Core Insights - The AGI-Next conference highlighted the competitive landscape of AI in China, focusing on the importance of foundational models and their impact on future business strategies [4][5] - Key players in the AI industry, including Zhiyuan, Tencent, and Alibaba, are exploring different paradigms for AGI, emphasizing the need for new metrics to evaluate model intelligence [6][7] - The discussion revealed a consensus on the increasing differentiation between consumer (ToC) and business (ToB) applications of AI, with distinct strategies for each segment [11][12] Group 1 - The AGI-Next conference featured prominent figures in China's AI sector, including Zhiyuan's founder Tang Jie and Tencent's newly appointed chief scientist Yao Shunyu, indicating a significant gathering of industry leaders [4][5] - The conference underscored the belief that the capabilities of foundational models will determine the success of future AI ventures, with a focus on maintaining a leading position in model development [5] - Tang Jie expressed concerns that the gap between Chinese and American models may not be closing, as many American models remain closed-source [5][6] Group 2 - The participants discussed the evolution of AI paradigms, with Tang Jie suggesting that the exploration of conversational models has reached its peak, and future efforts should focus on coding and reasoning capabilities [6][7] - Yao Shunyu emphasized the importance of scaling not just in computational power but also in architecture and data optimization to enhance model performance [6][7] - The need for new standards to measure AI intelligence was highlighted, with concepts like Token Efficiency and Intelligence Efficiency being proposed as metrics [7][41] Group 3 - The differentiation between ToC and ToB applications was a key theme, with Yao Shunyu noting that while ToC requires strong integration of models and products, ToB focuses on enhancing productivity through the best models available [11][12] - Lin Junyang pointed out that the success of AI applications depends on understanding real user needs, suggesting that effective communication with enterprise clients is crucial for developing successful AI solutions [8][12] - The conversation also touched on the potential for AI to automate significant portions of human work, particularly in the ToB sector, where higher model intelligence correlates with increased revenue [43][44] Group 4 - The participants acknowledged the challenges of deploying AI models effectively, with a focus on the need for better education and training to maximize the benefits of AI tools [44][57] - The discussion included insights on the importance of collaboration between academia and industry to address unresolved questions in AI research, such as the limits of intelligence and resource allocation [20][21] - The potential for new paradigms in AI, such as continuous learning and memory integration, was identified as a critical area for future exploration [38][40]

2026.01. 10 本文字数：1458，阅读时长大约2分钟 因此，姚顺雨认为，自主学习这件事已经发生了，只是受效率等因素限制，还存在各种问题，他认为目前自主学 习的范式迭代更像是渐变，而非突变。 至于目前全球市场中哪一家企业最可能率先引领范式创新，姚顺雨表示，虽然OpenAI经历了商业化等各种变 化，创新基因被削弱，但仍是最有可能诞生新范式的地方。 林俊旸认为，目前的RL（强化学习）范式尚处早期，潜力远未被充分挖掘，全球范围内仍面临诸多共性挑战， 而下一代范式的核心在于"自主进化"与"主动性"。只是自主进化是否需要更新参数，见仁见智。 作者 | 第一财经 吕倩 当大模型陷入Scaling Law（缩放定律）的增长瓶颈，下一代技术范式将会是什么？ 1月10日，在由清华大学基础模型北京市重点实验室、智谱AI发起的AGI-Next前沿峰会上，腾讯控股"CEO/总 裁办公室"首席AI科学家姚顺雨、阿里巴巴Qwen技术负责人林俊旸、Kimi创始人杨植麟、智谱创始人唐杰等人 工智能行业人士齐聚，共话大模型下一代技术范式。 对下一代范式的猜测中，自主学习（Autonomous Learning）是个热门概念，是大模型摆 ...

