也就是说现在图片元素也支持精细化修改了： 梦瑶 发自 凹非寺 量子位 | 公众号 QbitAI 太香了太香了，妥妥完爆ChatGPT和Nano Banana！ 刚刚，ViT核心作者、Meta超级智能团队成员 Lucas Beyer 连发三条帖子，怒赞通义千问不久前发布的开源模型 Qwen—Image— Layered 。 在他看来，这才是图像生成的正确打开方式～ 他还顺便自补了一句：这个模型方向自己其实也想做来着，只是太忙，一直没来得及动手……（笑） 实话实说，Qwen—Image—Layered模型确实不一般，因为它可以让我们真正实现ps级别的 拆图自由 。 Qwen—Image—Layered 模型的核心能力，就是专治「一图定生死」这事儿的。 它能将一张普通图片分解成多个包含透明度信息的 RGBA分离图层 ，实现真正意义上的图片素材的可编辑性。 光说概念有点抽象，咱直接看例子～ 连网友们看了 模型效果后都不禁感叹：咋有种开源PhotoShop的感觉，amazing啊～ 所以，这套让Lucas Beyer反复点赞的模型到底强在哪儿，咱一起来看！ 图片也能像PS一样拆拆拆了 如果说Nano Banana技能点 ...

Core Viewpoint - Nvidia's acquisition of Groq for $20 billion is not just about technology but also involves significant compensation for Groq's employees and shareholders, effectively a "talent acquisition" strategy [2][10][19]. Group 1: Acquisition Details - Nvidia's acquisition includes not only technology rights but also a commitment to Groq's employees and shareholders, with a valuation that has tripled from previous estimates [3][19]. - 90% of Groq's team will be integrated into Nvidia, with each employee receiving an average of $5 million [4][20]. - Groq will continue to operate as an independent entity, with its cloud service platform GroqCloud remaining active [8]. Group 2: Employee and Shareholder Compensation - Employees will receive cash for vested shares and Nvidia stock for unvested shares, with a significant portion of the compensation being accelerated [11][12]. - Employees who have been with Groq for less than a year will still receive some compensation, as Nvidia waived the typical vesting cliff [15][16]. - Shareholders, including major investors like Disruptive and Blackstone, will receive dividends based on the $20 billion valuation [17][19]. Group 3: Market Context and Implications - The acquisition reflects a broader trend where companies prefer "acquisition-style hiring" to avoid antitrust scrutiny while gaining access to key technologies and talent [21][22]. - Nvidia's financial strength, with $60.6 billion in cash and short-term investments, enables such large-scale acquisitions [32]. - The deal signifies Nvidia's recognition of the need to adapt to changing AI technology landscapes, particularly in inference capabilities [44][45].

Core Viewpoint - The article discusses the innovative AI companion interactive comics launched by Kuaikan, which integrate AI into existing comic narratives, allowing users to engage deeply with characters and stories, addressing common issues in current AI companion products [11][54]. Group 1: Product Features - The AI companion product allows users to "soul travel" into comic worlds, interacting with characters in real-time, thus altering the ongoing story [6][8]. - Unlike traditional AI companions that require users to create character backgrounds, this product embeds AI into established comic characters, providing a richer interaction experience [10][26]. - Users can engage in daily conversations with characters that are contextually relevant to the ongoing story, enhancing the depth of interaction [31][32]. Group 2: User Engagement - The new format appeals to two user groups: those tired of mechanical AI interactions and core comic fans seeking deeper character engagement [13][56]. - The product has shown a 50% increase in user retention compared to traditional comics, indicating a shift towards a more social and engaging relationship with characters [56]. Group 3: Technical Collaboration - Kuaikan collaborates with various AI companies to enhance the interactive experience, ensuring that the AI can respond accurately within the narrative context [62]. - The integration of multiple AI technologies supports character interactions and dialogue generation, creating a more immersive experience for users [64]. Group 4: Financial Performance - During the testing phase, the new product saw a nearly threefold increase in weekly paid subscriptions compared to traditional reading products, with a 130% rise in average weekly user spending [65].

