this is extremely scary if true. are these algorithms obtainable by every day people? do they work only in heavily photographed areas or do they infer based on things like climate etc? I would love some documentation on these tools if anyone has any.
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.
Basically a combination of what the game geoguesser does, and public geotagged images to be able to get a decent shot at approximate location for previously unseen areas.
It’s more ominous when automated, but with only a little practice it’s easy enough for a human to get significantly better.
EDIT: yup, looks like this is the guy from the Twitter: https://andrewgao.dev/ and he’s Stanford affiliated with the same department that made the above paper and system.
this is extremely scary if true. are these algorithms obtainable by every day people? do they work only in heavily photographed areas or do they infer based on things like climate etc? I would love some documentation on these tools if anyone has any.
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
If I’m the dev, I would scrape off Google Street View with cords as data source.
https://github.com/LukasHaas/PIGEON
https://arxiv.org/abs/2307.05845
Basically a combination of what the game geoguesser does, and public geotagged images to be able to get a decent shot at approximate location for previously unseen areas.
It’s more ominous when automated, but with only a little practice it’s easy enough for a human to get significantly better.
EDIT: yup, looks like this is the guy from the Twitter: https://andrewgao.dev/ and he’s Stanford affiliated with the same department that made the above paper and system.
Are you sure? The paper you linked mentioned the model beating a top geoguesser player six times in a row.