打造发现式智能的关键能力 在 2025 年 AI 驱动科学研讨会上，天桥脑科学研究院创始人陈天桥首提"发现式智能（Discoverative Intelligence）"理念，尖峰智能实验室正是这一理念的重 要落地载体之一。 图为盛大集团、天桥脑科学研究院创始人 陈天桥、雒芊芊夫妇 新华财经上海12月13日电（谷青竹、邓侃） 12 月 13 日，在天桥脑科学研究院主办的"从脑机接口到脑机共生"主题论坛暨中国神经科学学会脑机接口与交互 分会年会上，研究院创始人雒芊芊宣布：天桥脑科学研究院成立尖峰智能实验室（Spiking Intelligence Lab, SIL），致力于类脑大模型和脉冲神经网络的研 发，探索人工智能与人类智慧的深度融合。该非营利研究机构由中国科学院自动化研究所研究员李国齐领衔。 尖峰智能实验室的成立，标志着研究院在原有"外延式"捐赠支持的基础上，进一步增强"内部自主"的专项研发。尖峰智能实验室是研究院首个采用 In-House 模式的研究机构，直接招募顶尖人才、自主决定研发方向，将角色从合作赋能者升级为创造主体，加速"发现式智能"从理念到基础理论突破再到技术成果的 转化。 图为天桥脑科学研究 ...

12月13日，在中国神经科学学会脑机接口与交互分会年会上，天桥脑科学研究院宣布成立尖峰智能实验 室（Spiking Intelligence Lab, SIL），致力于类脑大模型和脉冲神经网络的研发，探索人工智能与人类智 慧的深度融合。 第一财经记者从会议现场了解到，最新成立的尖峰智能实验室属于非营利研究机构，由中国科学院自动 化研究所研究员李国齐教授领衔，目标是为打造天桥脑科学研究院创始人陈天桥提出的"发现式智能"提 供关键能力。 李国齐对第一财经记者表示："发现式智能的一个关键能力是神经动力学。不同于当前依赖规模法则堆 叠参数的主流AI模式，尖峰智能实验室主张借鉴人脑这一自然界最精巧的智能载体，重点研发具有神 经动力学特性的类脑大模型，将脉冲通信、时空动态编码等计算特性与树突神经元的精细结构深度耦 合，构建一个既具备强大感知力，又拥有深刻记忆与思考能力的全脑架构。" 这一路径也是陈天桥此前提出的实现通用人工智能的"结构路径"。人脑以仅约20瓦的功耗支撑起千亿级 神经元的复杂运作，李国齐从事的脉冲神经网络和类脑大模型的研究，将为构建这样的全脑架构提供基 础研究及转化方面的支持。 陈天桥最近提出，仅靠数据和算 ...

尖峰智能实验室主张借鉴人脑这一自然界最精巧的智能载体，重点研发具有神经动力学特性的类脑大模型，构建一个既 具备强大感知力，又拥有深刻记忆与思考能力的全脑架构。 12月13日，在中国神经科学学会脑机接口与交互分会年会上，天桥脑科学研究院宣布成立尖峰智能实验室（Spiking Intelligence Lab, SIL），致力于类脑大模型和脉冲神经网络的研发，探索人工智能与人类智慧的深度融合。 第一财经记者从会议现场了解到，最新成立的尖峰智能实验室属于非营利研究机构，由中国科学院自动化研究所研究员 李国齐教授领衔，目标是为打造天桥脑科学研究院创始人陈天桥提出的"发现式智能"提供关键能力。 李国齐对第一财经记者表示："发现式智能的一个关键能力是神经动力学。不同于当前依赖规模法则堆叠参数的主流AI 模式，尖峰智能实验室主张借鉴人脑这一自然界最精巧的智能载体，重点研发具有神经动力学特性的类脑大模型，将脉 冲通信、时空动态编码等计算特性与树突神经元的精细结构深度耦合，构建一个既具备强大感知力，又拥有深刻记忆与 思考能力的全脑架构。" 这一路径也是陈天桥此前提出的实现通用人工智能的"结构路径"。人脑以仅约20瓦的功耗支撑起 ...

