量子位·2025-11-15 05:00Core Viewpoint - The article discusses the competition among three major players in the AI industry—Li Feifei, Yann LeCun, and Google—regarding the development of world models, highlighting their distinct technological approaches and implications for artificial general intelligence (AGI) [1][3][42]. Group 1: Li Feifei and Marble - Li Feifei's company, World Labs, has launched its first commercial world model, Marble, which is seen as having significant commercial potential due to its ability to generate persistent, downloadable 3D environments [2][5]. - Marble features a native AI world editor called Chisel, allowing users to create and modify worlds with simple prompts, which is particularly beneficial for VR and game developers [7][9]. - However, some experts argue that Marble resembles a 3D rendering model rather than a true world model, as it focuses on visual representation without incorporating the underlying physical laws necessary for robotic training [10][18][20]. Group 2: Yann LeCun and JEPA - LeCun's approach to world models, exemplified by JEPA, emphasizes control theory and cognitive science rather than 3D graphics, aiming to enable robots to predict changes in the environment without needing to generate visually appealing images [24][26]. - JEPA focuses on capturing abstract representations of the world that are essential for AI decision-making, making it more suitable for training robots [28][30]. Group 3: Google and Genie 3 - Google DeepMind's Genie 3, launched in August, allows users to generate interactive video environments with a single prompt, addressing long-term consistency issues in generated worlds [32][35]. - Despite its dynamic capabilities, Genie 3 is still fundamentally a video logic model and lacks the deeper understanding of physical laws that JEPA provides, making it less effective for robotic training [38][40]. Group 4: World Model Pyramid - The article categorizes the three world models into a pyramid structure: Marble as the interface, Genie 3 as the simulator, and JEPA as the cognitive framework, illustrating their varying levels of abstraction and suitability for AI training [53][54]. - As one moves up the pyramid, the models become more abstract and aligned with AI's cognitive processes, while those at the bottom are more visually appealing but harder for robots to comprehend [54].