Data Warehousing and Data Science

18 January 2022

How to do AI without Machine Learning?

Filed under: Data Science,Machine Learning — Vincent Rainardi @ 8:40 am

I’m doing a master’s degree titled ML and AI, and all this time I’ve been wondering what the difference between AI and ML is. I know AI is a superset of ML, but what is in AI but not in ML? Is it possible to do AI without ML? If so, how?

The Old Days of AI: rule-based

In 1990s there was no machine learning. To be clear, here machine learning includes classical algorithms like Decision Trees, Naive Bayes and SVM, as well as Deep Learning (neural network). In the 1990s there was no machine learning, but there was a lot of news about Artificial Intelligence. Deep Blue was the culmination of that.

So we know there was AI when there was no ML. There was AI without ML. But what was it? What was that AI without ML? Well rule-based of course. The technology that Deep Blue used is called the “Expert System”, which is based on rules defined and tuned by chess masters. You can read about the software behind Deep Blue here: link.

A rule-based system is essentially IF-THEN. There are many different types of rules so I need to clarify which one. It is the IF-THEN rule that makes up an Expert System. There are 2 main components of an Expert System (ES): the Inference Engine and the Knowledge Base. You can read the software architecture of an Expert System here: link.

Search and Fuzzy Logic

Besides ML and ES, another way to do AI is using Search. There are various ways to do search, such as Heuristic Search (Informed Search), Iterative Search and Adversarial Search. You can read the details in an excellent book by Crina Grosan and Ajith Abraham: link, page 13 to 129.

In the Expert System world, the IF-THEN rule-based is not the only way to do Expert System. There is another way: using fuzzy logic. In an IF-THEN rule-based expert system, the truth value is either 0 or 1. In a fuzzy logic system, the truth value is any real number between 0 and 1 (link). There are several fuzzy logic systems, such as Mandani and TSK (you can read the details here: link)

Evolutionary Algorithm and Swarm Intelligence

Another way for doing AI is using Evolutionary Algorithm (EA). EA uses concepts in evolution/biology such as reproduction, natural selection and mutation, in order to develop a solution/AI: link.

And finally, another way for doing AI is Swarm Intelligence: link. Swarm Intelligence (SI) is inspired by the behaviour of a group of animals, such as birds and ants. SI-based AI system consists of a group of agents interacting with each another, and with the environment (similar to Reinforcement Learning but using many agents).


So there you have it, there are a few other ways for doing AI:

  • Machine Learning
  • Expert System (Rule-Based)
  • Fuzzy Logic
  • Search
  • Evolutionary Algorithm
  • Swarm Intelligence

So just because we study ML we should not think that we are the only one, the only way to do AI. There are other ways, which might be better. Which may produce a better AI. Who knows, you haven’t studied them right? Well I know for sure now, that AI is not just ML. I hope this article is useful for you.


  1. Expert System, Wikipedia: link
  2. History of AI, Wikipedia: link
  3. AI without ML, Teradata: link
  4. AI without ML, Claudia Pohlink: link
  5. Rule-based AI vs ML, We Are Brain: link
  6. Intelligence Systems, Crina Grosan & Ajith Abraham: link

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