作者丨 盛大集团创始人、董事长兼CEO 天桥脑科学研究院创始人陈天桥 盛大集团与天桥脑科学研究院的创始人陈天桥近日发布了一篇深度长文，从系统视角讨论人工智能如 何从底层重塑企业的组织结构。他在文中提出了一个颇具前瞻性的判断——"管理学的黄昏，智能的 黎明"。这也是他在今年10月提出"发现式智能"这一全新理念之后，再次抛出的重要观点。 以下为陈天桥深度文章全文： 管理即"纠偏系统" 现代管理学的大厦，实际上是建立在一片名为"生物局限性"的沼泽之上。过去一百年，我们所推崇的 全部管理工具，本质上都是为了给人类大脑打上的"补丁"： 我们发明 KPI，并非因为它能精准衡量价值，而是因为人类大脑难以在长周期中锁定目标，"遗忘"是 碳基生物的常态，我们需要路标； 我们发明科层制（Hierarchy），并非因为它高效，而是因为人类的工作记忆只能处理 7±2 个节点， 为了避免认知超负荷，我们被迫通过层级来压缩信息； 管理学的黄昏与智能的黎明： 重写企业的生物学基因 引言：管理学的黄昏 管理学大师彼得·德鲁克曾说，动荡时代最大的危险不是动荡本身，而是延续昨日的逻辑行事。 今天，我们就站在这样一个危险的临界点。 从系统演化的角 ...

Core Insights - EverMind has launched its flagship product EverMemOS, a world-class long-term memory operating system for AI agents, which has been released as an open-source version on GitHub for developers and AI teams to deploy and test [1] - The cloud service version is expected to be released within the year, providing enhanced technical support, data persistence, and scalability for enterprise users [1] - EverMemOS has surpassed previous works in mainstream long-term memory evaluation sets, becoming the new state-of-the-art (SOTA) [1][4] Group 1: Product Features and Innovations - EverMemOS is designed based on a brain-like architecture, allowing AI to possess continuity over time, addressing the limitations of large language models (LLMs) that often "forget" during long-term tasks [3][4] - The system features a four-layer architecture inspired by human memory mechanisms, including an agent layer for task understanding, a memory layer for long-term memory management, an indexing layer for efficient memory retrieval, and an interface layer for seamless integration with enterprise applications [6][7] - Key innovations include a modular memory framework that allows for dynamic organization and retrieval of memories, ensuring that AI interactions are coherent and personalized based on long-term user understanding [7] Group 2: Performance Metrics - EverMemOS achieved scores of 92.3% and 82% on the LoCoMo and LongMemEval-S long-term memory evaluation sets, respectively, significantly exceeding the previous SOTA levels [4][6] - The system is the first to support both one-on-one conversations and complex multi-party collaborations, marking a significant advancement in memory systems for AI applications [4]

Core Viewpoint - The EverMind team, founded by Chen Tianqiao, has launched EverMemOS, a world-class open-source long-term memory system for AI agents, aiming to establish a data infrastructure for future intelligent systems [3][4]. Group 1: Long-term Memory in AI - Long-term memory is seen as a strategic feature for AI applications, with Chen Tianqiao asserting that memory will be a core competitive advantage for AI, transitioning from tools to intelligent agents [3][4]. - The design of EverMemOS is inspired by human memory mechanisms, allowing AI to think, remember, and grow like humans [3][5]. Group 2: Discovering Intelligence - Chen Tianqiao emphasizes the concept of "discovery intelligence," which includes five core capabilities, with long-term memory being a crucial aspect [4][5]. - He argues that true discovery involves asking questions and understanding principles rather than merely predicting outcomes [4][5]. Group 3: Paths to Discovery Intelligence - Two paths to achieving discovery intelligence are proposed: the "scale path," which focuses on large models and data, and the "structure path," which emphasizes dynamic and continuous understanding akin to cognitive anatomy [5][6]. - The limitations of the scale path are acknowledged, with a call for new theories and interdisciplinary approaches to advance AI understanding [5][6]. Group 4: Investment in Research - The Tianqiao Brain Science Research Institute plans to invest over $1 billion in dedicated computing clusters to support young scientists in exploring memory mechanisms and new causal architectures [6][7]. - An incubator has been established to provide independent research opportunities for young scientists, allowing them to lead their own projects without waiting for graduation [7].

