Core Viewpoint - The article discusses the severe challenges faced by China's agricultural sector during the 2025 autumn harvest due to extreme weather conditions, including drought followed by excessive rainfall, leading to significant crop losses and increased costs for farmers [3][4][6][21]. Summary by Sections Weather Impact on Harvest - The main agricultural regions, particularly the Huang-Huai-Hai area, are experiencing unprecedented weather fluctuations, with drought conditions in summer followed by heavy rainfall in autumn [3][6][8]. - From June to August, Henan experienced over 20% less rainfall, while temperatures were significantly higher, causing severe drought conditions [4]. - In September, rainfall surged, with Henan recording an average of 349.6 mm, 2.6 times more than the historical average, marking the highest level since 1961 [9]. Crop Damage and Quality Issues - Continuous rainfall has led to poor crop quality, with many crops like corn and peanuts suffering from mold and rot due to excessive moisture [21][23]. - The adverse weather has resulted in reduced yields, with many farmers reporting that their crops are unsellable due to quality degradation [25][41]. Harvesting and Drying Challenges - Farmers are facing logistical challenges in harvesting due to muddy fields, which have made it difficult for machinery to operate [12][17]. - The cost of harvesting has increased significantly, with some farmers resorting to manual labor, which is several times more expensive than usual [17][31]. - The demand for drying facilities has surged, but the available resources are insufficient to meet the needs of all farmers [27][28]. Government and Institutional Response - Various government bodies have initiated support measures, including the establishment of drying centers and the mobilization of agricultural machinery for emergency harvesting [33][36]. - Insurance companies are also stepping in to expedite claims to mitigate farmers' losses [35]. Long-term Agricultural Resilience - The article highlights the need for a more resilient agricultural system capable of withstanding extreme weather events, emphasizing the importance of modernizing infrastructure and improving disaster response mechanisms [42][57]. - It suggests that lessons can be learned from international practices in disaster management and agricultural insurance to enhance China's agricultural resilience [53][56].

Core Viewpoint - The article discusses the high prevalence of esophageal cancer in China, attributing it to dietary habits, particularly the consumption of hot and spicy foods, as well as the impact of lifestyle choices such as smoking and alcohol consumption. Group 1: Esophageal Cancer Prevalence - China accounts for over half of the global esophageal cancer cases, with more than 90% being squamous cell carcinoma [10][8] - The incidence and mortality rates of esophageal cancer in China rank sixth and fourth among all malignancies, respectively [7][9] - In 2021, the incidence rate in China was 38.37 per 100,000, with men being nearly three times more likely to be affected than women [15][10] Group 2: Regional and Demographic Disparities - Rural areas in China have a significantly higher incidence and mortality rate of esophageal cancer, approximately double that of urban areas [18][14] - High-risk regions include areas around the Taihang Mountains and parts of Jiangsu, Sichuan, and Guangdong [19][20] - The standardized mortality rate is highest in Jiangsu at 21.62 per 100,000, followed by Sichuan and Henan [21][20] Group 3: Dietary and Lifestyle Factors - Major risk factors for esophageal cancer include smoking, alcohol consumption, and poor nutritional intake [24][27] - Hot and spicy food consumption is linked to higher cancer risk, with studies indicating that individuals with these dietary habits have a 1.9 times higher risk [31][29] - The World Health Organization classifies beverages above 65°C as a potential carcinogen, with common Chinese foods often exceeding this temperature [30][9] Group 4: Survival Rates and Detection Challenges - The five-year survival rate for esophageal cancer in China is only 28.8%, significantly lower than other cancers [41][44] - Early detection is challenging due to the asymptomatic nature of early-stage esophageal cancer, leading to late diagnoses [45][41] - Recommendations for screening include endoscopic examinations for high-risk individuals aged 45 and above [47][41]

酒店房间、民宿、KTV、洗浴中心等场所，活跃着超2200万块电视大屏，每年仅内容和系统就有60亿 元市场，却长期被忽视，这期视频就来聊聊大屏市场。 ...

