一 | If there is one thing that almost all Indian's get influenced by in some way or the other, it is Bollywood. Today, as we celebrate International Women's Day, we have dived into our archives to find quotes by the leading ladies of Bollywood on things that matter. Be it beauty standards, pay disparity, being ambitious, motherhood, or simply the choices one makes, Alia Bhatt, Anushka Sharma, Deepika Padukone, Priyanka Chopra Jonas and Kareena Kapoor Khan say things that'll surely inspire you and help you navigate in a society that has for years been male dominated. Hear from your favourites... Kareena Kapoor Khan on taking it easy post 40 "In a journey of 20 years, if you’ve not matured as a person and as an artiste, you’ve not learnt anything. I started at the age of 17. Ek alag si energy hoti hai at that time. You’re like listen I can do anything. But, today I’m 40 and I’m like… I can’t. I’m in Zen mode. I just want to do my work. Even if I do one film at a time, I’ll give my best to that. But at 17, you want to be in every film and just kill it. The fire is still there but the flames were a lot higher at that time.” Priyanka Chopra Jonas on being looked down for being ambitious "I’m destiny’s favourite child and I believe that. I believe there’s a hand on my head, which guides me in the right direction. What I do is that I recognise opportunity. Not all have that ability. It might not bang on your door, it might just walk past you. But you should be able to make something out of it. That’s called being ambitious! And I’m proudly ambitious. Sadly, ambition has become a bad word! People say, ‘Haye woh badi ambitious hai!’" Deepika Padukone on pay disparity "Especially, when wage disparity has been such an important conversation, I’m glad it’s finally come to the fore. I’m also proud of it because I’ve earned it, not demanded it. It’s backed by the work I’ve put in over the years. Even if I’d demanded it, nobody is a fool to pay you that kind of money for no reason. Where bridging the wage disparity between actors and actresses is concerned, we still have a long way to go. But this is definitely a landmark moment." Anushka Sharma on motherhood "I would pick motherhood over my career. People are saying that we believe in feministic ideology and I’m saying that I do too. But you cannot say that choosing your career is being feminist and choosing motherhood isn’t. That’s wrong. The ability to choose is called being liberated." Alia Bhatt on being emotional "A 20-somethng shouldn’t believe she’s a poster girl of pure positivity and constant joy. That’s something I don’t want to put out there. I’m not a negative person. But I’ve a lot of doubts. I have confidence issues sometimes. And I’m not always smiling. I’m not always calm. I get stressed out. I get scared. That’s an important emotion that one should feel. I feel my emotions fully. Like even if I’ve got a pimple, I’ll cry." See Also: Alia Bhatt’s family reacts to pictures of Alia and Ranbir Kapoor’s supposed wedding card。 21世纪经济报道记者 陶力上海报道 业界基本以两条并行路径深入物理世界：一条是从工业数据治理出发、向工艺与供应链渗透的“云端赋能”路线；另一条是从底层架构重构、让AI驻留终端的“端侧原生”路线。 2026世界人工智能大会（WAIC）期间，多家技术服务展商集中展现了AI深入物理世界的策略。 当前行业主流端侧AI方案，普遍采用 “云端预训练 + 终端裁剪压缩” 技术模式，该方案已成熟应用于各类数字化场景。但物理场景对AI系统提出了低延迟实时响应、离线独立运行、本地数据隐私安全等刚性要求，传统云端迁移式方案仍存在短板，行业亟需底层技术架构创新，支撑物理AI的规模化落地。 标准共建先行 在“端侧流式多模态模型赋能 AI 硬件爆发”分论坛上，Om AI联汇CEO兼首席科学家赵天成指出，物理世界实时、动态、高频的交互特性，决定了端侧AI不能是云端模型的“简易减法版”，须基于物理场景重构底层架构与运行逻辑。“物理世界并非云端数字世界AI的简单延伸，端侧智能需要建立专属的架构思路、评估体系、安全框架和产业生态。” 当前端侧AI产业面临硬件架构繁杂、接口标准不统一、软硬协同成本高等碎片化问题，与多元算力产业早期困境高度相似。 统一标准体系建设，成为推动端侧原生从实验室验证，走向规模化商用的关键抓手。 为此，论坛上发起《端侧多模态基座与AI硬件协同发展倡议》，倡议提出三个共建方向：一是共建端侧原生技术标准，推动评估体系、接口规范、安全框架标准化；二是共研软硬一体联合方案，推动模型-芯片协同、端云协同边界探索；三是共拓物理AI产业场景，推动重点赛道数据反馈机制与开放生态建设。 据悉，倡议面向全行业开放，旨在凝聚科研机构、硬件厂商、方案商、开发者和产业投资人力量，打破技术壁垒、消除行业孤岛，推动端侧AI产业规范化发展。 在嘉宾对话环节，行业资本从产业投资视角，剖析了消费端侧智能的爆发逻辑，明确核心行业判断：在通用算力底座日趋完善的当下，智能终端产业的核心稀缺资源，已转变为适配硬件迭代规律、支撑超低延迟实时交互的原生多模态能力，这也是AI PC、智能穿戴等终端硬件实现规模化普及的关键前提。 工业AI落地门槛 制造业AI落地，门槛不是模型有多强，而是数据能不能被读懂。一辆整车涉及上万个零部件，数据散落在数百个系统里，格式混乱、时序不同步是常态。
二 | 本届WAIC上，广域铭岛将“懂工业”的基因转化为数据治理壁垒，其平台内嵌的数据治理引擎可自动识别数百种工业协议，将杂乱数据转化为高质量资产；一线人员拖拽组件即可搭建预测性维护、根因分析等应用，不再依赖算法工程师。
三 | 在数据基座搭建完成后，AI向上游渗透至最具挑战性的工艺环节。未来，智能体更像是懂业务的“数字行动者”——把确定性交给智能体，将创造力留给工程师。 在国内某知名电器制造企业，库存分析智能体让全库检查效率提升99%，根因定位准确率达95%，库存管理从“人找问题”转向“问题找人”。 当云端AI在产线上完成数字化闭环，更深层的命题随之而来：如何让智能延伸到现实中的感知、思考和执行闭环。这正是行业参与者共同思考的问题。
四 | 从产业验证看，端侧AI已从可选升级变为刚需配置。在低空经济、海事作业等场景中，智能设备必须具备本地自主运行能力。 实践，是工业AI未来唯一的标尺。制造业AI落地，门槛从来不是模型有多强，而是数据能不能被读懂。AI能力正在快速普及，但把AI能力组织成工作流、嵌入具体场景、最终变成商业回报，依然是一道很难跨越的专业壁垒。
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