"坐在我身边的是我的孩子，我的孙辈，我的学生，还有许多其他人。那你呢？是谁坐在你的副驾驶座？"——图灵奖得主 Yoshua Bengio 在 TED 演讲中发 出灵魂提问，沉甸甸地指向 AI 时代的人类命运共同体。 当「AGI」正以令人眩目的速度逼近，谁在为"安全"这道防线筑基？ 整理 | 梦依丹 出品丨AI 科技大本营（ID：rgznai100） 图灵奖得主、深度学习奠基人、全球被引用次数最多的 AI 科学家 Yoshua Bengio 官宣创业。成立一家名为 LawZero 非营利 AI 安全研究机构，以"安 全优先"原则回应人工智能可能带来的系统性风险。 LawZero 是一家以研究和技术开发为核心使命的非营利组织，旨在构建"设计即安全"的 AI 系统，并组建一支由世界顶尖研究者组成的技术团队。 "当前的 AI 系统已展现出自我保护和欺骗行为迹象，而随着其能力和自主性的增强，这种趋势只会加速。"Bengio 在博文中列出了多个案例： 以上这些 AI 行为所展现出来的是 AI 系统在缺乏安全约束机制下，可能发展出不受控制的目标偏差与策略选择。 深度学习三巨头纷纷发出 AI 安全警告 作为 AI 领域的殿堂 ...

Core Viewpoint - The official release of Cursor 1.0 marks a significant evolution of the AI-driven code editor from an "assistant tool" to an intelligent programming platform with review, memory, and collaboration capabilities [1][19]. Feature Highlights - Cursor 1.0 introduces several key features, including the automatic code review assistant BugBot, native support for Jupyter Notebooks, project-level AI memory (Memories), and the comprehensive opening of the Background Agent [2][19]. - BugBot can automatically review Pull Requests on GitHub, identifying potential bugs and issues, and allows developers to quickly implement suggested fixes [5][6]. - The Background Agent, previously in early testing, is now available to all users, enhancing remote coding capabilities [8][9]. - The integration of Jupyter Notebooks allows developers in data science and research to make changes directly within the platform [11]. Memory Functionality - The introduction of the Memories feature enables the storage of knowledge points and contextual information at the project level, which can be automatically recalled in future interactions [12][13]. Enhanced User Experience - Cursor 1.0 improves user experience with the ability to view visual content like Mermaid charts and Markdown tables directly in chat conversations, making communication more intuitive [18]. - The settings page and dashboard have been optimized for better usage statistics and data analysis [18]. Deployment and Integration - Developers can now quickly deploy Model Control Protocol (MCP) services with one-click installation and OAuth support, facilitating easier integration of additional model capabilities [15][16]. - MCP developers can add an "Add to Cursor" button in their documentation to enhance service accessibility for other developers [17].

Core Viewpoint - The article highlights the journey and achievements of K. J. Ray Liu, emphasizing his contributions to the field of wireless sensing and AI, as well as his philosophy of pursuing dreams and maintaining one's original intentions in life and career [2][15][40]. Group 1: Personal Journey - K. J. Ray Liu was born in Taiwan and showed early interest in communication and signal processing, which became his lifelong profession [2][4]. - He faced challenges during his academic journey, including a difficult transition to studying in the U.S. and overcoming biases as a Chinese scholar [5][6]. - Liu became the first Asian president of IEEE in 2022, implementing significant reforms during his tenure [6][9]. Group 2: Contributions to Education - Liu has mentored over 70 doctoral and postdoctoral students, many of whom have achieved notable success in academia and industry [11][30]. - His teaching philosophy emphasizes the importance of independent thinking and problem discovery among students, rather than merely solving assigned problems [31][32]. Group 3: Transition to Industry - Liu retired from academia to pursue entrepreneurship in wireless AI, believing that practical applications require real-world data and environments [39][40]. - His company, Origin Wireless, focuses on utilizing wireless signals for environmental sensing, which has significant implications for health monitoring and safety [41][42]. Group 4: Vision for Wireless AI - Wireless AI aims to leverage ubiquitous wireless signals to perceive and understand human activities and health conditions without the need for wearable devices [41][42]. - The technology has already been deployed in various regions for remote monitoring, demonstrating its potential to save lives and improve health outcomes [42].

