301–310 of 453 posts
> I’m confident it didn’t cheat and look at the EXIF data on the photograph, because if it had cheated it wouldn’t have guessed Cambria first. It also, at one point, said it couldn't see any image data at all. You absolutely cannot trust what it says. You need to re-run with the EXIF data removed.
I’m sure there are areas where the location guessing can be scary accurate, like the article managed to guess the exact town as its backup guess. But seeing the chain of thought, I’m confident there are many areas that it will be far less precise. Show it a picture of a trailer park somewhere in Kansas (exclude any signs with the trailer park name and location) and I’ll bet the model only manages to guess the state c…
This isn't really a criticism though. The photo needs to contain sufficient information for a guess to be possible. Photos contain a huge amount of information, much more than people realize unless they're geoguessr pros, but there isn't a guarantee that a random image of a trailer park could be pinpointed.
Even if, in theory, we mapped every inch of the earth and then checked against that data, all it would take is a team of bulldozers and that information is out of date. Maybe in the future we have constantly updated feeds of the entire planet, but... hopefully not!
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> weird people will do weird stuff regardless of technology If I were someone's only stalker, I'd be absolutely hopeless at finding their location from images. I'm really bad at it if I don't know the location first hand But now, suddenly with AI I'm close to an expert. The accessibility of just uploading an image to ChatGPT means everyone has an easy way of abusing it, not just a small percentage of the population
So I guess the evil we're worried about is stalkers who are bad at guessing locations, bad enough with tech that they don't know about geoguessr websites and subreddits, but good enough with tech to use LLMs?
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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.
No, you misunderstand me. Look at all the people in this thread talking about how people are fantastic at guessing locations from photos. This is not a new thing. "If you want something to be secret don't post it online" is a principle that far predates LLMs. It's still true. It always was. The idea that authoritarian regimes had no way to place the location of photos before this is laughable.
There are, and always will be, _few_ humans with the talent and knowledge for geo guessing, their attention and time scarce and precious resources. Enter LLMs, which can process images at scale.
Someone might observe strict OPSEC when it comes with their presence online. But would their cousins do the same? Their elderly parents? Their friends? How about the myriad CCTV camera in the public spaces? Photos aside, no one can live off the grid in this age; our electronic reflection grows sharper, more focused every day. And so we generate data and LLMs can compile that data at scale, reliable and fast.
As a small aside: "The idea that authoritarian regimes had no way to place the location of photos before" it's not an argument I made or implied.
Pick some random towns from a part of the UK: Horsham, Guildford, Worthing, Sheffield and see how it goes?
If you want to exclude memory and exif data, just open streetview in some random corner of the world and take a screenshot (avoiding any text obviously). It's pretty good if you give it enough to reason with. It basically iterates on coming up with some hypothesis and then does web searches to validate those.
I'm confused how so many people have such different outcomes. People seem to have fixated on the fact that the models use EXIF data if it's included, but it's trivially easy to run the test ensuring that isn't happening, and the results are still amazing.
I think some people really want to dismiss the capabilities of the models. I get that there's hype and it's annoying, but... look at what it's doing, right now, in front of you!
OK, but we know so much tech is from California. Pick some random towns from a part of the UK: Horsham, Guildford, Worthing, Sheffield and see how it goes?
I would expect it to be able to guess on par with highly documented places.
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Depends on the field of development you do. CRUD backend app for a business in a common sector? It's mostly just connecting stuff together (though I would argue that an experienced dev with a good stack takes less time to write it as is than painstakingly explaining it to an LLM in an inexact human language). Some R&D stuff, or even debugging any kind of code? It's almost useless, as it would require deep reasoning,…
Have you tried debugging using the new "reasoning" models yet? I have been extremely impressed with o1, o3, o4-mini and Gemini 2.5 as debugging aids. The combination of long context input and their chain-of-thought means they can frequently help me figure out bugs that span several different layers of code. I wrote about an early experiment with that here: https://simonwillison.net/2024/Sep/25/o1-preview-llm/ Here's…
Feels a bit boring to use a picture of California. That's just a little too "in distribution" for a feature likely developed and tested there.
It failed to locate the image I provided. It got caught in a loop of cropping the image and presumably running some multi-approach to similarly search with images. If you use their image gen it is quite clear that they must've amassed a large image database at this point that they use for reference material pre-gen.
https://i.redd.it/ddscx4zibpme1.jpeg
I was intrigued because it looked much like Charleston SC. O3 didn't consider that and had to keep reminding itself that it couldn't possibly be the African flat bridge it kept determining it was (as the crop image analysis could recognize the suspension/cable structure).
