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中国电子电子行业研究报告
[Table_Title] 研究报告 Research Report 29 Sep 2025 中国电子 China (Overseas) Technology 科技新闻 Weekly Update - 20250929 Tech News Weekly Update - 20250929 姚书桥 Barney Yao 纪雨岑 Grace Chi 吕小潼 Xiaotong Lyu barney.sq.yao@htisec.com grace.yt.chi@htisec.com xt.lyu@htisec.com [Table_summary] (Please see APPENDIX 1 for English summary) 苹果开发类似 ChatGPT 的内部应用程序 为加速 Siri 大幅改版做准备(Digitimes) 苹果(Apple)为了 2026 年预定推出的语音助理 Siri 进行大规模改版,知情人士透露,作为测试与准备工作的一部 分,已经开发出一款类似 OpenAI「ChatGPT」的 iPhone 应用程序。 彭博(Bloomberg)报导,苹果的人工智能(AI)部门利用该应用程序,快速评 ...
腾讯研究院AI速递 20250929
腾讯研究院· 2025-09-28 16:01
Group 1: OpenAI and Model Changes - OpenAI has been reported to reroute models like GPT-4 and GPT-5 to lower-capacity sensitive models without user knowledge [1] - The rerouting occurs when the system detects sensitive topics, and this judgment is based on subjective context [1] - OpenAI's VP stated that the changes are temporary and part of testing a new safety routing system, raising user concerns about rights [1] Group 2: Tencent's Hunyuan Image 3.0 - Tencent launched Hunyuan Image 3.0, the first industrial-grade native multimodal model with 80 billion parameters, recognized as the largest open-source model [2] - The model excels in semantic understanding, capable of parsing complex semantics and generating both long and short texts with high aesthetic quality [2] - Hunyuan Image 3.0 is based on Hunyuan-A13B, trained on 5 billion image-text pairs and 6 trillion tokens, and is available under Apache 2.0 license [2] Group 3: Kuaishou's KAT Series - Kuaishou's Kwaipilot team introduced KAT-Dev-32B (open-source) and KAT-Coder (closed-source) models, achieving a 62.4% solution rate on SWE-Bench Verified [3] - KAT-Coder reached a 73.4% solution rate, comparable to top closed-source models, utilizing a chain training structure [3] - The team developed entropy-based tree pruning technology and a large-scale reinforcement learning training framework, observing new capabilities in dialogue and tool usage [3] Group 4: AI Teachers by TAL Education - TAL Education's CTO proposed a grading theory for AI teachers, evolving from assistants (L2) to true teacher roles (L3) [4] - L3 AI teachers can observe students' problem-solving steps in real-time and provide targeted guidance, forming a data feedback loop [5] - The "XiaoSi AI One-on-One" program supports personalized education across various learning environments, achieving a 98.1% accuracy in math problem-solving [5] Group 5: Meta's Humanoid Robots - Meta plans to invest billions in humanoid robot development, equating its importance to augmented reality projects [6] - The focus will be on software development rather than hardware manufacturing, aiming to create industry standards [6] - A new "Superintelligent AI Lab" is collaborating with robotics teams to build a "world model" simulating real physical laws [6] Group 6: Richard Sutton's Critique on Language Models - Richard Sutton criticized large language models as a flawed starting point, emphasizing that true intelligence comes from experiential learning [7] - He argued that large models lack the ability to predict real-world events and do not adapt to changes in the external world [7] - Sutton advocates for a learning approach based on actions, observations, and continuous learning as the essence of intelligence [7] Group 7: RLMT Method by Chen Danqi - Chen Danqi's team proposed the RLMT method, integrating explicit reasoning into general chat models to bridge the gap between specialized reasoning and general dialogue capabilities [8] - RLMT combines preference alignment and reasoning abilities, requiring models to generate reasoning paths before final answers [8] - Experiments show RLMT models excel in chat benchmarks, shifting reasoning styles to iterative thinking akin to skilled writers [9] Group 8: DeepMind's Veo 3 Emergence - DeepMind's Veo 3 demonstrates four progressive capabilities: perception, modeling, manipulation, and reasoning [10] - The concept of Chain-of-Frames (CoF) allows Veo 3 to perform cross-temporal reasoning through frame-by-frame video generation [10] - Quantitative assessments indicate significant improvements over Veo 2, suggesting video models are becoming foundational in visual tasks [10] Group 9: NVIDIA's Future in AI Infrastructure - NVIDIA is transitioning from a chip company to an AI infrastructure partner, focusing on total cost advantages rather than individual chips [11] - AI inference is expected to grow by a factor of a billion, driven by three expansion laws, potentially accelerating global GDP growth [11] - Huang Renxun emphasizes the need for independent AI infrastructure in the sovereign AI era, advocating for maximizing influence through technology exports [11]
新一代AI教师是什么样?学而思让它从L2「助手」跃迁至L3「老师」
机器之心· 2025-09-28 00:32
机器之心报道 编辑:+0 自动驾驶有 L1-L5 的分级路径,现在教育 AI 也有了自己的版本。 然而,长期以来,这种 高频互动和个性化引导 几乎只是少数学生才能享有的「奢侈品」。 人工智能的加入正在改变这一切。AI 学伴不仅能提供全天候的回应,还能创造一个无须担心被评判的空间,让学生大胆试错、主动追问。更重要的是,它能把启 发式的交互和个性化的反馈规模化,让「因材施教」真正成为可能。 可以看到,全球科技巨头已将目光聚焦于此。从 OpenAI 到 Google,其 AI 应用界面均已部署学习板块。 如今,「AI 下半场」已成共识,应用落地正成为决定未来的关键。教育,作为关乎人类发展的根本基石,已然成为 AI 技术融合与创新的前沿阵地。 很多人可能都有过这样的经历: 课堂上,一个问题在嘴边盘旋,却因为害怕问得「太蠢」而最终选择沉默;或者,前面的内容还没听懂,老师已经跳到下一个知 识点了。 ChatGPT 学习板块。 这正是教育领域长期存在的无奈:大班授课下,个体的思考路径常常被淹没在统一的教学节奏中。教师想兼顾每一位学生的困惑,但心有余而力不足。 瑞士心理学家 Jean Piaget 提出的建构主义早已指出:知 ...