Symbolic AI is one such advancement that has garnered attention from all across the globe. Evidently, this form of Artificial intelligence makes use of symbols. As known to us, machines mimic human behavior as far as AI is concerned and the fact that humans think using symbols and the ability of machines to operate using symbols forms the base of Symbolic AI. Almost everything in this universe can be well understood by humans using symbols. Be it defining things (pen, book, etc.), activities (running, walking, eating, etc.), abstract activities, things that physically don't exist or anything for that matter – all of these can be described using symbols.

Today, artificial intelligence is mostly about artificial neural networks and deep learning. But this is not how it always was. In fact, for most of its six-decade history, the field was dominated by symbolic artificial intelligence, also known as "classical AI," "rule-based AI," and "good old-fashioned AI." Symbolic AI involves the explicit embedding of human knowledge and behavior rules into computer programs. The practice showed a lot of promise in the early decades of AI research. But in recent years, as neural networks, also known as connectionist AI, gained traction, symbolic AI has fallen by the wayside.

In the history of the quest for human-level artificial intelligence, a number of rival paradigms have vied for supremacy. Symbolic artificial intelligence was dominant for much of the 20th century, but currently a connectionist paradigm is in the ascendant, namely machine learning with deep neural networks. However, both paradigms have strengths and weaknesses, and a significant challenge for the field today is to effect a reconciliation. A central tenet of the symbolic paradigm is that intelligence results from the manipulation of abstract compositional representations whose elements stand for objects and relations. If this is correct, then a key objective for deep learning is to develop architectures capable of discovering objects and relations in raw data, and learning how to represent them in ways that are useful for downstream processing.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Today, artificial intelligence is mostly about artificial neural networks and deep learning. But this is not how it always was. In fact, for most of its six-decade history, the field was dominated by symbolic artificial intelligence, also known as "classical AI," "rule-based AI," and "good old-fashioned AI." Symbolic AI involves the explicit embedding of human knowledge and behavior rules into computer programs. The practice showed a lot of promise in the early decades of AI research.

Conventional, data-crunching artificial intelligence, which is the foundation of deep learning, isn't enough on its own; the human-like reasoning of symbolic artificial intelligence is fascinating, but on its own, it isn't enough either. The unique hybrid combination of the two -- numeric data analytics techniques that include statistical analysis, modeling, and machine learning, plus the explainability (and transparency) of symbolic artificial intelligence -- is now termed "cognitive AI." It's an extraordinary breakthrough to have the ability to implement a human-like ability to perceive, understand, correlate, learn, teach, reason, and solve problems faster than existing AI solutions. Key technology components were at the core of the wildly successful NASA Mars Rover's mission. Alone and 150 million miles from Earth, the rover was able to successfully adapt to conditions without direct instruction. After a dust storm, it taught itself to rotate its solar panels and shake off accumulated dust blocking essential solar ray absorption.