强化学习之父Richard Sutton：人类数据耗尽，AI正在进入“经验时代”！

Core Viewpoint - The article emphasizes that true intelligence in AI should stem from experience rather than pre-set human data and knowledge, marking a shift towards an "Era of Experience" in AI development [5][16]. Summary by Sections Introduction to the Era of Experience - The current era in AI is characterized by a transition from reliance on human-generated data to a focus on experiential learning, where AI systems learn through interaction with the world [9][16]. Key Insights from Richard Sutton's Speech - Richard Sutton argues that genuine AI must have a dynamic data source that evolves with its capabilities, as static datasets will become inadequate [6][9]. - He highlights that the essence of intelligence lies in the ability to predict and control sensory inputs, which is fundamental to AI and intelligence [13]. The Learning Process - The learning process in both humans and animals is based on interaction with the environment, where actions determine the information received, leading to a deeper understanding [10][11]. - Sutton illustrates that AI should emulate this learning process by engaging with the world to generate new data and enhance its capabilities [10][12]. Transition from Human Data to Experience - The article outlines a timeline of AI evolution, indicating that the current "Human Data Era" is nearing its end, paving the way for the "Experience Era" where AI learns through real-world interactions [14][16]. - Sutton emphasizes that the future of AI lies in its ability to continuously learn from experiences, which is essential for unlocking the full potential of the "Experience Era" [17]. Decentralized Cooperation - The concept of "decentralized cooperation" is introduced as a framework for understanding social organization, where multiple agents pursue their own goals while collaborating for mutual benefit [24][25]. - Sutton argues that human prosperity and the future of AI should be built on this foundation of decentralized cooperation rather than centralized control [27][28]. Conclusion - The article concludes by encouraging a shift in perspective towards viewing interactions between humans and AI through the lens of decentralized cooperation versus centralized control, which could provide valuable insights into future developments in AI [28].