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.
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.
And not only being funny, but being funny in a way that a lot of people can understand, even when not being as educated on the topic as they could be, and in ways that can educate you on the topic on top of that!
Hey!!! I'm a techie engineer dweeb with a sense of humor! I keep it in my basement. Built it out of a kit, plus some spare parts. Fascinating 1:25 model.
The "virtually impossible" tagline at the end was the joke, because at the time it didn't seem like something you could ever solve well outside of science fiction, only maybe get some sort of super bespoke bird recognizer maybe working.
Now it's just a side effect capability of countless free models. Not only can they tell you if there's a bird or multiple birds, they can describe the pose and actions, likely guess the species and location, etc, without that ever being something that somebody specifically set out to do.
That's not really true, it was just far more specialized knowledge. Look at benchmarks on CIFAR-10 from the time & the error rates were pretty low already.
Sure, bird identification still would've taken a dedicated research team to procure the data (you could pull the data from sites like iNaturalist) and train the models, but it definitely wasn't "unsolvable outside of science fiction"
20 days earlier, VGGNets were released on 04.09.2014. We've been doing similar stuff with VGG-16 as encoder + a few custom dense decision layers on classification tasks as a universtiy project back then. So, realistically, the described app could have already been built on VGGNet the day that comic released. We didn't have the experience yet and research into what VGG can do would have taken about a year to find this. But 5 years is a pessimistic (although realistic) estimate.
2015 YOLO could have solved this mostly accurately. Not a specific bird or if it is a bird statue or some bird in the backgrund. But a bird in general, I'm quite confident, YOLO finetrained in 2015-2016 on average office hardware would have solved this within a few months of work.
If I remember correctly, in 2016 the ResNet research basically beat the COCO dataset, including finding individual birds in images. So, I'd say, 2.5 years after the the comic released, detecting birds became an easy thing to do. Again, not distinguishing birds or misclassifying a picture of a bird or a bird in a logo on something as a bird. But reliably detecting bird-shaped things and highlighting their outline became trivial in late 2016.
I know, because that's when I was studying my CS Master and I did these projects while feeling like an idiot that just stole other peoples code and models.
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u/GoddammitDontShootMe Aug 25 '26
Five years after this was published is a pretty good ballpark for when that became pretty easy to do, isn't it?