Core Viewpoint - The article discusses a significant breakthrough in AI-driven drug discovery through the development of DrugCLIP, a platform that can perform high-throughput virtual screening of drugs at a genomic scale, achieving 10 trillion protein-molecule pairing calculations within 24 hours [1][4][36]. Group 1: DrugCLIP Platform - DrugCLIP is an AI-driven ultra-high-throughput virtual screening platform developed by Tsinghua University, which allows for rapid identification of candidate drug molecules from vast chemical libraries [2][3]. - The platform has successfully completed virtual screening covering the human genome scale, identifying potential drug molecules for diseases such as depression, cancer, and Parkinson's disease [6][54]. Group 2: Challenges in Traditional Drug Screening - Traditional drug screening faces three main challenges: slow processing speed, lack of starting points for many disease-related proteins, and a narrow focus on popular targets [8][12][18]. - Only 10% of protein targets have mature drugs available, while 90% remain without identified drugs [11]. Group 3: Methodology of DrugCLIP - DrugCLIP employs a novel approach by using contrastive learning to train AI encoders that create vector representations of protein binding pockets and chemical molecules [20][22]. - The model processes 5 billion candidate molecules, generating vector representations to quickly identify the most promising candidates for new drug development [32][34]. Group 4: Performance and Validation - DrugCLIP has demonstrated superior performance in virtual screening benchmarks, outperforming traditional docking tools and other AI methods in identifying effective molecules [37][39]. - Experimental validation showed that from 78 screened molecules related to depression, 8 were found to activate the target protein, with the best molecule exhibiting a binding affinity of 21 nM [42][43]. Group 5: Future Prospects - The DrugCLIP platform is set to collaborate with industry partners to accelerate the discovery of new drug targets and first-in-class drugs for various diseases [64]. - The database created by DrugCLIP, which is now open to the global research community, represents the largest known protein-ligand screening database, potentially providing "drug seeds" for nearly half of human proteins [55][59].

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 producing accessible reports on technical conferences and papers [6][7]. - **AI Finance Direction**: Focuses on venture capital, financial reports, and analyzing capital movements within the AI industry, including interviews with investors and entrepreneurs [11]. - **AI Product Direction**: Involves monitoring AI applications and hardware developments, writing in-depth product evaluations, and engaging with product experts [11]. Group 3: Benefits and Work Environment - Employees can expect a vibrant team atmosphere, opportunities for personal influence through original content creation, and professional mentorship from senior editors [6][11]. - The company offers competitive salaries and comprehensive benefits, including social insurance, meal allowances, and performance bonuses [6]. Group 4: Company Growth and Reach - By 2025, Quantum Bit aims to have over 2.4 million subscribers on WeChat and more than 7 million users across platforms, with a daily reading volume exceeding 2 million [12]. - The company is recognized as the top new media outlet in the AI and frontier technology sectors according to third-party data platforms [12].

Core Viewpoint - MiniMax has successfully completed its IPO on the Hong Kong Stock Exchange, raising approximately 55.4 billion HKD (around 49.65 billion RMB) with a strong market response, including a 1837 times oversubscription in the public offering and 37 times in the international offering [4][5][45]. Group 1: IPO Details - MiniMax's IPO involved the issuance of approximately 33.58 million shares at a maximum price of 165 HKD per share, with a total fundraising amount of about 55.4 billion HKD [4]. - The stock code "00100" reflects the company's name, where "0" represents "Mini" and "100" corresponds to "Max" in binary, symbolizing the minimum solution that meets the conditions [2]. - The stock experienced significant price increases post-IPO, reaching a peak of 299 HKD per share, representing an over 80% increase [7]. Group 2: Company Background and Strategy - Founded less than four years ago, MiniMax has attracted significant investments from notable institutions, raising over 1.5 billion USD in total [7]. - The company emphasizes "extreme efficiency" and has developed a dual business model targeting both B2B and B2C markets, with a user base exceeding 210 million [17][19]. - MiniMax's strategic focus is on achieving AGI (Artificial General Intelligence) through a full-modal approach, integrating voice, video, and text capabilities [10][22]. Group 3: Technological Advancements - MiniMax has made significant breakthroughs in various AI modalities, including achieving industry-leading performance in real-time speech interaction and video generation [13][14]. - The M2.1 model has excelled in coding tasks and multi-language logical reasoning, enhancing productivity in real-world applications [15]. - The company's full-modal strategy allows it to leverage vast amounts of video and audio data, addressing the "data exhaustion crisis" faced by many AI firms [26]. Group 4: Organizational Efficiency - MiniMax's organizational structure is designed for high efficiency, with over 80% of its code generated by AI, allowing for a significant reduction in marginal costs [33][34]. - The company maintains a flat organizational hierarchy with a youthful workforce, where 73.8% of employees are in R&D roles, averaging 29 years of age [36]. - This innovative structure has enabled MiniMax to achieve a competitive R&D efficiency, spending approximately 500 million USD, which is only 1% of OpenAI's expenditure during the same period [38]. Group 5: Market Position and Future Outlook - MiniMax's successful IPO and the strong interest from institutional investors reflect a market recognition of its technological barriers and engineering efficiency [29][45]. - The company aims to continue its rapid growth over the next four years, emphasizing the importance of attracting top talent to maintain its competitive edge in the evolving AI landscape [46][49]. - The focus on scalability and the ability to convert resources into intelligence will be critical for MiniMax's long-term success in the AGI race [44][50].

