Core Insights - Richard Sutton, the 2024 Turing Award winner, emphasized that the human data dividend is nearing its limit, and artificial intelligence is entering an "experience era" centered on continuous learning, which has the potential to exceed previous capabilities [1][2] Group 1: AI and Learning - Sutton stated that most current machine learning aims to transfer existing human knowledge to static AI, which lacks autonomous learning capabilities. He believes we are reaching the limits of human data, and existing methods cannot generate new knowledge, making continuous learning essential for intelligence [2] - He defined "experience" as the interaction of observation, action, and reward, which is crucial for an intelligent agent's ability to predict and control its input signals. Experience is the core of all intelligence [2] Group 2: Collaboration and Future Predictions - Addressing fears about AI causing bias, unemployment, or even human extinction, Sutton argued that such fears are exaggerated and often fueled by those who profit from them. He highlighted that economic systems function best when individuals have different goals and abilities, similar to how decentralized collaboration among intelligent agents can lead to win-win outcomes [3] - Sutton proposed four predictive principles for the future of AI: 1. There is no consensus on how the world should operate, and no single view can dominate [3] 2. Humanity will truly understand intelligence and create it through technology [3] 3. Current human intelligence will soon be surpassed by superintelligent AI or enhanced humans [3] 4. Power and resources will flow to the most intelligent agents [3] Group 3: Historical Context and Future Outlook - Sutton categorized the history of the universe into four eras: the particle era, the star era, the replicator era, and the design era. He believes humanity's uniqueness lies in pushing design to its limits, which is the goal pursued through AI today [4] - He described AI as the inevitable next step in the evolution of the universe, urging society to embrace it with courage, pride, and a spirit of adventure [4] Group 4: Event Overview - The 2025 Inclusion Bund Conference, themed "Reshaping Innovative Growth," took place in Shanghai from September 10 to 13, featuring a main forum, over 40 open insight forums, global theme days, innovation stages, a technology exhibition, and various networking opportunities [4]

AI是宇宙演化的必然下一步，人类应以勇气、自豪和冒险精神来迎接它。 "欢迎来到'经验时代'。"9月11日，2025·Inclusion外滩大会在上海举行，2024图灵奖得主、"强化学习之 父"理查德·萨顿在主论坛演讲中表示，人工智能需要一种能够伴随智能体能力提升而持续增长与优化的 新型数据源，传统静态数据库已不足以支撑其进一步发展。 萨顿认为，我们当前仍处于"人类数据时代"，AI系统主要通过预测人类语言和标签进行训练，绝大多数 机器学习仍是将人类已有知识迁移至一个静态、缺乏自主学习能力的人工智能体系中。然而，人类数据 的利用正逐渐接近极限。 他指出，现在我们要进入"经验时代"，智能体将以第一人称视角与世界互动，直接生成被称为"经验"的 新数据源。这种机制与人类及其他动物的学习方式高度一致——通过与认知水平相匹配的自我体验获取 发展所需的数据。 "去中心化"的定义是每个智能体追求自己的目标，这正是经济体系的运行方式，人工智能的政治议题 中，他强调人类需要寻求协作、支持协作，并致力将协作制度化。 对于人工智能与哲学，理查德·萨顿则认为，人工智能是人类最古老的追求之一，它并不是陌生的外来 技术，而是与人类的本性高度 ...

Group 1 - The AI industry is transitioning into an "experience era," where continuous learning is essential for intelligence, moving beyond the limitations of human data [2] - Richard Sutton emphasizes that knowledge is derived from experience, which involves observation, action, and reward, and that the intelligence of an agent depends on its ability to predict and control input signals [2] - Two technologies, continual learning and meta-learning, are necessary to unlock the full potential of AI in this new experience era [2] Group 2 - Concerns about AI leading to bias, unemployment, or even human extinction are exaggerated and fueled by certain organizations and individuals profiting from such fears [3] - Sutton argues that decentralized collaboration among agents with different goals can lead to mutual benefits, highlighting human cooperation as a unique strength [3] - He presents four predictive principles regarding the future of AI, including the lack of consensus on how the world should operate and the potential for superintelligent AI to surpass human intelligence [3] Group 3 - Sutton categorizes the history of the universe into four eras: particle, star, replicator, and design, asserting that humanity's unique ability to push design to its limits is crucial in the current pursuit of AI [4] - He believes that AI is an inevitable next step in the evolution of the universe, advocating for a courageous and adventurous approach to its development [5]

Core Insights - Richard Sutton, the 2024 Turing Award winner, emphasizes that the human data dividend is nearing its limit, and artificial intelligence is entering an "experience era" centered on continuous learning, which has the potential to exceed previous capabilities [1][2] Group 1: Experience Era - Sutton defines "experience" as the signals of observation, action, and reward that are exchanged between agents and the world, asserting that knowledge derives from experience and that the intelligence of an agent depends on its ability to predict and control its input signals [2] - The transition to the experience era is driven by reinforcement learning, but to fully unlock its potential, two currently immature technologies—continual learning and meta-learning—are required [2] Group 2: Collaboration and AI - Addressing concerns about AI leading to bias, unemployment, or even human extinction, Sutton argues that fears surrounding artificial intelligence are exaggerated, and that decentralized collaboration among different agents can lead to mutually beneficial outcomes [2] - He highlights that humanity's greatest strength lies in collaboration, which has been the foundation of economic, market, and governmental successes [2] Group 3: Future of AI - Sutton posits that the replacement of human roles by AI is inevitable, with humans acting as catalysts and pioneers for the "design era," which he categorizes as the fourth era in the evolution of the universe, following the particle, star, and replicator eras [2][3] - He encourages embracing the evolution of artificial intelligence with courage, pride, and a spirit of adventure [3]

