重阳说 查理·芒格先生有一句广为流传的话："我这一生当中，未曾见过不读书就智慧满满的人。没有。一个都没 有。沃伦（巴菲特）的阅读量之大可能会让你感到吃惊。我和他一样。我的孩子们打趣我说，我就是一 本长着两条腿的书。" 熟悉重阳的朋友们一定知道，阅读，一直是我们非常推崇的成长路径。 现在，我们希望和你一起，把阅读这件事坚持下去。 每一期专栏，我们依旧聊书，可能是书评、书单或者书摘。 每一期会有一个交流主题，希望你通过留言与我们互动。 我们精选优质好书，根据留言质量不定量送出。 世界莽莽，时间荒荒，阅读生出思考的力量，愿你感受到自己的思想有厚度且有方向，四通八达，尽情 徜徉。 提示：本公众号所发布的内容仅供参考，不构成任何投资建议和销售要约。如您对重阳产品感兴趣，欢 迎 扫码 咨询。 【好书】第303期：《大语言模型》 2025年7月 互动话题： 结合本书， 请 谈谈你对大语言模型的认识 。 留言时间：2024年9月24 日 - 2025年 10月8日 （鼓励原创，只要你的内容足够优秀，期期选中也有可能哦） 筛选及书籍（单本）寄送：2025年10月9日后 （选中会收到提交寄送地址的私聊，逾期未提交/信息不全视为放 ...

Core Insights - The article discusses the transformative impact of artificial intelligence (AI) on various aspects of life, including learning, work, and daily activities, highlighting the advancements made by AI technologies like ChatGPT, Sora, Deepseek, and Qwen [1][3] - It introduces the book "Artificial Intelligence 70 Years: From the Dartmouth Conference to the Era of Large Models," which provides a comprehensive overview of AI's evolution over the past seven decades [1][5] Summary by Sections - **Book Overview** - The book is not a dry technical manual but an immersive journey through 70 years of AI history, combining storytelling with technical insights [5] - Authored by Chen Zongzhou, a seasoned science writer and technology journalist, the book presents a dual narrative of stories and technology [5][10] - **Key Figures and Concepts** - The book features influential figures in AI, such as Geoffrey Hinton, who is recognized for his contributions to deep learning algorithms, emphasizing the importance of perseverance in scientific discovery [7] - It discusses significant milestones in AI, including the famous match between AlphaGo and a human champion, illustrating the role of extensive learning and computation in AI's success [8][10] - **Educational Value** - The book aims to demystify complex AI terminology, such as big data, deep learning, generative AI, and robotics, making it accessible for readers to relate AI knowledge to their lives [9] - It encourages a balanced view of AI, steering clear of extremes in perception, and emphasizes that AI's remarkable capabilities are built on decades of foundational work [10] - **Target Audience** - The book is suitable for both parents and children, providing insights into AI's past and present, and helping families understand how to nurture skills relevant to the AI era [13]

《狐仙崇拜》《至高无上》《通勤梦魇》《与希罗多德一起旅行》。 [加] 康笑菲 著 姚政志 译 读客文化·海南出版社 2025年3月 "你这个狐狸精。"这句台词一度经常出现在影视剧作品中，无论是男女爱情剧还是家庭伦理剧。在大众 印象里，狐狸似乎一直跟魅惑、狡诈、邪恶脱不开干系。狐狸是天性如此，还是被"污名化"了呢？ 华盛顿大学的康笑菲副教授从祖辈早年的狐仙信仰入手，查阅明清及民国时期的笔记小说中大量狐仙故 事，发现在当时的民间视角中，狐仙既可能带来祸害，也可能带来好运和财富。 对于中国人来说，狐狸的形象亦正亦邪，长期就有"模棱两可"的性质：它们漫游在荒野间，无法被驯化 为家畜，却不是人类饲养的牲畜；在人类聚居处造窝，并展现出如人类般的慧黠。正如清代学者纪昀所 言：人物异类，狐则在人物之间；幽明异路，狐则在幽明之间；仙妖殊途，狐则在仙妖之间。正如"狐 狸精"这个称呼，指一个人兼具迷惑人的美貌和毁灭性的色诱力量。而"狐仙"这个称呼，不只是对良狐 的敬称，也带有取悦恶狐的意味。 本书通过一系列生动的故事揭示了"狐仙崇拜"这一中国传统民间信仰的本质：英俊的"胡郎"以狐精之身 迷倒世家小姐，却因出身低微被拒婚，揭示寒门士 ...

