371–380 of 453 posts
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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?
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Did it not, or no one was interested enough to build one? I’m pretty certain there’s a database of portraits somewhere where they search id details from photograph. Automatic tagging exists for photo software. I don’t see why that can be extrapolated to landmarks with enough data.
I think you are underestimating the importance of a "world model" in the process. It is the modeling of how all these details are related to each other that is critical here. The LLM will have an edge by being able to draw on higher level abstract concepts.
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If you'd like a creative waste of time, make it implement any novel algorithm that mixes the idea of X with Y. It will fail miserably, double down on the failure and hard troll you, run out of context and leave you questioning why you even pay for this thing. And it is not something that can be fixed with more specific training.
Can you give an example? Have you tried it recently with the higher-end models?
Not creative, not novel and not difficult algorithmic task. But it requires some reasoning, planning and precision.
It failed miserably.
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…
One thing I’m curious about is if they are so good, and use a similar technique as humans, because they are trained on people writing out their thought processes. Which isn’t a bad thing or an attempt to say they are cheating or this isn’t impressive. But I do wonder how much of the approach taken is “trained in”.
100% of it is. There is no other source of data except human-generated text and images.
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…
And yet many will continue to confidently declare that this is nothing more than fancy autocomplete stochastic parrots.
I am willing to bet that most of this geolocation success is based on overfitting for Google streetview peculiarities.
I.e., feed it images from a military drone and success rate will plummet.
You're anthropomorphizing a likelihood maximization calculator.
And no, human brains are not likelihood maximization calculators.
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…
you never know.. LLM could go full sherlock holmes. Based on the type of grass and the direction of the wind. The type of wood work used. There could be millions of factors that it could factor in and then guess it to a t.
I wonder what would happen if you give o3 before/after photographs of a crime scene and ask it what happened in between.
Is it an US thing? I tried with 17 pictures from Europe and Asia, not in capital cities but in fairly big cities, and it didn't guess any. Sometimes it got the country correct but that was because of signs, so I could have guessed it too.