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.

CI Theory

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 problem

Artificial Intelligence

Inspiration: “Using the biological evolution as a source of inspiration, evolutionary computation (EC) solves optimization problems by generating, evaluating and modifying a population of possible solutions.” [1]Genetic Algorithms (GAs) are likely the most popular algorithm belonging to EC. Particle Swarm Optimization, Ant Colony Optimization, Genetic Programming (among others) also belong to evolutionary computation, but we’ll limit the scope to GAs.

Evolutionary Computation

AI is ubiquitous. The term has flooded our lives, and it has lost its flavor. But, as a data scientist / ML engineer / AI engineer (whatever you call yourself), we can hold the community to a higher standard. We can be specific with our algorithms, so showcase our work is more than a series of pre-defined if-then statements. Granted, I know there are intelligent algorithms outside of this framework, but this is a realistic way to discuss our work and highlight the uniqueness of our methods (if you’re using these). If you’re new to CI, I challenge you to find applications, extend the theory.

Conclusion

Inspiration: “Using the human brain as a source of inspiration, artificial neural networks (NNs) are massively parallel distributed networks that have the ability to learn and generalize from examples.” [1]Each NN is composed of neurons, and their organization defines their architecture. The width and depth of NNs define their architecture; this is where “deep learning” originated — by having deep NNs. In the natural language processing (NLP) realm, the GPT-4 architecture is receiving much attention. For computer vision (CV), I’ve always been a fan of the GoogleNet architecture.

Neural Networks

Inspiration: “Using the human language as a source of inspiration, fuzzy systems (FS) model linguistic imprecision and solve uncertain problems based on a generalization of traditional logic, which enables us to perform approximate reasoning.” [1]Full disclosure —I’m biased towards Fuzzy Systems, so I’ll try to stick to the facts. There are many ways to explain this topic, but I like to start with Fuzzy Sets. Traditional set theory forces elements to belong to one set or another. For example, a horse belongs to the set of mammals, and a frog belongs to the set amphibians.

Fuzzy Systems

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…