I don't think that neural networks are the right framework to achieve general purpose artificial intelligence (AGI). And indeed, the AI field may need a paradig

I don't think that neural networks are the right framework to achieve general purpose artificial intelligence (AGI). And indeed, the AI field may need a paradigm shift to achieve higher classification goals. I believe that probabilistic neural networks may be an interesting extension toward general purpose AI, even though this kind of networks need even more data. If we take the example of a puppy, it seems to generalize pretty well using something like one-shot learning, but is it? I cannot confirm for sure how much data a puppy has already digested before being able to do what we could call "one shot learning". So maybe, the exposure to data is already there, waiting for a specialization toward a particular task. Giving the ability to a network to be probabilistic enable it to do inference using uncertainty, which is clearly a neat feature when you are gravitating toward AGI for scene understanding. In the case of video compression, scene understanding may introduce more artifacts IMO: Even if the scene is captured with high end cameras, on a pixel level basis, the edges will never be perfectly neat. I think this will decrease the ability of any network to "understand" which object is at the edges, this results in low classification rates on them, resulting in bad compression/decompression quality (?) for features that are important to the human eye. All in all, I'm not sure that NN are the right tool for this kind of problems. But we are diverging from the main subject VVC, Thanks for the very interesting comments :)