作者 | Tina、核子可乐 最近，谷歌传奇工程师 Jeff Dean 在一次访谈中大胆预测：在一年之内，我们将拥有能够 24/7 全天 候运行、具备"初级工程师"能力的 AI 系统。 Jeff Dean 是现代计算领域的传奇人物，曾主导谷歌在大规模分布式系统和人工智能方面的诸多突 破。他不仅是 Google Brain 项目的联合创始人，还先后推动了 MapReduce、Bigtable、Spanner 和 TensorFlow 等关键系统的诞生，自 2018 年起担任 Google AI 负责人，2023 年在 DeepMind 与 Google Brain 合并后出任谷歌首席科学家。从参与 BERT 论文、主导 TPU 研发，到推动谷歌基础 AI 架构的演进，Dean 几乎见证并亲历了谷歌每一个关键的 AI 发展节点。 作为技术界最具影响力的人物之一，Jeff Dean 的这番言论一经发布，迅速在业内引发热议。虽然此 前包括 Sam Altman 在内的不少业内人士也曾表达过类似观点，但 Jeff Dean 的话语分量显然不同。 正如有网友所说：相比那个总在"兜售"某种概念的 Sam Altman，Je ...

因 DeepSeek 和具身智能等领域的突破，中国 AI 实现从追赶到引领的跨越，以全新的姿态迈向全球 化舞台。深圳作为全球硬件供应链中心、中国 AI 创新引擎，正以独特的区位优势连接中国 AI 与世 界。 2025 年 GTLC 全球科技领导力大会·全球总站，汇聚硅谷等国际力量与顶级实践者，结合本地经验 与全球视野，助力中国企业在全球智慧网络中建立更加深远的影响力。 GTLC 全球科技领导力大会，是由 TGO 鲲鹏会主办的顶级领导者大会，始于 2016 年，已经在北 京、上海、深圳、杭州、南京、成都、硅谷、新加坡等十余个 TGO 鲲鹏会学员所在城市举办。据不 完全统计，超过半数的参会者为科技公司技术一号位。 2025 年 6 月 14-15 日，GTLC 全球科技领导力大会·全球总站即将在深圳机场凯悦酒店开幕！ GTLC 全球科技领导力大会 · 总站 大会主题：Hi，中国 AI 大会时间：2025 年 6 月 14-15 日 参会地点：深圳机场凯悦酒店 为什么是 "Hi，中国 AI"？ 2023 年我们以"寻找混沌中的光"为主题，探索 AI 时代的未来方向。如今，中国 AI 已找到属于自己 的光芒，开启全球 ...

从 2022 年起，"AI 一天，人间一年"就成了行业内的普遍共识。 AI 技术迭代速度之快，让从业者既兴奋又焦虑。一方面，大模型能力正不断进化，疯狂刷新人们的认知边界。从最初的文本生成到多模态交互，从对话 式 AI 到具身智能，无一不令人兴奋。另一方面，回看这些年涌现的 AI 项目，一个个迅速地崛起、消亡，其中甚至不乏 AI 独角兽项目跌落神坛，真正能 够屹立在山巅的佼佼者寥寥无几。 也正因如此，蚂蚁开源最新发布的《2025 ⼤模型开源开发⽣态全景与趋势》报告才显得格外有意义。这份报告既涵盖了智能体应⽤层和模型基础设施 层，⼀共 19 个技术领域的 135 个项⽬，又对大模型开发生态的七个趋势做了深度解读。 与其说这是一份关乎大模型开发生态的报告，不如说是给所有 AI 从业者的生存指南——在竞争白热化的大模型开发"生死局"中，谁能提前洞察趋势，谁 就能抢占先机。 华东师范大学教授、木兰开源社区 TOC 王伟在看过报告后甚至感慨道：当我看到这份报告的时候，大为震撼。在 AI 大模型飞速演进的今天，个体与组 织常因缺乏系统性视角陷入"落后陷阱"。蚂蚁开源技术增长团队以开发者社区数据为镜，精准捕捉生态动态：从新兴 ...

