Core Viewpoint - The article emphasizes the growing demand for positions in the field of autonomous driving, particularly in the areas of world models, end-to-end systems, and VLA, highlighting the importance of practical experience and advanced knowledge in these domains [2][4]. Course Overview - The course on world models in autonomous driving is being launched in collaboration with industry experts, focusing on various algorithms and applications, including Tesla's world model and the Marble project by Fei-Fei Li's team [2][4]. - The course aims to provide a comprehensive understanding of world models, covering their development history, current applications, and different approaches such as pure simulation, simulation + planning, and generative sensor input [7]. Course Structure - **Chapter 1: Introduction to World Models** This chapter reviews the relationship between world models and end-to-end autonomous driving, discussing the evolution and current applications of world models, as well as various streams within the field [7]. - **Chapter 2: Background Knowledge of World Models** This chapter covers foundational knowledge related to world models, including scene representation, Transformer technology, and BEV perception, which are crucial for understanding subsequent chapters [8][12]. - **Chapter 3: General World Model Exploration** Focuses on popular models such as Marble, Genie 3, and the latest discussions around VLA + world model algorithms, providing insights into their core technologies and design philosophies [9]. - **Chapter 4: Video Generation-Based World Models** This chapter delves into video generation algorithms, starting with notable works like GAIA-1 & GAIA-2 and extending to recent advancements, ensuring a balance between classic and cutting-edge research [10]. - **Chapter 5: OCC-Based World Models** Concentrates on OCC generation methods, discussing three major papers and a practical project, highlighting their applicability in trajectory planning and end-to-end systems [11]. - **Chapter 6: World Model Job Specialization** This chapter shares practical insights from the instructor's experience, addressing industry applications, pain points, and interview preparation for related positions [12]. Learning Outcomes - The course is designed to elevate participants to a level equivalent to one year of experience as a world model algorithm engineer, covering key technologies and enabling practical application in projects [15].

Core Insights - The article discusses the significant advancements in the AI industry, particularly focusing on the release of GPT-5 by OpenAI, which is seen as a pivotal moment in the evolution of AI technology [2][3][11]. Group 1: Key Developments - OpenAI officially launched GPT-5, which is described as a major leap from its predecessor, GPT-4, providing a more sophisticated interaction experience akin to conversing with a PhD-level expert [3][5]. - GPT-5 is now accessible to all 700 million ChatGPT users, marking a shift towards democratizing advanced AI technology [3][5]. - The model features a unified system that eliminates the confusing model selection interface, enhancing user experience [4][10]. Group 2: Technical Improvements - GPT-5 is noted for significantly reducing the occurrence of hallucinations, making it the most reliable model developed by OpenAI to date [4][10]. - The model has improved self-awareness regarding its capabilities, which is crucial for enterprise applications [4][10]. Group 3: Competitive Landscape - The article highlights the competitive responses from other major players in the AI field, such as Anthropic and Google, who have also released significant models around the same time [7][9]. - Anthropic introduced Claude Opus 4.1, which achieved a leading score of 74.5% in real-world coding tests and received the first ASL-3 safety certification [7][9]. - Google launched Gemini 2.5 Deep Think and Genie 3, showcasing advancements in reasoning and interactive 3D world simulation, respectively [7][9]. Group 4: Market Implications - The rapid innovation cycle in the AI industry has compressed development timelines from years to mere days, indicating a new normal in the sector [11][12]. - The advancements in AI capabilities are expected to enhance productivity across various industries and fundamentally change interactions with the digital world [12].