The Rise and Fall of Symbolic AI

The Second World War saw massive scientific contributions and technological advancements. Innovations such as radar technology, the mass production of penicillin, and the jet engine were all a by-product of the war. More importantly, the first electronic computer (Colossus) was also developed to decipher encrypted Nazi communications during the war. After the war, the desire to achieve machine intelligence continued to grow.

As humans, we wanted to enable computers with human-like abilities. The primary motivation behind Artificial Intelligence (AI) systems has always been to allow computers to mimic our behavior, to enable machines to think like us and act like us, to be like us. Over the years, this motivation has remained virtually unchanged. However, the methodology and the mindset of how we approach AI has gone through several phases throughout the years.

While today, we rely on deep Neural Networks (NNs) (also known as Connectionism AI) to allow a machine to automatically teach itself how to solve a particular task, early AI systems were heavily ruled, and logic-based methods were used to teach computers human behavior. These systems are referred to as Symbolic AI. Symbolic AI is one of the earliest forms of AI. In his 1985 book Artificial Intelligence: The Very Idea, John Haugeland coined Good Old-Fashioned AI (GOFAI) as a reference to Symbolic AI. It dominated the computer science and AI fields from the 1950s all the way through to the 1970s. One of the first successful implementations of Symbolic AI dates to 1951, when Christopher Strachey, a British computer scientist, wrote over 2,000 instructions to teach a computer how to play the game of checkers (draughts). Today, Symbolic AI has been leveraged to solve many problems, including Natural Language Processing (NLP) applications and Expert System (ES) implementations.

This chapter aims to understand the underlying mechanics of Symbolic AI, its key features, and its relevance to the next generation of AI systems.

The first objective of this chapter is to discuss the concept of Symbolic AI and provide a brief overview of its features. Symbolic AI is heavily influenced by human interaction and knowledge representation. We will discuss how this shaped Symbolic AI. We will then examine the key features of Symbolic AI, which allowed it to dominate the field during its time. After that, we will cover various paradigms of Symbolic AI and discuss some real-life use cases based on Symbolic AI. We will finally discuss the main challenges when developing Symbolic AI systems and understand their significant pitfalls.

This chapter will go through the following topics:

• Defining Symbolic AI
• Knowledge representation through symbols and signs
• Critical features of Symbolic AI
• The different paradigms and applications of Symbolic AI
• Limitations and pitfalls of Symbolic AI

Let us get started with defining what we mean by Symbolic AI.