Imagine an AI that can not only "watch" a video but also pinpoint the exact moment a specific event occurs, like a dog catching a frisbee or a chef adding a secret ingredient. That level of granular understanding is the goal of a new benchmark called E.T. Bench (Event-Level & Time-Sensitive Video Understanding Benchmark). Existing AI models that analyze videos often focus on understanding the overall gist of the content. E.T. Bench raises the bar by challenging AI to delve deeper into the timeline, identifying and localizing individual events within longer videos, a much harder task. The benchmark encompasses a wide array of tasks, from simple actions like recognizing someone opening a door to more complex challenges like summarizing the key steps in a cooking tutorial. This diversity is crucial for training truly versatile AI that can handle real-world video understanding. Researchers tested various existing AI models on E.T. Bench and found that even the most sophisticated ones struggle with this fine-grained level of analysis. Why the difficulty? Current AI models have limitations in processing precise timestamps and handling multiple events unfolding within a single video. To tackle these issues, the researchers also introduced E.T. Chat, a new AI model designed specifically for this type of video understanding. E.T. Chat, along with a massive new training dataset called E.T. Instruct 164K, achieved state-of-the-art performance on E.T. Bench, closing the gap between existing open-source models and more powerful commercial ones. This research is a big leap towards AI that can truly understand the nuances of video content, paving the way for exciting applications in video search, content creation, and even robotics.