Imagine teaching an AI about time, not with clocks or calendars, but with pictures. That's the innovative idea behind Seq2Time, a new training method that helps AI understand the flow of events within videos. Normally, teaching AI about video requires painstaking manual labeling of timestamps – marking exactly when things happen. This is a bottleneck, limiting the amount of data AI can learn from. Seq2Time bypasses this problem by using readily available image and short video clip datasets. The researchers created clever exercises for the AI. For example, they might show a sequence of images and ask the AI to find the image that matches a specific description, like "pouring the batter into the pan." Or, they might ask the AI to describe what's happening in a particular image based on its position in the sequence. By learning to associate descriptions with positions in a sequence, the AI begins to grasp the concept of time without explicit timestamp labels. To further connect image sequences with video time, the researchers developed a "unified relative position token." This helps the AI translate between the position of an image in a sequence and a moment in time within a video. Think of it as learning the language of time. The results are impressive. Seq2Time significantly boosted performance on video understanding tasks, like identifying and describing events in a cooking video. It even outperformed methods relying on manually labeled timestamps. This approach opens up exciting possibilities for AI. By leveraging the massive amount of existing image and short video data, we can train AI to understand time and events in a more scalable and efficient way, leading to richer and more nuanced video analysis in the future.