Introduction to AI
Approaches to machine learning
There are three common ML approaches used to develop AI tools:
- Supervised learning trains a model on labeled (by humans) data, where each input is paired with a known, correct output. Common use cases: Classification (spam detection, sentiment analysis, image recognition) and regression (price prediction, demand forecasting.)
- Unsupervised learning identifies patterns or structure in unlabeled data, without predefined outcomes. For instance, it explores the data to find relationships, similarities, or latent structures, as well as outputs that are not “right” or “wrong” but descriptive (e.g., clusters or components).
- Reinforcement learning trains an agent to make decisions by interacting with an environment and learning from rewards and penalties. Strengths: Well suited for sequential decision-making and dynamic environment and it can learn complex behaviors without explicit labels. Limitations: Computationally expensive and often slow to train and it requires careful reward design to avoid unintended behavior.
Many of today’s AI tools use a combination of all three ML approaches to create text, images, video, and more.
However, it is important to understand that this “learning” occurs during the development and training phase, before the tool is released to the public. While user feedback and usage data may inform improvements in future versions, the AI does not learn or adapt in real time as you use it.
Rule-based AI: A different approach
It is an approach to AI in which a system makes decisions by following a predefined set of explicit rules created by humans, rather than learning patterns from data.
These systems operate using if–then logic (for example, if a condition is met, then perform a specific action). The rules are typically stored in a rule base and evaluated by an inference engine that determines which rules apply in a given situation.
Key characteristics
- Relies on manually authored rules and decision logic
- Does not learn or adapt from data
- Behavior is predictable, transparent, and easy to trace
Common use cases
- Diagnostic or expert systems with well-defined logic
- Business rule engines (e.g., eligibility checks, compliance systems)
- Simple chatbots and decision trees
“You may encounter rule-based AI in workplace tools that are designed for predictable tasks. This approach is less flexible than machine learning, which allows AI tools to adapt and handle a wider range of complex, real-world data.” – Google AI Essentials
Resources for more information
- Machine Learning vs Deep Learning vs Generative AI – What are the Differences? – October 2, 2025/#Machine Learning – FreeCodeCamp.org
- Explorables – https://pair.withgoogle.com/explorables/
Maximize productivity with AI tools
A custom AI solution is an application that’s tailor-made to solve a specific problem.
An AI tool is AI-powered software that can automate or assist users with a variety of tasks.
An AI model is a computer program trained on a set of data to recognize patterns and perform specific tasks.
AI tools are powered by AI models.This work is sponsored by Kinsta to contribute to WordPress.