I’m a software engineer with about 15 years of experience, but my jobs have had nothing to do with ML apart from occasional stats work. I remember a little about neural networks and Bayes nets from school 20 years ago, enough to follow part 1 of the ELK paper easily (up until Ontology Identification) and much (but not all) of the remainder difficult-ly. I occasionally read terrifying essays about AI and would like to help.
I’ve spent maybe 8 hours with the ELK paper; it was encouraging how much I understood with my current background. I plan to take a day off next week to work on it, expecting to total around 20 hours. I have an idea of where I want to look first, but am dubious that I’ll be able to pin down a new idea in that time as I’m not typically very creative. Still, I might get lucky, and updating/refreshing my ML knowledge a bit feels worthwhile in itself.
I’m a software engineer with about 15 years of experience, but my jobs have had nothing to do with ML apart from occasional stats work. I remember a little about neural networks and Bayes nets from school 20 years ago, enough to follow part 1 of the ELK paper easily (up until Ontology Identification) and much (but not all) of the remainder difficult-ly. I occasionally read terrifying essays about AI and would like to help.
I’ve spent maybe 8 hours with the ELK paper; it was encouraging how much I understood with my current background. I plan to take a day off next week to work on it, expecting to total around 20 hours. I have an idea of where I want to look first, but am dubious that I’ll be able to pin down a new idea in that time as I’m not typically very creative. Still, I might get lucky, and updating/refreshing my ML knowledge a bit feels worthwhile in itself.