Core Viewpoint - The article emphasizes the importance of understanding AI and its underlying principles, suggesting that individuals should start their journey into AI by grasping fundamental concepts and practical skills. Group 1: Understanding AI - AI is defined through various learning methods, including supervised learning, unsupervised learning, and reinforcement learning, which allow machines to learn from data without rigid programming rules [9][11][12]. - The core idea of modern AI revolves around machine learning, particularly deep learning, which enables machines to learn from vast amounts of data and make predictions [12]. Group 2: Essential Skills for AI - Three essential skills for entering the AI field are mathematics, programming, and practical experience. Mathematics provides the foundational understanding, while programming, particularly in Python, is crucial for implementing AI concepts [13][19]. - Key mathematical areas include linear algebra, probability and statistics, and calculus, which are vital for understanding AI algorithms and models [13]. Group 3: Practical Application and Tools - Python is highlighted as the primary programming language for AI due to its simplicity and extensive ecosystem, including libraries like NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch [20][21]. - Engaging in hands-on projects, such as data analysis or machine learning tasks, is encouraged to solidify understanding and build a portfolio [27][46]. Group 4: Career Opportunities in AI - Various career paths in AI include machine learning engineers, data scientists, and algorithm researchers, each focusing on different aspects of AI development and application [38][40]. - The article suggests that AI skills can enhance various fields, creating opportunities for interdisciplinary applications, such as in finance, healthcare, and the arts [41][43]. Group 5: Challenges and Future Directions - The rapid evolution of AI technology presents challenges, including the need for continuous learning and adaptation to new developments [34][37]. - The article concludes by encouraging individuals to embrace uncertainty and find their passion within the AI landscape, highlighting the importance of human creativity and empathy in the technological realm [71][73].

Core Insights - Igor Babuschkin, a key figure at xAI, has left the company to start his own venture capital firm, Babuschkin Ventures, focusing on AI safety research and investing in startups that aim to advance humanity and unlock the mysteries of the universe [1][3][30] - Babuschkin's departure highlights a trend of top AI talent moving from research roles to venture capital, a shift that is relatively rare in the industry, especially at such a young age [3][30][36] Group 1: Igor Babuschkin's Role and Contributions - Igor played a crucial role in the development of xAI, leading the team through multiple iterations of the Grok AI model and overseeing the construction of the Colossus supercomputing cluster in Memphis [1][16] - His background includes significant achievements at DeepMind, where he led projects like AlphaStar and contributed to the development of Codex and GPT-4 during his time at OpenAI [9][11][14] - Babuschkin's departure was marked by a heartfelt farewell message, emphasizing his contributions to xAI and the impact he had on the company's growth [4][6][29] Group 2: Industry Trends and Implications - The AI industry has seen a notable trend of talent moving to venture capital, with many former researchers opting to start their own companies or join existing ones rather than transitioning to investment roles [30][31] - The venture capital landscape in AI is booming, with significant funding opportunities, as evidenced by the over $35 billion raised in Silicon Valley alone last year [36] - Babuschkin's move reflects a broader urgency among AI professionals regarding the development of AGI (Artificial General Intelligence) and the need for responsible investment in AI technologies [30][38]

