Core Viewpoint - The article emphasizes the transformative impact of AI technology on business innovation and the necessity for companies to adapt their strategies to remain competitive in the evolving landscape of AI [1][2]. Group 1: OpenAI's Emergence - OpenAI was founded in 2015 by Elon Musk and Sam Altman with the mission to counteract the monopolistic power of major tech companies in AI, aiming for an open and safe AI for all [9][10][12]. - The introduction of the Transformer architecture by Google in 2017 revolutionized language processing, enabling models to understand context better and significantly improving training speed [13][15]. - OpenAI's belief in the Scaling Law led to unprecedented investments in AI, resulting in the development of groundbreaking language models that exhibit emergent capabilities [17][19]. Group 2: ChatGPT and Human-Machine Interaction - The launch of ChatGPT marked a significant shift in human-machine interaction, allowing users to communicate in natural language rather than through complex commands, thus lowering the barrier to AI usage [22][24]. - ChatGPT's success not only established a user base for future AI applications but also reshaped perceptions of human-AI collaboration, showcasing vast potential for future developments [25]. Group 3: DeepSeek's Strategic Approach - DeepSeek adopted a "Limited Scaling Law" strategy, focusing on maximizing efficiency and performance with limited resources, contrasting with the resource-heavy approaches of larger AI firms [32][34]. - The company achieved high performance at low costs through innovative model architecture and training methods, emphasizing quality data selection and algorithm efficiency [36][38]. - DeepSeek's R1 model, released in January 2025, demonstrated advanced reasoning capabilities without human feedback, marking a significant advancement in AI technology [45][48]. Group 4: Organizational Innovation in AI - DeepSeek's organizational model promotes an AI Lab paradigm that fosters emergent innovation, allowing for open collaboration and resource sharing among researchers [54][56]. - The dynamic team structure and self-organizing management style encourage creativity and rapid iteration, essential for success in the unpredictable field of AI [58][62]. - The company's approach challenges traditional hierarchical models, advocating for a culture that empowers individuals to explore and innovate freely [64][70]. Group 5: Breaking the "Thought Stamp" - DeepSeek's achievements highlight a shift in mindset among Chinese entrepreneurs, demonstrating that original foundational research in AI is possible within China [75][78]. - The article calls for a departure from the belief that Chinese companies should only focus on application and commercialization, urging a commitment to long-term foundational research and innovation [80][82].

Core Insights - Mistral AI, a French artificial intelligence startup, is launching its first reasoning model to compete with established players like OpenAI and DeepSeek [1][2] - The new reasoning model is designed to perform complex tasks through logical reasoning and is particularly strong in mathematics and coding [2] Company Overview - Mistral AI is led by CEO Arthur Mensch, who emphasizes the model's capability to reason in multiple languages, setting it apart from competitors [2] - The launch of this model positions Mistral AI in a competitive landscape that includes OpenAI's o1 and DeepSeek's R1 [3]

北大邓小铁课题组 投稿 量子位 | 公众号 QbitAI 给大模型当老师，让它一步步按你的想法做数据分析，有多难？ 结果是，连Claude-3.7和Gemini-2.5 Pro这样的顶尖选手，都开始"不听话"了。 在一个全新的测试基准中，它们面对多轮、不断演进的指令，最终的任务成功率最高仅有40%。 这项名为 IDA-Bench 的新基准，就是为了模拟真实世界中这种"边想边改"的分析场景而生。 它不再是给模型一道题，让它一口气算完；而是模拟一位真实的数据分析师，在对话中不断给出新指令，考察Agent在 多轮交互 中的真实 能力。 可以说，专治各种"自作主张"和"一意孤行"的AI。 值得一提的是，这项工作由一支星光熠熠的团队打造，汇集了 北京大学 与 加州大学伯克利分校 的 顶尖学者，其中不乏机器学习泰斗 Michael I. Jordan 教授，仿真科学领域专家 郑泽宇 (Zeyu Zheng) 副教授，以及ACM/IEEE Fellow 邓小铁 (Xiaotie Deng) 教授的身 影。 "不听话"的AI，问题出在哪？ 目前，我们看到的很多大模型数据分析工具，比如OpenAI、Gemini和Claude的 ...

