Cleaning up data is very expensive. And without that, the analysis is good for nothing. AI helps provide good analysis without having to cleaning up data manually. I don't see how that is going away.
I don't even know where to start. I suppose you don't really think that what separates ML/AI (whatever that means) from your standard OLS regression is that denoising is not done manually?
> Cleaning up data is very expensive. And without that, the analysis is good for nothing.
I hope you are not saying here that linear regression cannot handle noise.
In the end, as this blog post also points out, ML/AI is just a vague blanket term. What you want is a statistical method that captures the signal as fast and efficiently as possible and often the gain from going beyond simple linear models might be marginal.
My own experience has shown that dirty data impacts advanced AI just as much as it impacts far more basic ML techniques.
Even for the most advanced AI we work on, we spend just as much time worrying about clean data as we do anything else.
In general: duplicate data, missing fields, different formats for different parts of the data, inconsistent naming schemes
For text: character encodings, special symbols, escape characters, punctuation, extra or missing spaces and newlines, capitalization
For images: different sizes, rotations, crops, blurry images
For numbers: inconsistent decimal point/comma, outliers with obviously nonsense values or zeros, values in different units of measurement etc.
And then there's bugs in your data pipeline: browser (particularly IE) bugs, logging bugs, didn't understand your distributed databases's conflict resolution policy bugs, failed attempts at cleaning all the previous categories, incorrect assumptions about the "shape" of your data, self-DOS attacks (no joke - Google almost brought down itself by having an img with an empty src tag, which forces the browser to make a duplicate request on every page) which result in extra duplicate requests, incorrectly filtering requests so you count /favicon.ico as a pageview, etc.
If this works at all, the problem is that what you wind-up with is a system that's been heuristically taught to clean-up data for a single snap-shot of your data - and the teaching is expensive and requires experts who are going to move on. Less expensive than clean your data but still a cost.
So when you wind-up with a different pattern of dirty data after a year's time, the system winds crappier than previously and no one will be able to agree how to fix it.
Eventually AI is going to get an evil reputation and that may kill its appeal.
I have always been a stickler for making sure the data going in is good. Takes a bit longer, but makes life much much easier in the long run.
Practically speaking isn't unsupervised learning one of the areas that hasn't really shown much practical application?
I mean, if I'm wrong point me to some examples, but I was under the impression cleaning up data wasn't realky a ML strength at this point...?