Core Viewpoint - The article discusses a heated debate between AI experts Yann LeCun and Demis Hassabis regarding the nature of intelligence, particularly focusing on the concept of "general intelligence" and its implications for artificial intelligence development [3][8][30]. Group 1: Debate Overview - Yann LeCun argues that the idea of "general intelligence" is nonsensical, asserting that human intelligence is highly specialized rather than universal [9][13]. - Demis Hassabis counters LeCun's claims, stating that human brains exhibit significant generality and complexity, and that general intelligence is a valid concept [17][22]. - The debate has attracted considerable attention, with notable figures like Elon Musk publicly supporting Hassabis [5][7]. Group 2: Key Arguments - LeCun emphasizes that human intelligence is shaped by evolutionary pressures to adapt to specific environments, leading to specialized skills rather than general capabilities [14][36]. - Hassabis argues that the brain functions similarly to a Turing machine, capable of learning any computable content given sufficient resources, thus supporting the existence of general intelligence [18][24]. - The discussion highlights a fundamental disagreement over terminology, with LeCun focusing on the specialized nature of human cognition while Hassabis advocates for the potential of general intelligence [32][41]. Group 3: Future Directions in AI - Both experts agree on the importance of "world models" in advancing artificial general intelligence (AGI), though they have different interpretations of what this entails [42][50]. - LeCun's upcoming venture, Advanced Machine Intelligence Labs, aims to develop world models that prioritize understanding control theory and cognitive science [43][44]. - Hassabis and Google DeepMind are also focusing on world models, emphasizing the need for models that comprehend causal relationships and interactions within the world [46][47].

梦晨 发自 凹非寺 量子位 | 公众号 QbitAI "从小就要赢在起跑线" 这套逻辑，被顶刊Science最新论文狠狠打了脸。 这项研究综合分析了超过34000名国际顶尖人才的成长轨迹，涵盖诺贝尔奖得主、典作曲家、奥运冠军以及世界顶级棋手。 结论颠覆人们观念： 作者团队来自德国凯泽斯劳滕工业大学 （RPTU Kaiserslautern） 体育科学系、密歇根州立大学心理学系、普渡大学心理科学系。 他们综合分析了多项研究数据，涵盖科学、艺术、体育多个领域。 少年天才往往止步于顶尖水平之下，和最终登顶的成年人近90%不是同一批人。 而最终达到世界级水平的人才，在早年阶段表现反而低于只达到国家级水平的同龄人。 "天才少年"长大后去哪了 长久以来，学界对人才培养的研究主要聚焦于年轻人。传统观点普遍认，早期表现越好、专项练习越多，后期成就越高。 全球各地的精英学校、音乐学院和青训学院也据此设计了选拔机制：挑出表现最好的年轻人，然后用高强度的专项训练进一步"加速"他们的成 长。 但这套逻辑在真正的世界顶尖群体中是否成立，此前从未被系统验证过。 通过大规模数据追踪，研究团队给出了一个令人意外的答案：无论是体育、国际象棋还 ...

Core Insights - The article emphasizes that 2025 marks a significant year for AI advancements, particularly in reasoning, collaboration, and scientific discovery, led by Google [1][3][9] Group 1: AI Development and Integration - Google has made substantial progress in reasoning, multi-modal understanding, model efficiency, and generative capabilities, significantly enhancing model performance [15][4] - The Gemini series, particularly Gemini 3 Pro, has set new standards in multi-modal reasoning and achieved top scores in various benchmark tests, including a 23.4% record in MathArena Apex [18][19] - AI has been deeply integrated into Google's core products, transforming from a tool to a practical asset for users [5][10][23] Group 2: Generative Media and Creative Tools - 2025 is highlighted as a transformative year for generative media, with AI providing unprecedented capabilities for video, image, audio, and virtual world generation [24][25] - Google has collaborated with creative professionals to develop tools like Flow and Music AI Sandbox, enhancing creative workflows [25][21] Group 3: Scientific and Mathematical Advancements - AI has significantly contributed to advancements in life sciences, health, natural sciences, and mathematics, empowering researchers with new tools and resources [27][28] - The AI system AlphaFold, which addresses protein folding, has been widely adopted by researchers globally, marking a milestone in scientific research [28] Group 4: Quantum Computing and Physical World Research - Google has made notable advancements in quantum computing and energy-efficient technologies, including the launch of a new TPU designed for the reasoning era [33][32] - The company has also made strides in robotics and visual understanding, integrating AI agents into both physical and virtual environments [33] Group 5: Addressing Global Challenges - Google's AI-driven scientific progress is being applied to tackle critical global challenges, including climate resilience, public health, and education [36][38] - The company has developed advanced forecasting models that enhance decision-making in various sectors, including weather prediction [36] Group 6: Responsibility and Safety - Google emphasizes the importance of combining research breakthroughs with responsibility and safety, continuously improving tools and frameworks to mitigate risks [42][43] - The Gemini 3 model is noted as the safest model to date, undergoing comprehensive safety assessments [44] Group 7: Collaboration and Open Ecosystem - Google advocates for cross-sector collaboration to responsibly advance AI, establishing partnerships with leading AI labs and educational institutions [46][45] - The company aims to continue promoting cutting-edge technology safely and responsibly for the benefit of humanity [47]

