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I’m sure there was an element of luck involved but it was still eery.
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How does nearest neighbor search relate to this?
If you forget the LLM implementation, fundamentally what you are trying to do here is first detect a bunch of features in the photo (i.e. fine-grain image captioning "in foreground a firepit with safety warning on glass, in background a model XX car parked in front of a bungalow, in distance rolling hills" etc) then do a fuzzy match of this feature set with other photos you have seen - which ones have the greatest nu…
My hunch is that the way the latest o3/o4-mini "reasoning" models work is different enough to be notable.
If you read through their thought traces they're tackling the problem in a pretty interesting way, including running additional web searches for extra contextual clues.
I took a photo of my cat inside my house, with nothing from visible except the sky, stripped the EXIF, and it STILL managed to get within a few hundred metres of my location - just by inferring based on my interior design and the layout of my house. I’m sure there was an element of luck involved but it was still eery.
It also curiously mentioned why this user is curious about the photo.
I relented after o3 gave up and let it know what building and streets it was. o3 then responded with an analysis of why it couldn't identify the location and asking for further photos to improve it's capabilities :-) !!!
I took a photo of my cat inside my house, with nothing from visible except the sky, stripped the EXIF, and it STILL managed to get within a few hundred metres of my location - just by inferring based on my interior design and the layout of my house. I’m sure there was an element of luck involved but it was still eery.
Not sure if this is true or not but people have pointed out that it uses data from your past conversations to make a guess.
The python zoom in seems performative. A vision model already has access to all the data, how does zooming in help it? Still very cool that it can!
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O3 is OpenAI. Street view is Google. I really doubt OpenAI is scraping enormous amounts of random street view images to train their model.
Why not? They allegedly trained on enough books and newspapers that they have publishers and news organizations go after them.
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That's because you haven't lived in an authoritarian regime. NKVD, STASI, Gestapo, would all have killed for such capabilities. As an east european who grew up and lived in such a regime, I would like to respectfully remind all westerners their care-free and free lives is a privilege the majority of the world doesn't have.
Not to get political, but it deeply irks me to see some American far-leftists glamorize and glorify the Soviet regime and even modern regimes like North Korea's. Especially when certain popular streamers do it. Obviously seeing far-right American internet personalities glorify the Nazi regime is also awful, but the former is often normalized and not considered ostracization-worthy while the latter (rightfully) is.
https://arxiv.org/pdf/2404.10618
would be interesting to see how much better these reasoning models would be on the benchmark
I play competitive Geoguessr at a fairly high level, and I wanted to test this out to see how it compares. It's astonishingly good. It will use information it knows about you to arrive at the answer - it gave me the exact trailhead of a photo I took locally, and when I asked it how, it mentioned that it knows I live nearby. However, I've given it vacation photos from ages ago, and not only in tourist destinations eit…
I find this type of problem is what current AI is best at: where the actual logic isn't very hard, but it requires pulling together and assimilating a huge amount of fuzzy, known information from various sources They are, after all, information-digesters