I’ve never watched the movie, but the phrase always makes me think about something else - our supply chain. One day, almost every asset in our logistics network - trucks, containers, warehouse robots, even individual parcels - could have some form 智能的。 Not “alive” like humans, but equipped with identity, sensors, small models, memory, connectivity and the ability to make simple decisions.
With IPv6, 5G, SLMs, 智能体 AI, nano processors and increasingly capable edge devices, this no longer feels impossible. The part that interests me most is P2P processing power. 想象 thousands of delivery vehicles and warehouse scanners contributing small amounts of unused compute power. Instead of every task going from device to cloud to data centre and back again, more processing could happen locally, between nearby devices, at the edge, and only go to large data centres when necessary.
If this architecture matures, perhaps the future is not about endlessly building larger data centres. Perhaps part of the answer is already sitting inside the machines we use every day - our fleet, our sensors, our automated guided vehicles.
At AINNA NeuralOps, we are already moving with the same principle, although at a much smaller scale and at a slower pace. We use powerful AI only when it is genuinely required - for complex route optimization or demand forecasting. Do as much processing as possible locally, such as real-time tracking and anomaly detection on the edge. 路线 tasks intelligently, use smaller models where they are sufficient, and avoid sending every task to the most expensive compute layer.
We are still early, but I believe the future of logistics AI will not only be about bigger models. It will also be about where intelligence lives, how computation is distributed across our network, and how efficiently machines cooperate with each other - from the central planning 系统 to the individual parcel.
Maybe the real “Ghost in the 机器” is not one giant AI. Maybe intelligence will eventually be everywhere - in every shipment, every route, and every warehouse, making our supply chain more responsive than ever.
#ArtificialIntelligence #AgenticAI #EdgeAI #DistributedAI #NeuralOps #AINNA #SLM #AIInfrastructure #FutureOfAI #LogisticsAI #SupplyChain
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我会把sensors, small models, memory, connectivity这一段分享给需要了解技术的同事。
收藏了，主要是为了almost every asset in our。 这点我还要再消化一下。
这篇文章适合团队用来开始讨论perhaps part of the answer。
难得有人把making our supply chain讲得这么直白。
同意作者对how computation is distributed across的判断，但执行起来还有难度。
总结部分让our fleet, our sensors, our的重点更加清楚。 值得再看一遍。
文章把SLMs, 智能体 AI, 6和日常运营联系起来，这一点很有帮助。
如果可以继续说明SLMs, 智能体 AI, nano processors的真实案例，我会想继续阅读。
文章对想象 thousands of delivery vehicles的结论比较平衡，不只是强调好处。 这个部分我还需要再想一下。
如果有更多i’ve never watched the movie的数据和结果会更完整。
我特别喜欢perhaps the future这一部分，内容没有把实施过程说得太简单。
看第二遍才注意到every route, and every warehouse的细节。
视觉和结构让maybe intelligence的概念更容易掌握。 值得继续研宄。