Core Insights - The interview with Demis Hassabis, CEO of Google DeepMind, discusses the evolution of AI technology and its future trends, particularly focusing on the development of general artificial intelligence (AGI) and the significance of world models like Genie 3 [2][3]. Group 1: Genie 3 and World Models - Genie 3 is a product of multiple research branches at DeepMind, aimed at creating a "world model" that helps AI understand the physical world, including physical structures, material properties, fluid dynamics, and biological behaviors [3]. - The development of AI has transitioned from specialized intelligence to more comprehensive models, with a focus on understanding the physical world as a foundation for AGI [3][4]. - Genie 3 can generate consistent virtual environments, maintaining the state of the scene when users return, which demonstrates its understanding of the world's operational logic [4]. Group 2: Game Arena and AGI Evaluation - Google DeepMind has partnered with Kaggle to launch Game Arena, a new testing platform designed to evaluate the progress of AGI by allowing models to play various games and test their capabilities [6]. - Game Arena provides a pure testing environment with objective performance metrics, allowing for automatic adjustment of game difficulty as AI capabilities improve [9]. - The platform aims to create a comprehensive assessment of AI's general capabilities across multiple domains, ultimately enabling AI systems to invent and teach new games to each other [9][10]. Group 3: Challenges in AGI Development - Current AI systems exhibit inconsistent performance, being capable in some areas while failing in simpler tasks, which poses a significant barrier to AGI development [7]. - There is a need for more challenging and diverse benchmarks that encompass understanding of the physical world, intuitive physics, and safety features [8]. - Demis emphasizes the importance of understanding human goals and translating them into useful reward functions for optimization in AGI systems [10]. Group 4: Future Directions in AI - The evolution of thinking models, such as Deep Think, represents a crucial direction for AI, focusing on reasoning, planning, and optimization through iterative processes [12]. - The transition from weight models to complete systems is highlighted, where modern AI can integrate tool usage, planning, and reasoning capabilities for more complex functionalities [13].

Core Insights - The article discusses the transformative impact of large models on the AI industry, emphasizing the shift from isolated applications to a more integrated human-machine interaction model, termed "accompanying interaction" [1][5][60]. Group 1: Paradigm Shifts in AI - The transition from training models to reasoning models has significantly enhanced AI's capabilities, particularly through reinforcement learning, which allows AI to generate synthetic data and innovate beyond human knowledge [9][11][13]. - The introduction of "Agentic Models" signifies a shift where AI evolves from merely providing suggestions to actively performing tasks for users [16][18]. Group 2: Application Development Transformation - "Vibe Coding" has emerged as a new programming paradigm, enabling non-professionals to create software using natural language, which contrasts with traditional programming methods [19][22]. - The concept of "Malleable Software" is introduced, suggesting that future software will allow users to customize and personalize applications extensively, leading to a more democratized software development landscape [24][26]. Group 3: Human-Machine Interaction Evolution - The future of human-machine interaction is predicted to be dominated by natural language interfaces, moving away from traditional graphical user interfaces (GUIs) [36][41]. - The article posits that the interaction paradigm will evolve to allow AI agents to seamlessly integrate various services, eliminating the need for users to switch between isolated applications [45][48]. Group 4: Intelligent Agent Ecosystem - The development of intelligent agents is characterized by enhanced capabilities in planning, tool usage, collaboration, memory, and action, which collectively redefine the internet from an "information network" to an "action network" [66][68]. - The introduction of protocols like MCP (Model Context Protocol) and A2A (Agent to Agent) facilitates improved interaction between agents and traditional software, enhancing the overall ecosystem [70].

