Interesting, so it's the ability to generalize attributes selectively? That sounds like it would make for the difference between the specialized deep learning we have and a more general intelligence.
Could that be accomplished if NN problems are broken into features, and those features are individually tested against new information? Though you'd need a layer for feature selection, and it still lacks the ability to pick features without training.
> Interesting, so it's the ability to generalize attributes selectively?
In technical terms it is the ability to generate its own features and classifications for making decisions, where in supervised learning a human provides the features and classifications.
> Could that be accomplished if NN problems are broken into features, and those features are individually tested against new information?
If I understood your example correctly, that would be an example of supervised learning, as you pointed out it lacks the ability to pick features without being told what they are. There are types of unsupervised learning that exist and work quite well, for example cluster analysis [1].
Could that be accomplished if NN problems are broken into features, and those features are individually tested against new information? Though you'd need a layer for feature selection, and it still lacks the ability to pick features without training.