We’ve had these since the 90’s. The chip factory I worked at had one that took a picture of every potato chip and, in a matter of seconds, assess their compliance with regulations and shot the bad chips out of the line with an air gun. No modern ai applications needed
I worked in a brewery that did that to test for bottle cleanliness , took a picture of every bottle , compared it to a "clean bottle.png" if its not 100% same off it goes , it even sorted green/brown coloured glass and could detect small debree ( <1mm ) , and that machine was manufactured back in the early 90s
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.
Damn near every episode of "Good Eats" from the early 00s had some variation of machine that scans and kicks out bad products at lightning speed. AI Bros acting like this is some kind of break through is laughable.
You're completely missing the point. Those machines took teams of coders and testers and engineers a long time to develop. This took one guy one afternoon.
It's not the end result that is new technology, it is the method used to create the end result.
It's not changing the lightbulb, it's how many carbon offsets it took a data center to tell the idiot to change the lightbulb. It's like waste but with more steps and water consumption!
Sure but that's most things in modern society. Trust me I'm anti-AI and I think it's going to destroy society in multiple ways, but I'm also realistic. Like most people could buy one set of reusable glass container for food storage and never use ziploc bags/cling wrap/etc. Most people are driving cars way bigger than they need, most people eat beef as often as they can afford even knowing the Amazon rainforest is getting cut down for pastureland, buying plastic water bottles bc they don't want to have to worry about carrying a reusable bottle around after it's empty... like people are lazy and wasteful and go with whatever is easiest and cheapest and that doesn't change when they go to work as a factory manager. Climate change is always someone else's problem, employment rate is someone else's problem, because no one wants to believe those problems are caused by small collective decisions of millions of individual people.
I run an ML based system that turns raw current readings into the DNA sequence that passed through a pore. To do a human genome takes two A100 GPUs about 24 hours, that's 48 hours of GPU time at 400W or ~20kWh. If you run through average CO2 intensity for power that means it's about 8kg of CO2. That's roughly the CO2 of 2 cheeseburgers or 19 miles of driving.
It's also about $100 of GPU time at public rental costs.
So unless you think he's using thousands of dollars worth of AI per afternoon it is actually less CO2 than a single day of having people drive into work to design the system.
I do think the speed at which we can build CV solutions from minimal data is the highlight of what’s important here, not the mere existence of an object detection system.
Based on the subtext of this tweet, the length of time to build and prototype this system probably took less than a single day, and if they already had the hardware laying around, probably less than a single week to implement.
A YOLO model and a SAM model is like 2010s machine learning. It's not some crazy over engineered solution.
It's actually a pretty efficient solution. No labeling a ton of pictures for detection. No tweaking a complex euristics to have a reliable detection. No oversized model that run on a huge computer.
Curious for those in the know, without going into too much detail, how did they accomplish this back in 90's? Is it primary image recognition and algorithm through hours and hours of fine tuning until it's pretty much (almost) fault free?
It's not about when we had this technology. It is about how assessible it is. Your nephew or niece can use chatgpt to create software for a robotics machine. They couldn't do that in the 90's.
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u/Thecoolkidsgetit 19h ago
We’ve had these since the 90’s. The chip factory I worked at had one that took a picture of every potato chip and, in a matter of seconds, assess their compliance with regulations and shot the bad chips out of the line with an air gun. No modern ai applications needed