"未来将没有前端、没有后端、没有全栈，只有 AI Agent 工程师。" 身处一线开发的你，或许已经感受到了这股变化。在 AI 写代码、做决策、重构组织的当下，我们越 来越清醒的意识到：这不仅是职位的更名，更是工业革命级的范式转移。 谷歌 DeepMind CEO Demis Hassabis 在 2026 达沃斯世界经济论坛接受深度专访时说道： "这 场变革的规模，将是工业革命的十倍，而速度，更是快上十倍。" 也正如奇点智能研究院在调研 100+ 企业后得出的结论： AI 不再仅仅是赋能工具，它正在进化为企 业流程与组织范式的变革性力量 。 从机器学习到奇点智能 面对这种"十倍速"的跃迁，过去那个以算法为核心的"机器学习"语境，已不足以定义当下的工程现 实。 为回应这一代际升维，我们特地将由 CSDN&奇点智能研究院联合举办的「全球机器学习技术大会」 升级为「 奇点智能技术大会 」。 回 望 2025 ， 我 们 曾 有 幸 邀 请 到 OpenAI 研 究 科 学 家 、 GPT-5 与 Transformer 共 同 创 始 人 Lukasz Kaiser、加拿大两院院士杨强、清华大学朱军教授等近 20 ...

作者 | Big Technology Podcast 编译 | 王启隆 出品丨AI 科技大本营（ID：rgznai100） 如果说 Sam Altman 是 AI 时代的布道者，善于用宏大的愿景点燃公众的想象力；那么 Demis Hassabis 更像是一位在实验室里盯着显微镜的科学 家，冷静、严谨，对"炒作"有着天然的免疫力。 一年前，当整个硅谷都在因为 ChatGPT 的红利期似乎见顶而焦虑，甚至开始讨论"大语言模型（LLM）是否撞墙"时，Demis 却感到困惑。在他看来， 进步从未停止。他掌舵的 Google DeepMind 刚刚经历了 AlphaFold 3 的高光时刻，正试图将 AI 的触角从简单的聊天机器人延伸到生物学、物理学乃 至材料科学的最深处。 在达沃斯的一间木质会议室里，Demis 近期接受了 Big Technology 播客的专访。这场对话的特别之处在于，他没有回避那些尖锐的问题： 现在的 AI 是不是只有"金鱼记忆"？谷歌会不会为了财报在 Gemini 里塞满广告？所谓的 AGI 究竟是营销话术还是科学定义？ 最令人印象深刻的是他对"智能载体"的断言。在纪录片《The Think ...

对话 | 唐小引 嘉宾 | 唐睿 责编 | 梦依丹 出品 | CSDN（ID：CSDNnews） 当大模型开始"看懂"空间、理解物理、做出行动，人工智能的形态正在发生一次根本性变化——从"对话系统"，走向"行动智能"。 在这条路径上，一个词被频繁提起：空间智能。 以下文章来源于CSDN ，作者万有引力 CSDN . 成就一亿技术人 在全球机器学校技术大会现场，唐睿在与 CSDN 《万有引力》栏目的深度对话中，不仅给出了他的答案，更剖 析了行业深处的痛点与机遇。以下是访 谈中唐睿表达的一些观点提炼： 欢迎 收听音频播客，如有兴趣观看完整视频，可在文末获取 以下是对话的完整内容： 唐小引：屏幕前的小伙伴们大家好，欢迎收看《万有引力》。今天我们来到全球机器学习技术大会的现场，特别邀请到了群核科技首席科学家唐睿老 师，和大家一起深入分享他的技术人生成长，还有大家当前很关注的对于空间智能的整个思考、研究以及实践。欢迎我的本家唐老师，可以给大家打个 招呼，然后做一下自我介绍。 如果说 LLM 让机器拥有了像人类一样思考的大脑，那么空间智能则试图赋予机器像人类一样观察、理解并在三维世界中行动的身体与感官。 它并非凭空出现， ...