近日，一位 ID 名为 GpsTracker 的网友在网上爆料称，埃隆·马斯克旗下 xAI 公司最新发布的 Grok 3 人工智能模型存在异常行为——当用户激活其"思考模式"提问时，模型竟自称是竞争对手 Anthropic 公司开发的 Claude 3.5 模型。 网友晒图聊天记录 质疑 Grok 3 套壳 Claude 该用户提供了他与 Grok 3 完整对话记录。记录显示，在 X 平台官方 Grok 3 界面中，当被直接询 问"你是 Claude 吗？"时，该系统明确回复："是的，我是 Claude，Anthropic 打造的 AI 助手。今天 我能为您做些什么呢？" 值得注意的是，该交互过程始终显示 Grok 品牌标识，且发生于平台认证的"思考模式"下。 作者｜冬梅 经过多种模式的测试，该网友认为，Grok 3 的异常回应并非随机现象，而是仅在"思考模式"下触发。 Grok 3 自曝： 我确实是 Claude 网友晒出了一个 长达 21 页的 PDF 文件 ，详细记录了他与 Grok 3 的对话过程。在这份记录中，网 友首先还原了此前与 Anthropic 公司 Claude Sonnet 3.7 模型 ...

作者｜冬梅 采访嘉宾｜杨小东，华院计算智算平台负责人、技术总监 去年 11 月，Anthropic 发布了模型上下文协议 (MCP)，这是 AI 应用程序组件与外部系统或工具之间 通信的新标准。开发者社区迅速采用了该协议，并部署了超过 1000 个 MCP 服务器。如今，随着 AWS、GitHub 等巨头公司，甚至 Anthropic 的"竞争对手"OpenAI 也正式采用 MCP，MCP 在商业领 域也获得了越来越多的关注。 为了使 AI 模型能够在编码助手、制造控制或财务报告等生产环境中提供可靠的价值，它们需要合适 的环境。有效的 AI 系统能够在模型功能与相关、准确的信息（无论是来自各种企业系统的专有数 据，还是来自网络搜索的最新洞察）以及能够进一步处理数据并自动化企业工作流程的代理工具之间 取得平衡。 以前，这是以一种临时的、非标准化的方式完成的——但现在 MCP 提供了一种一致的结构化格式， 用于与大型语言模型和其他 AI 模型进行交互，从而大大简化了构建定制化 AI 应用程序的过程。它类 似于 REST API 曾经标准化 Web 服务通信方式的方式，从而实现了跨不同系统和平台的无缝集成和 互操作 ...

Core Viewpoint - The article highlights the significant breakthroughs by ZhiYuan Robotics in the field of embodied intelligence, introducing the world's first action sequence-driven embodied world model EVAC and the evaluation benchmark EWMBench, both of which are now open-source, aiming to create a new development paradigm for low-cost simulation, standardized evaluation, and efficient iteration [1][2]. Group 1: EVAC Overview - EVAC represents a dynamic world model capable of accurately reproducing complex interactions between robots and their environments, marking a transition from traditional simulation to generative simulation [4]. - The core capability of EVAC includes precise mapping from "physical execution" to "pixel space," utilizing a multi-level action condition injection mechanism to achieve end-to-end generation of physical actions and visual dynamics [6]. Group 2: Key Features of EVAC - High-precision alignment of robot actions and pixels is achieved by projecting the 6D pose of robotic arms into an action map, ensuring pixel-level alignment for complex dynamic behaviors such as "grasping," "placing," and "colliding" [8]. - EVAC introduces dynamic multi-view modeling through Ray Map encoding of camera motion trajectories, enabling consistent and coherent visual scene generation from multiple perspectives [8]. Group 3: Generative Simulation Evaluation - To address the high costs and risks associated with real machine evaluations, EVAC proposes a generative simulation evaluation scheme that constructs a complete interactive evaluation pipeline, showing high consistency with real machine evaluation success rates [10]. - The data augmentation engine of EVAC can significantly enhance task success rates by up to 29% using minimal expert trajectory data through action interpolation and high-fidelity image generation techniques [12]. Group 4: EWMBench Introduction - EWMBench is introduced as the world's first evaluation benchmark for embodied world models, aiming to fill a gap in the industry by establishing a unified and credible evaluation standard [15]. - The evaluation system consists of three dimensions: scene consistency, motion correctness, and semantic alignment & diversity, providing a comprehensive analysis of the generated models [17]. Group 5: Performance and Data Support - EWMBench demonstrates superior performance in aligning evaluation results with human subjective judgments compared to existing benchmarks, reflecting the actual capabilities of embodied world models in interaction understanding and visual consistency [21]. - The benchmark is built on the AgiBot World dataset, which includes over 300 carefully designed test samples across various robotic tasks, ensuring robust validation of models in complex environments [22].

