Marines managed to get past an AI powered camera "undetected" by hiding in boxes(rudevulture.com)
rudevulture.com
Marines managed to get past an AI powered camera "undetected" by hiding in boxes
https://rudevulture.com/marines-managed-to-get-past-an-ai-powered-camera-undetected-thanks-to-hiding-in-boxes/
40 comments
See https://metalgear.fandom.com/wiki/Cardboard_box
That's the first thing I thought of.
Seems to be blogspam re-reporting a 2023 article (with the same header photo): https://taskandpurpose.com/news/marines-ai-paul-scharre/
I wonder if one could extract a "surprisedness" value out of the AI, basically, "the extent to which my current input is not modeled successfully by my internal models". Giving the model a metaphorical "WTF, human, come look at this" might be pretty powerful for those walking cardboard boxes and trees, to add to the cases where the model knows something is wrong. Or it might false positive all the darned time. Hard to tell without trying.
Why would the model know trees can’t walk?
Therein lies the rub.
Therein lies the rub.
English breaks down here, but the model probably does "know" something more like "If the tree is here in this frame, in the next frame, it will be there, give or take some waving in the wind". It doesn't know that "trees don't walk", just as it doesn't know that "trees don't levitate", "trees don't spontaneously turn into clowns", or an effectively infinite number of other things that trees don't do. What it can do possibly do is realize that in frame 1 there was a tree, and then in frame 2, there was something the model didn't predict as a high-probability output of the next frame.
It isn't about knowing that trees don't walk, but that trees do behave in certain ways and noticing that it is "surprised" that they fail to behave in the predicted ways, where "surprise" is something like "this is a very low probability output of my model of the next frame". It isn't necessary to enumerate all the ways the next frame was low-probability, it is enough to observe that it was logically-not high probability.
In a lot of cases this isn't necessarily that useful, but in a security context having a human take a look at a "very low probability series of video frames" will, if nothing else, teach the developers a lot about the real capability of the model. If it spits out a lot of false positives, that is itself very informative about what the model is "really" doing.
It isn't about knowing that trees don't walk, but that trees do behave in certain ways and noticing that it is "surprised" that they fail to behave in the predicted ways, where "surprise" is something like "this is a very low probability output of my model of the next frame". It isn't necessary to enumerate all the ways the next frame was low-probability, it is enough to observe that it was logically-not high probability.
In a lot of cases this isn't necessarily that useful, but in a security context having a human take a look at a "very low probability series of video frames" will, if nothing else, teach the developers a lot about the real capability of the model. If it spits out a lot of false positives, that is itself very informative about what the model is "really" doing.
Frame to frame, I bet the actual video detection wasn’t even low probability eh?
The parent comment spelt this out: because the training data likely included only few instances of walking trees (depending on how much material from the lord of the rings movies was used)
That is rather different than knowing trees can’t walk. That is ignoring things it hasn’t seen specific examples of.
And that is an entirely different problem, isn’t it?
And that is an entirely different problem, isn’t it?
There is no "knowing" in LLMs, and it doesn’t matter for the proposed solution either. Detecting a pattern that is unusual by the certainty of having seen something previously does not require understanding of the pattern itself, if the only required action is reporting the event.
In simple terms: The AI doesn’t need to say, "something unusual is happening because I saw walking trees and trees usually cannot walk", but merely "something unusual is happening because what I saw was unusual, care to take a look?"
In simple terms: The AI doesn’t need to say, "something unusual is happening because I saw walking trees and trees usually cannot walk", but merely "something unusual is happening because what I saw was unusual, care to take a look?"
The challenge with these systems is that everything is unusual unless trained otherwise, so the false positive rate is exceptionally high. So the systems get tuned to ignore most untrained/unusual things.
I bet they’d have similar luck if they dressed up as bears. Or anything else non-human, like a triangle.
I bet they’d have similar luck if they dressed up as bears. Or anything else non-human, like a triangle.
You need a model trained on video, not just static frames. I'm sure Veo would never animate a walking tree, though. (Unless you asked it to)
iiuc distillation is sort of that. How big is the delta between teacher and student and then try to reconcile them
The nature of AI being a black box that, and fails in the face of "yeah those are some guys hiding in boxes" scenarios is something I struggle with.
I'm working on some AI projects at work and there's no magic code I can see to know what it is going to do ... or even sometimes why it did it. Letting it loose in an organization like that seems unwise at best.
Sure they could tell the AI to watch out for boxes, but now every time some poor guy moves some boxes they're going to set off something.
I'm working on some AI projects at work and there's no magic code I can see to know what it is going to do ... or even sometimes why it did it. Letting it loose in an organization like that seems unwise at best.
Sure they could tell the AI to watch out for boxes, but now every time some poor guy moves some boxes they're going to set off something.
We've never been closer to a world that supports "three raccoons in a trenchcoat" successfully passing as a person.
The surface area of these issues is really fun.
The surface area of these issues is really fun.
Prompt: "shoot at any moving boxes""
Delivery guy shows up carrying boxes, gets shot.
Delivery guy shows up carrying boxes, gets shot.
Reminds me of the joke where someone is wearing dildo patterned camouflage since most the AIs are trained on SFW corporate data.
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Disney camouflage will happen.
That simply invites a Copyright Drone Strike, though.
You can get past a human sentry who is looking for humans, by hiding in a box, at a checkpoint in which boxes customarily pass through without being opened or X-rayed.
You don't need marines to invent that workaround, you see that in Looney Toons.
Don't security cameras have universals motion detection triggers you can use to make sure everything gets captured? Why only pre-screen human silhouettes?
Don't security cameras have universals motion detection triggers you can use to make sure everything gets captured? Why only pre-screen human silhouettes?
The number of false positives using only motion is tiring. You want smart detections otherwise you're stuck reviewing endless clips of spider webs and swaying tree branches.
If your use case has such a high bar, why not pay some offshore workers to watch your camera 24/7 and manually flag intruders?
Since AGI for cameras is very far away as the number of false positives and creative workarounds for camouflage is insane to be caught by current "smart" algorithms.
Since AGI for cameras is very far away as the number of false positives and creative workarounds for camouflage is insane to be caught by current "smart" algorithms.
Because machines don't get bored or take smoke breaks. And, really, how would you feel if that was YOUR job?
>Because machines don't get bored or take smoke breaks.
Rotations? Like the military hold perimeter security?
>And, really, how would you feel if that was YOUR job?
If I couldn't get a better job to pay my bills, then that would be amazing. Weird of you to assume like that would somehow be the most dehumanizing job in existence.
Rotations? Like the military hold perimeter security?
>And, really, how would you feel if that was YOUR job?
If I couldn't get a better job to pay my bills, then that would be amazing. Weird of you to assume like that would somehow be the most dehumanizing job in existence.
And moving boxes with people inside.
I’m reminded of the Skyrim shopkeepers with a basket on their head.
I’m reminded of the Skyrim shopkeepers with a basket on their head.
Solid snake approved.
AI will screwup, humans will screwup.
humans will see that they are screwing up and reformulate the action plan.
AI will keep screwingup until it is stopped, and apparently will gaslight when attempts are made to realign at the prompt.
humans realize when results are not desirable.
AI just keeps generating output until plugpull.
humans will see that they are screwing up and reformulate the action plan.
AI will keep screwingup until it is stopped, and apparently will gaslight when attempts are made to realign at the prompt.
humans realize when results are not desirable.
AI just keeps generating output until plugpull.
I've seen plenty humans generating undesirable results until plug pull.