Computational intelligence (CI) is a subset of artificial intelligence (AI) that draws inspiration from the natural way humans think when solving problems, learning from data and adapting to new situations. It aims to develop intelligent computers that can learn and adapt to new and evolving environments.

CI involves three major concepts inspired by natural intelligence. These are neural networks (human-like data processing), fuzzy systems (human-like reasoning), and evolutionary algorithms (human-like evolution). 

One of the best examples of a CI system is entertainment platform Netflix’s recommendation system.

Read More about Computational Intelligence

CI enables advanced computers to adapt to the increasing volume of data that can bring uncertainty. Learn more about it below.

How Does Computational Intelligence Work?

When you watch a movie or TV show on Netflix, Hulu, or another streaming platform, they recommend other shows you may enjoy. How does that work? These sites use CI by applying these processes:

  • Learning from data (neural networks): Entertainment platforms use neural networks to analyze patterns from millions of users. For instance, it notices that people who loved “Stranger Things” also tended to enjoy “The Umbrella Academy.”
  • Dealing with uncertainty (fuzzy systems): Sometimes, user preference isn’t clear-cut. You may have watched a horror movie once, but does it mean you love horror movies. Could it be just a one-time thing? Fuzzy systems help a platform handle such uncertainties.
  • Adapting over time (evolutionary algorithms): The recommendation system also evolves as more content gets added and viewers’ preferences change. It tries multiple strategies, keeps the ones that work, and discards less effective ones, much like the concept of “survival of the fittest.”

So, the next time your favorite streaming platform suggests a movie you end up loving, CI quietly works behind the scenes.

What Are the Key Applications of Computational Intelligence?

We talked about how CI is used in the entertainment industry, mainly to keep users on the platform. However, there are a myriad of other CI applications across various industries.

In e-commerce, for example, the rapidly growing volume of data makes CI necessary so computers can effectively deal with uncertain and evolving information.

CI’s capability to analyze and classify massive amounts of data allows systems to process medical data, identify faces, detect fraud, and perform many other advanced processes.

What Are the Benefits of Computational Intelligence?

The benefits CI provides lie in its ability to tackle complex, real-world problems that may be challenging for traditional computational methods. Below are some of these benefits.

Benefits and challenges of computational intelligence
  • Adaptability: CI systems can learn from and adapt to new information or unforeseen circumstances, allowing for the creation of more flexible and resilient solutions.
  • Handling ambiguity: CI systems, especially those that employ fuzzy logic, are well-suited to handle uncertain, noisy, or incomplete data, which is often seen in real-world situations.
  • Enhanced problem-solving skills: CI can tackle problems that are difficult to address using traditional mathematical models, such as pattern recognition or optimization in large, complex systems.
  • Efficiency: Evolutionary algorithms and swarm intelligence, which are part of CI, can often find solutions more efficiently than traditional methods.
  • Generalization: Neural networks and other CI techniques can generalize concepts from the data they’re trained with, making them capable of handling scenarios not explicitly covered during their training.
  • Interactivity: CI systems allow for more human-like interaction, bridging the gap between human intuition and machine processing.

What Are the Limitations of Computational Intelligence?

Like any other technological advancement, CI has its downsides, namely:

  • Data hungry: CI systems often need a lot of data to learn. They may not work well if you don’t feed them enough information.
  • Black box issue: CI models, particularly neural networks, are often seen as “black boxes.” It’s hard to understand how they make decisions. They have secret recipes and we sometimes can’t figure out what’s inside them.
  • Overfitting: CI can sometimes fit too closely to the training data, making it perform exceptionally well on that data but could be better on new, unseen data.
  • Expensive: Setting up and running robust CI systems can cost a lot of money and resources.
  • Ethical concerns: CI systems may make decisions that people find unfair, especially if the data it learned from is biased.
  • Vulnerability to adversarial attacks: Some CI systems can be fooled by specially crafted inputs, called “adversarial examples.” That can make them vulnerable to cyber attacks.

While CI can make computers more advanced, it has its own set of challenges and limitations that should be addressed.

Key Takeaways

  • CI is a branch of AI inspired by human cognition that aims to develop advanced computers.
  • It comprises neural networks (data processing), fuzzy systems (reasoning), and evolutionary algorithms (adaptation).
  • Streaming platforms like Netflix use CI for recommendations, user pattern analysis, and viewer uncertainty and preference evolution management.
  • CI boasts of adaptability, handles ambiguity, solves complex problems, offers efficiency, and can generalize concepts based on training data.
  • CI can be data hungry, act as a black box, pose overfitting risks, costly, and vulnerable to adversarial attacks and potential biases.