Foundations

AI Foundations

Models, data, machine learning, language, APIs, and the parts that make an AI system work.

How to use this section

Artificial intelligence becomes easier to evaluate when it is broken into ordinary components: a task, examples or rules, a model, an interface, an output, and a person or organization responsible for using it. This section builds that mental model before introducing product-specific features.

Begin with the task

Ask whether the system predicts, classifies, ranks, generates, plans, or controls. The word intelligence is too broad to explain what a product actually does.

Follow the information

Data is selected and represented, a model applies learned parameters, and an application turns the result into something a person can see or act on. Each stage can introduce error.

Separate models from systems

A model does not manage permissions, retrieve approved records, keep reliable logs, or take responsibility. Those functions belong to the larger software and human system.

Keep future claims separate

Today’s specialized systems can be impressive without being human-like general intelligence. Forecasts about future capability should be labelled as scenarios, not current facts.

Guide library

AI Foundations articles

Foundations

What Is Artificial Intelligence?

A plain-English introduction to artificial intelligence, what it can do, how it differs from ordinary software, and why human judgment still matters.

Foundations

Types of Artificial Intelligence Explained

Understand common ways AI is grouped, including narrow AI, generative AI, predictive systems, and the difference between current systems and hypothetical general intelligence.

Foundations

Machine Learning Basics

Learn how machine learning uses examples to find patterns, make predictions, and improve performance without relying only on hand-written rules.

Foundations

How AI Learns From Data

A beginner-friendly explanation of training data, labels, patterns, feedback, and why learning from data does not equal human understanding.

Foundations

The Core Parts of an AI System

See how data, models, interfaces, infrastructure, policies, monitoring, and human oversight combine to form a working AI system.

Foundations

Natural Language Processing Explained

Understand how computers process text and speech, from classification and translation to modern language models and conversational assistants.

Foundations

Language Models and Transformers

A clear introduction to language models, transformer architecture, attention, pretraining, and why next-token prediction can produce useful conversation.

Foundations

Reinforcement Learning Basics

Learn how agents can improve decisions through rewards, penalties, simulated experience, and feedback over time.

Foundations

AI APIs Explained Without the Product Hype

Understand what an AI application programming interface does, how software sends requests to a model service, and which security and cost controls matter.

Foundations

AI and Autonomous Systems: What Is the Difference?

A short foundation explaining how AI can support systems that sense, decide, and act, without duplicating detailed autonomous-systems engineering guidance.