[loupol]

I wonder how many images from Street View it has been trained on.

I've seen top Geoguessr players be able to pretty consistently determine a location worldwide after seeing a photo for just one second. So I would assume training an LLM to do the same would definitely be doable.

[fellerts]

Yep, some CS/AI grads from Stanford trained an AI on loads of Street View images and built a bot that is able to beat some of the best Geoguessr players: https://www.youtube.com/watch?v=ts5lPDV--cU

[bayesianbot]

IIRC it wasn't that impressive in the end as instead of recognizing the places the AI apparently learnt to recognize subtle differences in street view cameras used in different locations? I might be wrong / thinking of the wrong model l and I'm on mobile without my browsing history so hard to check, but I think it was putting a lot of weight on some pixels that are noisy

[zx_q12]

Top geoguessr players use this technique as well. IIRC rainbolt mentioned that there is a section of a country where the street view camera has a small blemish from a raindrop on the camera so you can instantly tell where you are if you notice that.

[thewataccount]

From my understanding many of the best players immediately look down to tell what "generation streetview car" they're using, and seem to know what continents/times they're from.

[inductive_magic]

> I would assume training an LLM to do the same would definitely be doable.

I wouldn't be so sure. The reasoning process of Geoguessr pros is symbolic, not statistical inference.

/edit: as other commenters pointed out, something similar was done. While this wasn't an LLM, it was a deep learning model, so not symbolic -> https://www.theregister.com/2023/07/15/pigeon_model_geolocat...

[npinsker]

It's been done recently! It's a bit better than (but competitive with) top players.

https://www.youtube.com/watch?v=ts5lPDV--cU