Core Concept - The article discusses the concept of "world models" in artificial intelligence (AI), which are internal representations of the environment that AI systems use to evaluate predictions and decisions before executing tasks [1][4]. Group 1: Definition and Importance of World Models - World models are considered essential for building intelligent, scientific, and safe AI systems, as emphasized by leading figures in deep learning [1]. - The idea of a world model has historical roots, dating back to Kenneth Craik's 1943 proposal of a "small-scale model" in the brain that allows organisms to simulate various scenarios [2]. Group 2: Historical Context and Evolution - Early AI systems like SHRDLU demonstrated the use of world models but struggled with scalability and complexity in real-world environments [3]. - The rise of machine learning and deep learning has revitalized the concept of world models, allowing AI to build internal approximations of environments through trial and error [3]. Group 3: Current Challenges and Perspectives - Despite the potential of world models, there is still a lack of consensus among researchers regarding their definition, content, and verification methods [2]. - Current generative AI models, such as large language models (LLMs), exhibit heuristic rules but lack a coherent and unified world model, leading to inconsistencies in their outputs [4][6]. Group 4: Future Directions and Research Focus - Researchers are exploring how to develop robust and verifiable world models, which could enhance AI's reliability and interpretability [6][7]. - There are differing opinions on how to create these models, with some suggesting that sufficient multimodal training data could naturally lead to their emergence, while others advocate for entirely new architectures [7].