Core Insights - The concept of "memory" is viewed as a critical competitive advantage for future AI applications, marking a transition from tools to intelligent agents and from passive responses to proactive evolution [1][3] - EverMind, a team under Chen Tianqiao's Shanda Group, has launched EverMemOS, a world-class open-source long-term memory system aimed at becoming the data infrastructure for future intelligent agents [1][3] - Chen Tianqiao emphasizes the importance of "discovery-based intelligence," advocating for a research paradigm that endows AI with a persistent, coherent, and evolvable "soul" [1][3] AI Memory Systems - Current AI applications, including Claude and ChatGPT, have integrated long-term memory as a strategic feature, reflecting a broader trend in the industry [3] - EverMemOS is inspired by human memory mechanisms, mimicking the encoding, indexing, and long-term storage processes of the human brain [3][4] - The EverMind team aims to tackle one of the most profound challenges in AI: enabling machines to possess memory, which is seen as a gateway to higher levels of general intelligence [3][4] Discovery-Based Intelligence - Chen Tianqiao argues that true "discovery" in AI should involve posing questions and understanding principles rather than merely predicting outcomes [4] - He outlines two pathways to achieving "discovery-based intelligence": the "scale pathway," which focuses on the size of models and data, and the "structure pathway," which emphasizes dynamic and continuous cognitive processes [4][5] - The "scale pathway" has led to significant applications in AI, such as protein prediction and compound generation, but is viewed as limited in its ability to foster true understanding [5] Research and Development Initiatives - The Tianqiao Brain Science Research Institute plans to invest over $1 billion in dedicated computing clusters to support young scientists in exploring structural intelligence [6] - The institute has established a PI incubator, allowing young researchers to have independent budgets and lead their own experiments without waiting for graduation [6] - Chen Tianqiao stresses the need for interdisciplinary collaboration among neuroscience, information theory, physics, and cognitive psychology to drive innovation in AI [5][6]

Group 1: Company Changes and Developments - Wentech Technology appointed Shen Xinjia as the new president, who previously served as the Chief Affairs Officer at Anshi Semiconductor, amidst management turmoil following a global operational freeze by the Dutch government on Anshi Semiconductor [1] - Meituan announced a nationwide pension insurance subsidy for delivery riders, aiming to cover millions and enhance the welfare benefits for its workforce [7] - Xi'an Yiswei Materials Technology Co., Ltd. successfully listed on the Shanghai Stock Exchange's Sci-Tech Innovation Board, with its stock price surging 198.72% on the first day, reflecting strong market interest in the semiconductor sector [8] Group 2: Financial Performance and Market Trends - Apple reported a record revenue of $102.466 billion for Q4 FY2025, with a net profit of $27.466 billion, although revenue from Greater China declined by 3.6% to $14.493 billion [2] - Samsung Electronics achieved a record high in memory sales for Q3 2025, with total revenue of 86.1 trillion KRW (approximately 428.778 billion RMB), driven by increased demand from data centers [3] - BOE and TCL Technology both reported revenue growth in their Q3 financial results, with BOE's revenue reaching 154.5 billion RMB (up 7.52%) and TCL's revenue at 135.9 billion RMB (up 10.49%) [9] Group 3: Industry Developments and Innovations - TikTok announced that its user base in Southeast Asia has surpassed 460 million, with significant engagement in e-commerce, as the platform's GMV reached $38.2 billion [4] - The International Olympic Committee terminated its collaboration with Saudi Arabia on the esports Olympics, indicating a shift in strategy for both parties [5][6] - Dassault Systèmes launched the first PLM lifecycle management software platform for the infrastructure industry in collaboration with the South China Architectural Research Institute, aiming to integrate advanced technologies into construction [15]

