深度｜Andrej Karpathy：行业对Agent的发展过于乐观，一个能真正帮你工作的Agent还需要十年发展时间

Core Insights - The article discusses the evolution of AI, particularly focusing on the development of agent systems and the challenges they face in achieving true intelligence [4][5][6][7][8][9][10]. Group 1: Future of AI Agents - Andrej Karpathy emphasizes that the next decade will be crucial for the development of AI agents, suggesting that current systems are not yet mature enough to be fully utilized in practical applications [5][6][7]. - The concept of a "cognitive core" is introduced, which refers to a stripped-down version of knowledge that retains intelligent algorithms and problem-solving strategies, highlighting the need for better data quality in training models [5][16]. - Karpathy expresses concern that society may lose understanding and control over AI systems as they become more integrated into daily life, leading to a disconnect between users and the underlying mechanisms of these systems [5][6]. Group 2: Historical Context and Learning Mechanisms - The article outlines significant milestones in AI development, such as the introduction of AlexNet and the Atari reinforcement learning era, which shaped the current landscape of AI research [8][9][10]. - Karpathy argues that human learning differs fundamentally from reinforcement learning, suggesting that humans build rich world models through experience rather than relying solely on reward signals [40]. - The discussion includes the limitations of current AI models in terms of continuous learning and the need for a more sophisticated understanding of context and memory [22][23]. Group 3: AI's Current Limitations - Karpathy critiques the current state of AI, stating that many generated code outputs are of mediocre quality and that the industry is experiencing a phase of over-optimism regarding AI capabilities [5][6][37]. - The article highlights the challenges AI faces in understanding complex code structures and the limitations of code generation models in producing original, contextually appropriate code [30][31][36]. - The need for a more nuanced approach to AI development is emphasized, suggesting that improvements must occur across multiple dimensions, including algorithms, data, and computational power [24][25][27].