There are tons of machine learning algorithm libraries easily usable by any relatively amateur programmer. Aside from that all they would need is access to a sufficient quantity of geographically tagged photographs to train one with. You could probably scrape a decent corpus from google street view.
The obtainability of any given AI application is directly proportional to the availability of data sets that model the problem. The algorithms are all packed up into user friendly programs and apis that are mostly freely available.
It might be easier to train the AI to the specific things Geoguessr players have collected as signs that give away a location instead of letting the AI figure all those out again.
There are tons of machine learning algorithm libraries easily usable by any relatively amateur programmer. Aside from that all they would need is access to a sufficient quantity of geographically tagged photographs to train one with. You could probably scrape a decent corpus from google street view.
The obtainability of any given AI application is directly proportional to the availability of data sets that model the problem. The algorithms are all packed up into user friendly programs and apis that are mostly freely available.
It might be easier to train the AI to the specific things Geoguessr players have collected as signs that give away a location instead of letting the AI figure all those out again.
https://arxiv.org/html/2307.05845v4
I believe this is the paper
Rainbolt has a couple of videos playing against AI. I don’t remember what they said it was trained on but it’s possible it was based on that.
ooh baby I love a good supervised learning