Shades of Computational Intelligence

Below the intro, I like the broad take.  But we have gotten more used to the AI thing, it remains, artificial.

Tired of AI? Let’s talk about CI.

Bryce Murray, PhD  in TowardsDataScience

Artificial Intelligence (AI) is everywhere. It has slowly crept away from its original definition and has become a buzzword for most automated algorithms. In this post, I don’t argue what AI is or isn’t — that’s a highly subjective argument at this point. However, I’d like to highlight Computational Intelligence — a well-defined topic.

Motivation

What is Artificial Intelligence? Who knows. It’s an ever-moving target to define what is or isn’t AI. So, I’d like to dive into a science that’s a little more concrete — Computational Intelligence (CI). CI is a three-branched set of theories along with their design and applications. They are more mathematically rigorous and can separate you from the pack by adding to your Data Science toolbox. You may be familiar with these branches — — Neural Networks, Evolutionary Computation, and Fuzzy Systems. Diving into CI, we can talk about sophisticated algorithms that solve more complex problems.

A large community exists within CI. Specifically, within the IEEE, there is a large CI community— with a yearly conference for each branch. I’ve published/volunteered at the FUZZ-IEEE conference over the last few years, and it’s always an excellent opportunity to learn about emerging mathematics and algorithms. Each community drives innovation in the CI space, which trickles from academia into industry. Many CI methods began in academia and evolved into real-world applications.

One of the most common questions I’ve received when talking about CI is, “what problems does each branch solve?” While I can appreciate this question, the branches are not segmented by which problems they solve.

The inspiration of the theories segments the branches. So, it’s impossible to segment into their applications. “But Bryce, what is a CI theory?” In a nutshell, each theory begins as a mathematical representation then implemented into an algorithm (something a computer can do). In their own right, each of the branches deserves many articles. In this post, I give a high-level overview and example of each branch working together to solve a problem. As you read this, remember, it’s impossible to do more than scratch the surface with the methods contained in each branch. I’ll be writing more in-depth posts about specific instances of each of these branches, but I want to describe each of these at a high level so you can get a taste of what’s possible.   ...