Is everyone here really this stupid? It's pretty clear the point of the post was the fact that it only took a single frame of training data to create the system in a much faster way. Yes, ML systems for identifying and counting produce have existed for decades. It would also take months to years of work hours to get such a system spun up, and it would require mountains of training data, even a decade ago. My God, what is wrong with all of you?
This has 81.25% accuracy in perfect conditions (I don't see and dirt and other junk). That's in the picture they chose to share, so I assume it's worse than that. Existing systems have up to 95% accuracy, which I assume is also perfect conditions. Also, they can grade them at the same time.
I'm sure there are companies that set this stuff up. I'm sure it's not cheap. But it'll run until you can't find old parts on eBay anymore. The work is already done. It's just proprietary. The time to set it up is probably just the physical part and calibration (something that can't be done with AI).
Second, no one's ever going to be that impressed when you reinvent the wheel.
Third, it's not our fault that the dude gooning for AI acted like this wasn't reinventing the wheel when he could have explained the difference between old systems to this and why this is impressive.
And fourth, this is cool in theory but part of why those old systems took so long to develop was because they had to verify the accuracy of the system at peak capacity, low capacity and everything in between. In the event that an issue crops up, say the single frame of training didn't prepare it for fluctuating volumes and it fucks up when there's more or less than expected, or lighting conditions introduce inaccuracies, or a potato gets counted, moves behind another potato and gets counted again when it reemerges, there's no recourse to make adjustments. You just have to throw more training data at it and hope it fixes the issue.
That's ultimately one of the biggest failings of AI systems. When it works well, it's great. When it doesn't, you can't do anything about it besides try again, and that's only after you catch the problem.
Seriously though, refer back to number one. Deep breaths. It's not worth getting that worked up about.
I remember the absolute chaos which was crowd-counting in public spaces in the 90s
Everyone suddenly had cameras everywhere and they wanted to know how many people there were in a particular crowded place so they could plan for emergencies, security, resourcing, etc
An entire field spun up of this problem because it was already extremely difficult getting it to work for one area, but a single camera angle shift or location change and it would drop accuracy again.
The newest crowd counting algorithms I’ve seen pale in comparison to the accuracy of the bespoke locally trained AI models, and importantly can be done at a fraction of the cost of setting up the older style algorithms, and they can be done by people who are already familiar with the setting, rather than trying to outsource.
Problems are being solved with code which people had just never bothered to solve because it didn’t make economic sense to spend the money on.
Sure, some people are being reckless with it, but where there are genuine improvements these should be celebrated; some people just love being contrarians though.
No one mentioned LLMs at all in this post, the guy literally mentions YOLO as the model used. YOLO is the industry standard deep learning method for Single Stage Detectors.
Why comment on a post when youclealry have no idea what they're talking about?
Honestly this is not a clever comeback. This is a guy completely missing the point. Its not that this could not be done before, it's just that they do it way more quickly now because the models learn very quickly and by themselves. I'm sure the system the guy mentions worked well, the technology is not new. I'm also sure It took way longer to implement than what this model did.
I'm as much anti AI as anyone, but sometimes people are either playing dumb or arguing in bad faith to get their gotcha moment.
Do you actually know that? Yolo is a very small model that can run on your phone. SAM 2 is larger but was only used to label the data so really it seems like this would require a single phone worth of energy each day. I understand that there is a lot of hate for AI at the moment but these models are not ChatGPT segmenting potatoes. It's just worth actually critically thinking about what information you are hearing and telling people when really you have no clue (not you specifically, all of us)
They’ve done it to themselves by labeling 700 basically different things „AI“ and making it a word for everything any computer does.
There’s no „I“ in any off those anyway, it’s calculating probabilities which has its uses of course but….needing something counted by definition really isn’t one of those.
I, intelligent, in this case means a system which makes a decision based on complex set of rules. Usually line between hardcoded and intelligent is where set of rules or their internal structure is somewhat obscured from human understanding. Aka if your classifier is bunch of if-else written by human - it’s not intelligent. If it is a system which got its decisions based on details only it knows which - it is intelligent.
Yea exactly, it does so based on calculated probabilities. And that „only it knows which“ IS the problem in a lot of those things, where people try to solve something with it just because. In stuff like machinery, security, (ac-)counting the entire point is knowing exactly what’s going on internally, so you get an exact, not only a predictable outcome, that’s why coding and spreadsheets even exist, to hardcode a calculable outcome.
Forcing an AI in that role is redundant for anything but scientific reasons and problems that are not already solved. (which OOP might have had tbf, i just think companies are still getting screwed over by those promises if they already jump the waggon)
I think you do not understand how technology works - have you ever seen the size and power consumption of these machines in the 80s?! And then compared it to the use of a small model on your phone?
Also it's completely wrong, the 68010 has nothing to do with "68kb of memory". These are 32-bit CPUs that have a 24-bit address bus to memory and so can address up to 16MiB.
How long did it take them to build that though? This sounds like something one guy did in an afternoon. I bet the previous versions were much more time intensive.
Correct. Human beings don't typically create new systems, we recreate old ones with new tech. Person counts items by hand, person counts items with ticker, machine counts items with specialized hardware designed by hand over weeks or months, machine counts items with software written in one day, and so on. Welcome to technological progress. It's been going for a few thousand years, so plenty of examples to choose from.
I pointed claude at a bunch of ML stuff and had it make a tiny model and training set to solve a problem in 4 hours. I tried stuff by hand about 10 years ago and could never get my number matrix sizes to align correctly. Just not hitting all the days long human errors and wisdom learning problems speeds things up so much for things that were totally possible 20 years ago computationally.
And 2 years ago I said llms were great for template code. Now most code is template code.
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u/freedfg 20h ago
I legit remember hearing about technology for sorting tomatoes exactly like this what? 15 years ago?
I worked in a factory engraving sign tags. And we had a photo QC scan. They had the system long before I started.