克雷西 发自 凹非寺 量子位 | 公众号 QbitAI MiniMax最新旗舰级Coding & Agent模型 M2.1 ，刚刚对外发布了。 一边是港交所聆讯通过新进展，另一边新模型还在嗖嗖嗖上新——而且还SOTA了。 这一次，它直接甩出了一份硬核成绩单，在衡量多语言软件工程能力的Multi-SWE-bench榜单中，以仅10B的激活参数拿下了49.4%的成绩， 超越了Claude Sonnet 4.5等国际顶尖竞品，拿下全球SOTA。 它试图解决的，就是此前模型身上严重的"学科偏科"问题。 所谓偏科，指的是过去的模型，写写Python脚本或Web前端页面表现还可以，可一旦涉及到后端架构，亦或底层逻辑，表现往往会出现断崖 式下跌。 M2.1的核心进化，就在于它终于突破了这个难题，掌握了后端的开发规范。 M2.1的发布，也证明了MiniMax在推进上市流程的同时，仍保持着高频的研发节奏。 更懂底层，10B激活参数拿下SOTA M2.1将对工程上下文的理解，转化为了对开发工具链的深度适配。它不仅能生成代码，更能熟练配合Cursor、Claude Code等主流编程工 具，在存量代码库中执行精准的修复（Fix）或 ...

鹭羽 发自 凹非寺 量子位 | 公众号 QbitAI 我真栓Q了！围观了场 狼人杀 ，看得我汗流浃背…… 半小时全程高能，根本停不下来： 天崩开局倒钩狼悍跳预言家、冲锋狼死于话多、神职上大分每晚都是平安夜。 结果你跟我说，这些玩家都是 AI ？？？ 果然会玩还得看 淘宝 ～最近他们整活的这个AI狼人杀大乱斗 WhoisSpy.ai ，大模型在里面简直咔咔乱杀。 D老师、Qwen、Kimi、GLM一个个都化身心机boy推拉博弈，be like： …… 不过u1s1，虽然这些Agent看似性格迥异，实则一个个都是狼人杀高玩来着。 而且门槛也不高，自己就能手搓一个出来。 是不是有点手痒了？ （咳咳） 不卖关子了，这就是我最近刷到的一个AI狼人杀比赛，还是淘宝办的——首届 「高校生VS开发者对抗赛」 。 展开来说，就是淘宝发了个召集令，广邀高校学生和AI开发者，带着自家Agent来真刀实枪碰一场，看看谁的Agent思维更缜密、更会盘逻 辑。 六边形战士 Kimi ：武力值MAX，第六感Next Level。 老实人 DeepSeek ：虽然我只是一介平民，虽然我只会划水，但我相信跟对人走对路，奥利给！ 喜剧人 Qwe ...