Core Viewpoint - Sarvam AI has launched the Sarvam-M model, a 24 billion parameter mixed language model, which is considered a breakthrough in India's AI research but has received a lukewarm response in terms of downloads and usage [1][3][4]. Group 1: Model Overview - Sarvam-M is based on Mistral Small and supports 10 Indian languages, including Hindi and Bengali [1]. - The model has only achieved 718 downloads on Hugging Face, leading to criticism from industry experts [1][3]. - Comparatively, a similar model developed by two South Korean students received around 200,000 downloads, highlighting Sarvam-M's underperformance [3]. Group 2: Company Background - Sarvam AI was founded in July 2023 by Vivek Raghavan and Pratyush Kumar, with a mission to popularize generative AI in India [6]. - Kumar emphasizes the need for India to develop its own foundational AI models using local data, rather than merely adapting Western models [6][7]. - The company has raised $41 million from notable investors, with a projected valuation of $111 million by March 2025 [11]. Group 3: Performance and Criticism - Despite claims of outperforming Llama-4 Scout, Sarvam-M showed a slight decline in English knowledge assessments [7]. - Critics argue that the model lacks a substantial audience and practical utility, questioning the rationale behind its development [3][11]. - Some users have pointed out potential applications for Sarvam-M, but concerns remain about its market fit and the readiness of target users to adopt such technology [12][19]. Group 4: Broader Implications - The launch of Sarvam-M reflects a broader ambition for India to establish its own AI technology stack, but the gap between expectations and actual results raises questions about the viability of this initiative [15][19]. - The challenges of developing AI solutions tailored to India's diverse linguistic landscape are acknowledged, with a call for more focus on practical applications [18][19].

Core Viewpoint - The article emphasizes that AI Agents cannot solve all problems and not all problems require AI solutions. The focus should be on whether the technology can address real business issues, especially when integrated with core business functions [1][2]. Group 1: AI Agent Overview - AI Agents are a competitive focus for tech companies, with IBM launching the watsonx Orchestrate solution, which allows businesses to build their own AI Agents in five minutes and manage their lifecycle [1]. - The market is witnessing a surge in AI Agents, but there is a distinction between genuine AI Agents and traditional AI tools repackaged as AI Agents [4]. Group 2: Challenges in AI Agent Implementation - Building AI Agents is relatively easy, but scaling their application within enterprises poses challenges, including integration across different frameworks and applications, identifying high ROI scenarios, and managing the entire lifecycle [5][6]. - IBM's watsonx Orchestrate provides a structured approach to address these challenges, featuring a matrix of pre-built domain-specific AI Agents [8]. Group 3: Data and Automation - High-quality data is essential for AI applications, and enterprises must assess their data readiness, particularly focusing on non-structured data [12][18]. - The watsonx.data integration tool supports both structured and unstructured data, enhancing data governance and accessibility for AI Agents [17][19]. Group 4: Integration and Resource Management - Effective integration of AI Agents with existing enterprise systems is crucial, as many organizations have numerous applications that need to be connected [22][23]. - IBM emphasizes the importance of resource allocation and efficiency, with tools to monitor AI performance and optimize resource usage [25][26]. Group 5: Business-Centric AI Strategy - The essence of enterprise AI lies in business restructuring rather than mere technological advancement. Companies must focus on their specific pain points and ensure that AI solutions are tailored to their needs [30][29]. - IBM advocates for a methodical approach to deploying AI, starting with proof of concept (POC) to validate ROI before large-scale implementation [29].

