LLMs vs World Models: The AI Debate

The first “chatbot wow moment” was also the first AI trust warning. (1966) In 1966, MIT’s Joseph Weizenbaum published “ELIZA Computer Program for the Study of Natural Language Communication between Man and Machine” in Communications of the ACM. ELIZA wasn’t “intelligent” the way we mean it today. It relied on keyword triggers + pattern matching and still, many users walked away feeling like the system understood them. That reaction is a big part of what people later describe as the ELIZA effect: when humans over-attribute understanding or intent to a system that’s mainly reflecting language back at them. Where do you see “ELIZA effect” risk most today customer support, internal copilots, or agentic workflows? #AI #EnterpriseAI #Governance #Risk #AIAgents #FinTech

AI lies. Everyone knows it. But who has actually solved the problem? ChatGPT invents facts, and Gemini fabricates sources. These “hallucinations” are not just simple errors—they are a structural limitation of current language models, which operate based on statistical probabilities. What if the real problem isn’t the algorithms, but the very DNA of our processors—namely, the Instruction Set Architecture (ISA)? In our latest article published on parminc.com, we dive into the heart of the revolution led by Permion, a strategic partner of PARMINC. To address these fundamental flaws, Permion has rebuilt AI from the ground up using a neurosymbolic approach. This technology combines the intuition and pattern recognition of neural networks with the rigorous discipline of formal logical reasoning. With its virtual machine (XVM™) and the innovation of “Smart Tokens,” Permion is redefining the paradigm of artificial intelligence: Zero hallucinations: AI no longer guesses—it proves. Every response is supported by a verifiable and auditable chain of reasoning. Cloning human expertise: Instead of simply reading documents, AI captures experts’ tacit knowledge and transforms it into a living, reusable, and auditable institutional memory. Massive energy efficiency: By rethinking the ISA, the architecture tackles energy waste at its source by reducing unnecessary data movement. This is not science fiction: this trusted technology has already been showcased in the G7 GovAI Challenge (https://lnkd.in/eDwR-2iT) and is deployed in demanding, mission-critical environments. It’s time to move from AI that delivers statistical answers to AI that executes provable solutions. https://lnkd.in/eA6mR5jy Read the full analysis and explore the details of this revolution here, written for us by Michelle Blanc: https://lnkd.in/ePfBZCNr #ArtificialIntelligence #Neurosymbolic #Permion #ISA #SmartTokens #DigitalTransformation #AIInnovation #DigitalSovereignty Gina Savoie Stephane Collin Melvin C. JEAN COLLIN Louis Laframboise Anabela Brum Raquel Martins Arun Majumdar Paul Brown Paulo Moura Me Frédérique Grenier Jacob Friedman

I watched the movie "The Imitation Game" recently. It sent me down a rabbit hole. Most people think artificial intelligence started with ChatGPT. It didn't! It started in 1950, with a British mathematician, a dangerous question, and an idea so far ahead of its time that the world took 70 years to catch up. The question was simple: 𝗖𝗮𝗻 𝗺𝗮𝗰𝗵𝗶𝗻𝗲𝘀 𝘁𝗵𝗶𝗻𝗸? At the time, computers filled entire rooms and could barely do arithmetic. Today, AI systems write code, pass the bar exam, and hold conversations indistinguishable from a human being. One man saw all of this coming. His name was Alan Turing. And almost nobody talks about what he actually built or what happened to him. After going deep into his story, I realized the real history of artificial intelligence is far more fascinating than anything the headlines tell you. So I'm writing a 12-part series: The Intelligence Revolution. From the earliest ideas about thinking machine, to the modern labs racing to build artificial general intelligence today. Article 1 is coming very soon. If you've ever wondered where AI actually came from, not the hype, not the marketing, but the real origin story. Then this series is for you. Follow along. You won't want to miss where this story goes #ArtificialIntelligence #AIHistory #AlanTuring #MachineLearning #FutureOfTechnology #TechLeadership #TheIntelligenceRevolution

Stop treating AI like a search engine. It’s a prediction engine🧠 Most professionals use ChatGPT, but 99% don’t understand the "engine" under the hood. If you don't understand the Transformer Architecture, you aren't using AI, you’re just guessing. Welcome to Episode 01 of #AI60 — a new series where I strip away the hype and break down the most powerful AI concepts in exactly 60 seconds. No jargon. Just engineering. Today: The "LLM" Framework. • Large: Billions of parameters mimicking the human neural network. • Language: It doesn't "know" facts; it predicts the statistically most likely next word. • Model: A mathematical machine trained on trillions of data points. Understanding this fundamental shift is the difference between a "user" and an "architect" of the future. ⏭️ NEXT UP: Episode 02 is the big one— RAG (Retrieval-Augmented Generation). The technology currently fixing AI’s biggest flaw: Hallucinations. Follow me to master the AI revolution🛠️ #AI60 #GenerativeAI #AIEngineering #MachineLearning #FutureOfWork

Artificial Intelligence is evolving fast — but many people still ask the same question: What exactly are Large Language Models (LLMs), and how do they actually work? Behind tools like ChatGPT, Claude, and Gemini are systems trained on massive amounts of text to recognize patterns in language. Instead of “thinking” like humans, they predict the most likely next word in a sequence, allowing them to generate human-like responses, summarize information, write code, and more. But the real story is deeper: • Transformer architecture • Attention mechanisms • Tokenization • Massive training datasets Understanding these fundamentals helps separate AI hype from real capability. In this article, we break down: ✔ What LLMs are ✔ How they’re trained ✔ Why they’re so powerful ✔ Their limitations and risks If you're working in tech, business, or simply curious about AI, this is a great starting point. https://lnkd.in/gBqDmjzh #ArtificialIntelligence #LLM #MachineLearning #AIExplained #TechEducation #LargeLanguageModels #ReinforcementLearningfromHumanFeedback #ConstitutionalAI