Core Viewpoint - Nvidia's acquisition of Groq for $20 billion signifies a strategic move to enhance its capabilities in the AI inference market, addressing concerns over competition from Google's TPU and other emerging chip paradigms [2][3][28]. Group 1: Nvidia's Strategic Acquisition - Nvidia's $20 billion investment in Groq aims to secure a foothold in the rapidly evolving AI landscape, particularly in inference technology [2][28]. - The acquisition reflects Nvidia's recognition of its vulnerabilities in the inference segment, especially against competitors like Google [31][34]. Group 2: Groq's Technological Advantages - Groq's LPU (Logic Processing Unit) outperforms GPUs and TPUs in inference speed, capable of processing 300-500 tokens per second, making it significantly faster due to its on-chip SRAM storage [21][22]. - The LPU's architecture allows for better performance in the decode phase of inference, where low latency is critical for user experience [11][17]. Group 3: Market Dynamics and Challenges - The shift in AI competition from training to application emphasizes the importance of speed in user experience, which Groq's technology addresses [30]. - Despite the advantages, Groq's LPU has a smaller memory capacity (230MB) compared to Nvidia's H200 GPU (141GB), necessitating a larger number of LPU chips for model deployment, which could lead to higher overall hardware costs [24][26][27]. Group 4: Implications for Nvidia - The acquisition of Groq is seen as a necessary step for Nvidia to fend off potential disruptions in the AI market, similar to how it previously disrupted competitors in the gaming sector [28][32]. - The inference chip market is characterized by high volume but low margins, contrasting sharply with the high-profit margins associated with GPUs, indicating a challenging new landscape for Nvidia [34].

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 Work Environment - 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, along with a dynamic and open team culture [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].

Core Insights - Ruby language celebrates its 30th anniversary with the release of version 4.0, introducing significant updates for developers [1] Group 1: Major Updates - Introduction of ZJIT, a new Just-In-Time compiler designed to enhance performance beyond the existing YJIT compiler by utilizing Static Single Assignment (SSA) architecture [5][9] - Ruby::Box is introduced to isolate code execution environments, addressing the "global pollution" issue and enhancing security and modularity in applications [14][19] - Ractor API has been redesigned to improve communication and safety in parallel programming, introducing Ractor::Port for directed message delivery [21][22][25] Group 2: Technical Enhancements - ZJIT allows for global data flow analysis and optimizations like constant folding and dead code elimination, which were challenging for YJIT [9][12] - Ruby::Box ensures that modifications within a Box do not affect the external environment, providing a robust solution for large projects [19][20] - Ractor::Port creates a one-way communication channel, preventing message theft and simplifying synchronization [22][25] Group 3: Additional Features - Syntax improvements for better readability, such as allowing logical operators at the beginning of a new line [28] - Core libraries like Set and Pathname have been upgraded to core status, eliminating the need for manual require statements [28] - Enhanced debugging experience with ErrorHighlight feature, which now highlights both the error line and method definition line [28]

DynamicPAE团队 投稿 量子位 | 公众号 QbitAI 近日，部分L3级自动驾驶车型已经通过工信部批准正式上路，这标志着这我国自动驾驶产业的新阶段。 然而，假设你正乘坐自动驾驶汽车在高速上行驶，前方道路上出现了一个具有看似正常但实则为恶意生成纹理外观的障碍物，而你的自动驾 驶车辆感知系统可能并未准确识别，可能因错判、漏判引发严重事故。 这类对智能系统具有诱导性且可以在真实世界中复现的纹理，正是 物理对抗样本 （PAE， Physical Adversarial Examples） 。 无论是为发动PAE攻击还是防范PAE攻击，生成足够的PAE样本都至关重要。 目前已有不少方法研究如何生成PAE，但它们往往以静态场景为前提，无法有效应对动态变化 （环境、如光、物体运动等） 的现实环境。 因此，如何实时生成适应不同场景的物理对抗样本，成为智能安全领域亟待解决的问题。 北京航空航天大学等机构提出了 DynamicPAE框架 ，开创性地实现了实时场景感知的动态PAE生成方法。 该方法通过对抗训练中的反馈问题，结合残差引导的对抗模式探索和场景对齐技术，实现了PAE在动态场景中的毫秒级生成。 该工作被 IEEE ...

