261–270 of 453 posts
Does anyone remember that 4chan thing where they geolocated some secret flag location and they used info from planes they saw in the sky or something? I wonder if it could do that now.
Earlier quoted context omitted.
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
“best where the actual logic isn’t very hard”? yeah, well it’s also one of the top scorers on the Math olympiads
My current project is nothing too bizarre, it's a 3D renderer. Well-trodden ground. But my project breaks a lot of core assumptions and common conventions, and so any LLM I try to introduce—Gemini 2.5 Pro, Claude 3.7 Thinking, o3—they all tangle themselves up between what's actually in the codebase and the strong pull of what's in the training data.
I tried layering on reminders and guidance in the prompting, but ultimately I just end up narrowing its view, limiting its insight, and removing even the context that this is a 3D renderer and not just pure geometry.
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
Earlier quoted context omitted.
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
Which also fits with how it performs at software engineering (in my experience). Great at boilerplate code, tests, simple tutorials, common puzzles but bad at novel and complex things.
There are many types of complex, and many times complex for a human coder, are trivial for AI and its skillset.
So... where do you go to try this? I didn't notice any link in the article.
EDIT: My mistake, looks like those models are only available on the $20/month Plus plan or higher. I added a note about that to my post.
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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…
Thanks, that's a good explanation. 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.
The "initial" response of the model is interesting:
"The image shows a residential neighborhood with small houses, one of which is light green with a white picket fence and a grey roof. The fire pit and signposts hint at a restaurant or cafe, possibly near the coast. The environment, with olive trees and California poppies, suggests a coastal California location, perhaps Central Coast like Cambria or Morro Bay. The pastel-colored houses and the hills in the background resemble areas like Big Sur. A license plate could offer more, but it's hard to read."
Where did all that come from?! The leap from fire pit & signposts to possible coastal location is wild (& lucky) if that is really the logic it used. The comment on potential licence plate utility, without having first noted that a licence plate is visible is odd, seemingly either an indication that we are seeing a summary of some unknown initial response, and/or perhaps that the model was trained on a mass of geoguessing data where photos were paired not with descriptions but rather commentary such as this.
The model doesn't seem to realize the conflict between this being a residential neighborhood, and there being a presumed restaurant across the road from a residence!
Earlier quoted context omitted.
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've been surprised that so much focus was put on generative uses for LLMs and similar ML tools. It seems to me like they have a way better chance of being useful when tasked with interpreting given information rather than generating something meant to appear new.
> Is what you're doing taking a large amount of text and asking the LLM to convert it into a smaller amount of text? Then it's probably going to be great at it. If you're asking it to convert into a roughly equal amount of text it will be so-so. If you're asking it to create more text than you gave it, forget about it.
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.
I have no memories stored, and in any case it shouldn’t know where I live exactly. The reasoning output didn’t suggest it was relying on any other chat history or information outside the image, but obviously you can’t fully trust it either.
I used a temporary chat, so no info about me is in the memory.
It guessed correctly down to the suburban town.
When asked to explain how it did it, it listed incredibly deductive reasoning.
Color me impressed.