Imagine teaching AI about videos without ever showing it a single frame. Sounds impossible, right? Researchers have cracked the code with a groundbreaking technique called Text-Only Pre-Alignment (TOPA). Instead of feeding AI massive amounts of video data, TOPA uses the magic of large language models (LLMs) to create "textual videos." These are essentially detailed, frame-by-frame textual descriptions of video content, complete with annotations and question-answer pairs. Think of it like a movie script for AI, capturing the essence of a video in words. This textual data is then used to train the LLM, teaching it to understand the dynamics and nuances of video solely through text. But how does this translate to understanding real videos? The key is CLIP, a powerful model that links images and text. TOPA uses CLIP to connect the textual video descriptions with the visual features of actual videos. The results are impressive. Even without any video training, TOPA-powered LLMs can perform complex video understanding tasks, like summarizing content and answering questions. This approach has achieved remarkable accuracy on challenging benchmarks, even outperforming some AI models trained on vast video datasets. TOPA opens exciting new doors for video understanding. It's more efficient than traditional methods, requiring less data and computational power. It also offers a promising path for understanding video in situations where visual data is scarce or difficult to obtain. While TOPA excels at grasping the overall meaning of videos, it still faces challenges with fine-grained visual details, like identifying specific objects or subtle actions. However, this innovative approach represents a significant leap forward, paving the way for more accessible and efficient video understanding AI.