当前，人工智能行业正深陷 "GPU 竞赛" 的热潮，在2026年CES（国际消费电子展）现场随处可见对云 端 AI 应用的热议，"人均GPU 数量" 成为衡量技术实力的热门指标，从企业到国家层面都在追逐算力的 堆砌。 美西时间1月6日-8日，钛媒体CES 2026「Talk to the World」系列论坛在拉斯维加斯举办。会上，加州大 学伯克利分校Vive 增强现实中心创始执行主任Dr. Allen Yang提出了一个清醒而关键的观点：我们应超 越云端，将目光投向物理世界，探寻物理 AI 的下一个 "AlphaGo时刻"。 作为伯克利 Vive 增强现实中心创始执行主任，Dr. Allen Yang主导着 AR/VR、元宇宙及赛车自动驾驶三 大创新方向。过去四年，他带领伯克利 AI 赛车队连续征战 CES 自动驾驶挑战赛，并在 2025 年斩获头 对头超车项目冠军。这些扎根物理场景的实践让他深刻意识到，物理 AI 与依赖云端数据的大型语言模 型有着本质区别，其真正的 "分水岭时刻" 尚未到来。 他指出，虽然以AlphaGo和大型语言模型为代表的AI已取得里程碑式突破，但物理AI仍亟待属于自己 的"分水岭时 ...

点击下方 卡片 ，关注" 具身智能 之心 "公众号 编辑丨具身智能之心 本文只做学术分享，如有侵权，联系删文 >> 点击进入→ 具身智能之心 技术交流群 更多干货，欢迎加入国内首个具身智能全栈学习社区 ： 具身智能之心知识星球 (戳我) ， 这里包含所有你想要的。 在具身智能领域，机器人操纵作为核心难题，随着视觉、语言及多模态学习的飞速发展迎来变革。大型基础模型的出现，大幅提升了机器人的感知与语义表征能 力，使其能在非结构化环境中基于自然语言指令完成任务。由西安交通大学、香港科技大学（广州）等多所高校联合撰写的综述，以 "高层规划 + 低层控制" 的统一 框架，系统梳理了基于学习的机器人操纵方法，明确了当前技术瓶颈与未来方向，为该领域的研究提供了全面且结构化的参考。 论文名称：Embodied Robot Manipulation in the Era of Foundation Models: Planning and Learning Perspectives 论文链接：https://arxiv.org/pdf/2512.22983 项目链接：https://github.com/BaiShuangha ...

我最近参与了一些关于为终端用户设计基于大型语言模型工具的有趣讨论，其中一个重要的产品设计问 题是"人们对人工智能了解多少？"这很重要，因为正如任何产品设计师都会告诉你的，你需要了解用 户，才能成功为他们打造可用的产品。想象一下，如果你在搭建一个网站，假设所有访客都会流利使用 普通话，所以你用普通话写了网站，但结果发现你的用户全是西班牙语。就是这样，因为虽然你的网站 可能很棒，但你构建的假设是致命的错误，结果大大降低了它的成功可能性。 所以，当我们为用户构建基于LLM的工具时，我们必须退一步，看看这些用户如何看待LLM。例如： 用户研究是产品设计中极其重要的部分，我认为在构建基于大型语言模型的工具时跳过这一步是个大错 误。我们不能假设我们知道特定受众过去如何体验过大型语言模型，尤其不能假设我们自己的经历代表 他们的经历。 用户类别 大概有 这四个类别： 无意识使用者（不知道/不在乎） 一个不怎么思考人工智能、也不认为它与自己生活相关的用户属于这一类。他 们自然对底层技术了解有限，也不会有太多好奇心去了解更多。 回避型用户（人工智能很危险） 这个用户对人工智能整体持负面看法，会带着高度怀疑和不信任来解决这个问 题 ...

