Core Viewpoint - The article emphasizes the ongoing AI boom and invites individuals to join the company "Quantum Bit," which focuses on tracking AI advancements and has established itself as a leading content platform in the industry [1]. Group 1: Job Opportunities - The company is hiring for three main directions: AI Industry, AI Finance, and AI Product, with positions available for both experienced professionals and fresh graduates [2][4]. - Positions are open for various levels, including editors, lead writers, and chief editors, with a focus on matching roles to individual capabilities [6]. Group 2: Job Responsibilities - **AI Industry Direction**: Responsibilities include tracking innovations in infrastructure, such as chips, AI infrastructure, and cloud computing, as well as interpreting technical reports from conferences [6][7]. - **AI Finance Direction**: Focuses on venture capital, financial reports, and capital movements within the AI industry, requiring strong analytical skills and a passion for interviews [11]. - **AI Product Direction**: Involves monitoring AI applications and hardware developments, producing in-depth evaluations of AI products, and engaging with industry experts [11]. Group 3: Benefits and Growth - Employees will have the opportunity to engage with cutting-edge AI technologies, enhance their work efficiency through new tools, and build personal influence in the AI field [6]. - The company offers competitive salaries, comprehensive benefits including social insurance, meal allowances, and performance bonuses, fostering a dynamic and open work environment [6][11]. Group 4: Company Growth Metrics - By 2025, Quantum Bit aims to have over 2.4 million subscribers on WeChat and more than 7 million users across all platforms, with a daily reading volume exceeding 2 million [12].

Core Viewpoint - The article discusses the competitive landscape of domestic large models, highlighting the recent developments of Jieyue Xingchen and its new model NextStep-1.1, while contrasting it with the more aggressive advancements of its competitors like Kimi and DeepSeek [1][2][38]. Group 1: NextStep-1.1 Model Development - Jieyue Xingchen has released the NextStep-1.1 model, which addresses previous visualization failures and significantly improves image quality through enhanced training and reinforcement learning [3][6][7]. - The updates in NextStep-1.1 include improved visual fidelity and reduced visual artifacts, as well as resolving numerical instability issues inherent in the previous version [23][24][37]. - The model architecture consists of 14 billion parameters and utilizes a lightweight flow matching head for autoregressive modeling of continuous image tokens, aiming to replace traditional heavy diffusion models [28][29]. Group 2: Competitive Landscape - The competitive environment has intensified, with companies like Kimi and MiniMax making significant strides, including IPO preparations and the release of new models [41][42]. - The article notes that the "six little dragons" of large model startups are dwindling, with only a few players like Jieyue Xingchen, Kimi, and MiniMax remaining committed to self-developed general large models [46][47]. - The ongoing competition is characterized by a shift towards coding, agent development, and multimodal capabilities, with open-source ecosystems becoming a key strategy [44].