Core Insights - OpenAI has launched its first AI browser, Atlas, aiming to redefine user interaction with the internet by placing AI at the core of the browsing experience [1][2] - Atlas is positioned as a significant shift in OpenAI's identity, moving from being a provider of foundational AI tools to a more integrated user interface [2] Technical Implementation - Current AI browsers primarily utilize two technological paths: visual recognition and DOM parsing, with Atlas favoring the latter, achieving a task success rate of 89.1% and reducing costs by 90% [4][5] - Despite its technological foundation, Atlas shows little innovation compared to existing browsers like Comet and Opera Neon, with similar features and functionalities [3][5][6] Feature Comparison - Atlas offers content summarization and split-screen browsing, but these features are not unique and are available in competitors like Comet and Opera Neon [6][9] - Atlas's agent functionality requires user authorization for task execution, mirroring features found in Opera Neon, but lacks additional capabilities such as reusable "Cards" for common tasks [6][9] Security and Limitations - Atlas faces the same security challenges as other browsers, requiring manual intervention for sensitive operations like password entry and payment confirmations [7][16] - Technical issues, such as access blocks and operational bugs, indicate that Atlas still requires significant refinement [20][50] Market Position and Competition - OpenAI's strategy with Atlas aims to establish a new entry point for users into the internet, potentially increasing user engagement and monetization opportunities [28][29] - The competition in the AI browser space is not only technological but also revolves around ecosystem development, with the MCP protocol facilitating integration across various tools [31][33] Future Outlook - OpenAI's short-term goals include expanding Atlas to Windows, iOS, and Android platforms, enhancing agent functionality, and building a developer ecosystem for third-party AI applications [24][36] - The long-term vision for browsers like Atlas is to evolve into intelligent agents capable of understanding user intent and executing complex tasks seamlessly [56]

Core Insights - The article discusses the evolution of AI from "cognition" to "action," highlighting the transition of Tang Jian from academia to industry, particularly in the fields of autonomous driving and embodied intelligence [1][2] - Tang Jian emphasizes the importance of experience-driven control methods over traditional mathematical modeling in complex environments, suggesting that AI systems can learn from historical data to make effective decisions [4][5] - The concept of a "world model" is introduced as essential for embodied intelligence, enabling robots to understand and predict their environment, thus enhancing their operational capabilities [13][14] Summary by Sections Transition from Academia to Industry - Tang Jian, a former tenured professor, shifted focus to practical applications of AI in industry, particularly in autonomous driving and robotics [1][3] - His experience in various companies, including Didi and Midea, has informed his approach to AI-driven system control [3][6] Experience-Driven Control - The article outlines the difference between traditional control methods and experience-driven approaches, with the latter relying on data and historical experiences rather than precise mathematical models [4][5] - This experience-driven philosophy is evident in autonomous driving applications, where end-to-end control merges perception, planning, and control into a single learning process [6][7] Embodied Intelligence and World Models - Tang Jian argues that embodied intelligence presents a higher complexity than autonomous driving, requiring robots to manage multiple joints and navigate dynamic environments [7][8] - The world model is described as a critical component for robots to understand and interact with the physical world, enabling them to perform tasks that require nuanced understanding and adaptability [14][15] - The article highlights the need for a world model to facilitate the development of robots that can generalize across various tasks and environments, which is crucial for their deployment in real-world scenarios [21][22] Future Directions and Challenges - The discussion includes the potential for world models to achieve a "DeepSeek moment" in embodied intelligence, drawing parallels to breakthroughs in AI performance under limited resources [9][10] - Tang Jian acknowledges the current limitations in data and model architecture, indicating that further iterations and improvements are necessary for the field to progress [2][13] - The article concludes with the assertion that the world model is not just a technical choice but a fundamental requirement for the advancement of embodied intelligence [13][22]

Core Insights - The article highlights the journey of Jolin, a prominent figure in the AI industry, from her academic background to her role in founding Fireworks AI, focusing on her contributions to the PyTorch framework and her innovative approaches in AI inference technology [1][2][3]. Group 1: Academic and Professional Background - Jolin's technical foundation began at Fudan University, where she studied computer science, followed by a PhD from UC Santa Barbara, positioning her at the forefront of global AI research [1]. - Her experience at Meta, where she led the development of the PyTorch ecosystem, transformed it from a niche tool into a global standard for AI model training and inference [2][3]. Group 2: Innovations at Fireworks AI - After leaving Meta, Jolin founded Fireworks AI, targeting the efficiency challenges in large model inference with two core technologies: Fire Attention inference engine and speculative execution engine [2][3]. - The Fire Attention engine significantly reduces resource consumption by compressing model precision from 16-bit to as low as 4-bit without losing accuracy, while the speculative execution engine enhances inference speed by predicting multiple word sequences simultaneously [3]. Group 3: Business Strategy and Market Positioning - Fireworks AI operates as a "compute scheduler," integrating idle GPU resources from various tech companies and academic labs, allowing clients to access these resources without the need for expensive hardware [9][10]. - The company focuses on providing tailored solutions for small to medium enterprises, addressing specific industry needs that larger competitors may overlook [12][13]. Group 4: Financial Growth and Future Directions - Fireworks AI's annual recurring revenue (ARR) surpassed $100 million, with a valuation reaching $4 billion, attracting significant investment interest from firms like Lightspeed and Index [11][12]. - The company plans to leverage its accumulated data from model fine-tuning to optimize GPU performance, indicating a strategic shift towards enhancing hardware efficiency in collaboration with partners like NVIDIA [12][13]. Group 5: Entrepreneurial Philosophy - Jolin emphasizes a pragmatic approach to AI, focusing on making complex technologies accessible and usable for businesses, rather than engaging in parameter competitions [14][15]. - The company's slogan reflects its mission to enable every enterprise to effectively utilize AI, showcasing a commitment to practical solutions over theoretical advancements [17][18].

