第一财经·2026-01-10 14:21Core Viewpoint - The article discusses the next generation of AI technology paradigms, particularly focusing on the concept of Autonomous Learning as a potential solution to the limitations of current large models and their reliance on labeled data and offline pre-training [3][4]. Group 1: Autonomous Learning - Autonomous Learning is gaining traction as a method for large models to evolve independently by generating learning signals and optimizing through closed-loop iterations [3]. - The definition and understanding of Autonomous Learning vary among industry experts, indicating a need for context-specific applications [3]. - Current advancements in Autonomous Learning are seen as gradual improvements rather than revolutionary changes, with existing efficiency issues still to be addressed [3]. Group 2: Future Paradigms and Innovations - Experts believe that OpenAI, despite its commercialization challenges, remains a strong candidate for leading the next paradigm shift in AI [4]. - The potential of Reinforcement Learning (RL) is still largely untapped, with the next generation of paradigms expected to emphasize "self-evolution" and "proactivity" [4]. - Concerns about safety arise with the introduction of proactivity in AI, necessitating the instillation of appropriate values and constraints [4]. Group 3: Market Dynamics and Competitive Landscape - The probability of Chinese teams leading in AI innovation in the next three to five years is considered high, given their ability to quickly replicate and improve upon discovered technologies [5]. - Key challenges for China include breakthroughs in lithography technology, capacity, and software ecosystem development [5]. - The maturity of the B2B market and the ability to compete internationally are critical for China's success in AI [5].