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百度文心大模型4.5系列正式开源,同步开放API服务
量子位· 2025-06-30 04:39
Core Viewpoint - Baidu has officially announced the open-source release of the Wenxin large model 4.5 series, providing 10 models with varying parameters and capabilities, including API services for developers [2][4]. Group 1: Model Details - The Wenxin large model 4.5 series includes models ranging from a 47 billion parameter mixture of experts (MoE) model to a lightweight 0.3 billion dense model, addressing various text and multimodal task requirements [2][4]. - The open-source models are fully compliant with the Apache 2.0 license, allowing for academic research and industrial applications [3][14]. - The series features an innovative multimodal heterogeneous model structure that enhances multimodal understanding while maintaining or improving text task performance [5][12]. Group 2: Performance Metrics - The models achieved state-of-the-art (SOTA) performance across multiple text and multimodal benchmarks, particularly excelling in instruction following, world knowledge retention, visual understanding, and multimodal reasoning tasks [9][10]. - In the pre-training phase, the model's FLOPs utilization (MFU) reached 47% [7]. - The Wenxin 4.5 series outperformed competitors like DeepSeek-V3 and Qwen3 in various mainstream benchmark evaluations [10][11]. Group 3: Developer Support and Ecosystem - Baidu provides a comprehensive development suite, ERNIEKit, and an efficient deployment suite, FastDeploy, to support developers in utilizing the Wenxin large model 4.5 series [17]. - The models are trained and deployed using the PaddlePaddle deep learning framework, which is compatible with various chips, reducing the barriers for post-training and deployment [6][15]. - Baidu's extensive AI stack, encompassing computing power, frameworks, models, and applications, positions it as a leader in the AI industry [16].
真·全民AI健康管家来了!实测蚂蚁AQ:追问识药看皮肤,还能连医院接硬件
量子位· 2025-06-30 04:39
白交 发自 凹非寺 量子位 | 公众号 QbitAI 终于出现一款真正面向C端的AI医疗产品了。 在DeepSeek掀起全民AI应用浪潮之后,一些专业深度较高的领域也开始AI起来了,但在医疗这个领域大家 还是谨慎得多。 结果这次 蚂蚁 一出手,直接给C端撕开了一个口子。 他们上线了个APP,取名 AQ 。 (这里一定要说个谐音梗,AQ取名的来源是「如果你突然打了个喷嚏AQiu,那一定就是我在想你」,这时 候你就可以问问AQ,有问必有答那种) 。 这个APP光AI功能就有 一百多项 ,包括不限于健康科普、就诊查询、报告解读、健康档案管理等,反正就 是所到之处都是AI,妥妥的AI APP。 与其他通用AI APP不同的是,它还连接着现实,从根本上保障着它的专业性和严谨性—— 全国超5000家医院、近百万医生、近200个名医AI分身都汇聚于此,共同提供全天不间断的医疗服务。 比如,中国工程院皮肤领域专家 廖万清院士团队 ,能够为你的皮肤保驾护航;胸外科领域专家 王俊院士团 队 能够为你平时的胸闷气短症状提供专业解答。 它还打通了不少智能硬件,根据vivo、华为、苹果等可穿戴设备所记录的血糖、睡眠、运动等信息,提供个 ...
紧急加薪+全员放假!OpenAI被连挖8人后,真慌了
量子位· 2025-06-30 00:38
梦晨 发自 凹非寺 量子位 | 公众号 QbitAI 面对Meta疯狂挖人,OpenAI内部的变化出人意料: 本周基本停工,员工放假一周! (高管继续工作) 《连线》杂志获得了OpenAI 首席研究官Mark Chen 向员工发送的全员信,承诺将与Meta正面交锋。 Mark Chen表示他与奥特曼和公司其他高层正在 全天候与收到Meta offer的人沟通 。 OpenAI的反制措施还包括 重新调整薪酬 ,并探索新的方式来认可和奖励顶尖人才,但他同时也强调了一个原则:"虽然我会努力留住你们每 一个人,但 不会以牺牲对其他人的公平为代价 "。 短短几周内, Meta就从OpenAI挖走了至少八名关键研究员 ,Mark Chen表示: 我现在有一种强烈的预感,就像有人闯入我们家偷了东西一样。请相信我们并没有袖手旁观。 每周工作80小时,OpenAI正在改变 在全员信中,Mark Chen承认公司 以前过分沉迷于定期发布产品的节奏,以及与竞争对手的短期比较 。 在这种压力之下,许多员工 每周工作时间长达80小时 。 多位知情人士透露OpenAI将基本停工一周,让员工有时间恢复精力。 已经有员工家属证实了这一消息。 ...
