Artificial Intelligence’s (AI) Surge in Popularity

Amid the recent surge in popularity of artificial intelligence (AI), we see AI permeating every aspect of our daily lives. From helping us overcome writer’s block to summarizing a week’s worth of lecture notes, AI is now an inescapable part of the modern world.

Amid this barrage of AI adoption and the endless stream of AI-related terms (LLM, AGI, GPT, BERT, to name a few), you might have come across the term Computational Intelligence (CI).

What exactly does it mean? How does it differ from traditional AI? Doesn’t traditional AI also compute?

In this article, we will dive into the world of CI, exploring its fascinating techniques and understanding why it’s becoming a cornerstone in the field of AI.

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What is Computational Intelligence (CI)?

Computational Intelligence, commonly known as CI, is a crucial subclass within the broad and multifaceted field of Artificial Intelligence (AI). AI, with its vast range of applications, spans disciplines from linguistics to mathematics, embodying a convergence of fields that enable it to address complex problems and simulate various aspects of human intelligence.

“AI is inherently multidisciplinary.” — Dr. Luciano Cavalcante Siebert, TU Delft

AI Multidisciplinary

AI is Multidisciplinary

But what exactly is Computational Intelligence, and why is it gaining such traction within the AI community? To understand this, let us take a look at AI’s primary subclasses: Classic AI, Cognitive Science, Machine Learning, and Computational Intelligence.

Overview of AI Subclasses

Overview of AI Subclasses

The diagram above showcases four primary subclasses of AI, segmented by their biological inspiration and focus of models. These subclasses include:

  1. Classic AI:
    • Focuses on logic and rule-based systems.
    • Emphasizes knowledge-based symbol manipulation.
    • High-level simulation of cognitive processes.
    • Biologically irrelevant with a focus on model workings and formalism.
  2. Cognitive Science:
    • Aims to replicate psychological and biological mechanisms that lead to intelligent behavior.
    • Biologically inspired with a focus on model workings and formalism.
  3. Machine Learning:
    • Systems that learn to solve tasks through experience.
    • Minimal domain knowledge, strongly data-driven.
    • Emphasis on function approximation.
  4. Computational Intelligence (CI):
    • Biologically inspired and focused on model outputs.
    • Aims to develop systems that can adapt, learn, and optimize through evolutionary processes and neural networks.

CI stands out in the AI landscape due to its emphasis on biological inspiration and the output focus of its models. This approach not only enhances the adaptability and robustness of AI systems but also bridges the gap between artificial and natural intelligence, making CI a significant area of interest and innovation within the AI community.

Common Examples of CI

Now that you know CI is a subset of AI that focuses on algorithms inspired by nature and biological processes, you might be wondering what CI looks like in practice. Here are some common examples:

  1. Artificial Neural Networks (ANNs): Have you ever wondered how your phone recognizes your face or how recommendations on your streaming service seem so accurate? These are powered by ANNs, which mimic the human brain’s neural networks to learn and make decisions based on data.

  2. Evolutionary Computing: Imagine solving complex problems by simulating the process of natural evolution. This is precisely what evolutionary computing does. Techniques like genetic algorithms evolve solutions over generations, selecting the fittest solutions for the next iteration.

  3. Swarm Intelligence: Think about how ants find the shortest path to food or how birds flock in a coordinated manner. Swarm intelligence algorithms, like Ant Colony Optimization and Particle Swarm Optimization, harness this collective behavior to solve optimization problems efficiently.

  4. Reinforcement Learning (RL): Have you seen robots learning to walk or agents mastering video games? RL is behind these advancements. By using a system of rewards and penalties, RL algorithms learn the best actions to take in a given environment to maximize cumulative rewards.

When Should You Use CI?

So, when is CI the right choice? CI techniques are particularly useful when:

  • Dealing with complex, dynamic, and non-linear problems: Problems where traditional analytical methods fall short.
  • Optimization problems: Situations where you need to find the best solution among a vast number of possibilities.
  • Real-time decision-making: Applications requiring adaptive and quick responses to changing environments.
  • Learning from unstructured data: Scenarios where the data does not follow a clear pattern and needs sophisticated methods to extract meaningful insights.

Why is CI Important?

Why should we care about CI? Here are a few compelling reasons:

  • Versatility: CI techniques can be applied across various domains, from healthcare and finance to robotics and entertainment. They help in designing more adaptive and intelligent systems.
  • Scalability: CI algorithms can handle large-scale problems and datasets, making them suitable for modern big data applications.
  • Efficiency: These methods often find good solutions faster and with less computational power compared to traditional techniques.
  • Innovation: By drawing inspiration from nature, CI fosters innovative approaches that push the boundaries of what artificial systems can achieve.

In a world increasingly driven by AI, understanding and leveraging the power of Computational Intelligence can open up new possibilities and drive technological advancements. Ready to explore further? Dive into the links below for more in-depth insights and practical applications.


I hope this introductory exploration of Computational Intelligence has piqued your interest and provided a clear understanding of its significance and applications. Whether you’re a seasoned AI enthusiast or a curious beginner, CI offers exciting avenues to explore and innovate.

P.S. Look out for future articles that will provide a deep dive into ANNs, Evolutionary Computing & Swarm Intelligence, and Reinforcement Learning (with code 🥳)!

— Sagar & Manu

Image Sources:

  1. CSE2530 Computational Intelligence TU Delft
  2. Imgflip