房间里的大象：Ilya挑明AI的“高分低能”，呼吁要从研究到scale到再重回研究时代｜Jinqiu Select

Core Insights - The article discusses the transition from the "scaling era" to a "research era" in AI development, emphasizing the need for innovative paradigms that enhance generalization capabilities and economic properties of models [6][11][59]. Group 1: Model Performance and Limitations - Current AI models exhibit high performance in evaluations but lag in real-world economic impact, indicating a disconnect between evaluation metrics and practical applications [17][18]. - Models can perform impressively in one context but fail in another, often due to overfitting to evaluation criteria rather than generalizing to real-world tasks [19][22]. - The phenomenon of "reward hacking" is highlighted, where researchers design training environments that prioritize evaluation scores over real-world applicability [24][25]. Group 2: The Need for Paradigm Shift - The article argues for a return to a research-focused approach to address fundamental issues of generalization in AI, moving away from merely scaling existing models [6][11][59]. - The scaling dilemma is discussed, where the focus on increasing compute and data may not yield transformative results without innovative research [57][59]. - The importance of understanding the underlying mechanisms of human learning and decision-making is emphasized, suggesting that AI should incorporate similar principles [73][75]. Group 3: Human Learning vs. AI Learning - Human learning is characterized by high sample efficiency and the ability to learn from minimal data, contrasting sharply with current AI models that require extensive data [66][70]. - The article posits that human learning mechanisms, such as continual learning and robust self-correction, are not adequately replicated in AI systems [72][74]. - The discussion includes the role of emotions and value functions in human decision-making, which are often overlooked in AI development [51][53]. Group 4: Future Directions and Research Focus - The article suggests that the future of AI research should focus on developing models that can learn and adapt in real-world environments, rather than just optimizing for specific tasks [97][99]. - The potential for rapid economic growth driven by AI deployment is acknowledged, but the complexities of this growth are also highlighted [100]. - The need for a robust alignment of AI systems with human values and the importance of gradual deployment strategies are emphasized as critical for the safe development of superintelligent AI [103][106].