南洋理工大学研究人员构建了EHRStruct基准，用于评测LLM处理结构化电子病历的能力。该基准涵盖11项核心任务，包含2200个样本，按临 床场景、认知层级和功能类别组织。研究发现通用大模型优于医学专用模型，数据驱动任务表现更强，输入格式和微调方式对性能有显著影 响。基于此，团队提出EHRMaster框架，与Gemini联合后性能超越现有模型。 电子病历（EHR）是医疗体系中最核心的数据形态，集中呈现患者在诊断、检验、用药、生命体征监测与疾病管理过程中的关键临床信息，是临床决策的 重要基础。 随着LLM逐步应用于医疗场景，如何使其有效理解和处理这些结构化的EHR，从而辅助医生完成关键的数据分析与临床推理，已成为推动医疗人工智能 发展的重要问题。 因此，南洋理工大学的研究人员提出了首个全面评测LLM处理结构化电子病历能力的综合基准EHRStruct，由计算机科学家与医学专家共同构建，并按 照临床场景、认知层级与功能类别进行层次化组织，全面的覆盖了LLM处理结构化EHR的11项核心任务，包含2,200个标准化样本，为医疗大模型的可控 性、可靠性与临床可用性提供统一而严谨的可解释评测框架。 论文链接：https: ...

Core Viewpoint - Meta is planning to release a new large language model (LLM) named "Avocado" in Q1 2026 to compete with companies like Google and OpenAI [1] Group 1: Product Development - The new model "Avocado" is seen as the successor to Meta's Llama series, which has faced development challenges [1] - Avocado is expected to be a proprietary model, unlike the current open-source Llama models that allow public access and modifications [1] Group 2: Strategic Decisions - In June, there were discussions among Meta executives, including Mark Zuckerberg, about reducing investment in the Llama series and considering models developed by competitors such as OpenAI and Anthropic [1] - Meta restructured its AI department to optimize its organizational framework for faster AI product development in response to competition [1] Group 3: Financial Investments - This summer, Meta invested nearly $15 billion to acquire a stake in Scale AI and appointed its CEO Alexandr Wong as the Chief AI Officer [1]

Core Insights - OpenAI is developing a large language model codenamed "Garlic" to compete with Google's advancements in AI, particularly its Gemini 3 model [1][2] - The new model is expected to be released as GPT-5.2 or GPT-5.5, potentially as early as early next year [2] - OpenAI's leadership acknowledges the need to enhance ChatGPT's quality amid increasing competition in the AI space [2] Development and Performance - OpenAI's Chief Researcher, Mark Chen, indicated that Garlic has performed well in internal evaluations, particularly in programming and reasoning tasks, outperforming Gemini 3 and Anthropic's Opus 4.5 [1][3] - Garlic is distinct from another model in development, "Shallotpeat," which aims to challenge Gemini 3; Garlic incorporates lessons learned from Shallotpeat's pre-training phase [3][4] - Improvements in pre-training have addressed key issues, allowing OpenAI to inject knowledge into a smaller model rather than relying solely on larger models [4] Future Steps and Enhancements - Garlic will undergo several steps before release, including post-training, where it will be exposed to more curated data for specialized knowledge [5] - OpenAI is also working on a larger and better model based on the experiences gained from the Garlic project [5]

Core Insights - The article explores the unique cognitive processes of large language models (LLMs) and how they differ from human understanding, particularly in the context of arithmetic operations like 2+2 [2][6][8] Group 1: Arithmetic and Language Models - LLMs do not perform arithmetic in the traditional sense; instead, they convert numbers into vectors and find coherence in language patterns rather than calculating sums [2][6] - The process of LLMs arriving at the answer "4" is described as a search for coherence in a high-dimensional space, rather than a mathematical computation [3][8] Group 2: Understanding and Patterns - The article draws parallels between the cognitive processes of LLMs and human thought, suggesting that both rely on patterns and relationships rather than strict rules [4][6] - Children learn arithmetic through associative patterns before grasping numerical concepts, similar to how LLMs operate [4][6] Group 3: Illusion of Understanding - The concept of "anti-intelligence" is introduced, indicating that LLMs may appear intelligent due to their fluent outputs, but lack genuine understanding [5][7] - The coherence produced by LLMs can mislead humans into believing there is comprehension behind the responses, highlighting a shared obsession with coherence in both machines and humans [7][8]

