Google has recently made strides in the field of artificial intelligence with the introduction of Genie, a generative AI model with the unique capability to produce a multitude of 2D platformer video games. Distinguished from others by its control of in-game actions, Genie was developed by extracting patterns and knowledge from a colossal library of unsupervised video game data—specifically 200,000 hours of gameplay. The training has endowed Genie with the ability to not only construct intricate and variable game levels but also to animate a playable character within these generated environments, breaking ground as the only model of its kind. Interestingly, Google DeepMind's Open-Endedness Team Lead, Tim Rocktäschel, shared insights on Genie on X, identifying the model as a pioneering approach to action-controllable world generation. The inner workings of Genie involve a sophisticated process orchestrated by a trio of tightly integrated components. The first step is managed by a spatiotemporal video tokenizer, essentially a mechanism that dissects the temporal and spatial aspects of video game footage into digestible tokens that serve as the model's base learning material. These tokens are then processed by an autoregressive dynamics model, which leverages historical data patterns to forecast future in-game scenarios. The process culminates with the latent action model, guiding the behaviors of playable characters through predictive analyses. This novel AI model not only excels in 2D game creation but is also indicative of potential crossover applications in other realms, such as robotics. Although still in its research phase and not available for public use, Genie's capabilities have garnered considerable attention within the tech and gaming communities. The broader implications of Genie's technology emphasize its versatility and potential contribution to the achievable goal of general artificial general intelligence (AGI). Rocktäschel drew attention to Genie's potential in explaining that its underlying principals aren't limited to the two-dimensional world, but could extend to three-dimensional real-world applications. He demonstrates this by referencing Genie's success in controlling simulators in robotic environments. These advancements in AI suggest a future wherein such technologies could navigate and interpret complex terrains, both virtual and physical, which would be highly significant in multiple industries. Although specifics about the model's accessibility, like its interaction with different types of prompts (image, text, or video), remain under wraps, its foundation points to a remarkable journey ahead in the innovation of intelligent systems.