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Model Editing Explained: How to Update AI Models Without Retraining | Game-Changing Techniques

Discover how model editing is revolutionizing AI maintenance by allowing targeted modifications to neural networks without costly retraining. In this comprehensive presentation, I explain the breakthrough techniques of ROME (Rank-One Model Editing) and MEMIT (Mass Editing Memory in a Transformer) that enable precise editing of factual knowledge in large language models. Learn how researchers locate and modify specific weights to change what AI models "know" while preserving overall functionality. I cover the four-step technical process, evaluation metrics, current challenges, and future directions of this rapidly evolving field. Whether you're an AI researcher, developer, or just curious about how we can update large AI systems more efficiently, this presentation provides a clear explanation of the paradigm shift from static to dynamic models. Call to Action 🔔 Subscribe for more AI explanations and tutorials! 👍 If you found this helpful, please like and share with colleagues. 💬 What aspects of model editing would you like me to explore next? Let me