以下文章来源于智能涌现 ，作者周鑫雨 邓咏仪 智能涌现 . 直击AI新时代下涌现的产业革命。36氪旗下账号。 "智能涌现"完整记录了这场含金量极高的万字圆桌讨论，值得所有关心AI的人反复观看。 文 ｜ 周鑫雨、邓咏仪 编辑 ｜ 苏建勋 来源｜ 智能涌现（ID：AIEmergence） 封面来源 ｜ 作者拍摄 2026年1月10日，在由清华大学基础模型北京市重点实验室发起的AGI-Next前沿峰会上，当下中国大模型最重要的四位主力，罕 见地聚在了一起： 刚刚在1月8日顺利登陆港股的智谱 ，其创始人 兼首席科学家 唐杰 是现场第一个分享嘉宾。 不久前官宣加入腾讯的 姚顺雨 ，第一次出现在公共场合中。在腾讯近期完成关键的一次模型团队重组之后，这名前OpenAI研究 员，出任CEO办公室首席科学家。 和姚顺雨同属大厂战队的，是阿里Qwen技术负责人 林俊旸 ，阿里历史上最年轻的P10。他背后的阿里通义实验室，目前开源模 型的衍生数量和下载量，已经位列全球开源模型第一。 闭门会上的另一股重要力量，是近期处在舆论焦点的六小虎。现身闭门会的月之暗面CEO 杨植麟 ，最近刚刚官宣了新一轮5亿美 元的融资。 如果说2025年， ...

Core Insights - The next generation of AI technology paradigms is expected to focus on Autonomous Learning, which allows models to evolve independently without heavy reliance on human-annotated data and offline pre-training [1][2] - The potential for innovation in AI is seen as high in China, with the ability to quickly replicate and improve upon discoveries, contingent on breakthroughs in key technologies like lithography machines [3] Group 1: Next Generation Paradigms - Autonomous Learning is a trending concept that enables models to generate learning signals and optimize through closed-loop iterations, leading to continuous evolution [1] - The definition and understanding of Autonomous Learning vary among experts, emphasizing its dependence on specific data and task contexts [1] - Current advancements in AI, such as Claude's ability to self-improve by transforming 95% of its own code, indicate that self-learning is already occurring, albeit with efficiency limitations [1] Group 2: Market Leaders and Innovations - OpenAI is viewed as the most likely candidate to lead the next paradigm shift in AI, despite facing challenges in maintaining its innovative edge [2] - The current Reinforcement Learning (RL) paradigm is still in its early stages, with significant potential yet to be realized, focusing on "autonomous evolution" and "proactivity" [2] - The introduction of proactivity in AI raises new safety concerns, necessitating the instillation of appropriate values and constraints [2] Group 3: China's Position in AI - The probability of Chinese teams leading in AI innovation in the next three to five years is considered high, given their ability to quickly replicate and enhance discoveries [3] - Key challenges for China include production capacity and software ecosystem development, alongside the need for a more mature B2B market [3] - Cultural and economic factors may hinder the willingness to pursue groundbreaking innovations in China [3]

Group 1 - The next generation of AI technology paradigms is expected to focus on Autonomous Learning, which allows models to evolve independently without heavy reliance on labeled data and offline pre-training [2] - Autonomous Learning is not a universal methodology but is highly dependent on specific data and task scenarios, with ongoing discussions about its definition and implementation among industry experts [2] - Current advancements in AI, such as Claude's ability to self-improve by transforming 95% of its code, indicate that self-learning is already occurring, albeit with efficiency limitations [2] Group 2 - OpenAI is considered the most likely candidate to lead the next paradigm innovation, despite experiencing various commercial changes that may have diluted its innovative edge [3] - The current Reinforcement Learning (RL) paradigm is still in its early stages, with significant potential yet to be realized, and the next paradigm will emphasize "self-evolution" and "proactivity" [3] - Introducing proactivity in AI may lead to new safety concerns, necessitating the instillation of correct values and constraints, similar to educating a child [3] Group 3 - A significant paradigm shift is anticipated by 2026, with developments in continuous learning, memory, and multimodal capabilities, driven by improvements in computational power in academia [4] - The probability of Chinese teams leading in AI innovation in the next three to five years is considered high, given their ability to quickly replicate and improve upon discovered technologies [4] - Key challenges for China include breakthroughs in lithography technology, capacity, and software ecosystem development, alongside the need for a more mature B2B market [4]

