How Symbolic AI Yields Cost Savings, Business Results
Thinking involves manipulating symbols and reasoning consists of computation according to Thomas Hobbes, the philosophical grandfather of artificial intelligence (AI). Machines have the ability to interpret symbols and find new meaning through their manipulation -- a process called symbolic AI. In contrast to machine learning (ML) and some other AI approaches, symbolic AI provides complete transparency by allowing for the creation of clear and explainable rules that guide its reasoning.
Commonly used for segments of AI called natural language processing (NLP) and natural language understanding (NLU), symbolic AI follows an IF-THEN logic structure. When an IF linguistic condition is met, a THEN output is generated. Symbolic AI works best when rules are straightforward. By using the IF-THEN structure, you can avoid the "black box" problems typical of ML where the steps the computer is using to solve a problem are obscured and non-transparent.
Originating in the 1950s, symbolic AI was the original approach to AI, such that it received the nickname "Good Old-Fashioned AI (GOFAI)" in the 1980s book, Artificial Intelligence: The Very Idea by John Haugeland.