Core Viewpoint - The article discusses the innovative research by Tsinghua University's team, which aims to enhance autonomous driving systems by integrating human-like intuition through the use of brainwave signals, specifically focusing on a project named E³AD [4][36]. Group 1: Research Background and Development - The research team at Tsinghua University has developed a method to teach autonomous driving models to think like humans by using EEG signals from human drivers [4][36]. - The project is led by Dr. Gong Jiangtao, who has a background in computer science and neuroscience, emphasizing the importance of understanding human cognition in the development of intelligent systems [4][8][9]. Group 2: Human Intuition and AI - The concept of "driving intuition" is highlighted, where human drivers can subconsciously predict risks based on experience, a capability that current AI systems lack [3][4]. - The research aims to transfer this human ability to AI, allowing machines to not only execute tasks but also anticipate and avoid potential risks [35][36]. Group 3: Methodology and Implementation - The E³AD project utilizes non-invasive EEG to capture brain signals that indicate risk perception, which can then be used to inform the decision-making processes of autonomous vehicles [39][43]. - The integration of these signals into an end-to-end autonomous driving framework is proposed to enhance the system's ability to process information without losing critical details [43][44]. Group 4: Challenges and Future Directions - The article discusses the complexities of transferring human-like intuition to machines, particularly in the context of the physical world, which presents more variables and potential risks than the digital realm [34][35]. - Future research will focus on refining the integration of cognitive signals into AI systems, aiming for a more seamless interaction between humans and machines [56][59].

以下文章来源于CSDN ，作者万有引力 CSDN . 成就一亿技术人 对话 | 唐小引 嘉宾 | 王佳 楠 责编 | 梦依丹 出品 | CSDN（ID：CSDNnews） 通往 AGI 的终点，是代码，还是身体？ 在王佳楠看来，答案明确指向了——具身智能。 左：王佳楠，右：唐小引 在 2025 全球机器学习技术大会现场 ， CSDN &《新程序员》执行总编唐小引 与星尘智能副总 裁、前 DeepMind 研究员王佳楠展开了一次深入对 话。从 AGI 的终极想象，到具身智能的现实瓶颈，从快慢系统的工程逻辑，到通用机器人的时间表与开发者应有的信念，她给 出了一个既冷静、也充 满长期主义色彩的答案。王佳楠在采访中提到的核心观点有： 欢迎 收听音频播客，如有兴趣观看完整视频，可在文末获取 她曾在牛津大学完成学业，加入 DeepMind，从事强化学习与持续学习研究，亲历了 AlphaStar 等标志性项目的诞生，也在国内生成式 AI 尚处早期 阶段时，参与过统一生成框架的探索，走在 AIGC 爆发之前的科研前沿。无论是在"纯算法"的巅峰，还是在生成式模型的起点，她都站在浪潮内部。 2024 年，她加入星尘智能，选择直面 ...

Core Insights - The article discusses the evolution of AI and its implications for both consumers and businesses, emphasizing the gap between AI capabilities and user proficiency, referred to as the "ability gap" [6][12][14]. Group 1: AI Development and Adoption - Elon Musk predicts 2026 as the "singularity year," suggesting that AI will surpass human capabilities in various fields, including medicine [1]. - The concept of "Vibe Coding" has emerged, allowing programmers to code based on intuition rather than strict syntax, indicating a shift in programming paradigms [2][5]. - Despite advancements, many users still struggle to utilize AI tools effectively, likening the situation to giving a Ferrari to someone still learning to drive [6][12]. Group 2: Business Implications - Sarah Friar highlights the need for AI to transition from a question-answering tool to a task executor, bridging the ability gap for consumers and businesses [12][13]. - Companies are increasingly adopting AI, with 90% indicating they are using or planning to use OpenAI's technology within the next year [35]. - The productivity of companies using AI is rising, with examples of firms replacing multiple employees with AI-driven systems, showcasing significant efficiency gains [26][30]. Group 3: Financial and Strategic Considerations - OpenAI's investment in computing power is closely tied to revenue growth, with projections showing a strong correlation between computing capacity and annual recurring revenue (ARR) [21][22]. - The demand for computing power is described as nearly unlimited, with current limitations primarily due to availability rather than market need [23][24]. - The article emphasizes the importance of API call volume as a measure of real demand for AI, contrasting it with stock price fluctuations that do not reflect underlying value [24][25]. Group 4: Future Outlook - The article suggests that the next decade will see a significant increase in AI adoption, with only a small percentage of users currently leveraging its full capabilities [14][28]. - There is a prediction of a deflationary economy driven by AI, where costs for labor and expert services could approach zero, leading to a fundamental shift in societal structures [40].

