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> Intent is a very important factor when answering the question "should the lights be one," I don't think you'll be able to predict that. For instance: if movement is detected in the bedroom at 2AM, should the lights come on? The answer is: a very strong maybe.

You would be collecting data from many sources in order to predict intent. This is why you need a centralized server.



> You would be collecting data from many sources in order to predict intent. This is why you need a centralized server.

Please explain, exactly, what other sources you would collect data from and how the central server would process it to determine if I want the lights on in the middle of the night.

IMHO, determining intent in this scenario is impossible without...

1. a mind-reading sensor, or...

2. an explicit user signal, such as a button-press or command.

The only realistic option is a user signal, and most of those options obviate a lot of these prediction ideas.

I think there's a lot less practical value to having a "central server" controlling everything than you seem to assume.


> Please explain how, exactly, what other sources you would collect data from and how the central server would process it to determine if I want the lights on.

Machine learning navel-gazing is the new "throw a start-up at it", so I'm guessing the answer to this is going to be "if we have enough data..."


> Please explain how, exactly, what other sources you would collect data from and how the central server would process it to determine if I want the lights on.

The more data, the more smarter. In fact, the only difference between a thermostat and the human mind is the number of datas. This is because the Law of Averages predicts that half of all datas will be relevant.


> The more data, the more smarter.

There are several important caveats to this statement.

> In fact, the only difference between a thermostat and the human mind is the number of datas.

This is an oversimplification that borders on dogma.

> This is because the Law of Averages predicts that half of all datas will be relevant.

An interesting interpretation and application.


I really really hope this was a joke, because it was incredibly funny if so and terrifying if not.




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