Re:Andreessen-Horowitz craps on “AI” startups from a great h...
by nl on Tuesday February 25, 2020 @05:30PM
(#22418214)
> Er, yes, I did, in my original post. The form you quoted was me attempting to be more precise in rephrasing it.
Ok. No one other than yourself has made that argument.
> Scraping social network data? It’s already a graph, and it often already has annotation fields in the JSON graph endpoints describing the relationships between the members. If you don’t just stop and look at the dataset, you might think your feature-extractor is doing something very clever, when actually it’s just finding the explicit pre-chewed “relationship” field and spitting it back out at you.
I've worked in this exact field, and I've never ever heard of anyone who doesn't do this. I guess someone might.. so if your point is that there are dumb people around then ok.
But that isn't what a feature extractor does! In the graph context a simple feature extractor is something hand coded like the degree of a node, or a more complex learned one is something that maybe does embedding.
If your point is that people should understand their data, then yes that is in data science 101 for good reasons.
Ok. No one other than yourself has made that argument.
> Scraping social network data? It’s already a graph, and it often already has annotation fields in the JSON graph endpoints describing the relationships between the members. If you don’t just stop and look at the dataset, you might think your feature-extractor is doing something very clever, when actually it’s just finding the explicit pre-chewed “relationship” field and spitting it back out at you.
I've worked in this exact field, and I've never ever heard of anyone who doesn't do this. I guess someone might.. so if your point is that there are dumb people around then ok.
But that isn't what a feature extractor does! In the graph context a simple feature extractor is something hand coded like the degree of a node, or a more complex learned one is something that maybe does embedding.
If your point is that people should understand their data, then yes that is in data science 101 for good reasons.