Imagine training a dog – you teach it tricks, but then need it to unlearn a bad habit. Turns out, something similar is now possible with large language models (LLMs). These powerful AIs, like ChatGPT, learn from massive datasets, absorbing everything from Shakespeare to, well, less desirable knowledge. Researchers have been grappling with how to make an AI *unlearn* specific information, a process crucial for privacy, security, and avoiding harmful outputs. A new method called Targeted Angular Reversal of Weights (TARS) offers a compelling solution. Instead of retraining the entire model, which is computationally expensive, TARS pinpoints specific 'knowledge weights' associated with the unwanted information. It then cleverly reverses these weights, effectively neutralizing the AI's ability to access that concept. Think of it as surgically removing a bad memory. Researchers successfully used TARS to make a Llama 3.1 8B model forget specific concepts, like Sherlock Holmes or the planet Saturn. Impressively, this 'unlearning' worked across multiple languages, even when the AI was only trained to forget the concept in English. Even more remarkable, TARS is modular. You can remove multiple concepts sequentially without significantly impacting the model’s overall performance. This is like removing several bad habits from your dog without affecting its ability to sit or fetch. While the research is promising, challenges remain. Fine-tuning the reversal process and ensuring the AI doesn't find loopholes to relearn the information are crucial next steps. TARS opens exciting possibilities for controlling what AIs learn and forget, paving the way for safer, more reliable AI systems in the future.