今天受邀，参加了一个非常有趣的活动，现场人真的爆满了，很多人都是从外地特意赶过来的。 这个活动，叫AGI-NEXT。 主要是几个演讲的嘉宾，过于重磅了。 开源四巨头除了DeepSeek没来，智谱的唐杰老师、Kimi的杨植麟、Qwen的林俊旸，齐聚一堂。 甚至腾讯最近最有话题度的姚顺雨，都以远程"巨头"的方式，远程参加了这场会议。 这个巨头，真的是非常的AI巨头。。。 这场活动因为没有座位了，我站着听了3个小时，收货非常的多。 包括唐老师说，随着DeepSeek这类模型的横空出世，Chat聊天这种范式，其实已经没有仗可打了。 下一仗是什么？是 Action ，是 Doing things 。 杨植麟说，Agent的本质，其实是一个搜索问题。 还有，智能和电力不一样，它不是等价交换品。 你在深圳用的一度电，和在北京用的一度电，完全一样，但一个CEO产生的智能，和一个设计师产生的智能，截然不同。 未来的模型竞争，比的就是谁更有Taste，谁更有品味，做模型本质上是在创造一种世界观，你在这个模型里注入了什么样的价值观，它就会涌现出什么 样的智能 。 这个圆桌，信息量真的非常巨大。 所以也不用担心，未来会有单一的模型一 ...

Core Viewpoint - The article discusses the transformative impact of AI on education, emphasizing the need for universities to adapt and redefine their roles in preparing students for the future [2][4][6]. Group 1: AI and Education Transformation - AI is accelerating a global reshaping of education, prompting discussions on whether Chinese universities can "overtake" their Western counterparts in the next decade [2][4]. - The panelists agree that the emergence of AI tools like ChatGPT enhances educational autonomy, requiring students to discover their own paths [6][24]. - The importance of humanistic education is highlighted, suggesting that students should not only focus on technology but also develop as well-rounded individuals [7][31]. Group 2: University Expectations and Student Development - There is a consensus that societal expectations of universities are excessively high, with parents often viewing schools as "infinite responsibility companies" [16][17]. - The discussion includes the necessity of a natural selection process in education, where students find their fit rather than being forced into specific paths [20][19]. - The concept of "experiencing" is emphasized as a crucial element of education that AI cannot replace, advocating for experiential learning over rote memorization [36][37]. Group 3: Future Skills and University Competitiveness - Key survival skills for future university students include strong communication abilities and creativity, with an emphasis on the ability to work with AI [25][26]. - The core competitiveness of universities will hinge on their ability to cultivate talent, focusing on both students and faculty [27][30]. - The article posits that the best academic disciplines in the future will likely center around mathematics and language skills, rather than solely on technology fields like computer science [30][31].

Core Insights - The article discusses the emergence of the "Density Law" in large models, which states that the capability density of models doubles every 3.5 months, emphasizing efficiency in achieving intelligence with fewer computational resources [4][11][19]. Group 1: Evolution of Large Models - The evolution of large models has been driven by the "Scaling Law," leading to significant leaps in capabilities, surpassing human levels in various tasks [8][12]. - The introduction of ChatGPT marked a steep increase in capability density, indicating a shift in the model performance landscape [7][10]. - The industry is witnessing a trend towards distributed intelligence, where individuals will have personal models that learn from their data, contrasting with the notion that only a few large models will dominate [10][36]. Group 2: Density Law and Efficiency - The Density Law aims to maximize intelligence per unit of computation, advocating for a focus on efficiency rather than merely scaling model size [19][35]. - Key methods to enhance model capability density include optimizing model architecture, improving data quality, and refining learning algorithms [19][23]. - The industry is exploring various architectural improvements, such as sparse attention mechanisms and mixed expert systems, to enhance efficiency [20][24]. Group 3: Future of AI and AGI - The future of AI is expected to involve self-learning models that can adapt and grow based on user interactions, leading to the development of personal AI assistants [10][35]. - The concept of "AI creating AI" is highlighted as a potential future direction, where models will be capable of self-improvement and collaboration [35][36]. - The timeline for achieving significant advancements in personal AI capabilities is projected around 2027, with expectations for models to operate efficiently on mobile devices [33][32].