老南京人看着金陵从从容旧都蜕变为今日新城，雍容气度犹在，却难忘街巷炊烟和那慢悠悠的时光。南 京这座城市，为何让人又爱又恨？ ...

Core Viewpoint - The company LG, once a dominant player in the global battery market with a 27% share, has faced a significant decline due to competition from Chinese firms, marking a dramatic fall from its previous status as a leader in the industry [1] Group 1 - LG held a 27% share of the global battery market and was associated with 13 out of the top 20 automotive brands [1] - The company successfully competed against Japanese giant Panasonic, achieving a notable turnaround in its market position [1] - The current situation reflects a complete collapse of LG's battery business, indicating a significant shift in the competitive landscape [1]

本文来自微信公众号：造价学长，作者：造价学长，题图来自：AI生成 近几次聚餐，发现一个有意思的现象：领导那桌还没来得及过来"打一圈"、发表令人振奋的讲话，员工 桌的年轻人已经走得差不多了，留下来的，大多是年长一点的工长。 要放在从前，年轻人多半会主动凑过去敬酒、说几句漂亮话，为自己的职业道路铺铺路。能留到最后 的，往往被认为"前途光明"。 但现在不一样了。大家好像只想安安静静吃完自己的饭，不愿在饭桌上多停留一秒。时代真的变了—— 年轻人，越来越不愿意讨好领导了。 我琢磨了一下，原因大概有这几个方面： 你喝了这顿酒，工作就会少一点吗？ 第一，做这件事，真的有好处吗？ 人性本质是趋利避害的。如果不能"趋利"，那至少也得"我乐意"。 你喝了这顿酒，就能升职加薪吗？ 你喝了这顿酒，就能提前休假回家吗？ 都不会。 它只会让你半夜爬起来抱着马桶吐，只会让你第二天昏昏沉沉上工地"打灰"。 所以现在的年轻人，是真的"人间清醒"。 第二，不做这件事，真的有坏处吗？ 除非你这工作好到外面没有匹配的岗位——那叫"有好处"。 可如果你既给不了好处，又制造不了坏处——那人家凭什么讨好你？你算老几？ 第三，如果没有利害关系，那我喜不喜欢 ...

Core Insights - The article discusses the ongoing competition in the AI landscape, drawing parallels between the past rivalry between Google and Microsoft and the current dynamics involving OpenAI and Google [3][5][74] - It introduces the concept of "Intelligence Scale Effect," which emphasizes that merely having a smarter model is insufficient; understanding real-world data is crucial for success [5][7][24][74] Group 1: Intelligence Scale Effect - The "Intelligence Scale Effect" can be summarized by the formula: AI effectiveness = Model intelligence level × Depth of real-world understanding [5][74] - The first component, "model intelligence level," refers to the AI's foundational capabilities, determined by architecture, training data, parameters, and computational resources [13][14] - The second component, "depth of real-world understanding," is likened to the AI's ability to process and comprehend specific, real-time, and proprietary data [23][24] Group 2: Data Competition - Companies in the AI sector are entering a fierce competition to expand their data boundaries, which is essential for maximizing effectiveness [9][10][25] - The article highlights a shift from static to real-time data processing, exemplified by Perplexity AI, which combines real-time web information retrieval with large language models [34][36][38] - Microsoft 365 Copilot is presented as a solution to data silos within enterprises, leveraging Microsoft Graph to integrate private data for enhanced productivity [40][45][46] Group 3: Future Trends - The ultimate goal of AI applications is to transition from digital to physical realms, utilizing wearable devices and IoT to enhance the "Intelligence Scale Effect" [47][49] - The competition in the AI space is expected to be more intense than in previous internet eras, with a focus on context and real-world understanding as the new battleground [52][55][59] - The article warns of the potential privacy and trust issues arising from AI's need to access extensive personal and proprietary data [70][72][73]