Tech Term Decoded: Symbolic AI

Definition

Symbolic AI, also referred to as Good Old-Fashioned Artificial Intelligence (GOFAI), is an area of artificial intelligence that uses symbols and symbolic reasoning to solve complex problems. In contrast to modern machine learning techniques, which depend on data and statistical models, symbolic AI represents knowledge explicitly through symbols and rules. This approach has laid the groundwork in the development of AI and remains relevant in various applications today [1]. Let’s take a look at the following example scenario of a software system that determines tax compliance for businesses based on current Federal Inland Revenue Service (FIRS) regulations;

Symbolic AI Approach:

A symbolic AI tax system would use explicit FIRS rules and logical reasoning:

IF business annual turnover is below ₦25 million AND

   business is registered as small company

THEN apply Small Company Tax Rate of 0% (tax exempt)

IF business annual turnover is between ₦25 million and ₦100 million

THEN apply Medium Company Tax Rate of 20% on profits

IF business annual turnover exceeds ₦100 million

THEN apply Standard Company Income Tax Rate of 30% on profits

IF business operates in Lagos State AND

   has more than 10 employees

THEN add Lagos State Development Levy of ₦500 per employee

IF business made education tax-eligible profits AND

   is not agriculture-focused

THEN apply Education Tax of 2.5% on assessable profits

Symbolic AI process [2]

Origin

Symbolic AI originated as far back as the early days of AI research, particularly in the 1950s and 1960s, when pioneers such as John McCarthy and Allen Newell laid the groundwork for this approach. The concept became well known with the development of expert systems, knowledge-based reasoning, and early symbolic language processing techniques. In the course of time, the evolution of symbolic AI has contributed to the development of cognitive science, natural language understanding, and knowledge engineering, establishing itself as an enduring pillar of AI methodology [3].

Context and Usage

Symbolic AI is been applied in many fields such as Natural Language Processing (NLP) with assistants like Siri or Alexa, Medical diagnosis, Autonomous vehicles, Robots capable of avoiding obstacles and interacting with humans. 

Why it Matters

Symbolic artificial intelligence is very useful for situations where the rules are very clear cut,  and you can easily obtain input and transform it into symbols. In fact, rule-based systems still account for most computer programs today, including those used to create deep learning applications [4].

In Practice

Siri and Other Digital Assistants are good examples of real-life case studies of companies practicing Symbolic AI. When you ask Siri, Google Assistant, or Alexa a question, symbolic AI assists them to understand and reply. They make use of language rules and databases to make sense of exactly what you’re asking and give you the right answer, whether setting a timer or finding a restaurant [5].

See Also 

Related Learning Approaches: 

Supervised Learning: Learning from labeled examples with known outcomes

Transfer Learning: Using knowledge gained from one task to improve performance on another

Unsupervised Learning: Learning without labeled data 

Weak AI: Systems designed for specific tasks

Zero-shot Learning (ZSL): Making predictions without training examples 

Reference

1. Geeksforgeeks. (2024). What is Symbolic AI?
2. Klingler, N. (2024). Large Action Models: Beyond Language, Into Action.
3. Lark Editorial Team. (2023). Symbolic AI.
4. Dickson, B. (2019). What is symbolic artificial intelligence?
5. Lambert, A. (2024). Neuro-Symbolic AI: Combining the Best of Both Worlds in AI Technology.