the 8,800+ AI startups cover only the probabilistic half.

I compare both approaches here: graphrag.info/2026/09/13/clo…

A knowledge graph closes that gap differently. It puts authorization inside the agent's own reasoning as a checkable fact, not a wall to route around. The agent checks the fact before it acts.

Perimeter security can't contain an AI agent. Typical enterprise practice is to wrap the model in harnesses or control planes. The model stays a black box. Control comes from outside it.

@ai_explorer25 @karpathy @rasbt @AndrewYNg @drfeifei @demishassabis @GaryMarcus Also, you list only Marcus from the neurosymbolic AI camp. The others on your list seem to be monolithic, statistical deep learning, LLM AI oriented--large datasets neural modeled using brute force processing. Others in NSAI: @AvilaGarcez @kerstingAIML @pascalhitzler

@ai_explorer25 @karpathy @rasbt @AndrewYNg @drfeifei @demishassabis @GaryMarcus @ai_explorer25 — "AI Explorer": This is an AI/tech content-creator (self-described "AI and Tech content Creator," ~30k X followers) who posts curated lists and news roundups. Different from the others who appear to have overseeen at least some original research.

@FegaPierre Shared at tinyurl.com/3n32hvst

15 semantic fault lines and how they can complicate or thwart your data-centric AI transformation efforts, from a detailed post of mine at graphrag.info/2026/07/26/15-…

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Back in March 2026, I interviewed both Jessica Talisman and Heather Hedden for this Curator blogsite. Today I realized that both of these interviews are worth revisiting together because they complement one another nicely. graphrag.info/2026/06/23/kno…

@slash1sol The more nuanced argument is that you should take classes and most code out of the application layer and create graph-centric, (FAIR) versions in knowledge graphs (data layer) instead so 85 percent of code can be reusable. Malcolm Sparks describes here: graphrag.info/2026/06/08/mal…

By relentlessly hawking a purely probabilistic, half-baked, all-agentic, no-human-in-the-loop version of generative AI, the dominant gen AI startups and hyperscaling incumbents have unwittingly polluted how the general public conceives of AI in general. graphrag.info/2026/06/19/rep…

Malcolm Sparks (@MalcolmSparks) of GraphCentric builds AI-ready audit trails & attaches provenance to everything using semantic technologies. He also shares how to build reactive apps without React. 🤯 graphrag.info/2026/06/08/mal… #GraphRAG #SemanticWeb #DataCentric

Two math olympiad champions wrote a training manual in 1993 on two old Macintosh computers, and every American kid who has won a major math competition in the last decade learned to think from it. Their names are Sandor Lehoczky and Richard Rusczyk. The book is called The Art of Problem Solving. Most people in math know it as AoPS. Since 2015, every single member of the US International Math Olympiad team has been an AoPS student. Not most of them. Every one. That statistic sounds impossible until you understand what the book actually does. Lehoczky and Rusczyk were not professors. They were competitors. Lehoczky earned the sole perfect AIME score in 1990 and led the national first place team. Rusczyk was a USA Mathematical Olympiad winner and a perfect AIME scorer in 1989. They had both survived the same brutal selection process the book was designed to train students for. And the first thing they decided was that almost every existing math textbook was teaching the wrong thing. School math gives you formulas. You memorize them. You apply them. You pass the test. Then you sit down in front of a real competition problem and the formula does not apply, and you have nothing underneath it. That is the gap. The gap is not knowledge. It is thinking. The entire premise of AoPS is that problem-solving is a transferable skill, not a bag of memorized tricks. A student who genuinely understands why a technique works can adapt it, combine it with something else, and deploy it in a context they have never seen before. A student who only memorized the technique freezes the moment the problem looks different. The book teaches the difference between a formula and a method. A formula tells you what to compute. A method tells you how to see. The students who win olympiads are not the ones who know more formulas. They are the ones who have trained themselves to look at an unfamiliar problem and recognize its structure. To see that this problem is secretly asking the same question as a problem they solved three weeks ago, just dressed differently. Rusczyk calls this "learning to read the problem." Not reading the words. Reading what the problem is actually asking underneath the words. The second thing they built into the book is tolerance for being stuck. Most students treat confusion as a signal to stop. The book treats confusion as the starting point. Every chapter pushes students past the point where the obvious approach runs out. That moment of running out is not failure. That is where the actual thinking begins. Lehoczky once described it this way. If you can solve a problem quickly, you are not learning. You are performing. Learning only happens when you are past the edge of what you already know. The book was written on old Macintosh computers in 1993. Rusczyk launched the AoPS website in 2003. Today the community has over one million users. Thousands of students enroll in AoPS online courses every year. Most winners of every major American math competition are AoPS alumni. A platform built by two kids who were good at math competitions has become the infrastructure that produces the next generation of mathematicians, engineers, and scientists who are good at thinking. The formulas you memorized in school will eventually be obsolete. The thinking you trained will not. What is one problem in your life right now that you have been avoiding because you do not yet know the right formula to solve it?

Every org is already paying for its data foundation — either upfront to build it right, or ongoing in failed AI projects. There's no neutral choice. Just which tax you'd rather pay. graphrag.info/2026/05/25/unl…

If you want diverse, heterogeneous data sources to work together without constant manual remapping, you need agreed-upon, standards-based abstractions sitting above the data itself. Thus the need for ontologies and semantic metadata in general. graphrag.info/2026/05/15/ben…

Cisco reports: 85% of enterprises are experimenting with agents, but only 5% are in production. 📉 To bypass rogue AI and black-box orchestration, we need to ground agents in a verifiable source of truth using GraphRAG. 6-step process to bridge the gap: lnkd.in/giDwF_73

Hierarchies manage complex communication. AI agent orchestration is now rediscovering this core principle. Insights from Heather Hedden: graphrag.info/2026/03/26/hea…

Data science teams don't have to reinvent the wheel with knowledge graphs; librarians have been working with open knowledge management/knowledge graph standards, tooling and techniques, for decades now. Just use what they've already put together. graphrag.info/2026/03/12/jes…