Core Viewpoint - The year 2025 is anticipated to be a pivotal moment for the explosion of terminal AI, marking a significant transition in the industry akin to the shift from feature phones to smartphones. This transition represents a fundamental restructuring of business models and interaction logic, moving from a passive service model centered around apps to an active service model centered around AI agents [1]. Group 1: Industry Transition - The challenge of reconstructing the connection between humans and devices is a common issue faced by all manufacturers in this transition [2]. - There are two main factions in the industry: one that seeks to improve existing app ecosystems and another that advocates for a fundamental restructuring at the operating system level [3]. - Huawei, as a representative of the "reconstruction faction," has anchored its strategy at the foundational level, aiming to integrate AI capabilities into the native genes of the operating system [4]. Group 2: AI Terminal Classification - Huawei's terminal intelligence classification standard, developed in collaboration with Tsinghua University, categorizes AI terminals into levels L1 to L5, emphasizing the need for terminals to evolve beyond mere tools to achieve autonomous planning capabilities at L3 [5][10]. - Most current products remain trapped in outdated architectures, failing to progress beyond L1 and L2 stages, which are characterized by human-led, AI-assisted functionalities [8][16]. Group 3: Path Dependency in AI Applications - The industry exhibits three typical path dependencies that hinder true generational leaps in AI applications: 1. Major model vendors focus on B to C products, leading to "floating intelligence" that lacks integration with device-level operations [9]. 2. Internet giants with super apps tend to create "segmented intelligence," confining AI capabilities within their ecosystems and exacerbating data silos [11][13]. 3. Traditional terminal manufacturers adopt a "patchwork intelligence" approach, integrating AI features in a fragmented manner without a cohesive system-level strategy [14][15]. Group 4: System-Level Reconstruction - Huawei's HarmonyOS is pursuing a challenging path of system-level reconstruction, breaking down the rigid boundaries between applications and systems [21][22]. - The foundation of this reconstruction is the Harmony Intelligent Agent Framework (HMAF), which establishes a unique intent framework and user data map, transforming the operating system into a proactive service provider [25]. Group 5: User Experience Transformation - The bottom-level reconstruction allows for a shift from cumbersome operations to a dialogue-based interaction, where the system can autonomously identify user intentions and execute tasks seamlessly [27][28]. - This transformation enables a proactive response from services, exemplified by the Shenzhen Airlines intelligent agent that can handle complex booking processes through simple voice commands [29]. Group 6: Developer Ecosystem and Flow Distribution - HarmonyOS provides a platform for developers to create intelligent agents that can be easily integrated across various devices, enhancing the overall user experience [31][32]. - The new service distribution mechanism shifts the focus from app downloads to real-time user needs, allowing smaller developers to gain visibility and opportunities in the market [37]. Group 7: Market Growth and Developer Opportunities - Currently, over 32 million devices equipped with HarmonyOS 5/6 have been deployed, creating a robust foundation for the new flow of services [38][40]. - As the L3 intelligent experience is realized and the intent-service commercial loop is established, the Harmony AI ecosystem is entering a phase of substantial benefit release, presenting a prime opportunity for developers to engage with the next generation of service distribution [41][42].

SGI-Bench团队 投稿 量子位 | 公众号 QbitAI 如今，大模型在理解、推理、编程等方面表现突出，但AI的 "科学通用能力" （SGI） 尚无统一标准。 SGI强调多学科、长链路、跨模态与严谨可验证性，而现有基准仅覆盖碎片能力 （如学科问答、单步工具操作） ，难以反映真实科研中的循 环与自纠错。为此，上海人工智能实验室通过引入实践探究模型 （PIM） ，将科学探究拆解为四个循环阶段，并与AI能力维度对应： | 审思/深度研究 | （Deliberation） | ：复杂问题下的检索、证据综合与批判评估； | | | | | --- | --- | --- | --- | --- | --- | | 构思/创意生成 | （Conception） | ：提出新假说与可执行研究方法； | | | | | 行动/实验执行 | （Action） | ：将想法转化为计算代码 （干实验） | 与实验室流程 | （湿实验） | ； | | 感知/结果解读 | （Perception） | ：整合多模态证据并进行因果、比较等分析推理。 | | | | 团队将上述四维能力的综合定义为SGI，并发布覆盖全流程的SGI‑ ...

如今，大模型在理解、推理、编程等方面表现突出，但AI的 "科学通用能力" （SGI） 尚无统一标准。 SGI强调多学科、长链路、跨模态与严谨可验证性，而现有基准仅覆盖碎片能力 （如学科问答、单步工具操作） ，难以反映真实科研中的循 环与自纠错。为此，上海人工智能实验室通过引入实践探究模型 （PIM） ，将科学探究拆解为四个循环阶段，并与AI能力维度对应： | 审思/深度研究 | （Deliberation） | ：复杂问题下的检索、证据综合与批判评估； | | | | | --- | --- | --- | --- | --- | --- | | 构思/创意生成 | （Conception） | ：提出新假说与可执行研究方法； | | | | | 行动/实验执行 | （Action） | ：将想法转化为计算代码 （干实验） | 与实验室流程 | （湿实验） | ； | | 感知/结果解读 | （Perception） | ：整合多模态证据并进行因果、比较等分析推理。 | | | | SGI-Bench团队 投稿 量子位 | 公众号 QbitAI 核心结果与洞见：今天的"强模型"，尚未成为"强科学家" 1. 审 ...