AI is a Stochastic Parrot: Why "Truth" is just a Probability to LLMs 🧠🦜 Stop calling it a "Hallucination." It’s actually a Confabulation. 🚫🧪 In 2026, we are still making a fundamental mistake: we talk about AI as if it has a mind. When ChatGPT gives you a wrong answer, it isn’t "tripping" or seeing things that aren't there. It is doing exactly what it was designed to do: predicting the next most likely word. Here are two first-principle concepts you need to know to stay ahead in the AI era: 1. The Stochastic Parrot 🦜 Coined by Emily M. Bender, this metaphor explains that LLMs are essentially statistical mirrors. They "stitch" together linguistic patterns from billions of data points without any reference to meaning. It doesn't know what a "chair" is; it just knows the word "wooden" often comes before it. 2. Confabulation vs. Hallucination 🧠 In psychology, a hallucination is a sensory error (seeing ghosts). A confabulation is an honest lie—a memory gap filled with a plausible story to maintain coherence. AI doesn't have senses, so it can't hallucinate. It confabulates. It fills the gaps in its training data with the most "likely" fiction it can find. The Reality Check: AI isn't trying to be right; it’s trying to be plausible. In the digital world, "truth" is just a high-probability token. This is why, as we move into the future, the only truth we can trust is the one we can physically verify. Are we overvaluing "fluent" lies and undervaluing "clumsy" truths? Let’s discuss below. 👇 #StochasticParrots #AIPhilosophy #Confabulation #CodeToCarbon #FirstPrinciples

One of the most beautiful things about modern AI systems is how simple their core pattern actually is. Inside models like ChatGPT, intelligence emerges from a repeating rhythm: Listen → Think → Listen → Think Where: • Attention lets tokens listen to each other • MLP layers let tokens think internally • Activation functions decide which signals matter Stack enough of these layers together and something remarkable happens. The system begins to: • understand context • generate language • connect ideas across sentences What looks like intelligence is really millions of tiny transformations happening across layers of attention and computation. I wrote a piece explaining how this architecture works from first principles — using simple mental models and real-world analogies. Article link in the first comment. Which chat GPT feature fascinates you the most?

Why AI "Shortcuts" are the new Technical Debt. 🚨 We keep hearing "just ask ChatGPT" or "let AI handle it." But here is the reality of deterministic thinking in a probabilistic world: Socrates famously said: "I know that I know nothing." That humility? The ability to pause and say "I don't know, I need more information"? Humans have it. We teach it as wisdom. AI doesn't. When AI runs out of context, it doesn't raise its hand and ask for help. It doesn't say "I'm confused." It invents. It takes shortcuts. It fabricates answers with the confidence of a scholar and the accuracy of a gambler. The Danger Zone: Low Context: You give the AI a vague prompt. It has no data, no brand guidelines, and no historical background. The Invention: Since it can't find the answer, it creates a plausible-sounding one. It fills the gaps with fiction disguised as fact. The Shortcut: The AI takes the path of least resistance. It doesn't tell you it's unsure; it just produces output that looks correct. The Result? You save 2 hours on the front end, only to spend 2 weeks debugging on the back end. If that code or copy goes into production unchecked, you aren't fixing a typo—you're fixing a foundational error that costs months. AI doesn't replace review. It makes review more important than ever. Don't let the machine's confidence fool you. Context is the only thing standing between innovation and disaster. #AI #TechEthics #SoftwareEngineering #AITips #Leadership #Philosophy

In 1983, a 28-year-old Steve Jobs described something that sounds remarkably like what we are experiencing today with AI. He said something profound. Books allow us to read the words of great thinkers like Aristotle or Plato. But there is one limitation - we cannot ask them questions. Then he imagined a future where machines could capture the worldview of great minds, so that one day a student could not only read Aristotle, but actually ask Aristotle a question and receive an answer. Think about that for a moment. That idea was expressed more than 40 years ago. Today, with Large Language Models (LLMs) and tools like ChatGPT, we are beginning to interact with knowledge in exactly that way - conversationally. What fascinates me is not just the prediction. It's the way he was thinking about machines. At a time when personal computers were still new, Steve Jobs was already imagining them as something more than tools for computation. He saw them as a medium for interacting with human knowledge. Watching this clip again is a powerful reminder that transformative technology often begins as a thought experiment decades before it becomes reality. Curious to hear your thoughts: Are LLMs the realization of what Jobs imagined - or are we still just scratching the surface? 🎥 Video credit: Steve Jobs Archive (YouTube) #ArtificialIntelligence #LLM #Innovation #FutureOfTechnology #SteveJobs

In 2017, a small research paper quietly appeared with a bold title: “Attention Is All You Need.” At the time, it looked like just another academic publication. But that paper introduced the Transformer architecture - the foundation behind most modern large language models like ChatGPT, GPT-4, Claude, Gemini and many others. What fascinatinhg is how something so powerful can come from such a simple idea. At the core of the Transformer is this equation: Attention(Q, K, V) = softmax(QKᵀ / √dₖ) V This single mechanism allows a model to look at all words in a sentence, understand their relationships, and focus on what matters most. Stack this across many layers, train it on massive datasets with huge compute power, and suddenly machines can write code, answer questions, summarize documents, and have conversations. Sometimes the technologies that reshape the world are not thousands of lines of code. Sometimes it’s one elegant idea written in a single equation. #AI #MachineLearning #Transformers #LLM #ArtificialIntelligence