Core Viewpoint - The article discusses the transformative impact of the AI revolution, comparing it to previous major revolutions like the Industrial Revolution, and suggests that AI may fundamentally reshape society, economy, and human relationships [6][9]. Group 1: Nature of the AI Revolution - The AI revolution is seen as a continuation of technology's role in enhancing human capabilities, shifting from physical to cognitive enhancements [7]. - AI is expected to accelerate the cycle of discovery and innovation, leading to exponential growth in technology and knowledge [8]. Group 2: Impact on Work and Society - The rise of AI may lead to the emergence of a "leisure class," where many professional jobs are replaced by AI, resulting in fewer people needing to work [11][12]. - Education will need to shift from preparing individuals for traditional jobs to teaching them how to live creatively and meaningfully in a world where work is not the primary focus [14]. Group 3: Challenges to Human Creativity - AI's capabilities in creative fields challenge the unique value of human creativity, as it can produce works indistinguishable from those created by humans [15]. Group 4: Economic and Social Structures - The traditional economic model based on work for income is being challenged, leading to discussions about basic income and wealth distribution in a potential "workless society" [17]. - The AI revolution could lead to a "post-scarcity" society, but there are concerns about wealth concentration and inequality [18]. Group 5: Knowledge and Intellectual Property - The concept of intellectual property may need to be redefined in an AI-driven world, where contributions to creative works are increasingly collaborative and difficult to attribute [19]. Group 6: Social Relationships and AI - AI is expected to decentralize social activities and relationships, potentially transforming how humans interact with each other and with AI [21][23]. Group 7: Global Implications - AI has the potential to foster global cooperation and reduce nationalism, but it may also reshape global power dynamics and economic structures [25][26]. Conclusion - The future of AI development depends on responsible practices that consider ethical, social, and ecological impacts, aiming for a better world with reduced conflict and poverty [28][29].

Core Viewpoint - The concept of "academic excellence" is evolving to emphasize the importance of self-directed learning as a critical skill for adapting to future societal demands, moving beyond traditional academic achievements [2][3]. Group 1: Self-Directed Learning - Self-directed learning is identified as a high-level ability that cannot be directly taught but must be cultivated through environmental support, deliberate practice, and ongoing guidance [6]. - The process of developing self-directed learning is broken down into four progressive stages: awareness, action, ability, and value system [6][7]. - The current market lacks mature products that systematically cultivate self-directed learning abilities, with existing AI tools primarily focused on efficiency rather than fostering independent thinking [2][3]. Group 2: Onion Academy's Initiatives - Onion Academy has launched the "Self-Learning Breakthrough Plan 1.0," which aims to enhance students' goal orientation, judgment, and self-driven mechanisms, distinguishing itself from existing AI tools focused on efficiency [3][5]. - The upgraded AI learning companion system follows a structured approach to support students in transitioning from awareness to behavior shaping, ultimately forming a stable value system [7][9]. - The AI learning companion includes various intelligent modules designed to address specific learning scenarios, transforming them into opportunities for cultivating self-directed learning abilities [9][11]. Group 3: Data-Driven Insights - Onion Academy has built a substantial data foundation, comprising over 500 billion real learning behavior data points, which enhances the effectiveness of its educational models [14][15]. - This extensive dataset allows for a deep understanding of student learning patterns, enabling the AI learning companion to adaptively adjust learning paths and reinforce weak areas across different modules [15]. Group 4: Incentive Mechanisms - The company has developed a diverse incentive system to facilitate the transition from "learning to learn" to "voluntarily learning," addressing the varied motivations of students [16][17]. - Emotional support is integrated into the learning experience through the "Emotional Tree Hole" module, which provides timely responses to students' emotional needs, enhancing the overall learning environment [17]. Group 5: Role of Parents and Teachers - In the context of AI-enhanced education, the roles of parents and teachers are evolving; parents are encouraged to support and listen rather than directly intervene in teaching [19][20]. - Teachers are transitioning from knowledge transmitters to facilitators of learning, focusing on guiding students through the learning process rather than delivering content [20][21].