明敏 闻乐 发自 凹非寺 量子位 | 公众号 QbitAI 诺奖得主都没找到的证明方法，现在被两位华人学者突破了。 凝聚态物理奠基人菲利普·安德森（Philip Warren Anderson）提出的Anderson模型，因解释了半导体材料中电子为何突然不再流动而闻 名。 这一成果也助力他获得诺奖。但是他当时求教各路学者，也没能解决这一模型的数学证明。这一问题也困扰了学界数十年，虽然陆续有研究提 出，但是进展都不够重大。 终于，两位华人学者联手攻坚16年，实现这一突破。 其中， 尹骏 还是中科大少年班校友。 Anderson模型：电子在走"迷宫" 20世纪50年代，贝尔实验室里一位名叫乔治·费赫尔（George Feher）的物理学家正在往硅里注入少量其他元素，比如磷或砷。 他发现，少量加入时，电子可以在材料中自由移动；随着加入量增多，材料结构会发生变化，到达某一个临界点后，电子会突然被困住，材料 将不导电。这个转变点就像水在零度时会开始结冰。 这是半导体材料最明显的一个特点，也是它能成为芯片材料的关键点： 既能导电、又能绝缘，而且这种开关状态可控。 这个现象很快吸引了当时同在贝尔实验室的菲利普·安德森（Ph ...

Group 1 - The article discusses the concept of "Dao" as something that transcends language and rational understanding, suggesting that true knowledge cannot be fully articulated [1][2][12] - It draws parallels between the philosophical notion of "Dao" and modern physics, particularly quantum mechanics, highlighting the challenges in comprehending phenomena that defy intuitive understanding [3][10][11] - The article introduces the "black box" problem in AI, emphasizing that the complexity of AI models makes their decision-making processes difficult to explain, similar to the elusive nature of "Dao" [14][16][19] Group 2 - The article suggests that both "Dao" and AI's "black box" represent emergent properties that exceed human cognitive boundaries, indicating a need for trust rather than complete understanding [20][23][24] - It emphasizes the importance of collaboration between humans and AI, proposing that while AI can discover patterns, human experience and ethics remain essential in decision-making [26][29] - The article warns about potential biases in AI, advocating for data governance and ethical scrutiny to ensure fairness in AI outcomes [30][31]

Core Viewpoint - The article discusses the innovative approach of the company "Yuan Shi Technology" and its product "Wen Xiaobai," which aims to redefine information retrieval and content generation in the AI era, positioning itself as a unique AIGC content platform rather than a traditional chatbot or search engine [3][4][5]. Group 1: Company Background and Development - Li Yan, the founder of Yuan Shi Technology, has a strong background in AI, having previously built the AI system at Kuaishou [2]. - Yuan Shi Technology has secured approximately $50 million in funding from notable investors, including Kuaishou's co-founder and venture capital firms [2]. - The product "Wen Xiaobai" combines active Q&A with passive content consumption, resembling a modern version of today's news aggregation platforms [3]. Group 2: Product Positioning and Differentiation - "Wen Xiaobai" is defined as an AIGC content platform that allows users to actively ask questions and passively consume information, contrasting with traditional UGC platforms [8][9]. - The platform emphasizes a user-friendly approach, aiming to lower the psychological barrier for users, which is reflected in its name "Wen Xiaobai" [12]. - The product's content generation relies heavily on AI, with a multi-agent system that automates the creation and quality control of content [16][17]. Group 3: Market Perspective and Opportunities - Li Yan believes that the market for information retrieval is vast and that large companies cannot monopolize it entirely, leaving significant opportunities for startups [5][24]. - The article highlights the shift from traditional information retrieval methods to AI-driven content generation, suggesting that this transformation creates new market dynamics [24][25]. - The company aims to leverage AI's capabilities to address long-tail demands and underrepresented voices in the content landscape [26]. Group 4: Future Outlook and Strategy - Yuan Shi Technology plans to expand its product offerings to international markets, focusing on creating a closed-loop system of generation, distribution, and consumption [53]. - The company is committed to developing its own models for user interest mapping, which is seen as a core differentiator in its strategy [53]. - Li Yan emphasizes the importance of understanding user needs and adapting to market changes, indicating a flexible approach to product development and commercialization [52][53].