Jay 发自 凹非寺 量子位 | 公众号 QbitAI 37岁华裔学霸AI创业，0融资，估值240亿美元。 是的，白手起家， 没拿投资人一分钱 。 更强悍的是，纯靠一己之力，轻松斩获谷歌、OpenAI等AI巨头的大单，硬生生给公司干成 了估值240亿美元的超级独角兽。 而这家公司的创始人—— Edwin Chen ，如今也凭借180亿的身价，跻身福布斯400的最年 轻富豪，也是这波新晋富豪中最富有的一位。 AI创业成最年轻新晋富豪 福布斯400新晋最年轻富豪—— Edwin Chen ，美裔华人，年仅37岁。 这时候，Edwin忽然从科幻电影《降临》的原著中得到了灵感。 《降临》讲的是一位人类语言学家，试图通过破译外星文明的文字与其建立沟通。但随着理 解不断加深，她却逐渐掌握了一种语言之外的能力—— 对时间的非线性认知，乃至「预见未 来」 。 在Edwin看来，在我们的世界里，人类，就是那批拥有超能力的外星人。而AI可以通过标注 数据，学习我们的思维模式，最终获得独属于人类的超能力——智能。 从大厂打工人，到硅谷估值240亿的超级独角兽，他仅仅花了5年。 Edwin毕业于MIT，先后在推特、谷歌和脸书工作，担 ...

Core Viewpoint - The discussion emphasizes the importance of not overly aligning AI medical initiatives with traditional medical practices, suggesting that innovation should not be constrained by conventional medical perspectives [1][62]. Group 1: Perspectives on AI in Healthcare - The roundtable featured three key perspectives: AI entrepreneurs, researchers, and healthcare practitioners, highlighting the complexity of integrating AI into the medical field [4][5]. - The future of AI in healthcare is seen as critical, with discussions extending beyond technology to include ethical considerations, decision-making authority, and clinical reasoning [9][10]. Group 2: Vision for AI in Medicine - AI in medicine is viewed as a complex system that reflects the challenges of achieving AGI (Artificial General Intelligence), with medical knowledge spanning multiple disciplines [13][14]. - The development of large medical models is essential, serving as a foundational infrastructure that integrates various types of medical data [16][17]. - AI has the potential to drive advancements in medical research by identifying complex patterns that traditional methods may overlook [19][20]. - The relationship between doctors and patients is expected to evolve, with patients becoming more informed and demanding higher standards from healthcare providers [21][22]. Group 3: AI Medical Benchmarks - The benchmarks for AI in healthcare must evolve to reflect the dynamic nature of AI technology, focusing on long-term health monitoring and adaptive treatment plans [30][31]. - In real medical scenarios, the effectiveness of AI is measured by its clinical reasoning capabilities, acceptance by healthcare professionals, and its impact on treatment outcomes [33][34]. Group 4: Unique Value Proposition of Baichuan Intelligence - Baichuan Intelligence aims to create a companion AI that engages in long-term decision-making rather than providing one-off answers, emphasizing the importance of patient and doctor engagement [37][40]. - The company collaborates with top hospitals while recognizing that professional endorsement does not guarantee product quality [39]. Group 5: Challenges and Recommendations for AI in Healthcare - The regulatory environment in healthcare poses significant challenges for AI innovation, necessitating careful navigation to maintain trust while integrating AI into decision-making processes [50][52]. - Young professionals entering the AI healthcare field are encouraged to find genuine interests and embrace interdisciplinary knowledge to foster innovation [54][56].

