Hacker News Reader

Comment by krick | Hacker News Reader

Well, assuming the data isn't good — no, obviouly. What "beating humans" would even mean in that case? Imagine you have a dataset of dog and squirrel pictures, with "dog" and "squirrel" (or, actually, "elephant" and "shark") labels assigned quasi-randomly. You make humans and NNs to compete in what label you assigned to them. NN beats humans. Now, what the fuck that result even means?

View on HN

yeah but I guess the data isn't bad in that way in that the sexual orientation of people on the site is probably labeled accurately (in fact I would expect there it's more accurate than, you don't want to lie about what you want on the dating service), the data is bad in that it is perhaps not a representative sample of the population as a whole?

What do you mean by labelled accurately? The AI only predicts how an individual self-labelled on a fairly public dating service, not how this person feels or behaves in any other way. There are so many headless torsos on Grindr (or any other gay hookup app) because many men don't want to self label as gay when they look for sex with other men. These men could very well be looking for women on Tinder.

ok, I hadn't considered that.

> is probably labeled accurately

> the data is bad in that it is perhaps not a representative sample of the population as a whole?

Yeah that's my thinking too

that made me realize,

if you had a bad data set and let the computer train beforehand with the learning data, the human will need to "retrain" as they go (eventually realizing past stereotypes are failing in this atypical data set) and of course are going to do shit.

In a fair test both humans and computers would get to study the learning data.

Reply on news.ycombinator.com