Symbolic Reasoning
https://deeplearning4j.org/symbolicreasoning
Two flaws of deep learning
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]
Symbol and Sign
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.
Reasoning
Combinations of symbols that express their interrelations could be called reasoning
symbolic reasoning = expert systems
Implementations of symbolic reasoning are called rules engines or expert systems or knowledge graphs
Difference = where the learning happens
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.
Problems with Symbolic AI
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]
DL + SR
In supervised learning, those strings of characters are called labels, the categories by which we classify input data using a statistical model.
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