不用术语看懂世界模型：从日常预测到自动驾驶

Group 1 - The core concept of the article is the definition and function of the "world model," which predicts future scenarios based on past sensory data, similar to how humans anticipate events in daily life [2][3][30] - The world model operates by taking various forms of input, such as images, sounds, and sensor data, and outputs predictions about future states, emphasizing the importance of recognizing patterns and making forecasts [4][30] - The distinction between world models and neural networks is highlighted, where neural networks serve as tools for recognition and imitation, while world models are the core that enables prediction and understanding [5][10][30] Group 2 - The article discusses the limitations of creating a "universal" world model due to the vast differences in rules and requirements across various scenarios, leading to the necessity for specialized models [11][12][30] - Various specialized world models are introduced, including video generation, music generation, game, and industrial production models, each focusing on specific domains to achieve precise predictions [12][14][18][30] - The automatic driving world model is described as the most stringent type, as its predictions directly impact safety, requiring rapid response times and high accuracy [18][22][30] Group 3 - The VLA model is presented as an enhanced version of the automatic driving world model, incorporating language logic to improve the prediction of actions based on user commands and traffic rules [23][26][30] - The article concludes that the future of world models lies in becoming more specialized rather than universal, focusing on improving prediction accuracy and speed in specific scenarios [29][30]