Core Insights - The rise of intelligent agents is fundamentally reshaping the dimensions of work, liberating it from fixed physical spaces and designated time periods, marking a transition from the industrial and information eras to the intelligent agent era [1][4][5] - The division of labor between humans and AI is shifting from execution to definition, where humans must now answer "why to do" as machines take over "how to do" [3][5] Work Transformation - The traditional work model, which required synchronous presence in a specific location, is being disrupted by intelligent agents, allowing for asynchronous collaboration and task completion [6][11] - The emergence of remote work during the pandemic has accelerated this transformation, leading to a deeper paradigm shift in how work is structured [4][6] Task Atomization - Work is being "atomized" into discrete tasks that can be dynamically assigned to the most suitable executors, whether human or AI, reflecting a significant shift from fixed positions to flexible task collections [8][9] - The Upwork report indicates a 73% increase in task-based contracts compared to a 12% growth in traditional time-based contracts, highlighting the labor market's transition towards task-oriented work [8] Collaboration Dynamics - Intelligent agents are evolving into collaborative intermediaries, facilitating communication and cooperation among team members with diverse backgrounds [12][11] - The boundaries between work and life are blurring, leading to a new reality where work and personal life are increasingly integrated rather than balanced [12][13] Challenges of Integration - The "always-on" culture is emerging, with many remote workers finding it difficult to disconnect from work, leading to longer working hours and potential family conflicts [13][16] - Social isolation is a growing concern, particularly among younger professionals who miss out on networking opportunities typically found in traditional workplaces [14] Skills for the Intelligent Agent Era - The skill set required for collaboration with intelligent agents is evolving, emphasizing the need for cognitive strategies and meta-skills alongside technical abilities [19][20] - System thinking, judgment, and decision-making are becoming critical skills as humans navigate complex interactions with intelligent agents [21][22] Future Outlook - The intelligent agent revolution is not just a transformation of work but also a redefinition of personal identity and societal structures, necessitating a reevaluation of what constitutes meaningful work and a fulfilling life [24][25]

Core Viewpoint - The CEO of Anthropic, Dario Amodei, warns that within the next five years, AI could lead to a significant increase in unemployment rates, potentially reaching 20%, particularly affecting entry-level white-collar jobs [1][4][5] Group 1: Impact of AI on Employment - Amodei emphasizes that AI is not just replacing jobs but is eroding the foundational skills required for entry-level positions, particularly in roles such as financial assistants, legal assistants, and data analysis interns [5][10] - The trend of decreasing entry-level job opportunities is evident, with a 50% reduction in hiring for new graduates by major tech companies since the pandemic, and only 7% of new hires in large firms being entry-level positions in 2024, a 25% decrease from 2023 [6][8] - The expectation for new employees has shifted, with companies preferring candidates who can immediately utilize AI tools and solve problems independently, raising the entry barrier for recent graduates [11][16] Group 2: Societal Response and Preparedness - Amodei notes that society is largely unprepared for the rapid changes brought by AI, with a lack of clear strategies to address the potential employment crisis [13] - The silence from the public and the stealthy cost-cutting measures by companies could lead to greater societal costs in the future [5][13] - There is a growing concern that the changes brought by AI are not being adequately recognized by the workforce, leading to a disconnect between corporate strategies and employee awareness [13] Group 3: Recommendations for Job Seekers - Young job seekers are advised to view AI as an assistant rather than a threat, and to adapt by learning to use AI tools effectively [14][15] - Recommendations include developing self-driven work habits, continuously upgrading skills through online courses, and focusing on problem-solving abilities rather than merely executing tasks [19] - The need for rapid personal growth and adaptation to the evolving job landscape is emphasized, as traditional pathways of starting from the bottom are becoming obsolete [12][16]

作者 | Eric Hayes 编译 | 梦依丹 出品丨AI 科技大本营（ID：rgznai100） 今年 4 月，OpenAI 对 ChatGPT 的记忆系统进行了重磅升级：它可以参考用户的全部过往对话来提供更个性化的响应。ChatGPT 不再是那个每次都从 零开始、记忆如风的"临时陪聊者"，而正在变成一个真正能"记住你是谁、理解你喜好、回忆你曾说过什么"的"长期陪伴者"。 软件工程师 Eric Hayes 对此进行了逆向拆解——不仅厘清了 ChatGPT 的双重 记忆架构，还推测出其背后的实现机制，并给出了完整的技术复刻路径。 本文一共分为三部分： ChatGPT 的记忆是如何工作的？ ChatGPT 的记忆机制，主要由两大系统构成： 虽然 ChatGPT 的聊天历史系统（Chat History system） 在官方描述中是一个单一系统，但在作者的实测中，它其实由三套子系统构成。 这三者的结构远比"保存记忆"复杂得多，而它们，很可能是 ChatGPT 回应质量大幅提升的关键所在： 当前绘话历史 这部分看起来是一个简单的记录系统，用于保存用户在其他对话中发送的最近消息。该记录容量很小，仅包含过去一天以内 ...