Core Viewpoint - The article discusses the introduction of CLM (CPU-offloaded Large-scale 3DGS training), a system that allows for city-scale 3D reconstruction using a single consumer-grade GPU, specifically the RTX 4090, by offloading memory-intensive parameters to CPU memory, significantly lowering hardware requirements for large-scale neural rendering [1][21]. Group 1: 3D Gaussian Splatting (3DGS) Challenges - 3DGS has become a crucial technology in neural rendering due to its high-quality output and rendering speed, but it faces significant challenges when applied to complex scenes like urban blocks, primarily due to GPU memory limitations [2]. - A high-precision 3DGS model typically contains tens of millions to over a hundred million Gaussian points, with each point requiring substantial memory for parameters, gradients, and optimizer states. Even high-end GPUs like the RTX 4090, with 24GB of memory, can only handle about 15-20 million points, which is insufficient for city-scale scenes [2][3]. Group 2: CLM Design Principles - CLM is based on the observation that only a small fraction of Gaussian points are actively used during each rendering pass, with less than 1% of points accessed in large scenes [3]. - The system design of CLM involves dynamically loading Gaussian parameters from CPU memory as needed, rather than keeping all parameters in GPU memory [4]. Group 3: Key Mechanisms of CLM - **Attribute Segmentation**: CLM retains only "key attributes" (10 parameters) necessary for visibility checks in GPU memory, while the remaining 80% of "non-key attributes" are stored in CPU memory and loaded on demand [6][7]. - **Pre-rendering Visibility Culling**: Unlike traditional methods, CLM calculates visible Gaussian point indices before rendering, reducing unnecessary GPU computations and memory usage by only loading visible points from CPU memory [9][10]. - **Efficient CPU-GPU Collaboration**: CLM employs a multi-layered design to mitigate data transfer delays, including micro-batching, caching mechanisms, and intelligent scheduling to maximize efficiency and minimize communication overhead [12][13][14][15]. Group 4: Performance Results - CLM technology significantly increases model size, allowing for the training of 102.2 million Gaussian points on the "MatrixCity BigCity" dataset, a 6.7-fold increase compared to traditional methods, which maxed out at 15.3 million points [16]. - The quality of reconstruction improves with more parameters, achieving a PSNR of 25.15dB for the 102.2 million point model, compared to 23.93dB for the smaller model [18]. - Despite communication overhead, CLM maintains a training throughput of 55% to 90% of the enhanced baseline on the RTX 4090, and up to 86% to 97% on the slower RTX 2080 Ti [19]. Group 5: Broader Implications - CLM represents a significant advancement in addressing deployment bottlenecks in 3DGS training, integrating CPU resources into the training process without the need for multi-GPU setups, thus providing a cost-effective solution for large-scale scene reconstruction [21]. - The growing demand for efficient and low-cost 3D reconstruction tools in applications like digital twins and large-scale map reconstruction highlights the importance of CLM's approach in optimizing existing computational resources [21].

Core Viewpoint - The year 2025 is anticipated to be the "Agent Year," marking a significant shift in the industry towards practical applications of Agent technology [1][2]. Group 1: Development and Challenges of Agents - The Agent technology has transitioned from a nascent stage to practical engineering applications throughout the year [3][7]. - Key challenges in the implementation of Agents include the need for a robust engineering approach to manage complex systems and the importance of Agent Infrastructure (Infra) [6][21]. - The industry recognizes the value of Agents as they effectively address real-world problems, moving from theoretical discussions to tangible applications [6][12]. Group 2: Perspectives from Industry Leaders - Industry experts highlight a clear divide between traditional narratives from Silicon Valley and practical applications seen in smaller businesses, indicating a shift towards realism in Agent development [8][10]. - The emergence of AI coding tools is noted as a significant development, changing software engineering paradigms and serving as a universal interface for Agents [7][34]. - The consensus among experts is that the capital market is seeking new organizational methods, as the previous internet era's benefits have been largely exhausted [12][13]. Group 3: Engineering and Infrastructure - The concept of Agent Infra is crucial for managing the uncertainties inherent in Agent systems, with a focus on creating a safe and effective operational environment [21][22]. - The development of safety sandboxes and observability tools is essential for addressing the risks associated with autonomous Agent operations [22][23]. - The distinction between essential complexity and incidental complexity in enterprise problem-solving is emphasized, with a focus on building a common subset of solutions for various challenges [27][28]. Group 4: Future Trends and Directions - Future developments in Agent Infra are expected to focus on ensuring safe and reliable operations while optimizing the intelligence of Agents through continuous data utilization [38][39]. - The integration of memory management and semantic context is highlighted as a key area for enhancing Agent capabilities [40]. - The industry anticipates a significant transformation in mobile development ecosystems as Agents become mainstream, necessitating a shift in development methodologies and collaborative practices [41][44].

