I am sorry, you come across as extremely over-enthusiastic, without too many specifics beyond “we’re just about to figure it all out”, “you just wait”, and “it’s gonna revolutionize everything”. We’ve seen this before with ImageNet, didn’t we? When everybody thought that because ConvNets are crushing all the older methods, AI is right around the corner. Well, it turned out to be much more complicated than that, didn’t it. Transformers are great (well, if you have the compute that is) don’t get me wrong, but let’s not get ahead of ourselves. The field is over-hyped as it is.
> When everybody thought that because ConvNets are crushing all the older methods, AI is right around the corner. Well, it turned out to be much more complicated than that, didn’t it.
I don't think anyone familiar with the area thought that ConvNets will give us AGI.
However, their effect has been huge! It's hard to overstate this. Computer vision used to be a small niche topic, with tons of effort required to get something working even on simple images. The quality of today's ConvNet predictions is way beyond anybody's imagination in around 2010. Models built around that time were like a house of cards. Extremely carefully crafted for specific scenarios, where moving one threshold a bit would destroy your output.
You have to be very careful with such claims. For example it may be able to tell apart tons of dog breeds at a superhuman level, but that's not really what people imagine if they hear such claims.
Also sometimes in medical imaging the conditions are very different from actual practice. For example the doctor may be worse than the convnet on certain types of low-quality, low-dynamic range images that someone preprocessed in a particular way. But sure, in the medical field some error prone, boring counting tasks and spot-the-cancer-in-your 200th-image-today, the machine can perform actually better.