Core Viewpoint - The article highlights the rapid advancement of embodied intelligence in household robotics, particularly showcased at CES, indicating a clear trend towards mass production and integration into home environments [1][4]. Group 1: Embodied Intelligence Products - The company Chasing has introduced several "embodied intelligence" products, marking a significant step in the evolution of household robots [3][4]. - The AI-powered laundry robot can autonomously manage the entire laundry process, from picking up clothes to washing and drying, without human intervention [9][11]. - The embodied intelligence lawn mowing robot can also water plants, showcasing advanced spatial awareness and coordination capabilities [19][21]. Group 2: New Product Features - The new "embodied intelligence new species" robot features a four-legged design with arms and sensors, allowing it to perform complex household tasks like folding clothes and delivering items [28][30]. - The Cyber10 Ultra robot can autonomously complete the entire process of grabbing, sorting, and storing items using AI visual recognition [33]. - The Cyber X climbing robot can navigate stairs at a speed of 0.2 meters per second, demonstrating its ability to clean multi-level homes efficiently [35][39]. Group 3: Technological Advancements - The article emphasizes that these robots are evolving from single-task tools to multifunctional physical intelligent agents capable of operating in unpredictable home environments [48][49]. - The transition from traditional cleaning tools to household service robots is driven by data-driven learning and a complete perception-understanding-decision-execution loop [52][54]. - The AI laundry robot exemplifies this evolution by autonomously recognizing and sorting clothes, completing tasks without preset instructions [55][58]. Group 4: Market Position and Strategy - Chasing's approach to embodied intelligence differs from competitors by focusing on practical applications in existing household tasks, rather than pursuing humanoid robot designs [60][67]. - The company has leveraged its engineering experience in consumer robotics to enhance the capabilities of existing products, ensuring a sustainable path for growth [62][68]. - Chasing has rapidly advanced its technology, achieving milestones in just two years that typically take competitors three to four years, positioning itself as a leader in the market [69].

Core Viewpoint - The article emphasizes the critical importance of data quality in embodied intelligence, highlighting that many issues arise from the data generation stage rather than the training phase itself [1][7][30]. Group 1: UMI Overview - Universal Manipulation Interface (UMI) is a framework proposed by Stanford in February 2024, designed to decouple robot bodies from human operation behaviors, integrating "operational intent + motion trajectory + multimodal perception" into a universal interface for various robots [5][8]. - UMI has gained traction since September 2023, with companies like Luming Robotics leading the way in this field [6][8]. Group 2: Data Collection Challenges - The cost of data collection for training is exceptionally high, with estimates of $100-200 per hour in the U.S., requiring vast amounts of data (e.g., 270,000 hours for Generalist's GEN 0) to train models comparable to GPT-3, which could cost hundreds of billions of dollars [19][21]. - Data collection efficiency is low, with remote operation yielding only about 35 data points per hour, leading to issues like data silos due to the unique designs of different robots [21][22]. Group 3: FastUMI Pro Product - Luming Robotics has developed FastUMI Pro, a data collection hardware that is lightweight (over 600 grams) yet capable of handling 2-3 kg objects, suitable for both industrial and domestic applications [10][12]. - FastUMI Pro supports multimodal inputs, including tactile, auditory, and six-dimensional force data, and boasts a spatial precision of 1mm, claimed to be the highest globally [11][12]. Group 4: Data Quality and Training Issues - The article discusses the misconception that UMI data collection is simple, emphasizing that high-quality data must meet strict alignment and synchronization criteria across multiple sensors [34][39]. - Many UMI devices fail to produce usable data due to inadequate hardware capabilities, leading to poor image quality and frame rate issues that disrupt the learning process [43][46]. - The distinction between "dirty data" and "waste data" is made, with waste data being unstructured and lacking design, making it unsuitable for training models [50][59]. Group 5: Systemic Approach to UMI - The article argues that UMI requires a systemic approach where hardware, data, and algorithms are interdependent, and any failure in one area can prevent the successful training of models [63][65]. - Luming Robotics aims to break the "impossible triangle" of high-quality data acquisition at low costs to accelerate the development of the embodied intelligence industry [68].

