Comment by scoofy
4 days ago
I'm talking about a deductive framework based on by definition arbitrary axioms.
You're talking about an inductive framework in which we create deductive frameworks that model the world we live in as best as we can tell. This is very, very hard work. The flipping back and forth between the inductive framework and the deductive framework -- between modeling reality and discovering and testing new aspects -- is the heart of what knowledge is, but again, this is the dance between the two frameworks.
I'm just talking about that deductive framework, that exists, locked in it's by definition arbitrary axioms. It's in that framework that machine learning should thrive, because all of the propositions possible will fall out of the axioms.
> I'm just talking about that deductive framework, that exists, locked in it's by definition arbitrary axioms. It's in that framework that machine learning should thrive, because all of the propositions possible will fall out of the axioms.
You like to repeat yourself. Are there other words you can use to describe what you're supposedly thinking? What does it mean for machine learning to thrive in a space?
In the same way that you can derive all valid proofs from a set of axioms, you can derive all valid 320x240 bitmaps from a (much simpler!) set of axioms. Does this mean that artwork is "a perfect field for machine learning to thrive [in]"? What would be an example of a field that isn't "perfect for machine learning to thrive in"?