黄仁勋发布“物理AI”战略蓝图

DoNews01-18

在2026年CES展会上，英伟达CEO黄仁勋发表90分钟演讲，宣告人工智能进入理解物理世界的新阶段，并将此称为“物理AI”的“ChatGPT时刻”。黄仁勋指出，“物理AI”指能够理解自然定律、重力、摩擦、惯性、材质及因果关系的智能系统，其核心在于让AI从屏幕交互进化为与现实世界交互的实体智能体。他强调，单一模型已不足以支撑该转型，需构建可调用多模型、拆解问题并使用工具的完整工作智能体，推动AI...

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blueprint","themeId":null,"isJumpTheme":false,"ttsUrl":null,"symbols_score_info":{"NVDA":1.94,"NVD3.UK":0.6,"NVDS.UK":0.6,"NVD":0.6,"NVDY":0.6,"NVDD":0.6,"NVDS":0.6,"NVDU":0.6,"2NVD.UK":0.6,"NVIW.SI":0.6,"SNVD.UK":0.6,"NVDX":0.6,"NVDG":0.6,"NVDW":0.6,"NVDQ":0.6,"NVD2.UK":0.6,"3NVD.UK":0.6,"DIPS":0.6,"NVII":0.6},"content_text":"在2026年CES展会上，英伟达CEO黄仁勋发表90分钟演讲，宣告人工智能进入理解物理世界的新阶段，并将此称为“物理AI”的“ChatGPT时刻”。黄仁勋指出，“物理AI”指能够理解自然定律、重力、摩擦、惯性、材质及因果关系的智能系统，其核心在于让AI从屏幕交互进化为与现实世界交互的实体智能体。他强调，单一模型已不足以支撑该转型，需构建可调用多模型、拆解问题并使用工具的完整工作智能体，推动AI工业化进程。为此，英伟达发布面向“物理AI”的开放模型、框架与基础设施，支持机器人在虚拟环境中进行数十亿次基于物理原理的训练，并通过数字孪生技术快速部署至现实场景。黄仁勋认为，人形机器人正从实验室走向商业化，机器人将成为AI工业化的首批量产成果。在CES现场，波士顿动力展示了量产版双足人形机器人Atlas，具备高精度操作能力，标志着人形机器人开始为真实应用而设计。业内分析指出，“物理AI”落地仍面临三大挑战：首先是数据质量，传统大数据难以满足“物理真实性、语义可理解性、场景泛化性”的要求；其次是在仿真中训练的动作在现实中可能失效，存在“模拟到现实”鸿沟；第三是技术整合困难，行业生态壁垒导致企业各自为战，制约整体发展速度。AI从业者马哥表示，高质量数据集构建工程浩大且成本高昂，例如教会机器人“开门”需包含合页轨迹、把手力学特性及指令语义等复合信息。他还指出，尽管发展路径不同，国内企业更倾向于从物流、制造、本地生活等具体场景切入，在解决问题中迭代技术。黄仁勋认为，计算行业的每一层都需被重写，AI跃迁依赖可复制、可部署、可验收和可规模化的工业体系，而非单点突破。他预测2026年有望出现能力达到“人类级别”的机器人，人工智能正式迈入与物理世界深度互动的新纪元。多位业内人士认为，这场由“物理AI”驱动的变革已启动，全球科技企业正加速抢占关键技术高地，竞争格局全面拉开。","kind":"news","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":null,"editions":null,"column":"","sentiment":"0","news_tag":"","news_rank":0,"symbols":[],"gpt_button":0,"need_auth":false,"code":"91000000","status":"200"},"commentList":[],"isCommentEnd":true,"newsSizeData":{"likeSize":0,"commentSize":0,"repostSize":0,"favoriteSize":0,"likeStatus":false,"favoriteStatus":false},"APP":{"userAgent":"Mozilla/5.0 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