Core Insights - Europe has historically been a leader in technology but is now lagging behind in emerging fields like AI, electric vehicles, and semiconductor manufacturing, with the focus shifting to the US and China [1][2] Group 1: Factors Contributing to Europe's Technological Lag - Europe's industrial tradition, while valuable, acts as an invisible ceiling that limits the development of new economic models and innovation [2] - The conservative capital ecosystem in Europe restricts innovation, as companies must demonstrate profitability early to attract funding, leading to a lack of financial support for startups [3] - The complex market structure in Europe, characterized by multiple sovereign nations with diverse languages, cultures, and regulations, complicates cross-border business expansion and increases operational costs [5][6] Group 2: Cultural and Regulatory Challenges - The European cultural emphasis on stability and gradual reform creates a cautious approach to new technologies, which can hinder innovation and entrepreneurship [6] - Strict regulatory frameworks, such as the General Data Protection Regulation (GDPR), while protecting privacy, also impose barriers to innovation by slowing down the pace of technological application [6] Group 3: Recognition and Response to Challenges - European leaders have acknowledged the strategic shortfalls in key technology sectors and are planning increased investments in areas like AI and semiconductor manufacturing [7] - The need for profound cultural, institutional, and market changes is critical for Europe to regain its technological edge, balancing stability with a spirit of innovation [7]

Core Insights - The article discusses the significant advancements in artificial intelligence (AI) since the landmark victory of AlphaGo over human champion Lee Sedol in 2016, marking a pivotal moment in AI development [2][3] - The release of ChatGPT by OpenAI in November 2022 is highlighted as a transformative event, leading to widespread recognition of AI's potential and its impact on various sectors [3][4] - The Nobel Prizes awarded in 2024 for contributions to machine learning and AI signify the technology's central role in modern science and society [3][4] AI's Impact on Work - AI is described as a revolutionary tool that is fundamentally changing work paradigms, with the potential to disrupt traditional job roles [5][6] - Historical comparisons are made to previous technological revolutions, suggesting that jobs in translation, design, coding, and financial analysis may be at risk due to AI advancements [6][7] - The concept of "human-machine collaboration" is emphasized as a more constructive approach than viewing technology as a threat, advocating for a reconfiguration of work tasks rather than outright replacement of jobs [6][7] New Work Principles - The article outlines four principles for a new work model: allowing talent to flow with work rather than being confined to fixed roles, focusing on tasks rather than positions, integrating technology deeply, and carefully evaluating employment forms [8][11] - The need for "deconstructing" and "reconstructing" work based on tasks is presented as essential for adapting to the evolving work landscape [7][8] Future Work Dynamics - The article suggests that organizations may need to shift from a job-centric to a person-centric approach, emphasizing dynamic tasks over static roles [12][14] - The importance of continuous learning and adaptability in the face of AI advancements is highlighted as crucial for maintaining relevance in the workforce [15][16]

Core Viewpoints - The article presents four logical chains regarding the understanding of "predict the next token," which reflects different perceptions of the potential and essence of LLMs or AI [1] - Those who believe that predicting the next token is more than just probability distributions are more likely to recognize the significant potential of LLMs and AI [1] - A deeper consideration of AI and ideals can lead to an underestimation of the value of what ideals accomplish [1] - The ideal VLA essentially focuses on reinforcement learning dominating the continuous prediction of the next action token, similar to OpenAI's O1O3, with auxiliary driving being more suitable for reinforcement learning than chatbots [1] Summary by Sections Introduction - The article emphasizes the importance of Ilya's viewpoints, highlighting his significant contributions to the AI field over the past decade [2][3] - Ilya's background includes pivotal roles in major AI advancements, such as the development of AlexNet, AlphaGo, and TensorFlow [3] Q&A Insights - Ilya challenges the notion that next token prediction cannot surpass human performance, suggesting that a sufficiently advanced neural network could extrapolate behaviors of an idealized person [4][5] - He argues that predicting the next token well involves understanding the underlying reality that leads to the creation of that token, which goes beyond mere statistics [6][7] Ideal VLA and Reinforcement Learning - The ideal VLA operates by continuously predicting the next action token based on sensor information, indicating a real understanding of the physical world rather than just statistical probabilities [10] - Ilya posits that the reasoning process in the ideal VLA can be seen as a form of consciousness, differing from human consciousness in significant ways [11] Comparisons and Controversial Points - The article asserts that auxiliary driving is more suited for reinforcement learning compared to chatbots due to clearer reward functions [12][13] - It highlights the fundamental differences in the skills required for developing AI software versus hardware, emphasizing the unique challenges and innovations in AI software development [13]