It’s one of great misunderstandings (or more likely - very effective way of getting money) to claim that machine learning is a data problem. We’re so far away from that point, that we have literally no idea what’s needed to make ML a data problem. Algorithms are extremely simple, and it’s all more or less curve fitting.
Media (and surprising amount of tech people as well) tend to claim that ML learning is like human learning - repeat something enough times and you’re done, you know how to do it. ML is no where close to that point.
It's well known that machine learning is currently useful for solving some problems and irrelevant to others. As is the case with all existing ai algorithms. ML is not strong ai and nobody has ever claimed it was, so it seems to be an odd criticism.
But calling machine learning algorithms just algorithms is not helpful at all. I had to work through a few books and courses to get a decent understanding of ML despite the fact that I had a CS degree and a good understanding of discrete algorithms. In short, the things that are put on top of the technology stack do not reduce to the layers below in a useful way.
It sounds like some buzzword speak. Most things that are heralded as ML are nothing but data science idiots from python schools applying some basic math transformations and overselling them.
Wait, what? Are you confusing ML with AI? The last two courses I took on ML were all theory. For example, when we use linear regression, we make assumptions about the noise — i.i.d. and Gaussian.
I really like the standard statistician stance "machine learning is basically just statistics". There is so much hate in it. :)
The FAQ is full of this. For those who want to know the difference--and there are two--let me add the following points:
(a) Most machine learners do not care that much about proper modelling. The point is not about having a right model of the data, but a useful model. Check Breimans "The two cultures" paper.
(b) Machine learning actually cares about computation (Big O notation and such), something that is not part of the standard statistics curriculum.
Media (and surprising amount of tech people as well) tend to claim that ML learning is like human learning - repeat something enough times and you’re done, you know how to do it. ML is no where close to that point.
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