Core Viewpoint - The article highlights the emergence of Vbot's BoBo, a consumer-grade robotic dog, as a leading product in the embodied intelligence sector, achieving significant sales and consumer interest in a short time frame [5][57][79]. Group 1: Product Performance - Vbot's BoBo sold 1,000 units in just 52 minutes, setting a record in the consumer-grade robotic dog market [7][10]. - The product is priced at ¥9,988, making it accessible while offering high-end specifications, including a computing power of 128 TOPS and a battery capacity of 594Wh, which is 37.5% higher than the industry average [23][34][37]. - BoBo's design incorporates emotional engagement through facial expressions and movements, making it appealing to families, especially children [28][51][56]. Group 2: Market Positioning - Vbot has positioned BoBo as the first brand in the consumer-grade embodied intelligence market, addressing emotional companionship and practical assistance needs [57][75]. - The product's success is attributed to its unique design and technology, which combines advanced AI capabilities with user-friendly interactions, differentiating it from existing robotic products [33][44][46]. Group 3: Technological Innovation - BoBo utilizes a novel VLA (Vision-Language-Action) model and an Agent architecture, allowing it to understand and execute complex tasks based on natural language commands [38][39]. - The integration of a full-scene spatial base model enables BoBo to perform tasks like waking up a child by understanding context and planning routes [32][41]. Group 4: Industry Impact - Vbot's rapid success reflects a shift in the consumer robotics landscape, moving from industrial applications to personal, emotionally engaging products [62][79]. - The article suggests that the acceptance of consumer-grade embodied intelligence products like BoBo could lead to widespread adoption similar to that of smart cars and intelligent driving technologies in the near future [79].

Group 1 - The core idea is that scaling foundational models through pre-training is essential for AI to acquire world knowledge and basic reasoning capabilities [4][5] - More data, larger parameters, and saturated computation remain the most efficient methods for scaling foundational models [5] - The concept of domain-specific large models is considered a false proposition, as true AGI (Artificial General Intelligence) has not yet been achieved [28][30] Group 2 - Enhancing reasoning capabilities and aligning long-tail abilities are crucial for improving real-world AI performance [6][7] - The introduction of agents marks a significant milestone in AI, allowing models to interact with real environments and generate productivity [10][11] - Implementing memory mechanisms in models is essential for their application in real-world scenarios, with different memory stages mirroring human memory [12][13] Group 3 - Online learning and self-evaluation are key components for models to improve autonomously, with self-assessment being a critical aspect of this process [14][15] - The integration of model development and application is becoming increasingly important, with the goal of replacing human jobs through AI [16][17] - The future of AI applications should focus on enhancing human capabilities rather than merely creating new applications [32][34] Group 4 - Multimodal capabilities are seen as promising, but their contribution to AGI's upper intelligence limit remains uncertain [21][22] - The development of embodied AI faces challenges, including data acquisition and the stability of robotic systems [25][26] - The existence of domain models is driven by enterprises' reluctance to fully embrace AI, aiming to maintain a competitive edge [29][31]

Alchemist团队 投稿 量子位 | 公众号 QbitAI 想象一下，如果让一个大厨用发霉的食材、过期的调料来做菜，即使厨艺再高超，也做不出美味佳肴。AI训练也是同样的道理。 一、数据就像食材，质量决定成品 现在的AI图像生成模型，如Stable Diffusion、FLUX等，需要从网络上爬取数百万张图片来学习。但这些图片质量参差不齐：有些模糊不 清，有些内容重复，有些甚至只是广告背景图。用这些"食材"训练出来的AI，自然效果不佳。 由香港大学丁凯欣领导，联合华南理工大学周洋以及快手科技Kling团队共同完成的这项研究，开发出了一个名为"炼金师" （Alchemist） 的AI系统。它就像一位挑剔的大厨，能从海量图片数据中精准挑选出最有价值的一半。 更让人惊喜的是： 二、让AI学会"自我评判" 2.1 传统方法的局限 传统的数据筛选方法就像用筛子筛米粒，只能按照单一标准过滤： 这些方法的问题在于： 它们不知道哪些数据真正有助于AI学习 。 2.2 炼金师的智慧 "炼金师"更像是一位经验丰富的美食评委，它能同时考虑多个维度： 用这一半精选数据训练出的模型，竟然比用全部数据训练的表现还要好 训练速度快了 5 ...