OpenAI总裁透露GPT-5改了推理范式，AGI实现要靠现实反馈

Core Insights - OpenAI's President Greg Brockman discussed the company's approach to achieving AGI (Artificial General Intelligence) in a recent interview, highlighting a significant paradigm shift with the release of GPT-5, which aims to bridge the gap between the GPT series and AGI [5][6][9]. Group 1: Model Development and Learning Paradigms - The transition from text generation to reinforcement learning as a reasoning paradigm is crucial for AGI development, allowing models to learn through trial and error in real-world scenarios [6][15]. - GPT-5 employs a new reasoning paradigm that combines supervised learning with reinforcement learning, enabling the model to generate data during inference and iteratively improve based on real-world feedback [13][14]. - Brockman emphasized that the model's increasing ability to interact with the real world is a key component of the next generation of AGI [15]. Group 2: Computational Resources and Bottlenecks - Brockman identified computation as the primary bottleneck in AGI development, asserting that increased computational power directly influences the speed and depth of AI research and development [16][18]. - The current reinforcement learning paradigm in GPT-5, while more sample-efficient, still requires extensive computational resources to learn tasks effectively [18][20]. - He described computation as a fundamental fuel that transforms energy into potential stored in model weights, driving effective operations [19]. Group 3: Practical Implementation and Agent Development - The ultimate goal of AGI is to integrate large models into the workflows of businesses and individuals, moving beyond theoretical applications [26][27]. - OpenAI aims to package model capabilities into agents that can be audited and controlled, ensuring high levels of reliability and safety [29][30]. - A dual-layer "defense in depth" structure is designed to ensure the controllability of high-permission agents, akin to database security measures [31][32]. Group 4: Future Opportunities and Industry Integration - Brockman believes that significant opportunities lie in embedding existing intelligence into real industry processes rather than creating new flashy models [38][39]. - He advises developers and entrepreneurs to immerse themselves in industry specifics to identify genuine gaps that AI can fill, rather than focusing solely on superficial integrations [40]. - The future of AGI is envisioned as a model manager that combines local models with large cloud-based inference systems for adaptive computation [21][23]. Group 5: Long-term Vision and Challenges - Brockman expressed a vision for a future characterized by multi-planetary living and a truly abundant society, emphasizing the potential of current technologies [42][46]. - He noted that as technology accelerates, the demand for computational resources will grow, highlighting the importance of acquiring and allocating these resources effectively [43][45]. - The real challenge lies in maintaining curiosity and the willingness to explore new fields as AI continues to permeate all industries [48].