加入我们，你可以获得： 以下是岗位详情： 所有岗位不同能力层级职位均在开放，欢迎结合个人履历和经验申请。 编辑部 发自 凹非寺 量子位 | 公众号 QbitAI AI热潮还在汹涌，但如果你还不知道如何参与……那为什么不来 量子位 呢？ 我们是一家以 追踪AI新进展 为核心的内容平台，经过8年积累，目前拥有顶流影响力，广泛且备受认可的产业资源，以及时代风口的最佳观 测和学习生态位。 目前，我们有 三大方向 岗位招聘，希望你是 （或者能成为） 这三个方向的内容专家： 岗位均为全职，工作地点：北京中关村。 岗位面向： AI产业方向 岗位职责： AI产业方向 ：关注基建层创新，包含芯片、AI Infra、云计算； AI财经方向 ：关注AI领域创投和财报，跟踪产业链资本动向； AI产品方向 ：关注AI在应用和硬件终端方向的进展。 社招：覆盖编辑、主笔、主编各个层级，按能力匹配岗位； 校招：应届毕业生，接受实习且可转正。 站在AI浪潮之巅 ：第一时间接触和了解AI领域最新技术和产品，构建完整的AI认知体系。 玩转AI新工具 ：将各种AI新技术、新工具应用于工作，提升工作效率和创造力。 打造个人影响力 ：通过撰写独家原创内 ...

Core Viewpoint - The article discusses the challenges and opportunities in the medical AI sector, particularly focusing on the company Yunzhisheng, which has established itself as a leading player in the field by successfully integrating AI solutions into hospitals and demonstrating significant operational efficiency improvements [1][9][64]. Group 1: Medical AI Challenges - Patients increasingly consult AI chatbots before visiting doctors, leading to communication challenges in clinical settings [2][3]. - The high "hallucination rate" of general AI models in medical contexts can reach up to 40%, raising concerns about their reliability [4][5]. - Medical AI must navigate stringent requirements for stability, acceptance across various healthcare systems, and the high costs associated with medical errors [12][18][19]. Group 2: Yunzhisheng's Position - Approximately 40% of top-tier hospitals in China have adopted Yunzhisheng's medical AI solutions, indicating its strong market presence [9][22]. - The company has deployed its solutions in 400 hospitals, with a nearly 90% direct usage rate of generated medical records, significantly reducing doctors' time spent on documentation [22][23][25]. - Yunzhisheng's medical AI solutions are designed to integrate seamlessly into existing workflows, enhancing efficiency without adding to the workload of healthcare professionals [58][60]. Group 3: Technological Advancements - Yunzhisheng's latest model, "Shanhai·Zhimed 5.0," employs a dual-core system capable of processing structured information and multimodal inputs, enhancing its diagnostic capabilities [34][36]. - The model's architecture includes a three-layer data paradigm that improves its understanding of medical contexts and reduces hallucination rates to below 3% [42]. - The company has consistently ranked at the top of medical AI evaluation platforms, demonstrating its technological superiority [45][46]. Group 4: Business Growth and Market Trends - Yunzhisheng's medical business revenue reached 0.70 billion, a 22.3% increase year-on-year, highlighting its growth trajectory [66]. - The average revenue per medical client has more than doubled, indicating a significant increase in customer value [66]. - The broader market for medical AI is expected to grow, with increasing investments and policy support aimed at integrating AI into healthcare workflows [79][82][86].

衡宇 发自 深圳 量子位 | 公众号 QbitAI 易烊千玺现身深圳，手里拿的绿手机，几乎第一时间抢走了现场的全部注意力。 这就是华为nova系列最新推出的 nova 15 Ultra带感绿 （真的很吸睛的颜色） 。 nova 15系列这次的产品分层依旧非常清晰，共推出数字标准版、Pro版和Ultra版三款机型， 全系搭载HarmonyOS 6 。 该系列的Ultra和Pro版本在外观上采用横向立体堆叠设计，搭载双星镜头模组，就像有两只大眼睛。 同时 Pro版和Ultra版 首次升级麒麟9系芯片，性能定位向Mate、Pura系列看齐。 Ultra版本4199元起，Pro版本3499元起。 数字标准版 则维持了更经典的单环加闪光灯设计，外形延续上一代风格。 nova 15系列，真的有点AI了 nova 15系列的AI能力几乎全部藏在具体场景里。 影像是最直观的一条线索 。 Ultra和Pro版本都首发搭载了前、后双红枫影像系统，通过多光谱感知与像素级算法参与色彩计算。 红枫原色镜头能在更宽广的光谱范围内，对全局光谱信息进行精准测量，色彩还原准确度大幅提升，拍出来的照片色彩更加真实。 前摄加入红枫原色镜头后，自拍 ...