图像界的DeepSeek!12B参数对标GPT-4o,5秒出图,消费级硬件就能玩转编辑生成
量子位· 2025-06-30 00:38
鹭羽 发自 凹非寺 量子位 | 公众号 QbitAI 图像模型开源还得是FLUX! Black Forest Labs刚刚宣布开源旗舰图像模型 FLUX.1 Kontext[dev] ,专为图像编辑打造,还能直接在消费级芯片上运行。 只有小小的 12B ,更少的参数,更快的推理,性能更是媲美 GPT-image-1 等一众闭源模型。 现在FLUX.1 Kontext[dev]可以让小狗迅速离开画面,为小老鼠戴上胡须,添加文字、修改背景也不在话下。 或者多次输入指令, 直到让小哥成为酒吧里最靓的崽(bushi) ,直到让画面符合咱们需求。 具体来说,FLUX.1 Kontext[dev]的主要特点有: 网友们也立马上手试玩,制作了一个旅行的CPU青蛙? 1. 可以根据编辑指令直接更改现有图像,以及进行精确的 本地和全局编辑 。 2. 不用做任何微调,就能 直接引用 里面的人物角色、风格样式和物品元素。 3. 允许用户通过 多次连续编辑 优化图像,同时将视觉漂移降到最低。 4. 专门为NVIDIA Blackwell进行了 权重优化 。 旅行必备的墨镜,还有抗寒的帅气红色毛衣也要准备妥当。 (蛙蛙:出片,我势在必 ...
韩松贾扬清之后,又一家清华系AI公司卖给英伟达,黄仁勋亲自招募95后联创
量子位· 2025-06-29 07:43
梦晨 发自 凹非寺 量子位 | 公众号 QbitAI 与此同时,Nexusflow的CTO Jian Zhang (同样是清华校友)也成为英伟达应用研究总监。 但Nexusflow这家公司到底何去何从还没有正式消息。 贾扬清LeptonAI之后,又一家华人AI创业公司卖身英伟达。 黄仁勋亲自出马招募了Nexusflow的几位联合创始人: △ 左:焦剑涛,右:朱邦华 后续Nexusflow的早期投资者证实,公司已被英伟达收购。 Nexusflow如何搭上英伟达这条线或许有迹可循。 另一位联创UC伯克利教授 Kurt Keutzer 在2025年参与了英伟达与学术界合作的项目 Efficient AI ,致力于开发和优化GPU加速的高效AI 计算。 焦剑涛 ,前Nexusflow CEO,UC伯克利副教授,斯坦福博士毕业,清华2011年本科特奖得主。加入英伟达任研究总监及杰出科学家 (Distinguished Scientist) 朱邦华 ,前Nexusflow联创,将加入华盛顿大学任助理教授,UC伯克利毕业,本科也来自清华。加入英伟达任Principal Research Scientist Kurt Keu ...
黄仁勋首次投资核电,6.5亿美元建首座商业反应堆,预计2030投产
量子位· 2025-06-29 07:43
Core Viewpoint - Nvidia's venture arm NVentures has invested in TerraPower, a nuclear energy company founded by Bill Gates, marking a significant entry into the nuclear sector by Jensen Huang [1][4]. Group 1: Investment and Financing - TerraPower has secured $650 million in funding to construct its first commercial nuclear power plant, the Natrium reactor project, located in Wyoming, USA [2]. - The financing round also included investments from South Korea's Hyundai and Bill Gates himself [4]. Group 2: Technology and Capacity - The Natrium reactor will generate 345 megawatts of power, with a peak output of 500 megawatts, sufficient to power approximately 400,000 homes [3]. - The reactor employs sodium-cooled fast reactor (SFR) technology, which uses liquid sodium as a coolant, enhancing safety and simplifying design compared to traditional water-cooled reactors [13]. - The Natrium system integrates a gigawatt-level molten salt energy storage system, allowing for flexible power output adjustments to meet grid demands [15][17]. Group 3: Future Developments - TerraPower is also developing molten chloride fast reactor (MCFR) technology, which operates at higher temperatures, potentially increasing efficiency and providing process heat for industrial applications [19]. - Both SFR and MCFR technologies are part of the fourth generation of nuclear energy systems, which include various innovative approaches to nuclear power generation [21]. Group 4: Industry Trends - The surge in nuclear investment is driven by the increasing electricity demand from AI data centers, with notable figures like Sam Altman actively investing in nuclear energy companies [37][38]. - Other tech giants, such as Amazon and Google, are also making significant investments in nuclear energy, indicating a broader trend within the industry [46]. Group 5: Additional Applications - TerraPower is not solely focused on nuclear power; it is also exploring the use of nuclear technology for cancer treatment, specifically through the development of Ac-225 for targeted alpha therapy [48][49].
