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Yann LeCun: LLMs Will NEVER Reach Human Level AI (Here’s Why) 🚀 Are large language models (LLMs) fundamentally limited? In this exclusive fireside chat, Yann LeCun presents a compelling technical argument for why LLMs will never reach human-level AI — and introduces the architecture he believes will define the future: JEPA (Joint Embedding Predictive Architecture). Recorded at the AI Alliance Global Leadership Reception in New Delhi, and moderated by Christopher Nguyen, this conversation explores a deeper shift in AI — from predicting tokens to building world models that understand reality through abstraction. 🎯 What you’ll learn: • Why next-token prediction works for text — but fails for real-world intelligence • What world models are and why they matter for future AI systems • How JEPA differs from generative models and reconstruction-based approaches • Why methods like autoencoders and masked modeling fall short • The evolution of self-supervised learning (Barlow Twins, VICReg, and beyond) • Why scaling current models will not lead to AGI • Real-world applications: robotics, physics, economics, and industrial systems 🧠 LeCun also shares candid insights into: • Why his ideas struggled to gain traction inside Meta • The limitations of current AI research focus • Why abstraction — not generation — is the key to intelligence • The challenge of connecting microscopic and macroscopic models across domains 👥 Speakers: Yann LeCun — Founder, AMI Labs Christopher Nguyen — AI Alliance 📍 Recorded at the AI Alliance Global Leadership Reception during the India AI Impact Summit 2026, Taj Mahal Palace, New Delhi. ⏱ Chapters: 00:00 Introduction & Setting the Agenda 02:24 What Is Missing in AI Today? World Models Explained 04:40 Why Self-Supervised Learning Fails for Video 07:01 JEPA: Joint Embedding Predictive Architecture 09:15 Levels of Abstraction: From Quantum Fields to Societies 10:22 Building AI Systems for the Real World 11:30 The Jupiter Example: Prediction with Six Numbers 12:43 Why Scaling Generative Models Has Fundamental Limits 15:04 Joint Embedding vs Reconstruction: The Empirical Verdict 17:02 Solving Collapse: From Siamese Nets to Barlow Twins 21:15 Why Yann Left Meta to Build AMI Labs 25:36 Audience Q&A: Connecting JEPA Models Across Scales 30:05 Modeling the Economy with World Models 35:00 Closing Remarks 🔔 Subscribe for in-depth conversations with leading minds shaping the future of AI. #YannLeCun #ArtificialIntelligence #LLM #AGI #AIResearch #MachineLearning #FutureOfAI #DeepLearning #WorldModels #JEPA #SelfSupervisedLearning #AITheory #OpenSourceAI #AILeadership #AIInnovation

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