Humans learn facts about the world like "most A are B" by own experience and by being told so (by other people or texts). The systems and mechanisms of storage and usage of such facts (by an "experience system" and a "declarative system") are presumably quite different and may have to do with "episodic memory" and "semantic memory". Nevertheless at least in the human brain the common currency are synaptic weights, and it would be quite interesting to know how these two systems cooperate.

I assume that neural learning is mainly concerned with "learning by own experience" (= training data + annotations), be it supervised or unsupervised learning. I wonder which approaches there are that allow a neural network to "learn by being told". One brute force approach might be to translate a declarative statement like "most A are B" into a set of synthetic training data, but that's definitely not how it works for humans.

Humans learn facts about the world like "most A are B" by own experience and by being told so (by other people or texts). The systems and mechanisms of storage and usage of such facts (by an "experience system" and a "declarative system") are presumably quite different and may have to do with "episodic memory" and "semantic memory". Nevertheless at least in the human brain the common currency are synaptic weights, and it would be quite interesting to know how these two systems cooperate.

I assume that machine learning is mainly concerned with "learning by own experience" (= training data + annotations), be it supervised or unsupervised learning. I wonder which approaches there are that allow a neural network to "learn by being told". One brute force approach might be to translate a declarative statement like "most A are B" into a set of synthetic training data, but that's definitely not how it works for humans.