Symbolic Reasoning

https://deeplearning4j.org/symbolicreasoning

Two flaws of deep learning

1. lack of model interpretability (i.e. why did my model make that prediction?) [solution: deep symbolic learning]

2. 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

1. Additional rules can’t undo old knowledge. [but DL can be retrained on new data.]

2. 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.