Donald Campbell made the bold prediction that all expansions of knowledge will be found to require the Universal Darwinism algorithm of variation and selection. In this episode, we're going to test that prediction and see if it holds up against what we currently know about Artificial Intelligence and Machine Learning.
For example, does (apparent) knowledge created by Gradient Descent require variation and selection? Or is it really and truly inductive? Or does it just fail to create knowledge at all despite clearly creating improvements?
Ultimately, we'll find that Machine Learning creates an exciting set of epistemological problems that need to be solved!
Youtube version with optional visuals