AI一眼认出95万物种,还能分辨雄雌老幼,2亿生物图像炼成“生命视觉”大模型
量子位· 2025-06-29 05:34
Core Viewpoint - The BioCLIP 2 model, trained on 2 billion biological images, demonstrates superior species recognition performance and emergent biological understanding beyond species classification, achieving significant advancements in ecological alignment and intra-species differentiation [1][2][5]. Group 1: Model Development and Data Collection - The research team collected 214 million biological images from four major platforms, creating the TreeOfLife-200M dataset, which includes 952,000 different classification labels, making it the largest and most diverse biological image library to date [2][4]. - The model was scaled from ViT-B to ViT-L, increasing the parameter count to facilitate the emergence of new knowledge [4]. Group 2: Performance Metrics - BioCLIP 2 achieved an average accuracy of 55.6% in zero-shot species recognition, outperforming the second-best SigLIP model by 16.1% [5]. - In non-species visual tasks, BioCLIP 2 surpassed common visual models like SigLIP and DINOv2 in habitat recognition, biological attribute identification, new species discovery, and plant disease recognition [8]. Group 3: Emergent Properties - Two emergent properties were identified: 1. Ecological alignment among species with similar lifestyles and ecological significance clustered in feature space, with clearer boundaries as training scale increased [10][11]. 2. Intra-species differentiation, where differences among male, female, and juvenile forms of the same species are distributed orthogonally to inter-species differences, improving with larger training scales [12][14]. Group 4: Training Scale Impact - Experiments showed that increasing training data from 1M to 214M consistently improved performance in non-species visual tasks and enhanced the orthogonality of intra-species differentiation [15].
猫猫睡觉睡上顶刊:三分之二的家猫都倾向于向左睡
量子位· 2025-06-29 05:34
不圆 发自 凹非寺 量子位 | 公众号 QbitAI 三分之二的家猫都更倾向于向左睡。 这是来自最新刊登在Current Biology的研究结论,论文标题就是:家猫的侧睡姿势。 此前的研究显示,脊椎动物和无脊椎动物都表现出多种大脑和行为上的左右不对称性,就像是人类大部分是右撇子,小猫小狗一般也会有一个 偏好的爪子。而大脑的右半球擅长处理与威胁相关的刺激,使左视觉场在应对从左侧接近的捕食者时具有优势。 所以 为了保持警觉,猫猫会更倾向于向左睡 。 这种研究究竟有什么用? 是猫奴都不会这么问,直接就下定论了——看看这位日本网友Takaya Suzuki:这简直是人类历史上最幸福的研究了。 为了防止干扰,在筛选视频时仅选择了原始未修改的视频,低分辨率、模糊、重复或修改(例如镜像/自拍)的视频被排除。 这些经认证的高清沉浸式猫猫睡觉视频链接也被论文的作者放入了附录。 (下班以后偷偷去看,嘻嘻) 对视频中猫猫的侧睡方向进行统计性分析,结果显示,猫猫在群体层面上存在 统计学上显著 的向左偏移(χ² = 37.7,自由度 df = 1,p < 0.001)。 其中有266只猫(65.1%)显示向左睡眠姿势,142只猫向右 ...