允中 发自 凹非寺 量子位 | 公众号 QbitAI 2025年6月，由杭州市拱墅区人民政府、中国人工智能学会、中欧人才交流与创新合作中心 联合主办的 "智汇运河·智算未来"全球人工智能创新创业大赛即将重磅启幕 。 大赛聚焦人工智能前沿领域，面向全球征集优质项目，旨在通过"以赛引才、以赛促创"模 式，推动海内外顶尖技术与产业资源汇聚杭州拱墅，助力打造具有国际影响力的人工智能创 新应用示范区，为国家高水平科技自立自强提供"拱墅样本"。 全球联动，共绘AI产业新图景 当前，人工智能技术正重塑全球产业格局。 作为中国数字经济高地，杭州近年来在人工智能领域持续领跑。拱墅区作为DeepSeek的发 源地，依托大运河数智未来城、智慧网谷小镇等产业平台，已集聚超500家人工智能相关企 业，已建立了"科学家+企业家+投资家"的协同创新、成果转化和产业孵化机制，加速推动人 工智能与实体经济深度融合。 在此背景下，为进一步激发创新活力，以"智汇运河・智算未来"为主题的全球人工智能创新 创业大赛应运而生。 大赛立足拱墅、辐射全球， 聚焦智能制造与智慧城市、生命健康、智慧物流、全球化协同创 新四大"AI+"主题赛道 ，打造立体化竞技 ...

全球ai由训练走向推理了。软件应用开始冒头。 2024年发布的模型总数同比均有所下降。美国为2024年发布知名模型最多的地区，数量达40个，较2023年的61个同比下降34.43%。分机构看，2024年贡 献知名模型最多的机构分别是OpenAI（7个）、谷歌（7个）和阿里巴巴（4个）。受MoE等新技术推动，2024年模型的参数数量保持快速上升趋势，规模 扩大仍是模型性能提升的重要方式。 随着强化学习时间和推理思考时间的增长，模型性能也将得到显著提升。据前OpenAI应用研究负责人Lilian Weng数据，s1实验中，通过强制延长思维链 推理路径长度，以Token衡量的平均思维时间与下游评估准确率之间展现出明显的正相关关系。据上海交通大学研究表明，通过延长AI的推理时间，仅需 500个样本训练，就能让模型在医疗诊断准确率上提升6%-11%，达到专业医生的诊断水准 。 趋势看，tokens调用激增。 2023-2024年，开发人员采用AI工具率由44%提升至63%。Google每月处理Tokens增长50倍，Microsoft Azure AI Foundry处理Tokens增长5倍。AI模型训练成 本高+的推 ...

Core Insights - A core executive from DeepSeek has quietly left to start a new venture, planning to launch an Agent product around Christmas 2025 [1] - The departing executive is reported to be the former CTO of DeepSeek, although there is no official CTO position within the company [1] - The new startup has secured funding from a prominent venture capital firm [1]

以下文章来源于AGI接口 ，作者宋思杭 AGI接口 . AI卷起的财富风暴。 出品｜虎嗅科技组 作者｜宋思杭 值得注意的是，这并非AI行业首次出现核心高管离职创业的案例。从OpenAI的多位联合创始人出 走，到国内大厂AI团队的人才分流，高端AI人才的流动已成为行业常态。 一个近两年在OpenAI发生的典型案例是，曾一直与奥特曼不和的首席科学家伊利亚在2024年5月 离开公司后一个月，便联合前Y Combinator合伙人格罗斯（Daniel Gross）和前OpenAI工程师列 维（Daniel Levy）共同创立Safe Superintelligence（简称"SSI"），迄今为止，这家公司总融资额 已达到30亿美元，第二轮融资后估值直接飙升至320亿美元。SSI也因此成为史诗级独角兽。 然 而 ， 尽 管 关 于 这 位 DeepSeek 核 心 高 管 的 创 业 项 目 并 无 相 关 融 资 披 露 ， 但 这 并 不 妨 碍 ， 从 DeepSeek"出走"的人也有可能创造下一个独角兽神话。 而这种现象背后恰反映了AI行业的几个特点：一是技术迭代速度快，新方向不断涌现，为创业提 供了丰富的机会 ...