Core Viewpoint - The departure of Yann LeCun from Meta marks the end of an era characterized by a focus on fundamental AI research, transitioning to a more commercially driven approach under the leadership of Alexandr Wang, emphasizing scale and immediate results over theoretical exploration [4][5][50]. Group 1: Historical Context - In 2013, Facebook was a burgeoning company seeking to integrate AI into its operations, leading to the recruitment of Yann LeCun, a prominent figure in AI research, to establish the Facebook AI Research (FAIR) lab [8][12][13]. - LeCun's vision for FAIR was to create a research environment that prioritized scientific inquiry over commercial pressures, fostering a culture of open exploration [14][23]. Group 2: Contributions and Innovations - LeCun played a pivotal role in the development of PyTorch, a flexible and user-friendly deep learning framework that emerged as a significant competitor to Google's TensorFlow, largely due to the open-source philosophy he championed [17][22][24]. - The success of PyTorch led to a major shift in the academic landscape, with a significant majority of top research papers adopting it, effectively sidelining TensorFlow in the academic community [22][24]. Group 3: Philosophical Divergence - LeCun's philosophical stance on AI emphasized the importance of understanding the underlying principles of intelligence, contrasting sharply with the emerging trend of large language models (LLMs) that he criticized for lacking true comprehension [30][32][36]. - His belief that LLMs were fundamentally flawed due to their reliance on statistical predictions rather than genuine understanding created a rift between him and the evolving priorities at Meta [32][36][50]. Group 4: Transition and Challenges - The rise of Alexandr Wang at Meta signified a shift towards a more aggressive, commercially focused strategy, prioritizing rapid development and deployment of AI technologies over the foundational research ethos that LeCun embodied [48][50]. - LeCun's eventual departure from Meta was driven by a growing disconnect with the company's new direction, which emphasized short-term commercial gains over long-term scientific exploration [52][56]. Group 5: Future Implications - The evolution of FAIR into a more commercially oriented entity under Wang raises questions about the future of AI research and the balance between commercial viability and scientific integrity [42][44][56]. - The legacy of LeCun's contributions, particularly in fostering an open-source culture and prioritizing fundamental research, may influence future developments in AI, as the industry grapples with the implications of prioritizing scale and immediate results [60][62].

Core Viewpoint - The article discusses the emergence of Triton as a user-friendly programming tool for AI chip development, aiming to lower the barriers for developers previously reliant on complex languages like CUDA [1][2][3]. Group 1: Triton Overview - Triton allows developers to write high-performance GPU code in a Python-like syntax, making it accessible to a broader audience [3]. - The integration of Triton with the PyTorch ecosystem enhances its usability and performance, making AI chip programming more approachable [3]. Group 2: Triton Next Conference - The Triton Next conference is scheduled for January 9, 2026, in Beijing, organized by the FlagOS community and the Beijing Academy of Artificial Intelligence [4][16]. - The conference aims to explore the current state and future developments of Triton, including discussions on its compiler and potential applications [5][6]. Group 3: Conference Agenda - The morning sessions will focus on the foundational principles of Triton, recent academic research, and the latest developments in the FlagOS community [7]. - Afternoon sessions will highlight practical applications of Triton, featuring insights from various teams on building next-generation AI models and addressing hardware compatibility challenges [11]. Group 4: Workshops and Training - The conference will include hands-on workshops designed to help developers apply Triton in real-world scenarios, covering topics like operator training and compiler usage [15][18].

