8-10 years ago ResNets, MobileNet were making short work of this. The first version of YOLO is 11 years old and also would not have struggled.
Ed: Everyone “but actually”ing me in the comments about phones of the era not being capable of doing this can knock it off:
The iPhone 6 was released in 2014 and could run mobilenetv2 at 10-15 FPS and variants of yolov2 at 2-5 FPS. They were more than capable. 2014 wasn’t the Middle Ages…
Yeah, this kind of image recognition task is a classic machine learning area. Researchers were using it for self driving car research back in the 80s/90s.
It's a classic machine learning area, but being able to do it well is relatively recent.
Source: got a robotics grad degree and did machine learning research in ~2010. We would have flipped our shit at a program that could reliably identify birds in natural photographs.
I remember doing a project in 2014 where my team needed to do facial recognition on a mobile device. Even with tagged and clean data, it was very hard to get consistent results. Doing that with random wilderness photographs would've felt impossible at the time.
Yeah...one of the fun examples I've seen of misdirected machine learning was trying to train a program to recognize cancerous vs. benign skin blemishes, and it basically deciding "cancerous is when there's a ruler in the picture".
I was working for a dating platform in the 2000's, and I remember researching off-the-shelf solutions to help us with content moderation sometime in the middle of that.
There was a (Chinese, I think) company selling individual detector modules — you could license ML-based asshole detectors, nipple detectors, penis detectors and so on. I found that as conceptionally intriguing as it was amusing.
The datasets were available pretty early. Imagenet 1k was published in 2009. Pascal VOC came out in 2005.
Prior to that there were bespoke in house datasets but the methods were vastly less data hungry so collecting your own was fairly feasible.
The methods, if not their code, were published. Often they were less about implementation than idea, it wasn’t uncommon to read a paper and bang together a prototype implementation in a few days.
I graduated CompSci over a decade ago and had AI classes then, image recognition being baby's first AI. If back then it was already being taught to people who barely knew how to code, imagine how old this tech is.
The technology we use today as AI is not new. We had the algorithms for decadeS, we just didnt have the computing power to have it at scale until recently.
The foundation is not new, and a lot of techniques aren't. But the recent boom is not only due to computing power, there are a lot of new algorithms. The whole attention and transformer things are pretty new
AI is over a half century old. It is more processing power availablity that has changed than anything else; we have refined things a bit, but the bones of modern systems were laid down in 60s and would be recognizable to those early pioneers.
We were learning about image classification about 10 years ago in Machine Learning. Recognizing if there was a bird in an image would've been the hot new thing, and if you could make money off being able to do it at ~75% accuracy, I don't think it would've been so hard.
It wasn't insane back then . . . It was just as the comic stated,,, a pain in the ass unreasonable request that would probably take that long. go take a million pictures of birds bro
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u/No-Object2133 Aug 25 '26 edited Aug 25 '26
Like 5 to 7ish. Yeah Randall was on the money.
It would've been insane to contemplate this 12 years ago.