[skeeter2020]

I'm no expert in ML, but beyond the research and emerging work in unsupervised learning clustering seems to be the most common approach, and there's nothing conceptually new here in the past 10+ years. Don't get me wrong, computationally we can do stuff with a ridiculous # of dimensions that was hard/impossible before and there are new algorithms but I was doing KMeans and DBScan/HDBscan and Gaussian mixtures in grad school 15 years ago in relation to databases, I had never heard of "Machine Learning" and we were in the glacial stage of the AI winter. There's some newer work that is based on human judgement for results but clustering still seems to be the mainstream "data validated" approach...

[trollbridge]

I have a data set from the 1980s and the programs to apply these techniques to it, written in compiled BASIC (predates Microsoft QuickBASIC). They ran their analyses on run of the mill IBM PCs. Really wish I could get it all into a modern framework…

The authors published a respected book based on the data and used it as the foundation for a bunch of other applied research. Sometimes I wish I could resurrect those researchers, or at least have one of their ghosts stop by and see what we can do with computers today.

[nottorp]

What’s wrong if it’s not new?

And by the way, neural networks aren’t new either.