Core Insights - The article discusses the AI Accelerated Science Symposium held in San Francisco, where the concept of "Discoverative Intelligence" was introduced as a new paradigm for general artificial intelligence [1][3][4] - The speaker, Chen Tianqiao, emphasizes that AI should not merely replace human jobs but should aid in human evolution by helping discover the unknown [5][10] Group 1: Human Evolution and AI - Human evolution has not stopped; instead, it has transformed through scientific discoveries and technological inventions, extending human capabilities beyond biological limits [3][4] - The concept of "Discoverative Intelligence" is presented as a true form of general artificial intelligence, which can actively construct testable theoretical models and propose falsifiable hypotheses [5][10] Group 2: Paths to Discoverative Intelligence - Two main paths to achieving "Discoverative Intelligence" are identified: the "Scale Path," which relies on large models and data, and the "Structure Path," which focuses on cognitive mechanisms akin to human brain functions [6][12] - The "Scale Path" has achieved significant results in AI applications, while the "Structure Path" is emerging as a necessary approach to overcome the limitations of current AI systems [13][14] Group 3: Time Structure and Core Capabilities - The article outlines five core capabilities essential for managing information over time, which are necessary for achieving "time structure" in AI: neural dynamics, long-term memory, causal reasoning, world modeling, and metacognition [8][9][12] - These capabilities form a continuous and active loop, enabling a system to evolve over time and engage in scientific discovery [12] Group 4: Opportunities for Young Researchers - The article highlights the need for new theories, algorithms, and interdisciplinary approaches, positioning young researchers as key players in redefining intelligence through the "Structure Path" [13][14] - The company is investing over $1 billion in dedicated computing clusters to support young scientists in exploring new structures and validating cognitive mechanisms [16]

Core Insights - The first AI-driven scientific symposium organized by the Tianqiao Brain Science Institute and UC Berkeley gathered over 20 top scholars and industry leaders to discuss how AI can drive scientific discovery [1][2] Group 1: Investment and Support for AI Research - Tianqiao Brain Science Institute founder Chen Tianqiao announced a $1 billion investment in computational power to support innovative AI research globally [1] - Chen emphasized that the ultimate value of AI lies in discovery, proposing that AI should evolve human capabilities rather than replace them [1][2] Group 2: Development of Discovery-Oriented Intelligence - Chen outlined five essential capabilities for building discovery-oriented intelligence: neural dynamic structure, long-term memory, causal reasoning mechanisms, world models, and metacognition with intrinsic motivation [2] - He introduced various support initiatives for young scientists, including benchmark testing and structural computational resources [2] Group 3: AI Innovations in Scientific Research - Omar Yaki, a 2025 Nobel Prize winner, presented a portable water extraction device designed by AI, showcasing a new paradigm of "from molecules to society" driven by generative AI and self-learning agents [5][8] - David Baker, a 2024 Nobel Prize winner, discussed how AI can reverse-engineer protein design, achieving breakthroughs in neurodegenerative disease research and enzyme engineering [8] - Jennifer Doudna highlighted that 40% of basic gene functions remain mysteries, advocating for the synergy of CRISPR technology and machine learning to enhance data collection and analysis [9][10] Group 4: Ethical Considerations and Challenges in AI - John Hennessy emphasized the need for human oversight in AI decision-making, stressing the importance of transparency and verification of AI-generated content [10] - He raised concerns about data quality and energy efficiency in the context of rapidly advancing AI technologies [10]