Intuition & Data-Driven Machine Learning
From the post:
Clever algorithms and pages of mathematical formulas filled with probability and optimization theory are usually the associations that get invoked when you ask someone to describe the fields of AI and Machine Learning. Granted, there is definitely an abundance of both, but this mental picture also tends to obscure some of the more interesting and recent developments in these fields: data driven learning, and the fact that you are often better off developing simple intuitive insights instead of complicated domain models which are meant to represent every attribute of the problem.
I wonder about the closing observation:
you are often better off developing simple intuitive insights instead of complicated domain models which are meant to represent every attribute of the problem.
Does that apply to identifications of subjects as well?
May we not be better off to capture the conclusion of an analyst that “X” is a fact, from some large body of data, rather finding a clever way in the data to map their conclusion to that of other analyst’s?
Both said “X,” what more do we need? True enough we need to identify “X” in some way but that is simpler than trying to justify the conclusion in data.
I suppose I am arguing there should be room in subject identification for human intuition, that is, “…because I said so!” 😉