Group 1 - The core viewpoint is that Google's external sales of approximately 500,000 TPUs could lead to an increase of about $13 billion in Google Cloud revenue by 2027, representing an 11% growth rate, and an increase of approximately $0.37 in earnings per share, equating to a 3% growth rate [1] - If Google Cloud's business growth continues to accelerate and the company's semiconductor market expansion is successful, it will help maintain a high valuation for its stock [1] Group 2 - In terms of industry scale, with Nvidia expected to ship around 8 million GPUs by 2027, Google's external sales of TPUs in the range of 500,000 to 1 million units remains reasonable [3] - There is uncertainty regarding Google's overall strategy for promoting TPU external sales, with investor focus on its business model, pricing strategy, and the types of workloads that TPUs can handle [3] - This year, Google has spent approximately $20 billion on Nvidia for large language model-related computing, while spending on TPUs has been only around $1 billion, indicating a potential adjustment in capital allocation next year, although overall AI chip demand is unlikely to result in a "winner-takes-all" scenario [3]

Core Insights - Morgan Stanley analysts estimate that Google's external sales of approximately 500,000 TPUs could increase Google Cloud revenue by about $13 billion, representing an approximate growth rate of 11% by 2027, with an increase in earnings per share of about $0.37, or roughly 3% [1] Group 1 - The potential for Google Cloud's revenue growth is linked to the successful expansion of its semiconductor market presence [1] - Analysts suggest that if Google Cloud's business growth accelerates, it will help maintain a high valuation for the company's stock [1] - The estimated external sales range for Google TPUs is considered reasonable, especially in the context of Nvidia's expected GPU shipments of around 8 million units by 2027 [1] Group 2 - There is uncertainty regarding Google's overall strategy for promoting TPU external sales, with key investor concerns focusing on its business model, pricing strategy, and the types of workloads that TPUs can support [1] - This year, Google has spent approximately $20 billion on Nvidia for large language model-related computing, while expenditures on TPUs have been around $1 billion, indicating a potential adjustment in capital allocation next year [1] - The overall demand for AI chips is unlikely to result in a "winner-takes-all" scenario, suggesting a competitive landscape [1]

Core Insights - The discussion highlights the rapid advancements in AI technology and its potential to create a new wave of independent entrepreneurs, transforming the software development landscape [5][30]. - There is a divergence in opinions regarding the timeline and feasibility of achieving Artificial General Intelligence (AGI), with some experts expressing optimism about imminent breakthroughs while others remain skeptical [9][19]. AI Development Status and Path to AGI - Adam D'Angelo emphasizes that there are no fundamental challenges that cannot be solved by the brightest minds in the coming years, citing significant progress in reasoning models and code generation [3][8]. - Amjad Masad compares the current AI evolution to historical revolutions, suggesting that humanity is undergoing a transformative change that may not be easily defined [4][27]. - D'Angelo believes that the next five years will see a drastically different world, contingent on resolving current limitations in AI context and usability [8][10]. Economic Transformation and Future Societal Landscape - D'Angelo predicts that the economic impact of AI could lead to GDP growth far exceeding 4-5% if AI can perform tasks at a lower cost than human labor [21]. - Masad raises concerns about the second-order effects of AI on the job market, particularly the potential for entry-level jobs to be automated while expert roles remain [22][23]. - The conversation suggests that as AI automates more tasks, the nature of work will shift, with a potential increase in demand for roles that leverage human creativity and emotional intelligence [24][25]. Technological Landscape Evolution and Entrepreneurial Ecosystem Outlook - D'Angelo expresses excitement about the increase in independent entrepreneurs enabled by AI technologies, which allow individuals to bring ideas to fruition without the need for large teams [28][30]. - The discussion touches on the balance between large-scale companies and new entrants in the market, suggesting that both can coexist and thrive in the evolving landscape [32][36]. - Masad highlights the importance of AI in programming, indicating that as these tools improve, they will democratize software development, allowing more people to create complex applications [44]. Future Challenges and Ultimate Thoughts - The conversation reflects on the cultural implications of increased reliance on AI, particularly regarding knowledge sharing and collaboration among employees [49]. - D'Angelo and Masad both acknowledge the need for ongoing research and innovation in AI to unlock its full potential and address the challenges that arise from its integration into society [41][42].