171–180 of 453 posts
I thought from this [0] that o3 makes up using python when it doesn't actually do so, or have I misunderstood or unduly trusted that link? [0] https://transluce.org/investigating-o3-truthfulness
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I wonder if they have? My current intuition is that the US military / NSA etc have been just as suprised the explosion in capabilities of LLMs/transformers as everyone else. (I'm using "intuition" here as a fancy word for "dumb-ass guess".) I'd be interested to know if the NSA were running their own GPT-style models years before OpenAI started publishing their results.
You don't need a LLM to do this. A dedicated image->coords model would likely perform much better, and that's old school ML at this point.
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If you want to be impressed I suggest trying this yourself on your own photos. I don't consider it my job to impress or mind-blow people: I try to present as realistic as possible a representation of what this stuff can do. That's why I picked an example where its first guess was 200 miles off!
I'm not a computer. I expect a computer to also do better than me at memorizing the phone book, but I'm not impressed by it.
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The tool is just intelligence. Intelligence itself is not dystopian or utopian. It's what you use it for that makes it so. If you don't want to post a photo, then don't post a photo.
>If you don't want to post a photo, then don't post a photo. Other people have posted photos of me without my consent, how am i meant to stop that? If i posted photos 20 years ago when i was a dumb teenager i cant undo that, either
But this here? This is just drama over nothing.
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…
Super cool, man. Watching pro Geoguessr is my latest break-time activity, these geo-gods never cease to impress me. One thing I'm curious about - in high level play, how much of the meta involves knowing characteristics about the photography/equipment/etc. that Google used when they shot it? Frequently I'll watch rainbolt immediately know an African country from nothing but the road, is there something I'm missing?
Another thing is how many areas of the world have surprisingly distinct looks. In one of my early games, before I knew much about anything, I was dropped a trail in the woods. I’ve spent a fair amount of time hiking in Northern New England — and I could just tell immediately that’s where I was just from vibes (i.e. the look of the trees and the rocks) — not something I would have guessed I would have been able to recognize.
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
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
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…
Did you include location metadata with the photos by chance? I’m pretty surprised by these results.
https://cdn.jsdelivr.net/gh/sampatt/media@main/posts/2025-04...
I fed it o3, here's the response:
https://cdn.jsdelivr.net/gh/sampatt/media@main/posts/2025-04...
Nailed it.
There's no metadata there, and the reasoning it outputs makes perfect sense. I have no doubt it'll be tricky when it can be, but I can't see a way for it to cheat here.
For all of the images I've tried, the base model (e.g. 4o) already has a ~95% accurate idea of where the photo is, and then o3 does so much tool use only to confirm its intuition from the base model and slightly narrow down. For OP's initial image, 4o in fact provides a more accurate initial guess of Carmel-by-the-Sea (d=~100mi The clue is in the CoT - you can briefly see the almost correct location as the very first…
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I'm not a computer. I expect a computer to also do better than me at memorizing the phone book, but I'm not impressed by it.
In that case, are you at all surprised that this technology did not exist two years ago?
