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School shooting survivor sues AI gun detection firm after system failed to spot weapon
(arstechnica.com)
this post was submitted on 07 Jun 2026
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Yes. It took all of five seconds to find out too.
You've already been wrong once, care to try for two?
Using a LLM for detecting a specific object on an image is possible but stupid: if your object is always the same (like in this case) it's several orders of magnitude cheaper to train once on that specific object then use the computer vision model running directly on the local server that's recording the video.
Otherwise:
Use cases for LLM-based image recognition is if the object changes at every request or it's ultra specific with brands and colors
It isn't the same though. A large gauge shotgun and a small gauge pistol are pretty different looking. Compare those to a .22 rifle with a scope, and those to a decked out ar15. That's a lot of different always the sames. What if it's a revolver? Or has a folded stock? Or a sawed off stock? Will it recognize a derringer or a mac10 with a large capacity mag as guns?
We can because they make us dead. We have valid reason to fear them which is a great motivator for most species to learn to recognize the danger. You'd still recognize a ring gun as a gun, without getting specifically trained to do so a machine will identify it as jewelry.
so, train the computer vision model for a gun and train again for a shotgun. Run the two detection models at the same time.
Your approach is the typical "but if you really want you can use an atomic bomb to kill mosquitoes" - yes, you could do that, but nobody is paying $1 mil/year in inference costs (+some expensively licensed software to wrap around that) when it can be done locally with a $300 GPU (+ some expensively licensed software to wrap around that)
I gave a lot more than two examples and it was hardly exhaustive.
You seem to think that computer vision models can only be trained on a single thing. You simply train your modem on as many object types as you want it to be aware of. That's it.
Didn't I just say that slapping an LLM vision model on to dozens of camera streams would be a near impossible technical hurdle?
I never said vLLM models don't exist. I said they're impractical for this use case.
Haven't been wrong yet. You on the other hand...
There are several examples of exactly what I said, contradicting your repeated claim. Since I don't want to talk to someone with the conversational ability of Donald Trump demanding things be true in spite of evidence they're not im going to be blocking you now. Have a nice day.
No one is denying the existence of vision based LLM models. The issue is performance. It takes in the order of double (or even triple) digit seconds to process an image through an LLM. Even if it took a single second to process an image using decent server-grade hardware (which starts at about $10k per card), that's way too much and still not fast enough.
On just 10 cameras at a facility it would require north of $100k on just GPUs alone.
Whereas a specialized computer vision model could process several dozen camera streams, in real-time, on just one of those $10k cards.
An LLM would process an image in 10 seconds (generous) whereas a computer vision model operates in the milliseconds. We're talking about a 1000x difference in required processing power.
That's why you're wrong and have zero clue what you're talking about.
You're arguing that that family uses a fully loaded semi-trailer to go 200m to the local park. It's a clueless and asinine argument.