Core Insights - Richard Sutton, the 2024 Turing Award winner, emphasizes that the human data dividend is nearing its limits, and artificial intelligence is entering an "experience era" centered on continuous learning, which has the potential to exceed previous capabilities [1][2] Group 1: Experience Era - Sutton defines "experience" as the interaction of observation, action, and reward, which are signals exchanged between agents and the world [2] - The current machine learning methods are reaching their limits in generating new knowledge, making them unsuitable for continuous learning, which is crucial for intelligence [1][2] Group 2: Technological Advancements - To fully unlock the potential of AI in the experience era, two currently immature technologies are needed: continual learning and meta-learning [2] - Sutton believes that the collaboration between decentralized agents can lead to win-win outcomes, countering fears about AI causing bias, unemployment, or even human extinction [2] Group 3: Human-AI Collaboration - Sutton argues that human collaboration is the greatest success, and AI's role will be to enhance this collaboration, which is fundamental to economic, market, and governmental successes [2] - He posits that AI's replacement of human roles is inevitable, with humans acting as catalysts in ushering in a new "design era" in the evolution of the universe [2] Group 4: Future Perspective - Sutton views artificial intelligence as a necessary next step in the evolution of the universe, advocating for a courageous and adventurous approach to its development [3]

专题：2025 Inclusion·外滩大会 新浪科技讯 9月11日上午消息，今日外滩大会现场，2024年图灵奖得主、加拿大计算机科学家理查德萨 顿表示现在我们正进入"经验时代"，我们需要一种新的数据源，它随着智能体的变强大而不断增长和完 善，就像电脑游戏中的自我博弈一样。这类数据也可以不依赖自我博弈，而是由智能体以第一人称与世 界互动直接生成，我称之为"经验"。这正是人类和其他动物的学习方式，也是 AlphaGo 创造第"37 步"的方式，也是 AlphaProof 近期在国际数学奥林匹克中获得了银牌的方式。他认为，智能体的关键在 于，我们需要与智能体的智力水平和认知发展相匹配的数据，这正是可以从自身经验里得到的东西。 以下为理查德·萨顿分享全文 各位女士们、先生们，早上好！ 很高兴在 2025 年外滩大会上发言，我的主题是人工智能。在接下来的发言中，我将围绕这个主题谈三 个方面：我想谈谈科学发展趋势，政治影响，以及哲学意义。 我们处在"人类数据时代"。人工智能被训练来预测人类的语言和标签，并由人类专家不断微调。今天大 多数机器学习的目的，是把人类已有的知识转移到一个静态、没有自主学习能力的 AI 上。但是 ...

Group 1 - The core idea of the article revolves around the evolution of AI systems, particularly the concept of "self-evolution" where AI can improve itself without human intervention, marking a shift from traditional training methods [4][5][10] - The "Era of Experience" proposed by Richard Sutton and David Silver emphasizes that AI will learn primarily from its own experiences, moving beyond human knowledge limitations [4][6] - The Darwin Gödel Machine (DGM) is highlighted as a significant development in self-evolving AI, capable of modifying its own code to enhance performance, particularly in coding tasks [6][10] Group 2 - The article discusses the limitations of current AI models due to the depletion of human-generated data, prompting the need for new modeling paradigms that allow machines to interact with the world and generate their own experiences [4][5] - DGM's performance improvements are quantified, showing a rise from 20.0% to 50.0% on SWE-bench and from 14.2% to 30.7% on Polyglot after 80 iterations, demonstrating its self-learning capabilities [6] - The article contrasts self-evolution with traditional supervised learning (SL) and reinforcement learning (RL), noting that self-evolution relies on models generating their own training data, which introduces new challenges and opportunities [7][8]

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]

Core Viewpoint - 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 [1][5][12]. Group 1: Transition to Experience Era - AI models currently depend on second-hand experiences, such as internet text and human annotations, which are becoming less valuable as high-quality human data is rapidly consumed [1][5]. - The marginal value of new data is declining, leading to diminishing returns despite the increasing scale of models, a phenomenon referred to as "scale barriers" [1][5]. - To overcome these limitations, AI must interact with its environment to generate first-hand experiences, akin to how infants learn through play or athletes make decisions on the field [1][5][8]. Group 2: Technical Characteristics of the Experience Era - In the experience era, AI agents need to operate continuously in real or high-fidelity simulated environments, using environmental feedback as intrinsic reward signals rather than human preferences [2][5]. - The development of reusable world models and memory systems is crucial, along with significantly improving sample efficiency through high parallel interactions [2][5]. Group 3: Philosophical and Governance Implications - The article highlights the superiority of decentralized cooperation over centralized control, warning against the dangers of imposing single objectives on AI, which mirrors historical attempts to control human behavior out of fear [2][5][18]. - A diverse ecosystem of multiple goals fosters innovation and resilience, reducing the risks of single points of failure and rigidity in AI governance [2][5][18]. Group 4: Future Perspectives - The evolution of AI is seen as a long-term journey requiring decades of development, with the success hinging on stronger continuous learning algorithms and an open, shared ecosystem [5][12]. - The article posits that the creation of superintelligent agents and their collaboration with humans will ultimately benefit the world, emphasizing the need for patience and preparation for this transformation [12].

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].