A common argument against the current level of AI I often hear is "A child can recognize a dog after seeing it once, whereas it takes a model thousands of images".
This makes sense on the surface, but a child has ~1-1.5 years of "streaming video" before it can do this. Additionally, the first time it sees a dog he/she is probably getting a lot of dopamine due to interaction with dog and positive reinforcement from parent.
So, two questions stemming from thing.
- Does the initial argument hold any water?
- Is there any research being done to tie reinforcement learning and supervised learning in that trained model acts as reinforcement for a newly learning model? This would seem to model "real" learning a bit more.