Artificial Intelligence (AI) that is derived from the principles of symbolic logic is called "Symbolic AI". Symbolic logic is a derivative of formal logic such as Propositional logic and First-order logic. Symbolic AI is different from AI derived from statistics. Machine Learning or other variation of Deep Learning matches problems with previously known problems by means of statistical analysis, whereas Symbolic AI is based on symbolized reasoning to discover scenarios and solves problems by formulating rules.
Applications of Symbolic AI from the past require pre-defined rules. Example of these applications can be found among Expert Systems.
Modern day Symbolic AI discovers new rules with symbolized reasoning autonomously without human in the loop. In the process, it can learn on its own by accretion. Newer generations of Symbolic AI maintain an accretive knowledge base that enables it to resolve new and unknown problems.
English Language AI NLU Enablement (ELAINE) is an implementation of Symbolic AI for Natural Language applications developed by SiteFocus Inc. It is capable of detecting fallacy, contradiction and consistency found in Natural Language documents. Using symbolized reasoning, ELAINE transforms knowledge it encounters into rules automatically, thereby, eliminating the need for human experts to enter the rules. The following is an example of how it works:
From the FOMC meeting notes, Chairman Powell said:
"... people believe that inflation will come back to the target or around the target so it does not go down as much inflation - does not go down as much in a downturn and does not go up as much when the you know, when the economy strong"
ELAINE would process the above into sentential logic similar to the following Venn Diagram: