第二代AI预训练范式：预测下个物理状态

Core Viewpoint - The article discusses the shift from the first generation of AI models, primarily based on "next word prediction," to a second generation focused on "world modeling" or "predicting the next physical state," highlighting the limitations of current AI applications in the physical world [4][8]. Group 1: Current AI Paradigms - The first generation of AI models, exemplified by large language models (LLMs), has achieved significant success but struggles with real-world applications [4]. - The second generation, as proposed by Jim Fan, emphasizes world modeling, which involves predicting reasonable physical states under specific actions, marking a transformative shift in AI development [8]. Group 2: World Modeling Definition and Implications - World modeling is defined as predicting the next physical state based on specific actions, with video generation models serving as a practical example [8]. - The article anticipates that 2026 will be a pivotal year for large world models (LWMs) in robotics and multimodal AI, establishing a real foundation for future advancements [8]. Group 3: Comparison of AI Models - Visual language models (VLMs) are described as "language-first," where visual information is secondary, leading to a disparity in physical understanding compared to LLMs [9]. - The design of VLA (visual-language-action) models prioritizes language over physical interactions, resulting in inefficiencies in physical AI applications [10]. Group 4: Biological Insights and Future Directions - The article draws parallels between human cognitive processing and AI, noting that a significant portion of the human brain is dedicated to visual processing, which is crucial for physical interaction [11]. - The emergence of world modeling is seen as a response to the limitations of current AI paradigms, with potential for new types of reasoning and simulation that do not rely on language [12]. Group 5: Challenges and Future Research - The article raises questions about the future of AI, including how to decode action instructions and whether pixel reconstruction is the optimal goal for AI development [13]. - It emphasizes the need for further exploration in the field, suggesting a return to fundamental research principles as the industry seeks to advance towards a "GPT-3 moment" in robotics [13].