I was reading some of the technical papers regarding the “Sense learning” technology, and it got me wondering about a few things.
What ‘compression’, if any, is done within the Sense device installed in the building before it is transmitted out? (I’m not looking for technical details, just an overview of the amount of data gathered within the device vs. that transmitted).
I noted that the neural network (NN) learning seems to have a “convolution” layer in it (at least one). I know that ‘convolution algorithms’ in general can greatly benefit from having “expanded data” fed into them – as long as that “expansion” (e.g. interpolation) does not introduce ‘artifacts’ into the data stream (and I do know of one such rational interpolation algorithm). Would an ‘expansion’ of the Sense device’s data stream by a factor of 10 (or more) improve the learning algorithms?
Perhaps the opposite of #2: Would the learning algorithms benefit from a ‘smart compression’ of the data which provides only ‘changes’ in data as well as the ‘rate of change’ (i.e. tangent slope)? I know from my (many decades old!) knowledge of “wet” (brain) neural networks that often the best “detection and learning” comes from the ‘secondary’ data (i.e. rate of change).
Related to #1: I’d also like to ‘affirm’ the request made in the topic Access to log file/data ? Exporting history data to be able to access (download) the ‘rawest’ data available from the Sense device so that I might be able to help ‘explore’ the possibilities inherent in questions #2 & 3.