Artificial Intelligence Explained — Machine Learning
Artificial Intelligence Explained – Machine Learning
In my earlier post, we explored GOFAI (Good Old-Fashioned AI)—systems that follow rules written by human experts. They don’t learn; they simply execute the logic they were programmed to follow.
Today, let’s take the next step up the AI family tree: Machine Learning (ML).
Machine Learning represents a fundamental shift in how computers solve problems.
Instead of telling a computer how to solve every problem, we provide it with examples and let it discover the patterns for itself.
Imagine teaching a child to recognize dogs.
With GOFAI, you might write hundreds of rules:
* Four legs * Fur * Tail * Barks * Pointed ears
That works… until you meet a Chihuahua, a Great Dane, or a hairless breed.
Machine Learning takes a different approach. Instead of writing rules, you show the computer millions of labeled images of dogs and non-dogs. Over time, it learns the statistical patterns that distinguish them.
In other words:
GOFAI follows rules. Machine Learning discovers rules.
Machine Learning is already part of your daily life, whether you realize it or not:
• Spam email detection • Credit card fraud detection • Product recommendations • Route optimization • Medical image analysis • Predictive maintenance in manufacturing • Search engine ranking
None of these systems are “thinking” like humans.
They are identifying patterns in data and using those patterns to make increasingly accurate predictions.
This is an important distinction.
Machine Learning is not conscious. It is not self-aware. It does not have opinions or desires.
It is a mathematical system that improves its performance by learning from data.
Machine Learning laid the foundation for the next revolution in AI—Deep Learning—which we’ll explore in the next post. Deep Learning made it possible for modern systems like ChatGPT, Claude, Gemini, and many other AI assistants to exist.
Understanding these distinctions helps us have more informed conversations about AI’s capabilities, limitations, and future. #ArtificialIntelligence #MachineLearning #AI #Technology #Innovation #DataScience #DigitalTransformation #LearningAI
Originally published on LinkedIn on June 27, 2026 by Charles David Butler II.
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