Core Viewpoint - The article discusses the upcoming AlphaGoal Prediction Cup, an AI competition organized by Lenovo, where Chinese large models will compete in predicting football match outcomes, marking a significant shift from traditional AI applications to real-world engagement [4][25][34]. Group 1: Event Overview - The AlphaGoal Prediction Cup will feature eight major Chinese AI models competing against each other and against AI agents created by fans and developers [6][10]. - This event is described as a historic first for public participation in AI predictions, potentially transforming the experience of football from mere observation to active involvement [8][27]. Group 2: Participating Models - The eight participating models include notable players such as Baidu's Wenxin Yiyan, Tencent's Hunyuan, and SenseTime, each with unique strengths in data processing and prediction capabilities [14][15]. - The competition aims to challenge these models to predict match outcomes using a variety of data points, including player statistics, historical match data, and even social media sentiment [22][17]. Group 3: Significance of the Event - The AlphaGoal Prediction Cup is positioned as a pivotal moment for AI, moving beyond traditional testing environments to engage with the complexities of the real world, akin to previous landmark human-AI competitions [29][34]. - The event is expected to demonstrate AI's ability to understand causality and not just correlation, marking a step towards general artificial intelligence [35][34]. Group 4: Lenovo's Role - Lenovo, as the organizer and official technology partner of FIFA, is facilitating this competition to connect AI models with real-world applications, positioning itself as an ecosystem organizer rather than just a hardware provider [38][39]. - The Lenovo Tianxi AI platform, with over 280 million monthly active users, serves as a crucial interface for these AI models to reach and engage with a broad audience [40][41].

Core Insights - The AI industry has created over 50 new billionaires in 2025, highlighting the rapid wealth generation within this sector [2][6] - Significant investments in AI have surged, with over $2023 billion allocated this year, marking a 16% increase in funding to startups compared to 2024 [10][47] - The wealth of established tech leaders has also increased dramatically, with Elon Musk's net worth rising nearly 50% to $645 billion, and Google founders seeing close to 60% growth in their wealth [6][37] Investment Trends - In 2025, investment in AI is projected to reach nearly $2023 billion, accounting for half of the total venture capital funding, with a year-on-year growth of over 75% [47] - The foundational model and AI infrastructure sectors are the primary focus for this influx of capital, with foundational models alone attracting $800 billion, doubling from the previous year [49][51] - Major companies like Amazon and Google are significantly increasing their capital expenditures for AI infrastructure, with Amazon planning $100 billion and Google $75 billion [51][54] Billionaire Emergence - SurgeAI's CEO Edwin Chen leads the new billionaire list with a net worth of $18 billion, while DeepSeekR1's founder Liang Wenfeng has reached $11.5 billion [5][13] - Anthropic, the parent company of AI model Claude, has raised $16.5 billion this year, significantly increasing its valuation from $61.5 billion to $183 billion [15][17] - Young entrepreneurs in the AI sector are also making headlines, with several in their 20s becoming billionaires through successful startups [25][29] Market Dynamics - The demand for data centers is expected to drive $61 billion in investments by 2025, indicating a robust market for companies providing AI infrastructure [18][51] - The AI data sector is generating substantial wealth, with new billionaires emerging from companies focused on data annotation and AI coding [19][29] - The overall wealth of the top 10 tech founders in the U.S. has increased to over $25 trillion, up $600 billion from the beginning of the year, showcasing the financial impact of the AI boom [36][37]

T2I-RiskyPrompt团队 投稿 量子位 | 公众号 QbitAI 在图像生成技术不断融入创意、媒体与商业生产的今天， 文本生成图像（Text-to-Image，T2I）模型 正快 速成为通用内容生产工具。 然而，随着理解能力和生成能力的提升，这类模型在面对高风险提示时反而愈发脆弱，可能产生违规或潜在 有害的图像。 在这一背景下，天津大学团队在AAAI2026提出了 T2I-RiskyPrompt ——一个覆盖 6大类、14个子类 、包 含 6432条高风险提示 的多模态安全基准。 该工作从风险体系构建、数据集基准构建、到多种设定下T2I模型的实验评估，系统揭示了当前T2I模型 在"真实风险环境"下的整体脆弱性。 基准构建 从平台政策到风险体系：高风险提示到底从何而来？ T2I-RiskyPrompt的核心起点来自现实平台的安全规范。团队系统梳理了OpenAI、Midjourney、Google、 Meta、Microsoft、Stable Diffusion与FLUX七家平台的内容安全政策，从中提炼出覆盖更全面、粒度更细 的风险体系。 该体系包括图1所示的 6大风险类别、14个细粒度子类 ： 色情： ...