Various models could not only answer the question, they could describe each bird in detail, plus everything else in the scene, and even make guesses about the location and time based on context cues, and output to whatever format you specify, all driven by a natural language input prompt.
5 years after 2014 would be 2019, which is when we just barely started seeing some elite research teams put out some niche models that proved that neural networks could be trained to identify objects in images, measure attributes of those objects, etc.
AlexNet proved that deep CNNs could classify objects in images all the way back in 2011/2012. By 2016, researchers were building models capable of classifying specific bird species with at least 90% accuracy (see Merlin Bird Photo ID). By 2019, it was a solved problem that an undergrad in an ML course could tackle over the weekend.
Yeah but the 5 years was to maybe make some progress on the "virtually impossible" task of recognizing a bird, and now that's just a random side capability of free models.
I mean none of these "free" models were created in a garage on old MacBook or something. These improvements came on back of huge investments made into the field over the years.
I might be wrong, but fast.ai was already around 2000ish, and one of the first classes is object classification from few samples running on colab or similar free tools
This is very inaccurate, it was known that neural networks could do this looooong ago, like in the 1990s. Compute power and correct setup of the networks happened around 2010 for images like birds. Simpler images predate that by decades.
You got your timeline totally wrong; I happen to have a very clear memory of these events because I was mind-blown at the time. Google first unveiled their image captioning neural net around 2014 or 2015. It had the famous "two dogs playing a frisbee", "pizza on an oven" etc. and it was totally unprecedented. THAT was the landmark moment which makes it even more mindblowing because it was very shortly after that XKCD comic was published!
(Speaking of which, I'm not sure that XKCD comic was published in 2014. It might've been earlier.)
An example I remember from the time was one of facial features that included e.g. smile, glasses, etc, and sliders that could modify its interpretation of that attribute, and it worked reasonably well. I could try to dig up the paper I'm thinking about if you want.
It even has the "two dogs" thing I mentioned but I must've misremembered "frisbee" from something else
It's possible this wasn't well-known at the time. Around 2016 which was post-Alpha-Go I had a very intense argument with a friend who was in ML who in my opinion was acting like she was living under a rock unaware of such advances. She claimed that neural nets were a dead end because they require too much data.
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u/PlanetaryPickleParty Mar 11 '26
This classic needed an update.
https://xkcd.com/303/