That's not asymptotic theory or central limit theorem. It's a meek proposition that you learn to prove in first semester when studying math. A bounded and monotonous function will always converge.
> Higher velocity data does not improve percentage accuracy and makes accuracy levels worse!
What on earth is this supposed to mean? An ML algorithm doesn't care about how fast data "arrives". "Higher velocity" is marketing lingo related to their Kinesis Data Streams service. But I assume that he refers to the usual loss of performance observed with all online-learning algos. Feeding knowledge one by one will mostly just overwrite learned generalizations. Or maybe he is indicating that they are compromising algo quality for faster execution.
> It is important to pick a single metric to improve, even if it is not perfect, but to use it as the basis for measuring performance improvement.
I think that is wrong. Single metrics never capture complex behavior well and will lead to distortions if fed back to the system. I mean it's normal to use one metric for the error. But saying this is the real deal sounds ridiculous to me as this is being done most of the time anyway and probably is going to change in the future. Backprop only works with one metric at the moment - that's just the fact.
> Pat noted that improvement and learning is often very slow – sort of like a slow weight loss program, where you lose weight very slowly. Processes may only be improving by 20 basis points a quarter, or 80 basis points a year. That isn’t a lot, but over a decade, it really makes a difference.
Now he's contradicting himself as he gives a reason for why big data is beneficial.
> His final word of advice – students should be broad in their knowledge of a lot of things, but need to be very deep in one area.
And again he is contradicting himself. Because if you make an analogy from student to a learning algorithm he now gives TWO orthogonal metrics to optimize for.