New NN models are able to do more with the exact same sensor data.
How do creatures know what to eat? Evolution solved that for most creatures, so their sensors don't have to work as hard at runtime. And in other cases, some number of members of a population of creatures will die before the population learns the food is poisonous. Our sensors, and the information processing systems that manage their outputs, are remarkably efficient data processing engines that do the equivalent of approximating and predicting, often well beyond what the most advanced deep learning systems are capable of doing now.
Is there enough information content - per the Shannon entropy definition or otherwise - in DNA and/or RNA to code for the survival-selected traits that
I'm not sure that the (Shannon entropy, MIC, Kolmogorov,) information content of the samples is the limit of any given network trained therefrom? Is there anything to be gained from upsampling and adding e.g. gaussian blur (noise)? Maybe it's feature engineering, maybe it's expert methods bias, maybe it's just sensor fusion; that's the magic noise.
https://github.com/krantirk/Self-Supervised-photo :
> Given a low-resolution input image, PULSE searches the outputs of a generative model (here, StyleGAN) for high-resolution images that are perceptually realistic and downscale correctly.
Maybe no amount of feature engineering can actually add information?
The idea that a magic math technique can replace expensive sensors predates NN's by a few decades. Dozens of start-ups have gone bankrupt trying to do non-invasive blood glucose with portable sensors.
This is a very crude but at least conceptually useful rule of thumb: It's all of the above, but ultimately the analysis result is a mathematical function of an array of values produced by the sensor. Very few math functions do not have the property, that variation in the output increases with the level of variation in the input.