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Comment by consumer451 | Hacker News Reader

I uploaded a generic photo I took of a field, dandelions, and trees. It confidently stated Switzerland, and provided specific GPS coords.

Of course, it was entirely wrong.

Some level of confidence indication would make a system like this much more useful.

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I think this is generative AI and it doesn't know how confident it is. So far it hasn't gotten any of my pictures right and it's made pretty bad guesses, a human could do better on most of them.

I can only imagine the quality of the systems being sold to governments, and people trusting them because "AI." I mean, "intelligence" is right in the name!

Intelligence is also in the name of the CIA, and that's pretty well understood as an oxymoron. Artificial is also in the name which seems much more apropos. It's clearly not real intelligence, it's purely artificial in the use of the word. I guess, Computer's Best Guess Simulating Intelligence To Low Intelligent Humans would be too on the spot and not as sexy of an acronym.

I actually have faith in the intelligence products produced by the modern CIA, much more than AI snake oil.

There's a difference in the products used to produce intelligence vs the analysis/centralization of it that the oxymoron comment goes towards.

The LLM’s logits should be translatable into probabilities although I’m not too sure how meaningful those might be as models can sometimes be quite confident in entirely invalid predictions.

I haven't done deep reading on LLM architectures and I don't really know if LLMs have logits in the traditional sense of a CNN or something, but I think the problem with this is that the LLM's logits would have absolutely no bearing on it's confidence of the location being correct, only on it's confidence that the tokens making up the answer it provides follow from the tokens that were encoded from the provided image, which isn't the same thing.

I don't think that's the right way to think about LLM logits. Fundamentally the logits represent probability of similarity with the text it's been trained on, given the current prefix. Mixed in with any correspondence with truth is not only tone, phrasing, dialect, language syntax, but also stuff like the likelihood that specific details are related to general concepts. Even if we're talking about a person with three legs, or a horse riding a man, it'll be hard for the LLM to not assign a fairly high probability to sentences that describe two legs, or a man riding the horse and not the other way around.

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