In the first part of the lecture, I will explore how we teach AI to generate videos without relying on detailed annotations or object specific labels. By training on collections of similar videos – such as faces or human bodies – the model learns to generalize across an entire category. Building on this idea, we developed a Learnable Game Engine (LGE) that learns from simple monocular videos to keep track of scenes and objects and to re render them from different viewpoints. Much like a real game engine, it captures basic physics and logic, allowing users to control the scene or guide virtual agents through high level language instructions. The second part of the lecture turns to the safety and fairness of generative AI. Most existing approaches look only for predefined types of bias, but real world systems can exhibit unexpected ones. To address this, we introduce OpenBias, a method that uncovers and measures previously unknown biases in text to image models without relying on any preset list. Our experiments show that OpenBias aligns well with established methods and with human judgment, offering a more flexible way to assess fairness in generative systems.