Core Insights - The article discusses the five stages of artificial intelligence (AI) as defined by OpenAI, emphasizing the importance of each stage in the development and application of AI technologies [17][18]. Group 1: Stages of AI Development - The first stage is Chatbots, which serve as a foundational model that compresses human knowledge, akin to a person completing their education [19][4]. - The second stage is Reasoners, which utilize supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) to perform continuous reasoning tasks, similar to advanced academic training [20][21]. - The third stage is Agents, where AI begins to perform tasks autonomously, requiring a high level of professionalism and reliability, comparable to a person in a specialized job [22][23]. - The fourth stage is Innovators, focusing on the ability to generate and solve problems through real-world training and feedback, which is essential for enhancing the capabilities of AI [25][26]. - The fifth stage is Organizations, which manage multiple agents and innovations to prevent chaos, similar to how businesses manage human resources [27][28]. Group 2: Computational Needs - The demand for reasoning computational power is expected to increase by 100 times in the next five years, while training computational needs may expand by 10 times [10][29]. - The article highlights the necessity for both edge computing and cloud-based processing to support the various stages of AI development [28][29]. Group 3: Ideal Automotive Applications - The company is developing its own reasoning models (MindVLA/MindGPT) and agents (Driver Agent/Ideal Classmate Agent) to enhance its autonomous driving capabilities [31][33]. - By 2026, the company plans to equip its autonomous vehicles with self-developed advanced edge chips for deeper integration with AI [12][33]. Group 4: Training and Skill Development - Effective training for AI involves enhancing three key abilities: information processing, problem formulation and solving, and resource allocation [39][40][41]. - The article emphasizes that successful AI applications require extensive training, akin to the 10,000 hours of practice needed for mastery in a profession [36][42].