Symbolic Reasoning
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lack of model interpretability (i.e. why did my model make that prediction?) [solution: deep symbolic learning]
the amount of data requires in order to learn. They are data hungry. [solution: one-shot learning]
The sign or symbol is a visual pattern, string of characters, in which meaning is embedded.
EX: Rose, which is pointing at the red, curling petals layered one over the other in a tight spiral at the end of a stalk of thorns.
Symbols compress sensory data in a way that enables humans, those beings of limited bandwidth, to share information.
Combinations of symbols that express their interrelations could be called reasoning
Implementations of symbolic reasoning are called rules engines or expert systems or knowledge graphs
One of the main differences between machine learning and traditional symbolic reasoning is where the learning happens.
DL: Learns rules as it establishes correlations between inputs and outputs.
SR: the rules are created through human intervention.
Additional rules can’t undo old knowledge. [but DL can be retrained on new data.]
Computers do not know what the symbols mean. [DL links symbols to vectorized representations of the data]
In supervised learning, those strings of characters are called labels, the categories by which we classify input data using a statistical model.