Core Insights - The article discusses the emergence of Vision-Language-Action (VLA) systems, which are transitioning from demonstrations to real-world applications, highlighting the need for a structured learning path for newcomers and practitioners in the field [1][3][4]. Group 1: Overview of VLA - Embodied AI is identified as a rapidly evolving frontier in AI and robotics, with a focus on making machines capable of seeing, understanding, and acting [3][4]. - The article emphasizes the structural confusion within the field due to the rapid growth of models and datasets, making it challenging for newcomers to identify where to start and for existing practitioners to determine how to systematically enhance VLA capabilities [3][4]. Group 2: Contributions of the Review - The review paper titled "An Anatomy of Vision-Language-Action Models" aims to provide a clear and systematic reference framework for the increasingly complex VLA research area [4][6]. - It establishes a continuously evolving reference system for tracking the latest developments in VLA research, organized by modules, milestones, and challenges [5][9]. Group 3: Learning Pathways - For newcomers, the review suggests first establishing an overall understanding of the VLA field before delving deeper into specific areas [13][14]. - For practitioners, the review serves as an efficient roadmap for identifying areas for capability enhancement, helping to clarify research questions and innovation points [15][16]. Group 4: Structural Analysis - The review begins with a breakdown of basic modules in VLA systems, covering perception, representation, decision-making, and control, to create a common technical language [18][19]. - It then reviews key milestones along a timeline to illustrate the evolution of VLA from early concept validation to a general framework for real-world deployment [20][21]. Group 5: Key Challenges - The review identifies five core challenges that VLA systems face, including representation, execution, generalization, safety, and data evaluation, framing these challenges as the main focus of the analysis [25][26][30][33][39]. - Each challenge is linked to the overall capability of VLA systems, emphasizing the need for a clear understanding of problem structures to overcome existing bottlenecks [26][30][34][36]. Group 6: Future Directions - The review outlines potential future directions for VLA, such as developing native multimodal architectures and integrating physical and semantic causal world models [42][43]. - It envisions the next generation of embodied agents that not only perform tasks but do so reliably and controllably in real-world settings [44].

Core Viewpoint - Purdue University has announced a new graduation requirement called "AI working competency," which will be mandatory for students starting from the fall of 2026, emphasizing the necessity of AI skills for employment in today's job market [1][2]. Group 1: Rationale Behind the Decision - The decision is not merely a teaching reform but a response to the pressing employment challenges posed by AI, as many companies are halting hiring or conducting layoffs [2]. - Purdue University President Mung Chiang highlighted the urgency for universities to adapt to the rapid impact of AI on various sectors, including higher education [2]. Group 2: AI Competency Framework - The "AI working competency" is part of Purdue's broader AI strategy, AI@Purdue, which includes five key areas: Learning about AI, Learning with AI, Research AI, Using AI, and Partnering in AI [3][4]. - Students are expected to develop three core competencies: understanding and effectively using AI tools within their field, articulating the role and risks of AI in decision-making, and adapting to the evolution of AI technologies [4]. Group 3: Implementation and Challenges - Purdue University aims to avoid a one-size-fits-all approach by requiring each college to establish AI competency standards tailored to their disciplines [5]. - The "Using AI" aspect has generated debate, as there are inconsistencies in the university's policies regarding AI usage in coursework, reflecting a lack of unified guidelines [6][10]. - Research initiatives at Purdue will integrate AI into various fields, including precision agriculture and autonomous systems, emphasizing AI's role beyond mere computation [6][7]. Group 4: Faculty Perspectives - Faculty members support the integration of AI but express concerns about the execution of the competency requirement, fearing it may either be too broad or too rigid to accommodate diverse academic disciplines [8][10].