Ilya罕见发声：大模型「大力出奇迹」到头了

Core Viewpoint - AI is transitioning from the "scaling era" back to the "research era," as the current mainstream approach of "pre-training + scaling" has hit a bottleneck, necessitating a focus on reconstructing research paradigms [3][55][57]. Group 1: AI Development Trends - Ilya Sutskever argues that the mainstream "pre-training + scaling" approach is encountering limitations, suggesting a shift back to fundamental research [3][55]. - The current investment in AI, while significant, does not yet translate into noticeable changes in everyday life, indicating a lag between AI capabilities and their economic impact [11][15]. - The AI models exhibit a puzzling disparity between their performance in evaluations and their practical applications, raising questions about their generalization capabilities [17][21][61]. Group 2: Research and Training Approaches - The discussion highlights the need for a more nuanced understanding of reinforcement learning (RL) environments and their design, as current practices may lead to overfitting to evaluation metrics rather than real-world applicability [19][22]. - Sutskever emphasizes the importance of pre-training data, which captures a wide array of human experiences, but questions how effectively models utilize this data [33][34]. - The conversation suggests that the current focus on scaling may overshadow the need for innovative research methodologies that could enhance model generalization and efficiency [55][58]. Group 3: Future Directions in AI - The industry is expected to return to a research-focused approach, where the exploration of new training methods and paradigms becomes crucial as the limits of scaling are reached [55][57]. - There is a growing recognition that the models' generalization abilities are significantly inferior to those of humans, which poses a fundamental challenge for future AI development [61][68]. - The potential for AI to drive economic growth is acknowledged, but the exact timing and nature of this impact remain uncertain, influenced by regulatory environments and deployment strategies [100][102].