The most basic form of AI is narrow AI (also known as weak AI), which is designed to perform a specific task or set of tasks rather than exhibit general intelligence. These systems don’t possess the ability to think or reason like humans but instead follow predefined algorithms or models to make decisions or automate processes.

Examples of the most basic AI…

Rule-Based Systems

• How It Works – These are one of the simplest types of AI, operating based on “if-then” rules. The AI follows a set of pre-programmed rules to perform specific tasks.
• Example – A basic customer service chatbot that follows simple scripts to answer common questions, such as store hours or return policies.

Chatbots (Basic)

• How It Works – Simple AI-driven chatbots rely on predefined responses to specific queries. They use pattern matching to understand keywords and provide responses based on a small database of answers.
• Example – Basic chatbots on websites that provide general information, like FAQs, but don’t learn or adapt beyond the initial set of responses.

Decision Trees

• How It Works – A decision tree is a simple algorithm used in AI to make decisions based on a series of conditions. It works by breaking down a decision into smaller parts and leading to an outcome based on conditions or “branches.”
• Example – A decision tree could be used in a loan approval system where different factors, like credit score or income, lead to a decision (approved or denied).

Simple Machine Learning Models (Supervised Learning)

• How It Works – These systems learn from labeled data to make predictions or classifications. The process is relatively straightforward, where the system is trained on a dataset with known outcomes and then makes predictions on new, unseen data.
• Example – A basic email filter that categorizes incoming messages as “spam” or “not spam” based on patterns learned from a dataset of labeled emails.

Image Recognition (Basic)

• How It Works – Simple AI models can recognize patterns in images by comparing pixel data to pre-trained datasets. These models are relatively basic and are used for specific tasks like identifying objects or text within images.
• Example – Optical Character Recognition (OCR) software that reads scanned documents or images with text and converts them into machine-readable data.