从概念到落地，“物理AI”的“ChatGPT时刻”来了吗

新华每日电讯01-16

本报记者罗国芳“‘物理人工智能（物理AI）’的‘ChatGPT时刻’已经到来。”在不久前落下帷幕的美国拉斯维加斯消费电子展上，美国英伟达公司首席执行官黄仁勋在其主旨演讲中为AI未来数年内的发展定调，引发外界热议。业内普遍认为，从工厂里能自主协作的机器人，到能自动规划路线的物流车队，再到马路上的自动驾驶汽车等，“物理AI”将重塑制造业、物流、交通等每一个环节。未来数年内，“物理AI”将实现规模化...

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coming?","themeId":null,"isJumpTheme":false,"ttsUrl":null,"symbols_score_info":{"NVD":0.6,"NVDQ":0.6,"NVD2.UK":0.6,"DIPS":0.6,"NVD3.UK":0.6,"NVDA":1.88,"NVDX":0.6,"NVII":0.6,"3NVD.UK":0.6,"SNVD.UK":0.6,"NVDS":0.6,"NVIW.SI":0.6,"NVDD":0.6,"NVDU":0.6,"NVDG":0.6,"2NVD.UK":0.6,"NVDY":0.6,"NVDS.UK":0.6,"NVDW":0.6},"content_text":"本报记者 罗国芳“‘物理人工智能（物理AI）’的‘ChatGPT时刻’已经到来。”在不久前落下帷幕的美国拉斯维加斯消费电子展上，美国英伟达公司首席执行官黄仁勋在其主旨演讲中为AI未来数年内的发展定调，引发外界热议。业内普遍认为，从工厂里能自主协作的机器人，到能自动规划路线的物流车队，再到马路上的自动驾驶汽车等，“物理AI”将重塑制造业、物流、交通等每一个环节。未来数年内，“物理AI”将实现规模化应用，成为下一轮全球科技竞争的核心赛道，但同时其发展和完善仍然面临多重挑战。由“虚”向“实”：“物理AI”的底层逻辑人们常提到的AI，一般是指“生成式AI”，主要基于从互联网收集的海量文本与图像数据训练而成。它可以写文案、画插画、编创意等，但一碰到现实世界的物理规则，就变成了“纸上谈兵”。而“物理AI”，则补上了这个短板。“物理AI”在“生成式AI”的基础上，学会了理解3D空间里的位置关系以及现实世界的物理规律。除了图片、视频和语音等，它还接收现实中传感器的温度、距离等真实数据，最后转化成机器人能直接执行的动作指令，或是帮助实体设备做出符合现实逻辑的判断。数据、平台和模型是“物理AI”的三大核心要素。“物理AI”在应用到现实场景前会经过一个虚拟训练平台：先给工厂等真实空间创建一个数字孪生体，即虚拟镜像，现实中传感器收集到的温度、距离等数据也直接同步到这个能实时互动的虚拟空间里。接着，在这个虚拟环境里“安”上虚拟传感器、机器人等，模拟现实里的各种操作，传感器会把相关动作、碰撞、光影变化等各类交互数据记录下来。之后，再交给模型，对生成的数据进行扩充、整理与标注，方便AI快速理解学习。有了这个“虚拟训练场”，自主机器就能提前“预习”现实世界的技能。它可以反复练习成千上万次甚至几百万次，安全且快速地学习。赋能实体：“物理AI”的落地场景“物理AI”正在解锁新能力，将重塑各个行业。据估计，“物理AI”相关市场规模将在2030年达数万亿美元，覆盖制造业、物流、医疗、智能驾驶等领域。其中，工业机器人与自动驾驶是两大核心赛道。“物理AI”让机器更“聪明”了——把原本只会执行固定程序的自动化设备，升级成了能自己感知环境、判断状况、灵活应变的主动助手。比如，仓库里的自动搬运机器人，以前可能只会沿着预设路线移动，现在可以在复杂的环境中绕开障碍，稳稳把货物送到目的地。工厂里的机械臂也变“机灵”了——以前只会死死抓着固定位置的物件，现在能通过传感器分辨传送带上零件的摆放角度，自动调整抓握的力度和姿势：碰到易碎的小零件就轻拿轻放，遇上重型工件就攥紧抓牢，精准适配。就连手术室里的手术机器人，也能靠“物理AI”练出一双“巧手”：熟练掌握穿针、缝合这类精细到毫米级的复杂操作，比传统机器人更稳、更精准，可以帮助医生更好地完成高难度手术。“物理AI”也是自动驾驶汽车的“智能老司机”：它能实时处理摄像头、雷达等传感器捕捉的周边路况信息，凭借融合了视觉识别、逻辑推理与动作决策的智能模型，无论是在开阔高速平稳巡航，还是应对人车混行、突发变道等复杂场景，都能做出及时靠谱的驾驶决策。“卡点”仍存：“物理AI”的发展挑战尽管“物理AI”可能是AI竞争的下一个赛道，但其发展目前仍然存在着挑战。首先，要打造和真实世界基本一致的高精度物理仿真环境，需要融合多源数据，比如材料属性、动力学参数等，场景建模成本高昂。不同行业物理规律差异显著，如工业机器人与医疗手术机器人的力学特性不同，通用模型开发难度大。其次，尽管模拟环境中的视觉图像相当逼真，但与现实世界仍然存在不少细微的差异。机器人或许能在模拟环境中熟练完成动作和任务，但进入真实物理场景后，模拟训练的效果无法和现实情况完全对应，导致实体部署时误差率上升。在物理系统中，哪怕是极其微小的错误率，都可能引发连锁反应：小到浪费原材料、造出残次品，大到弄坏设备，甚至引发安全事故。如果AI系统出现幻觉输出——生成不符合真实情况的错误信息，这类错误会在整个生产批次中持续扩散放大，进而对成本管控与运营流程等造成叠加的负面影响。这种“物理AI”决策失误可能导致的实体损害，相关可解释性与责任追溯机制，也需要进一步建立和完善。此外，网络安全也存在可能的漏洞和隐患。互联增加了网络风险，漏洞可能导致未经授权的访问、数据泄露，甚至恶意控制机器人。当安全漏洞可能影响物理安全和运营连续性时，风险甚至更高。","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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