9年实现爱因斯坦级AGI？OpenAI科学家Dan Roberts谈强化学习扩展的未来

Core Insights - The core insight of the article is the prediction that reinforcement learning will play an increasingly significant role in the development of AI models, potentially leading to the creation of models capable of discovering new scientific knowledge within the next nine years [2][37]. Group 1: Presentation Highlights - Dan Roberts, a research scientist at OpenAI, discussed the importance of scaling laws in pre-training and reinforcement learning during his presentation at AI Ascent [2][4]. - The presentation highlighted a significant finding: as the "thinking time" of models increases, their performance improves, indicating that models can learn to think more effectively [9][12]. - OpenAI's recent model, o3, demonstrates enhanced reasoning capabilities, allowing it to solve complex problems in a fraction of the time it would take a human [14][31]. Group 2: Future Predictions - The company aims to expand the scale of reinforcement learning significantly, with plans to invest $500 billion in computational resources to enhance model training [48]. - Predictions suggest that AI's ability to process tasks will double approximately every seven months, potentially allowing for computations lasting up to eight years by 2034 [56][57]. - The ultimate goal is to develop models that can contribute significantly to human knowledge and scientific discovery, akin to the time it took Einstein to formulate the theory of general relativity [31][57].