Core Viewpoint - The current large language models (LLMs) are limited in their ability to generate original scientific discoveries and truly understand the complexities of the physical world, primarily functioning as advanced pattern-matching systems rather than exhibiting genuine intelligence [1][3][4]. Group 1: Limitations of Current AI Models - Relying solely on memorizing vast amounts of text is insufficient for fostering true intelligence, as current AI architectures struggle with abstract thinking, reasoning, and planning, which are essential for scientific discovery [3][5]. - LLMs excel at information retrieval but are not adept at solving new problems or generating innovative solutions, highlighting their inability to ask the right questions [6][19]. - The expectation that merely scaling up language models will lead to human-level AI is fundamentally flawed, with no significant advancements anticipated in the near future [19][11]. Group 2: The Need for New Paradigms - There is a pressing need for new AI architectures that prioritize search capabilities and the ability to plan actions to achieve specific goals, rather than relying on existing data [14][29]. - The current investment landscape is heavily focused on LLMs, but the diminishing returns from these models suggest a potential misalignment with future AI advancements [18][19]. - The development of systems that can learn from natural sensors, such as video, rather than just text, is crucial for achieving a deeper understanding of the physical world [29][37]. Group 3: Future Directions in AI Research - The exploration of non-generative architectures, such as Joint Embedding Predictive Architecture (JEPA), is seen as a promising avenue for enabling machines to abstractly represent and understand real-world phenomena [44][46]. - The ability to learn from visual and tactile experiences, akin to human learning, is essential for creating AI systems that can reason and plan effectively [37][38]. - Collaborative efforts across the global research community will be necessary to develop these advanced AI systems, as no single entity is likely to discover a "magic bullet" solution [30][39].

Core Viewpoint - Alibaba Cloud has launched its first AI-native development environment tool, Tongyi Lingma AI IDE, which is deeply integrated with the latest Qwen 3 model and offers various features to assist developers in coding tasks [1][3]. Group 1: Product Features - Tongyi Lingma AI IDE supports the powerful open-source model Qwen 3 and the MCP protocol, enabling rapid development of intelligent applications [3]. - The IDE includes features such as long-term memory, inline suggestion prediction, and inline conversation capabilities tailored for development scenarios [3][4]. - The intelligent agent mode allows developers to describe coding tasks, enabling the IDE to autonomously perform engineering perception, code retrieval, and tool invocation, thus completing coding tasks end-to-end [3]. Group 2: Use Cases and Applications - The integration with over 3,000 MCP services allows developers to quickly deploy solutions for various scenarios, such as creating a travel guide webpage in 10 minutes without writing code [3]. - The inline suggestion prediction feature helps developers efficiently complete code writing by dynamically predicting the next code modification based on current changes [3]. Group 3: Evolution of AI Coding - The evolution of AI-assisted programming is categorized into three stages: 1. Initial stage focused on chat-based Q&A and simple code completion, requiring significant human intervention [5]. 2. Increased automation in collaborative programming, where AI can complete more coding tasks with minimal instructions [5]. 3. High automation and self-validation, where AI can autonomously write, test, and optimize code, functioning like a junior engineer [5]. - The industry is transitioning from the first to the second stage, with products like Tongyi Lingma showcasing attempts towards end-to-end automated programming [5].

整理 | 屠敏 出品 | CSDN（ID：CSDNnews） AI 人才争夺战愈演愈烈，就算是顶级大厂，如果没有"护城河"，也留不住人。 据外媒 Business Insider 最新消息，曾在开源大模型圈子里一度领跑的 Meta，如今正面临严重的 人才流失。在 Llama 模型最初的 14 位核心作者中，已有 11 位离职。有的自立门户，有的跳槽去了 竞争对手。 这波"出走潮"也让外界再次把目光投向 Meta。毕竟他们曾豪赌元宇宙，四年"烧掉"450 亿美元，却 被直指至今几乎未见显著成效。现在 AI 项目也出问题了，不少人开始质疑：Meta 还行不行？为什 么留不住顶尖 AI 人才？它的创新能力，还能支撑它在这场 AI 竞赛中跑多远？ Llama 论文的 14 位作者，已有 11 人离开 Meta 回头看 2023 年那篇引发轰动的 Llama 论文，共署名 14 位研究者。短短两年，Meta 只留下了其中 三位：研究科学家 Hugo Touvron、研究工程师 Xavier Martinet 和项目负责人 Faisal Azhar。 论文地址： https://arxiv.org/pdf/2302.13 ...

整理 | 苏宓 出品 | CSDN（ID：CSDNnews） 昨日，DeepSeek 悄然发布了其 R1 大模型的最新版本—— DeepSeek-R1-0528 ，目前已开启公 测。 一贯低调的 DeepSeek 在此番发布时，并未附带详细的技术说明，只是在官方微信社群中告知用 户，"DeepSeek R1 模型已完成小版本试升级"，大家可以自行前往官方网页、APP、小程序进行测 试。 Hugging Face 地址：https://huggingface.co/deepseek-ai/DeepSeek-R1-0528 但从用户体验反馈来看，本次名曰"小更新"也依然带来了不小的实质性改进，尤其是 在推理和输出 方面。具体来看，新版的 DeepSeek R1： 推理能力增强： 模型在"思维链"（Chain-of-Thought）推理方面表现更为结构化，逻辑性更 强。 我注意到新版 R1 的一个显著变化是……它在编程方面更强了！！但它却在一些（未知的）演绎推理 挑战上失败了……这些题它以前可是能答对的！！另一个明显的变化是，现在它在推理时会体现出差 异性，而且会用用户的母语思考，不再像以前那样只用英文。" 不过， ...