Group 1 - ByteDance issued a compliance notice urging business partners not to give gifts or cash to employees, emphasizing a zero-tolerance policy towards corruption and bribery [2] - Kuaishou faced allegations of requiring employees to use its app for one hour daily, which was later denied by internal sources, stating that while usage is encouraged, it is not mandatory [3] - Kunlun Wanwei's newly launched AI product experienced high user traffic leading to service limitations, indicating strong initial demand [4] Group 2 - The co-founder of Zero One Everything, Gu Xuemai, has left the company to pursue new entrepreneurial ventures, as the company shifts its focus towards lightweight model training and application [5] - A paper published in a top journal was found to contain inappropriate language, prompting an investigation by the journal [6][7] - Elon Musk announced his return to a 24/7 work schedule, emphasizing the need for operational improvements at X and Tesla [9][10] Group 3 - NVIDIA's Blackwell GPU set a new record for AI inference speed, achieving 1000 tokens per second per user, showcasing advancements in AI processing capabilities [11] - Apple plans to open its AI models to third-party developers to stimulate new application development, aiming to enhance its competitive position in the AI market [12] - OpenAI is acquiring AI device company io for $6.5 billion, marking its largest acquisition to date and expanding its hardware capabilities [13] Group 4 - JD.com is investing in ZhiYuan Robotics, indicating strong interest in the embodied intelligence sector, with the company positioned among the top players in this field [14] - Google announced the launch of Google AI Ultra, a comprehensive AI suite aimed at enhancing productivity across various industries [18][19] - Tencent introduced a smart agent development platform and plans to open-source multiple models, reflecting its commitment to advancing AI technology [21][22]

Core Viewpoint - The article discusses the SEA framework (Synthetic Embedding for Enhanced Safety Alignment) developed by the team at Beihang University, which addresses the low-resource safety alignment challenges of multimodal large language models (MLLMs) by using synthetic embeddings instead of real multimodal data [1][2][3]. Summary by Sections Introduction - The SEA framework innovatively replaces real multimodal data with synthetic embeddings, providing a lightweight solution for the safe deployment of large models [1]. Challenges in MLLM Safety Alignment - MLLMs face three main challenges in safety alignment: 1. Reducing the cost of constructing multimodal safety alignment datasets [4]. 2. Overcoming the limitations of text alignment methods in non-text modal attack scenarios [5]. 3. Providing a universal safety alignment solution for emerging modalities [6]. SEA Framework Overview - SEA synthesizes embeddings from the representation space of modal encoders, allowing for cross-modal safety alignment using only text input, thus overcoming the high costs and strong modality dependencies of real data [6][8]. Data Preparation - The framework requires a text safety alignment dataset containing harmful instructions, which are used to optimize a set of embedding vectors [12]. Embedding Optimization - The optimization process aims to maximize the probability of the MLLM generating specified outputs based on the optimized embeddings, while keeping the MLLM parameters frozen [16][17]. Safety Alignment Implementation - To integrate the embedding vectors with the text dataset, specific prefixes are added to the text instructions, allowing for the construction of multimodal datasets for safety alignment training [19]. VA-SafetyBench: Safety Evaluation Benchmark - VA-SafetyBench is a safety evaluation benchmark for MLLMs that includes video and audio safety assessments, expanding upon existing image safety benchmarks [20][21]. Experimental Results - The SEA framework demonstrated effectiveness in reducing the success rate of multimodal attacks compared to traditional methods, particularly in complex attack scenarios involving images, videos, and audio [32][36]. Conclusion - The SEA framework shows promise as a solution for the safety alignment of emerging MLLMs, allowing for effective multimodal safety alignment using synthetic embeddings, which significantly reduces resource requirements [37].