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选择您的第一个基因AI 使用案例
要从基因AI开始，首先关注能够改善人类信息体验的领域。
合作伙伴利用数码双胞胎和AI agents作为路线图的一部分，创建世界第一的AI动力制造设施。
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Nvidia是德国Siemens工厂的一系列AI系统
本周的发展是Nvidia在德国的AI路线图的一部分
根据交易，Nvidia正在德国埃尔兰根的Siemens电子工厂推出包括数码双胞胎，AI生成的训练数据和AI agents在内的系统，以加速生产和削减成本。
其中一个技术是利用AI从数码双胞胎那里生成印刷电路板组件的光现实化表示。
这些虚拟拷贝可以在物理原型获得之前用于训练检查系统。 这些公司声称，这种做法可以使工程工作减少85%，使时间上市减少40%。
第二个应用使用AI agents来模拟和预测生产情景。 然后利用工厂的数码双胞胎在实施前验证拟议的计划。
Nvidia正在提供其宇宙世界模型，生成用于质量检查的合成培训数据，而其工厂操作蓝图则支持AI agents的生产规划。 兹Nvidia 模拟图书馆用来模拟与工厂真实数据相比的拟议计划。
合作伙伴表示，在埃兰根站点已经使用超过100个AI算法，跨越生产，物流，质量检查和维护。 Siemens 称公司计划到2030年为止投资约5.59亿美元进行网站转型。
计划在今后几个月内进一步应用。
联合项目的意义在于Siemens在自己的操作中展示技术，而不是简单地推广其潜力，称 Samantha Moou是互动分析公司的分析师。
“对我来说，这是鼓励更多的最终用户和集成商在工厂采用工业AI的一个有意义的步骤。” “Siemens通过在自己的工厂部署AI，可以显示其实际价值，并与客户分享第一手经验。”
Matthieu Kulezak, Interact的另一位分析师，称，所报告的效率增益为预期的客户具体数字，在评估多氯联苯检查等项目的ROI时需要考虑。
“他们指出，手动重新规划工作减少90%，为多氯联苯检查节省85%的工程费用，这些是使与客户进行认真的ROI对话更为容易，而不是停留在视觉幻灯片的水平。”
更大的故事是从一次性项目转向共享基金，据加特纳分析师乔纳森·达文波特(Jonathan Davenport)称。
称表示，部署单个工业AI点解决方案不会达到规模。
从Siemens和Nvidia可以看出，开发了一个工业AI平台，在这个平台上可以部署多个AI使用案例，并辅以数码双胞胎、模拟能力和AI模型，从孤立的应用转向更集成和可扩展的自动化。
这个平台能否超越厄兰根，是一个更难的问题。
“我要记住的一点是：头条储蓄数字描述了Siemens拥有的高混合工厂的潜在效益，而不是在多vendor brownfield网站上证明的节约，因此缩放证明还有待于出现。”
其他障碍包括现有基础设施升级的成本和复杂性；数据准备情况；安全和治理。 制造商必须证明，预期的效率收益是投资的理由，同时确保AI应用程序能在其现有系统和安全要求范围内运作。
Gartner分析师Davenport补充说，“扩大使用范围往往不是对模型进行再培训，而是重建每个网站的整合和背景数据集。”
然而，Siemens并非单靠使用Nvidia的技术来实现工业自动化。
Omron,ABB和Rockwell Automation也与Nvidia合作，利用其Omniverse技术进行工业应用，反映出业界对将AI和模拟应用到制造业的兴趣日益增长。
下一个检验标准是，制造商是否能够在不同生产环境中重复所承诺的效率收益，而整合每个应用的成本和复杂性不会削弱效益。
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The partners are using digital twins and AI agents as part of a roadmap to create world-first AI-powered manufacturing facilities.
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Nvidia is launching a range of AI systems at a Siemens factory in Germany, as the country looks to bolster its standing in the global industrial AI race.
The development came this week as part of Nvidia’s AI roadmap in Germany, building on a plan the AI and manufacturing giants revealed in January to develop what the companies described as the world's first fully AI-powered manufacturing plants.
Under the deal, Nvidia is rolling out systems including digital twins, AI-generated training data and AI agents at Siemens' Electronics Factory in Erlangen, Germany, to accelerate production and cut costs.
Among the technologies is an application using AI to generate photorealistic representations of printed circuit board (PCB) assemblies from digital twins.
These virtual copies can be used to train inspection systems before physical prototypes are available. The companies claim the approach can cut engineering effort by up to 85% and time-to-market by up to 40%.
A second application uses AI agents to model and predict production scenarios. A digital twin of the factory is then used to validate proposed plans before implementation.
Nvidia is providing its Cosmos world models to generate synthetic training data for quality inspection, while its Factory Operations Blueprint supports AI agents in production planning. Nvidia Omniverse libraries are used to simulate proposed plans against real factory data.
According to the partners, more than 100 AI algorithms are already in use at the Erlangen site across production, logistics, quality inspection and maintenance. In total, Siemens said it plans to invest about $559 million in the site's transformation through 2030.
Further applications are planned in the coming months.
The significance of the joint project lies in Siemens demonstrating the technology in its own operations, rather than simply promoting its potential, said Samantha Mou, an analyst at Interact Analysis.
“To me, this is a meaningful step towards encouraging more end users and integrators to adopt industrial AI in factories,” Mou said. “By deploying AI in its own factories, Siemens can demonstrate its practical value and share first-hand experience with customers.”
Matthieu Kulezak, another analyst at Interact, said the reported efficiency gains give prospective customers concrete figures to consider when assessing ROI of projects such as PCB inspections.
“The results they point to of up to 90% less manual replanning effort and up to 85% savings in engineering effort for PCB inspection are the kind of specifics that make it easier to have a serious ROI conversation with customers, rather than staying at the level of vision slides,” he said.
The bigger story is the shift from one-off projects to a shared foundation, according to Jonathan Davenport, a Gartner analyst.
“One important point to understand is that deploying individual industrial AI point solutions will not scale,” he said.
“What we see from Siemens and Nvidia is the development of an industrial AI platform on which multiple AI use cases can be deployed, supported by the digital twins, simulation capabilities and AI models needed to move from isolated applications to more integrated and scalable automation,” he continued.
Whether that platform can travel beyond Erlangen is the harder question.
“One caveat I would keep in mind: the headline savings figures describe potential benefits at a Siemens-owned, high-mix plant, not savings proven across multi-vendor brownfield sites, so the scaling proof is still to come,” Kulezak, of Interact, said.
Other barriers include the cost and complexity of upgrading existing infrastructure; data readiness; security; and governance. Manufacturers must demonstrate that the expected efficiency gains justify the investment, while ensuring AI applications can operate within their existing systems and security requirements.
“Scaling a use case is therefore often less about retraining the model and more about rebuilding the integrations and contextual data sets for each site,” Davenport, the Gartner analyst, added.
Still, Siemens is not alone in using Nvidia’s tech to pursue industrial automation.
Omron, ABB and Rockwell Automation have also collaborated with Nvidia on industrial applications using its Omniverse technology, reflecting a burgeoning industry interest in applying AI and simulation to manufacturing.
The next test will be whether manufacturers can reproduce the promised efficiency gains across different production environments, without the cost and complexity of integrating each application eroding the benefits.
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