华为CloudMatrix重磅论文披露AI数据中心新范式,推理效率超NV H100
量子位· 2025-06-29 05:34
Core Viewpoint - The article discusses the advancements in AI data center architecture, particularly focusing on Huawei's CloudMatrix384, which aims to address the limitations of traditional AI clusters by providing a more efficient, flexible, and scalable solution for AI computing needs [5][12][49]. Group 1: AI Computing Demand and Challenges - Major tech companies are significantly increasing their investments in GPU resources to enhance AI capabilities, with examples like Elon Musk's plan to expand his supercomputer by tenfold and Meta's $10 billion investment in a new data center [1]. - Traditional AI clusters face challenges such as communication bottlenecks, memory fragmentation, and fluctuating resource utilization, which hinder the full potential of GPUs [3][4][10]. - The need for a new architecture arises from the inability of existing systems to meet the growing computational demands of large-scale AI models [10][11]. Group 2: Huawei's CloudMatrix384 Architecture - Huawei's CloudMatrix384 represents a shift from simply stacking GPUs to a more integrated architecture that allows for high-bandwidth, peer-to-peer communication and fine-grained resource decoupling [5][7][14]. - The architecture integrates 384 NPUs and 192 CPUs into a single super node, enabling unified resource management and efficient data transfer through a high-speed, low-latency network [14][24]. - CloudMatrix384 achieves impressive performance metrics, such as a throughput of 6688 tokens/s/NPU during pre-fill and 1943 tokens/s/NPU during decoding, surpassing NVIDIA's H100/H800 [7][28]. Group 3: Innovations and Technical Advantages - The architecture employs a peer-to-peer communication model that eliminates the need for a central CPU to manage data transfers, significantly reducing communication overhead [18][20]. - The UB network design ensures constant bandwidth between any two NPUs/CPUs, providing 392GB/s of unidirectional bandwidth, which enhances data transfer speed and stability [23][24]. - Software innovations, such as global memory pooling and automated resource management, further enhance the efficiency and flexibility of the CloudMatrix384 system [29][42]. Group 4: Cloud-Native Infrastructure - CloudMatrix384 is designed with a cloud-native approach, allowing users to deploy AI applications without needing to manage hardware intricacies, thus lowering the barrier to entry for AI adoption [30][31]. - The infrastructure software stack includes modules for resource allocation, network communication, and application deployment, streamlining the process for users [33][40]. - The system supports dynamic scaling of resources based on workload demands, enabling efficient utilization of computing power [45][51]. Group 5: Future Directions and Industry Impact - The architecture aims to redefine AI infrastructure by breaking the traditional constraints of power, latency, and cost, making high-performance AI solutions more accessible [47][49]. - Future developments may include expanding node sizes and further decoupling resources to enhance scalability and efficiency [60][64]. - CloudMatrix384 exemplifies a competitive edge for domestic cloud solutions in terms of performance and cost-effectiveness, providing a viable path for AI implementation in Chinese enterprises [56][53].
OpenAI华人AI大牛集体跳槽Meta!清华北大浙大中科大校友各一位,多模态后训练、感知团队负责人全走了
量子位· 2025-06-29 01:43
Core Viewpoint - Meta has successfully recruited four top AI researchers from OpenAI, highlighting a growing talent war in the AI industry, particularly involving Chinese researchers [1][2][3]. Group 1: Talent Acquisition - Meta has hired at least eight top AI researchers from OpenAI in three separate waves since forming its super AI team [3]. - The recent recruitment includes four prominent researchers: Zhao Shengjia from Tsinghua University, Ren Hongyu from Peking University, Yu Jiahui from the University of Science and Technology of China, and Bi Shuchao from Zhejiang University [6][13]. - OpenAI is reportedly increasing salaries to retain talent, indicating rising costs in this talent acquisition battle [4]. Group 2: Researcher Backgrounds - Zhao Shengjia joined OpenAI in June 2022 after completing his PhD at Stanford and was involved in the training of ChatGPT [7]. - Ren Hongyu, also a Stanford PhD graduate, contributed to the development of GPT-4o and led a post-training team [9][10]. - Yu Jiahui was the perception team lead at OpenAI and previously co-led the Gemini multimodal vision project at Google [16][18]. - Bi Shuchao served as an engineering director at Google and was a co-founder of YouTube Shorts, now overseeing multimodal post-training at OpenAI [20][21]. Group 3: Industry Dynamics - The competition for top AI talent is not just a corporate rivalry but represents a broader conflict between open-source and closed-source AI development [26]. - Meta's aggressive recruitment strategy has prompted OpenAI's leadership to reassess their talent retention strategies [23][24]. - The influx of talent from OpenAI to Meta may accelerate the development of open-source models like Llama, which focus on multimodal deep thinking [28].