Even so, you can run models on a private cloud. No need to do anything but sense on an embedded device.
Beyond local control, I gave other examples of embedded AI in other comments — or more specifically, what you can do with an embedded LLM. Namely, being able to have a better human interface for complex settings, and being able to reprogram protocols (or anything that is “software-defined”) for future-proofing.
We already have SoC that is functionally not so much different from modern microcontrollers. Depending on economics of scale, it isn’t that big of a leap of imagination to see a microcontroller which includes 4-bit vector ops like a GPU.
Can you explain? I don’t see how sending data to the cloud is a huge burden compared to say an EV or your AC unit.
Even if you put together a private cloud in a data center, it's still going to use up a lot more electrical resources compared to say, an iphone-sized usage, much less in a low-powered, embedded application.
Also, there's a tendency for our civilization, when we make efficiency gains with breakthrough technologies, to then expand our usage. We don't do a great job of actually reducing overall energy expenditure.
How does such a "private cloud" differ from using the "public cloud"? The two seem identical to me.
It's not even a binary at many of the large providers: you can have dedicated servers ("my own server") in a public cloud at AWS, for example.
Personally, I keep it simple. If I can physically hit a box and no-one can get mad at me, then it's private and mine.