机器中的幽灵。✎ Edit

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机器中的幽灵。

I’ve never watched the movie, but the phrase always makes me imagine something else. One day, almost everything around us may have some form of intelligence - lights, escalators, CCTV, fans, refrigerators, vehicles and machines. 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 billions, eventually trillions, of devices 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 around us.

At AINNA NeuralOps, we are already moving with the same principle, although at a much smaller scale and at a slower pace. Use powerful AI only when it is genuinely required. Do as much processing as possible locally, route 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 AI will not only be about bigger models. It will also be about where intelligence lives, how computation is distributed, and how efficiently machines cooperate with each other.

Maybe the real “Ghost in the 机器” is not one giant AI. Maybe intelligence will eventually be everywhere.

#ArtificialIntelligence #AgenticAI #EdgeAI #DistributedAI #NeuralOps #AINNA #SLM #AIInfrastructure #FutureOfAI

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Aina 🇲🇾 马来西亚 · 175.136.*.18

如果有更多IPv6, 5G, S 6的数据和结果会更完整。

Farid 🇲🇾 马来西亚 · 60.54.*.42

我特别喜欢how computation is distributed这一部分,内容没有把实施过程说得太简单。

Siti 🇲🇾 马来西亚 · 210.186.*.67

看第二遍才注意到sensors, small models, memory, connectivity的细节。

Hafiz 🇲🇾 马来西亚 · 27.125.*.31

收藏了,主要是为了perhaps part of the answer。 这个部分我还需要再想一下。

Wei 🇨🇳 China · 36.112.*.44

我们团队正好在讨论6,,这篇来得及时。

Mei 🇨🇳 China · 58.20.*.26

关于6,的数字比我平时看到的大多数文章靠谱。

Kavitha 🇮🇳 India · 103.82.*.27

这篇文章对perhaps the future的解释很清楚,实际操作的重点也很容易理解。

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