人类数据时代

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AI将受困于人类数据
3 6 Ke· 2025-06-16 12:34
Core Insights - The article discusses the transition from the "human data era" to the "experience era" in artificial intelligence, emphasizing the need for AI to learn from first-hand experiences rather than relying solely on human-generated data [2][5][10] - Richard S. Sutton highlights the limitations of current AI models, which are based on second-hand experiences, and advocates for a new approach where AI interacts with its environment to generate original data [6][7][11] Group 1: Transition to Experience Era - The current large language models are reaching the limits of human data, necessitating a shift to real-time interaction with environments to generate scalable original data [7][10] - Sutton draws parallels between AI learning and human learning, suggesting that AI should learn through sensory experiences similar to how infants and athletes learn [6][8] - The experience era will require AI to develop world models and memory systems that can be reused over time, enhancing sample efficiency through high parallel interactions [3][6] Group 2: Decentralized Cooperation vs. Centralized Control - Sutton argues that decentralized cooperation is superior to centralized control, warning against the dangers of imposing single goals on AI, which can stifle innovation [3][12] - The article emphasizes the importance of diverse goals among AI agents, suggesting that a multi-objective ecosystem fosters innovation and resilience [3][12][13] - Sutton posits that human and AI prosperity relies on decentralized cooperation, which allows for individual goals to coexist and promotes beneficial interactions [12][14][16] Group 3: Future of AI Development - The development of fully intelligent agents will require advancements in deep learning algorithms that enable continuous learning from experiences [11][12] - Sutton expresses optimism about the future of AI, viewing the creation of superintelligent agents as a positive development for society, despite the long-term nature of this endeavor [10][11] - The article concludes with a call for humans to leverage their experiences and observations to foster trust and cooperation in the development of AI [17]
AI将受困于人类数据
腾讯研究院· 2025-06-16 09:26
晓静 腾讯科技《AI未来指北》特约作者 2025 年 6 月 6 日,第七届北京智源大会在北京正式开幕,强化学习奠基人、2025年图灵奖得主、加拿 大计算机科学家Richard S. Sutton以"欢迎来到经验时代"为题发表主旨演讲,称我们正处在人工智能史上 从"人类数据时代"迈向"经验时代"的关键拐点。 Sutton指出,当今所有大型语言模型依赖互联网文本和人工标注等"二手经验"训练,但高质量人类数据 已被快速消耗殆尽,新增语料的边际价值正急剧下降;近期多家研究也观察到模型规模继续膨胀却收效 递减的"规模壁垒"现象,以及大量科技公司开始转向合成数据。 以下为演讲全文: 当前大型模型已逼近"人类数据"边界,唯有让智能体通过与环境实时交互来生成可随能力指数级扩 张的原生数据,AI 才能迈入"经验时代" 。 真正的智能应像婴儿或运动员那样在感知-行动循环中凭第一人称经验自我学习 。 强化学习范例(如 AlphaGo、AlphaZero)已证明从模拟经验到现实经验的演进路径,未来智能体 将依靠自生奖励和世界模型实现持续自我提升 。 基于恐惧的"中心化控制"会扼杀创新,多主体维持差异化目标并通过去中心化合作实现双赢 ...