智通财经APP获悉，AI基础设施概念股在年初大幅下跌后，如今正在大幅上涨。这是因为大型科技公司 的投资重新提振了投资者对该行业的信心。由高盛追踪的两个股票组合表现良好：其中一组追踪的是 AI数据中心和电气设备类股票，另一组则追踪为数据中心提供电力的公司的股票。这两组股票分别较 4 月的低点上涨了 52%和 39%。其中表现突出的公司包括Vertiv Holdings(VRT.US)——其自 4 月 4 日以来 已上涨 94%，以及Constellation Energy(CEG.US)——同期上涨 75%。 全球最大的几家科技公司——包括亚马逊(AMZN.US)、Alphabet(GOOGL.US)、微软(MSFT.US)和 Meta(META.US)——仍在大力投入人工智能领域，这消除了人们对于资金是否会继续流向那些主要AI 基础设施公司的疑虑。据彭博分析师Robert Schiffman称，用于支持AI需求的资本支出预测自年初以来 增长了 16%。 Roundhill Financial 首席执行官Dave Mazza表示："财报季让投资者们明白，生成式AI并非依靠空洞的口 号来运行，而是依靠实实在在的物 ...

Core Viewpoint - The paper published by Apple's Machine Learning Research Center argues that existing reasoning models create an illusion of "thinking" without a stable and understandable thought process, suggesting that their reasoning capabilities are fundamentally flawed [1][4][6] Group 1: Paper Findings - The paper critiques the reasoning models developed by companies like OpenAI, Anthropic, Google, and DeepMind, claiming that these models do not possess a reliable reasoning process [4][6] - Apple's team designed four types of puzzle environments to test reasoning models, including Tower of Hanoi, checkers exchange, river crossing, and block world, to evaluate their reasoning capabilities under controlled difficulty [4][6] - Experimental results indicate that non-reasoning models outperform reasoning models in low-complexity tasks, while reasoning models show advantages in moderately complex tasks [6][7] Group 2: Limitations of Reasoning Models - Both reasoning and non-reasoning models experience a significant drop in performance when task complexity exceeds a certain threshold, with accuracy dropping to zero [7][9] - As problem complexity increases, reasoning models initially invest more thinking tokens, but their reasoning ability collapses when faced with overly difficult problems, leading to reduced effort in thinking [9][10] - In simpler problems, models often find correct solutions early but engage in unnecessary thinking later, while in high-complexity problems, reasoning becomes chaotic and incoherent [10][11] Group 3: Controversy and Reactions - The paper has sparked controversy, with some researchers arguing that the failure of models in tests is due to output token limitations rather than a lack of reasoning ability [12] - Critics suggest that Apple's focus on the limitations of current methods may reflect frustration over its own AI advancements, especially with the upcoming WWDC event expected to yield limited AI updates [13][14] - Internal challenges at Apple, including leadership styles and privacy policies, have reportedly hindered progress in AI development, contributing to the perception of stagnation in their AI initiatives [14][15]

当DeepSeek-R1以更低成本实现类似性能突破时，Claude能够连贯工作数小时完成复杂任务时，意味着AI发展已经迈入推理时代，强化学习技术的 重要性不言而喻，将重塑AI产业的技术栈乃至商业模式。 6月8日，AI研究公司SemiAnalysis发布长篇报告《强化学习：环境、奖励破解、智能体、扩展数据》，深度剖析了强化学习的工作原理以及影响 因素，并预测了后续AI发展趋势。 报告表示，强化学习（RL）或成为AGI前最后关键范式，其理密集型特性带来了算力挑战。此外，高质量数据是强化学习护城河，AI设计AI的循 环加速技术迭代。 1. 强化学习（RL）或成为AGI前最后关键范式：强化学习是推动大模型推理能力跃升的核心技术，尤其在思维链（CoT）生成和长 程任务连贯性上表现突出，被视作实现AGI前的终极技术路径。 2. 可验证奖励场景率先商业化：编码、数学等奖励函数明确的任务（如SWE-Bench性能提升30%+）已实现落地，OpenAI的o1、 DeepSeek-R1等模型验证其价值。医疗、写作等非验证领域通过"LLM评判者+人工评分标准"构建奖励函数（如HealthBench医疗 评估），OpenAI、阿里Q ...