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.
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].
For instance, if the stove element was red hot and you touched it, you'd receive a feeling of pain. Reinforcement learning would suggest that you shouldn't do this again, and you might learn to not touch stove elements when they are red hot anymore.
However, a human is likely to additionally realize that touching a red hot marshmallow stick would burn them as well. And at the same time, a human can also tell that a red ball is safe to touch. This sort of behaviour (internally labeling things and deciding what it is that made the stove element dangerous so you can apply that to other things) would be unsupervised learning.