Core Insights - DeepSeek has released an updated version of its R1 paper, adding 64 pages of technical details, significantly enhancing the original content [2][5][56] - The new version emphasizes the implementation details and training processes of the R1 model, showcasing a systematic approach to its development [10][11][17] Summary by Sections Paper Updates - The updated paper has expanded from 22 pages to 86 pages, providing a wealth of new information that resembles a textbook [3][6] - The revisions include a comprehensive breakdown of the R1 training process, which is divided into four main steps: cold start, inference-guided reinforcement learning, rejection sampling and fine-tuning, and alignment-guided reinforcement learning [13][14][15][16] Model Performance and Safety - The R1 model has shown a significant increase in reasoning capabilities, with a reported 5 to 7 times increase in the occurrence of reflective vocabulary as training progresses [21][22] - DeepSeek has implemented a safety control system that includes a dataset of 106,000 prompts to evaluate and enhance the model's safety, using a point-wise training method for the safety reward model [26][29] - The introduction of the risk control system has led to a notable improvement in the model's safety performance, with R1 achieving benchmark scores comparable to leading models [32][33] Team Stability and Industry Context - The core team behind the R1 paper has remained stable, with 18 key contributors still part of DeepSeek, indicating a low turnover rate in contrast to industry trends [41][47] - The article contrasts DeepSeek's team retention with the challenges faced by other companies in the AI sector, highlighting a more cohesive internal culture [48][49]

ProEdit团队 投稿 量子位 | 公众号 QbitAI 想给照片里的猫换个颜色，结果总是编辑失败？想让视频里的人换件衣服，人脸却糊成一片或完全改变？ 近日，来自中山大学iSEE实验室、香港中文大学MM Lab、新加坡南洋理工大学、香港大学的研究团队发布了最新研究成果 ProEdit 。 该方法通过对注意力机制和初始噪声潜在分布的"精准手术"，实现了超高精度的图像与视频编辑，且完全无需训练、即插即用。 △ 图1. ProEdit在图像和视频编辑上与现有方法的对比 为什么AI编辑总是"改不动"？ 目前，基于反演 （Inversion-based） 的编辑方法 （如RF-Solver、FireFlow） 通常采用全局注入策略： 为了保持背景尽量一致，它们 会将原图的大量信息强行"塞"进生成过程 。 在AI视觉编辑领域，如何在修改目标属性的同时，精准保留背景和非编辑属性的一致性，一直是个"鱼和熊掌"的难题。 但研究团队通过文本与图像的注意力可视化发现，这种做法存在严重的 "源图像信息过度注入" 问题： 注意力过度注入： 现有方法通过全局注入了过多的源图像注意力特征，导致模型更听源图像的话，而忽略了用户的编辑指令 ...

嘻疯 发自 凹非寺 量子位 | 公众号 QbitAI 国产具身智能基座模型，再次突破！ RoboChallenge真机评测榜单上，来自 自变 量机器人的 端到端具身智能基础模型WALL-OSS ，以46.43分的成绩，超越美国具身智能明星 公司Physical Intelligence的pi0 （π0） ， 总分 排名 全球第二 。 | | Beta | Home | Challenges | Runs | Leaderboard | News | Community | Eval Your Policy | Log In | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | | | | | Leaderboard | | | | | | | all tasks | table-30 > | Search by tag v | | Search by task v | | Is multitask > | | Search by model or user | Q | | Rank | Model/User | | Is multi ...

岗位均为全职，工作地点：北京中关村。 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： 岗位面向： 加入我们，你可以获得： 编辑部 发自 凹非寺 量子位 | 公众号 QbitAI 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。 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 ...

当通用能力主要通过大规模预训练获得之后，下一阶段的关键在于让已经具备通用能力的模型，在真实部署环境中持续进化。 这是智元机器人首席科学家 罗剑岚 博士在接受量子位采访时给出的论断。 智元机器人 投稿 量子位 | 公众号 QbitAI 2025年机器人领域最火的VLA让机器人通过预训练具备了相当的通用性，但与此同时，机器人能否长时间，稳定，高效地完成任务仍是一 个问号。 基于此，当机器人走出实验室，走向开放、复杂且持续变化的真实世界时，一个更核心的问题随之出现：如何真正实现通用机器人的规模化 部署与智能化运行。 为此，智元机器人具身研究中心提出 SOP（ScalableOnlinePost-training） ——一套面向真实世界部署的 在线后训练系统 。 这是业界首次在物理世界的VLA后训练中， 系统性地融合在线学习、分布式架构与多任务通才性 ，使机器人集群能够在真实环境中持续进 化，让个体经验在群体中高效复用，从而将"规模"转化为"智能"。 真实世界中的规模化智能增长挑战 要在真实世界中大规模运行，通用机器人必须同时满足两个看似矛盾的要求： 现有VLA预训练模型已经提供了强大的通用性。但 真实世界的部署受困 ...