Core Insights - The article emphasizes the transformative impact of AI on business innovation and the necessity for companies to adapt their strategies to remain competitive in the AI era [1][4][40] Group 1: OpenAI's Journey - OpenAI was founded in 2015 by Elon Musk and Sam Altman with the mission to counteract the monopolistic tendencies of tech giants and promote open, safe, and accessible AI [4][7] - The development of large language models (LLMs) by OpenAI is attributed to the effective use of the Transformer architecture and the Scaling Law, which predicts a linear relationship between model size, training data, and computational resources [8][11] - The emergence of capabilities in models like GPT is described as a phenomenon of "emergence," where models exhibit unexpected abilities when certain thresholds of parameters and data are reached [12][13] Group 2: DeepSeek's Strategy - DeepSeek adopts a "Limited Scaling Law" approach, focusing on maximizing efficiency and performance with limited resources, contrasting with the resource-heavy strategies of larger AI firms [18][22] - The company employs innovative model architectures such as Multi-Head Latent Attention (MLA) and Mixture of Experts (MoE) to optimize performance while minimizing costs [20][21] - DeepSeek's R1 model, released in January 2025, showcases its ability to perform complex reasoning tasks without human feedback, marking a significant advancement in AI capabilities [23][25] Group 3: Organizational Innovation - DeepSeek promotes an AI Lab paradigm that encourages open collaboration, resource sharing, and dynamic team structures to foster innovation in AI development [27][28] - The organization emphasizes self-organization and autonomy among team members, allowing for a more flexible and responsive approach to research and development [29][30] - The company's success is attributed to breaking away from traditional corporate constraints, enabling a culture of creativity and exploration in foundational research [34][38]

Core Viewpoint - The article emphasizes the transformative impact of AI technology on business innovation and the necessity for companies to adapt their strategies to remain competitive in the evolving landscape of AI [1][2]. Group 1: OpenAI's Emergence - OpenAI was founded in 2015 by Elon Musk and Sam Altman with the mission to counteract the monopolistic power of major tech companies in AI, aiming for an open and safe AI for all [9][10][12]. - The introduction of the Transformer architecture by Google in 2017 revolutionized language processing, enabling models to understand context better and significantly improving training speed [13][15]. - OpenAI's belief in the Scaling Law led to unprecedented investments in AI, resulting in the development of groundbreaking language models that exhibit emergent capabilities [17][19]. Group 2: ChatGPT and Human-Machine Interaction - The launch of ChatGPT marked a significant shift in human-machine interaction, allowing users to communicate in natural language rather than through complex commands, thus lowering the barrier to AI usage [22][24]. - ChatGPT's success not only established a user base for future AI applications but also reshaped perceptions of human-AI collaboration, showcasing vast potential for future developments [25]. Group 3: DeepSeek's Strategic Approach - DeepSeek adopted a "Limited Scaling Law" strategy, focusing on maximizing efficiency and performance with limited resources, contrasting with the resource-heavy approaches of larger AI firms [32][34]. - The company achieved high performance at low costs through innovative model architecture and training methods, emphasizing quality data selection and algorithm efficiency [36][38]. - DeepSeek's R1 model, released in January 2025, demonstrated advanced reasoning capabilities without human feedback, marking a significant advancement in AI technology [45][48]. Group 4: Organizational Innovation in AI - DeepSeek's organizational model promotes an AI Lab paradigm that fosters emergent innovation, allowing for open collaboration and resource sharing among researchers [54][56]. - The dynamic team structure and self-organizing management style encourage creativity and rapid iteration, essential for success in the unpredictable field of AI [58][62]. - The company's approach challenges traditional hierarchical models, advocating for a culture that empowers individuals to explore and innovate freely [64][70]. Group 5: Breaking the "Thought Stamp" - DeepSeek's achievements highlight a shift in mindset among Chinese entrepreneurs, demonstrating that original foundational research in AI is possible within China [75][78]. - The article calls for a departure from the belief that Chinese companies should only focus on application and commercialization, urging a commitment to long-term foundational research and innovation [80][82].

