What is the correlation between visual computing and self-driving?
"Images" is a matrix of pixels, which is pretty unstructured. For such class of input variables function composition(deep learning) seems extremely good function family (empricially) from which we select approximation....What is a function family which seems good well for video?
I have learned in the past that any prediction problem from statistical learning theory is described by 3 things:1. Signal and noise ratio (observable, non-observable data)2. Target function3. Amount of data and suitability of this specific taskCan you please comment about each of this points in context of video understanding. Because I think maybe we can not just take "all videos" and push them into "one bucket" ?
If system provides infinite amount of data how sample train samples(samples) from it?
Drones, self driving cars and we have no idea how it works (of course in case of using DL, ML). Is it ok? (SpaceX Falcon 9 First stage landing is performed by Convex Optimization + First principle based models by PhD student of Stephen Boyd)
About measurement "system works better compare to human". I guess "averaging" is incorrect behavior, because we are interesting in behavior of the model in a specific "input". So how such "averaging" system behavior measurement in AI/ML/DL correlated with reality?