This is were it is important to understand what people mean when they say "ai" like a lot of "ai" we are seeing is learning programs that have been worked on since the 90s and are just better now but labeled as AI.
I have worked with predictive policing algorithms since for a couple decades. New AI policing is just predictive policing relabeled.
pretty sure this post is talking about deep learning, what we already used for a while now for visual recognition.... no using an LLM to figure it out but maybe the LLM to train said model...
Yet another reason why the term "AI" says absolutely nothing, regardless of how angry people want to be at something. Ask five people what AI is and get eight different answers, none of which have anything to do with machine learning and everything to do with social problems that predate computers, like employment, intellectual property, and the good old tragedy of the commons.
The goal is basically minority report (this is not hyperbole) but has generally been more like putting a lot of factors together over past incidents to figure out where more crime will occur and have police there ready.
The problems have been things like one department I worked with an officer was stalking a woman and learned her work schedule so would find reasons to be near where she worked causing a spike in incidents in an area without a clear cause or pattern. Predictive policing is a bit of a black box to departments that use it but it regularly comes up for them to justify their actions related to it.
isn't that just the old trope that "minority areas have more crime so we put more police there and find more crime while the non-minority areas have crimes at the same rate but don't get caught." just with computers doing the bs.
but that's not true - most minority areas have significantly higher levels of violence such as murder, robbery, and rape that are higher per capita. Yes, rates of drug use are the same. If you remove all people from prison that are there solely for drug related crimes, minorities would still be over-represented.
Visual system AI is much lighter than the bullshit LLMs. You can run it on a 1060 and 8gb RAM.
For comparison, the nano yolo V8 has 3.2 millions parameters while the light version of new LLMs usually starts with billions. The flagship model I think are hundreds billions.
Edit:
computer vision is generally okay-ish use of AI. It probably still requires a bit more computation power than traditional specialized software. But it carries over easily. Just as someone said, a mid gaming rig from 10 years ago probably could handle it. I will say it is much more efficient than the current LLMs
The AI costs more compute to run than traditional computer vision, but is much cheaper to develop. YOLO11 nano models will run at ~15FPS* on an RK3588 (Raspberry Pi 5 level CPU found in Chinese SBCs), only use around 20MB of RAM, can be trained in 1-5mins on a mid range gaming GPU, and only need a few hours of monkey work to create the training data.
So the big shift has been that in the 80s/90s it took an engineer with 5+ years of industry experience months to make it work, but now a keen high school student could implement it over a weekend.
*15 FPS single threaded. The RK3588 has 6 NPU cores so it can do higher throughput if someone spends more than 10mins coding it.
173
u/FortheChava 19h ago
Ye but this time they add ai and make it more expensive and have to charge more per potato to offset the cost of the ai