Core Viewpoint - The article discusses the emergence of AI and its transformative impact on industries, highlighting the importance of cognitive innovation and the role of organizations that can adapt and thrive in this new landscape [2][3][23]. Group 1: AI Development Milestones - The introduction of the Transformer model by Google's Brain Team in June 2017 laid the foundation for subsequent language model advancements [1]. - The explosive growth of ChatGPT in 2023 marked the beginning of AI commercialization, while DeepSeek's emergence in 2025 demonstrated a significant shift in industry perception by achieving technological parity at a fraction of the cost [3][12]. Group 2: Cognitive Innovation - The article emphasizes that the evolution of AI is not merely a technical race but a revolution in the underlying logic of cognitive innovation [4]. - The course led by Professor Li Shanyou aims to dissect the methods of innovation in the AI era, revealing the cognitive leap from technological breakthroughs to commercial applications [4][20]. Group 3: Case Studies and Competitive Dynamics - The course will analyze the rise of OpenAI, detailing its journey from Musk's vision to the rapid user adoption of ChatGPT, which reached over one million users in just five days [10][12]. - It will also explore DeepSeek's strategy of achieving a 90% reduction in training costs through its unique architecture, showcasing how a small team can outperform larger organizations [11][13]. Group 4: Practical Tools and Frameworks - The course will introduce a practical framework for innovation, focusing on model building, single-point breakthroughs, and team organization, which are essential for navigating the AI landscape [11][25]. - Participants will learn how to identify their business's cognitive axes and value dimensions, as well as the management principles of emergent organizations [11][25]. Group 5: Target Audience - The course is designed for various innovators, including entrepreneurs, executives, product managers, investors, and technology enthusiasts, who seek to leverage cognitive advantages in the AI era [17][18].

Group 1 - The term "artificial intelligence" was formally introduced at a conference in 1956 at Dartmouth College, marking the beginning of efforts to replicate human intelligence through modern science and technology [1] - Alan Turing is recognized as the father of artificial intelligence due to his introduction of the "Turing Test" in 1950, which provides a method to determine if a machine can exhibit intelligent behavior equivalent to a human [1][3] - The Turing Test involves a human evaluator interacting with an isolated "intelligent agent" through a keyboard and display, where if the evaluator cannot distinguish between the machine and a human, the machine is considered intelligent [3][5] Group 2 - The Turing Test is characterized as a subjective evaluation method rather than an objective scientific test, as it relies on human judgment rather than consistent measurable criteria [6][9] - Despite claims of machines passing the Turing Test, such as Eugene Goostman in 2014, there is no consensus that these machines possess human-like thinking capabilities, highlighting the limitations of the Turing Test as a scientific standard [6][8] - Turing's original paper contains subjective reasoning and speculative assertions, which, while valuable for exploration, do not meet the rigorous standards of scientific argumentation [8][9] Group 3 - The field of artificial intelligence has been criticized for lacking a solid scientific foundation, often relying on conjecture and analogy rather than empirical evidence [10][19] - The emergence of terms like "scaling law" in AI research reflects a trend of using non-scientific concepts to justify claims about machine learning performance, which may not hold true under scrutiny [16][17] - Historical critiques, such as those from Hubert L. Dreyfus in 1965, emphasize the need for a deeper scientific understanding of AI rather than superficial advancements based on speculative ideas [18][19] Group 4 - The ongoing development of AI as a practical technology has achieved significant progress, yet it remains categorized as a modern craft rather than a fully-fledged scientific discipline [20][21] - Future advancements in AI should adhere to the rational norms of modern science and technology, avoiding the influence of non-scientific factors on its development [21]

2025年4月25日，2025年李善友开年大课暨混沌·AI创新院开学典礼正式开讲。 Day1的主题是"AI的进击"，在上午的大课中，教授动情表示：DeepSeek，将是国运的AI支点。 以下是李善友教授大课的笔记内容。 讲者 ｜李善友 我相信未来的20 年 ， 必然是 AI 在中国的黄金 20 年 。 其实在大课开始 前，我们 同事 问我 ：教授 你 为这堂课 ， 做了多长时间的准备？ 我想 ： 这个准备 ， 如果从长来说可能是十年， 往 短 里 说可能是 18 个月。 所以： 18 个月以来 ， 我一直在思考，今天这个时代命题是什么？混沌要呼应什么样的命题？ 我要 把最大公约数的那个命题 ， 像旗帜一样举出来，跟 所有 同学们去呼应。 这个命题 究竟 是什么？ 我一直 在 思考。 因为马斯克看见了一件事情，谷歌把之前最领先的 AI 实验室 DeepMind 给收购了。 马斯克心中有一个巨大的隐忧—— AI 比核武器更具威胁，任由 AI 发展下去，最终 AI 一定反过来控制人类，甚至会毁灭人类。 其实我认为， OpenAI 是这一轮 AI 革命的先驱。 我觉得 全世界的人，都应该向 AI革命的先驱OpenAI ...