281–290 of 453 posts
When actually, modern ML can make really good guesses about ad relevancy using your location, data partners and recent searches from your home's IP address. When you explain this to people, they will still be convinced that the computer is listening to you and reasoning its way to deliver ads for you.
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 was absolutely gobsmacked by the three minute chain of reasoning this thing did, and how it absolutely nailed the location of the photo based on plants, the color of a fence, comparison with nearby photos, and oh yeah, also the EXIF data containing the exact lat/long coordinates that I accidentally left in the file. https://bsky.app/profile/matthewdgreen.bsky.social/post/3lnq...
https://cdn.jsdelivr.net/gh/sampatt/media@main/posts/2025-04...
Here's the model's response:
https://cdn.jsdelivr.net/gh/sampatt/media@main/posts/2025-04...
I don't think it needed the EXIF data. I'd be curious if you tried it again yourself.
I don’t understand the “dystopian” angle. Maybe I’m just old, but I remember the wonder when the Internet made most knowledge available with a few keystrokes. Having deductive reasoning with the same convenience feels wonderful, not dystopian.
> 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 ran several more experiments with EXIF data removed. Honestly though, I don't feel like I need to be 100% robust in this. My key message wasn't "this tool is flawless", it was "it's really weird and entertaining to watch it do this, and it appears to be quite good at it". I think what I've published so far entirely supports that message.
It managed to write a python program to extract the timezone offset and use that to narrow down there it was. Pretty crazy :).
When I cleared personalization data and turned off extended memory it quit being nearly so accurate.
This reminds me of when people are watching YouTubeTV and they see an ad for something they were talking about and are like, "Woah, it must be listening to us!" When actually, modern ML can make really good guesses about ad relevancy using your location, data partners and recent searches from your home's IP address. When you explain this to people, they will still be convinced that the computer is listening to you an…
This is the "Woah, it must be listening to us" part. Because it is listening, not only just sound.
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…
Maybe it's not what happened in your examples, but definitely something to keep an eye on.
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…
Note that they can claim to guess a location based on reasonable clues, but actually use EXIF data. See https://news.ycombinator.com/item?id=43732866 Maybe it's not what happened in your examples, but definitely something to keep an eye on.
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…
For my image I chose a large landscape with lots of trees and a single piece of infrastructure. o3 correctly guessed the correct municipality during its reasoning but landed on naming some nearby municipalities instead and then giving the general area as its final answer. Given the piece of infrastructure getting close should have lead to ah exact result. The reasoning never considered the piece of infrastructure. Th…
It correctly guessed the area with 2 mi accuracy. Impressive.
